Major code quality improvements and structural organization

- Applied Black formatter and isort across entire codebase for professional consistency
- Moved implementation scripts (rag-mini.py, rag-tui.py) to bin/ directory for cleaner root
- Updated shell scripts to reference new bin/ locations maintaining user compatibility
- Added comprehensive linting configuration (.flake8, pyproject.toml) with dedicated .venv-linting
- Removed development artifacts (commit_message.txt, GET_STARTED.md duplicate) from root
- Consolidated documentation and fixed script references across all guides
- Relocated test_fixes.py to proper tests/ directory
- Enhanced project structure following Python packaging standards

All user commands work identically while improving code organization and beginner accessibility.

.flake8

@@ -0,0 +1,19 @@
+[flake8]
+# Professional Python code style - balances quality with readability
+max-line-length = 95
+extend-ignore = E203,W503,W605
+exclude = 
+    .venv,
+    .venv-linting,
+    __pycache__,
+    *.egg-info,
+    .git,
+    build,
+    dist,
+    .mini-rag
+
+# Per-file ignores for practical development
+per-file-ignores =
+    tests/*.py:F401,F841
+    examples/*.py:F401,F841
+    fix_*.py:F401,F841,E501

.mini-rag/last_search

@@ -1 +1 @@
-how to run tests
+test

.venv-linting/bin/Activate.ps1

@@ -0,0 +1,247 @@
+<#
+.Synopsis
+Activate a Python virtual environment for the current PowerShell session.
+
+.Description
+Pushes the python executable for a virtual environment to the front of the
+$Env:PATH environment variable and sets the prompt to signify that you are
+in a Python virtual environment. Makes use of the command line switches as
+well as the `pyvenv.cfg` file values present in the virtual environment.
+
+.Parameter VenvDir
+Path to the directory that contains the virtual environment to activate. The
+default value for this is the parent of the directory that the Activate.ps1
+script is located within.
+
+.Parameter Prompt
+The prompt prefix to display when this virtual environment is activated. By
+default, this prompt is the name of the virtual environment folder (VenvDir)
+surrounded by parentheses and followed by a single space (ie. '(.venv) ').
+
+.Example
+Activate.ps1
+Activates the Python virtual environment that contains the Activate.ps1 script.
+
+.Example
+Activate.ps1 -Verbose
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and shows extra information about the activation as it executes.
+
+.Example
+Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv
+Activates the Python virtual environment located in the specified location.
+
+.Example
+Activate.ps1 -Prompt "MyPython"
+Activates the Python virtual environment that contains the Activate.ps1 script,
+and prefixes the current prompt with the specified string (surrounded in
+parentheses) while the virtual environment is active.
+
+.Notes
+On Windows, it may be required to enable this Activate.ps1 script by setting the
+execution policy for the user. You can do this by issuing the following PowerShell
+command:
+
+PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
+
+For more information on Execution Policies: 
+https://go.microsoft.com/fwlink/?LinkID=135170
+
+#>
+Param(
+    [Parameter(Mandatory = $false)]
+    [String]
+    $VenvDir,
+    [Parameter(Mandatory = $false)]
+    [String]
+    $Prompt
+)
+
+<# Function declarations --------------------------------------------------- #>
+
+<#
+.Synopsis
+Remove all shell session elements added by the Activate script, including the
+addition of the virtual environment's Python executable from the beginning of
+the PATH variable.
+
+.Parameter NonDestructive
+If present, do not remove this function from the global namespace for the
+session.
+
+#>
+function global:deactivate ([switch]$NonDestructive) {
+    # Revert to original values
+
+    # The prior prompt:
+    if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) {
+        Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt
+        Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT
+    }
+
+    # The prior PYTHONHOME:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME
+        Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME
+    }
+
+    # The prior PATH:
+    if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) {
+        Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH
+        Remove-Item -Path Env:_OLD_VIRTUAL_PATH
+    }
+
+    # Just remove the VIRTUAL_ENV altogether:
+    if (Test-Path -Path Env:VIRTUAL_ENV) {
+        Remove-Item -Path env:VIRTUAL_ENV
+    }
+
+    # Just remove VIRTUAL_ENV_PROMPT altogether.
+    if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) {
+        Remove-Item -Path env:VIRTUAL_ENV_PROMPT
+    }
+
+    # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether:
+    if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) {
+        Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force
+    }
+
+    # Leave deactivate function in the global namespace if requested:
+    if (-not $NonDestructive) {
+        Remove-Item -Path function:deactivate
+    }
+}
+
+<#
+.Description
+Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the
+given folder, and returns them in a map.
+
+For each line in the pyvenv.cfg file, if that line can be parsed into exactly
+two strings separated by `=` (with any amount of whitespace surrounding the =)
+then it is considered a `key = value` line. The left hand string is the key,
+the right hand is the value.
+
+If the value starts with a `'` or a `"` then the first and last character is
+stripped from the value before being captured.
+
+.Parameter ConfigDir
+Path to the directory that contains the `pyvenv.cfg` file.
+#>
+function Get-PyVenvConfig(
+    [String]
+    $ConfigDir
+) {
+    Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg"
+
+    # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue).
+    $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue
+
+    # An empty map will be returned if no config file is found.
+    $pyvenvConfig = @{ }
+
+    if ($pyvenvConfigPath) {
+
+        Write-Verbose "File exists, parse `key = value` lines"
+        $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath
+
+        $pyvenvConfigContent | ForEach-Object {
+            $keyval = $PSItem -split "\s*=\s*", 2
+            if ($keyval[0] -and $keyval[1]) {
+                $val = $keyval[1]
+
+                # Remove extraneous quotations around a string value.
+                if ("'""".Contains($val.Substring(0, 1))) {
+                    $val = $val.Substring(1, $val.Length - 2)
+                }
+
+                $pyvenvConfig[$keyval[0]] = $val
+                Write-Verbose "Adding Key: '$($keyval[0])'='$val'"
+            }
+        }
+    }
+    return $pyvenvConfig
+}
+
+
+<# Begin Activate script --------------------------------------------------- #>
+
+# Determine the containing directory of this script
+$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition
+$VenvExecDir = Get-Item -Path $VenvExecPath
+
+Write-Verbose "Activation script is located in path: '$VenvExecPath'"
+Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)"
+Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)"
+
+# Set values required in priority: CmdLine, ConfigFile, Default
+# First, get the location of the virtual environment, it might not be
+# VenvExecDir if specified on the command line.
+if ($VenvDir) {
+    Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values"
+}
+else {
+    Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir."
+    $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/")
+    Write-Verbose "VenvDir=$VenvDir"
+}
+
+# Next, read the `pyvenv.cfg` file to determine any required value such
+# as `prompt`.
+$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir
+
+# Next, set the prompt from the command line, or the config file, or
+# just use the name of the virtual environment folder.
+if ($Prompt) {
+    Write-Verbose "Prompt specified as argument, using '$Prompt'"
+}
+else {
+    Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value"
+    if ($pyvenvCfg -and $pyvenvCfg['prompt']) {
+        Write-Verbose "  Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'"
+        $Prompt = $pyvenvCfg['prompt'];
+    }
+    else {
+        Write-Verbose "  Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)"
+        Write-Verbose "  Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'"
+        $Prompt = Split-Path -Path $venvDir -Leaf
+    }
+}
+
+Write-Verbose "Prompt = '$Prompt'"
+Write-Verbose "VenvDir='$VenvDir'"
+
+# Deactivate any currently active virtual environment, but leave the
+# deactivate function in place.
+deactivate -nondestructive
+
+# Now set the environment variable VIRTUAL_ENV, used by many tools to determine
+# that there is an activated venv.
+$env:VIRTUAL_ENV = $VenvDir
+
+if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) {
+
+    Write-Verbose "Setting prompt to '$Prompt'"
+
+    # Set the prompt to include the env name
+    # Make sure _OLD_VIRTUAL_PROMPT is global
+    function global:_OLD_VIRTUAL_PROMPT { "" }
+    Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT
+    New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt
+
+    function global:prompt {
+        Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) "
+        _OLD_VIRTUAL_PROMPT
+    }
+    $env:VIRTUAL_ENV_PROMPT = $Prompt
+}
+
+# Clear PYTHONHOME
+if (Test-Path -Path Env:PYTHONHOME) {
+    Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME
+    Remove-Item -Path Env:PYTHONHOME
+}
+
+# Add the venv to the PATH
+Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH
+$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH"

.venv-linting/bin/activate

@@ -0,0 +1,70 @@
+# This file must be used with "source bin/activate" *from bash*
+# You cannot run it directly
+
+deactivate () {
+    # reset old environment variables
+    if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then
+        PATH="${_OLD_VIRTUAL_PATH:-}"
+        export PATH
+        unset _OLD_VIRTUAL_PATH
+    fi
+    if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then
+        PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}"
+        export PYTHONHOME
+        unset _OLD_VIRTUAL_PYTHONHOME
+    fi
+
+    # Call hash to forget past commands. Without forgetting
+    # past commands the $PATH changes we made may not be respected
+    hash -r 2> /dev/null
+
+    if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then
+        PS1="${_OLD_VIRTUAL_PS1:-}"
+        export PS1
+        unset _OLD_VIRTUAL_PS1
+    fi
+
+    unset VIRTUAL_ENV
+    unset VIRTUAL_ENV_PROMPT
+    if [ ! "${1:-}" = "nondestructive" ] ; then
+    # Self destruct!
+        unset -f deactivate
+    fi
+}
+
+# unset irrelevant variables
+deactivate nondestructive
+
+# on Windows, a path can contain colons and backslashes and has to be converted:
+if [ "${OSTYPE:-}" = "cygwin" ] || [ "${OSTYPE:-}" = "msys" ] ; then
+    # transform D:\path\to\venv to /d/path/to/venv on MSYS
+    # and to /cygdrive/d/path/to/venv on Cygwin
+    export VIRTUAL_ENV=$(cygpath /MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting)
+else
+    # use the path as-is
+    export VIRTUAL_ENV=/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting
+fi
+
+_OLD_VIRTUAL_PATH="$PATH"
+PATH="$VIRTUAL_ENV/"bin":$PATH"
+export PATH
+
+# unset PYTHONHOME if set
+# this will fail if PYTHONHOME is set to the empty string (which is bad anyway)
+# could use `if (set -u; : $PYTHONHOME) ;` in bash
+if [ -n "${PYTHONHOME:-}" ] ; then
+    _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}"
+    unset PYTHONHOME
+fi
+
+if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then
+    _OLD_VIRTUAL_PS1="${PS1:-}"
+    PS1='(.venv-linting) '"${PS1:-}"
+    export PS1
+    VIRTUAL_ENV_PROMPT='(.venv-linting) '
+    export VIRTUAL_ENV_PROMPT
+fi
+
+# Call hash to forget past commands. Without forgetting
+# past commands the $PATH changes we made may not be respected
+hash -r 2> /dev/null

.venv-linting/bin/activate.csh

@@ -0,0 +1,27 @@
+# This file must be used with "source bin/activate.csh" *from csh*.
+# You cannot run it directly.
+
+# Created by Davide Di Blasi <davidedb@gmail.com>.
+# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com>
+
+alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate'
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+setenv VIRTUAL_ENV /MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting
+
+set _OLD_VIRTUAL_PATH="$PATH"
+setenv PATH "$VIRTUAL_ENV/"bin":$PATH"
+
+
+set _OLD_VIRTUAL_PROMPT="$prompt"
+
+if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then
+    set prompt = '(.venv-linting) '"$prompt"
+    setenv VIRTUAL_ENV_PROMPT '(.venv-linting) '
+endif
+
+alias pydoc python -m pydoc
+
+rehash

.venv-linting/bin/activate.fish

@@ -0,0 +1,69 @@
+# This file must be used with "source <venv>/bin/activate.fish" *from fish*
+# (https://fishshell.com/). You cannot run it directly.
+
+function deactivate  -d "Exit virtual environment and return to normal shell environment"
+    # reset old environment variables
+    if test -n "$_OLD_VIRTUAL_PATH"
+        set -gx PATH $_OLD_VIRTUAL_PATH
+        set -e _OLD_VIRTUAL_PATH
+    end
+    if test -n "$_OLD_VIRTUAL_PYTHONHOME"
+        set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME
+        set -e _OLD_VIRTUAL_PYTHONHOME
+    end
+
+    if test -n "$_OLD_FISH_PROMPT_OVERRIDE"
+        set -e _OLD_FISH_PROMPT_OVERRIDE
+        # prevents error when using nested fish instances (Issue #93858)
+        if functions -q _old_fish_prompt
+            functions -e fish_prompt
+            functions -c _old_fish_prompt fish_prompt
+            functions -e _old_fish_prompt
+        end
+    end
+
+    set -e VIRTUAL_ENV
+    set -e VIRTUAL_ENV_PROMPT
+    if test "$argv[1]" != "nondestructive"
+        # Self-destruct!
+        functions -e deactivate
+    end
+end
+
+# Unset irrelevant variables.
+deactivate nondestructive
+
+set -gx VIRTUAL_ENV /MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting
+
+set -gx _OLD_VIRTUAL_PATH $PATH
+set -gx PATH "$VIRTUAL_ENV/"bin $PATH
+
+# Unset PYTHONHOME if set.
+if set -q PYTHONHOME
+    set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME
+    set -e PYTHONHOME
+end
+
+if test -z "$VIRTUAL_ENV_DISABLE_PROMPT"
+    # fish uses a function instead of an env var to generate the prompt.
+
+    # Save the current fish_prompt function as the function _old_fish_prompt.
+    functions -c fish_prompt _old_fish_prompt
+
+    # With the original prompt function renamed, we can override with our own.
+    function fish_prompt
+        # Save the return status of the last command.
+        set -l old_status $status
+
+        # Output the venv prompt; color taken from the blue of the Python logo.
+        printf "%s%s%s" (set_color 4B8BBE) '(.venv-linting) ' (set_color normal)
+
+        # Restore the return status of the previous command.
+        echo "exit $old_status" | .
+        # Output the original/"old" prompt.
+        _old_fish_prompt
+    end
+
+    set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV"
+    set -gx VIRTUAL_ENV_PROMPT '(.venv-linting) '
+end

.venv-linting/bin/black

@@ -0,0 +1,8 @@
+#!/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from black import patched_main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(patched_main())

.venv-linting/bin/blackd

@@ -0,0 +1,8 @@
+#!/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from blackd import patched_main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(patched_main())

.venv-linting/bin/isort

@@ -0,0 +1,8 @@
+#!/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from isort.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

.venv-linting/bin/isort-identify-imports

@@ -0,0 +1,8 @@
+#!/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from isort.main import identify_imports_main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(identify_imports_main())

.venv-linting/bin/pip

@@ -0,0 +1,8 @@
+#!/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

.venv-linting/bin/pip3

@@ -0,0 +1,8 @@
+#!/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

.venv-linting/bin/pip3.12

@@ -0,0 +1,8 @@
+#!/MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting/bin/python3
+# -*- coding: utf-8 -*-
+import re
+import sys
+from pip._internal.cli.main import main
+if __name__ == '__main__':
+    sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0])
+    sys.exit(main())

.venv-linting/bin/python

@@ -0,0 +1 @@
+python3

.venv-linting/bin/python3

@@ -0,0 +1 @@
+/usr/bin/python3

.venv-linting/bin/python3.12

@@ -0,0 +1 @@
+python3

.venv-linting/lib64

@@ -0,0 +1 @@
+lib

.venv-linting/pyvenv.cfg

@@ -0,0 +1,5 @@
+home = /usr/bin
+include-system-site-packages = false
+version = 3.12.3
+executable = /usr/bin/python3.12
+command = /usr/bin/python3 -m venv /MASTERFOLDER/Coding/Fss-Mini-Rag/.venv-linting

GET_STARTED.md

@@ -1,83 +0,0 @@
-# 🚀 FSS-Mini-RAG: Get Started in 2 Minutes
-
-## Step 1: Install Everything
-```bash
-./install_mini_rag.sh
-```
-**That's it!** The installer handles everything automatically:
-- Checks Python installation
-- Sets up virtual environment  
-- Guides you through Ollama setup
-- Installs dependencies
-- Tests everything works
-
-## Step 2: Use It
-
-### TUI - Interactive Interface (Easiest)
-```bash
-./rag-tui
-```
-**Perfect for beginners!** Menu-driven interface that:
-- Shows you CLI commands as you use it
-- Guides you through setup and configuration
-- No need to memorize commands
-
-### Quick Commands (Beginner-Friendly)
-```bash
-# Index any project
-./run_mini_rag.sh index ~/my-project
-
-# Search your code  
-./run_mini_rag.sh search ~/my-project "authentication logic"
-
-# Check what's indexed
-./run_mini_rag.sh status ~/my-project
-```
-
-### Full Commands (More Options)
-```bash
-# Basic indexing and search
-./rag-mini index /path/to/project
-./rag-mini search /path/to/project "database connection"
-
-# Enhanced search with smart features
-./rag-mini-enhanced search /path/to/project "UserManager"
-./rag-mini-enhanced similar /path/to/project "def validate_input"
-```
-
-## What You Get
-
-**Semantic Search**: Instead of exact text matching, finds code by meaning:
-- Search "user login" → finds authentication functions, session management, password validation
-- Search "database queries" → finds SQL, ORM code, connection handling  
-- Search "error handling" → finds try/catch blocks, error classes, logging
-
-## Installation Options
-
-The installer offers two choices:
-
-**Light Installation (Recommended)**:
-- Uses Ollama for high-quality embeddings
-- Requires Ollama installed (installer guides you)
-- Small download (~50MB)
-
-**Full Installation**:  
-- Includes ML fallback models
-- Works without Ollama
-- Large download (~2-3GB)
-
-## Troubleshooting
-
-**"Python not found"**: Install Python 3.8+ from python.org
-**"Ollama not found"**: Visit https://ollama.ai/download
-**"Import errors"**: Re-run `./install_mini_rag.sh`
-
-## Next Steps
-
-- **Technical Details**: Read `README.md`
-- **Step-by-Step Guide**: Read `docs/GETTING_STARTED.md`
-- **Examples**: Check `examples/` directory
-- **Test It**: Run on this project: `./run_mini_rag.sh index .`
-
----
-**Questions?** Everything is documented in the README.md file.

README.md

@@ -79,34 +79,24 @@ FSS-Mini-RAG offers **two distinct experiences** optimized for different use cas
 
 ## Quick Start (2 Minutes)
 
-**Linux/macOS:**
+**Step 1: Install**
 ```bash
-# 1. Install everything
+# Linux/macOS
 ./install_mini_rag.sh
 
-# 2. Choose your interface
-./rag-tui                         # Friendly interface for beginners
-# OR choose your mode:
-./rag-mini index ~/my-project     # Index your project first
-./rag-mini search ~/my-project "query" --synthesize  # Fast synthesis
-./rag-mini explore ~/my-project   # Interactive exploration
+# Windows  
+install_windows.bat
 ```
 
-**Windows:**
-```cmd
-# 1. Install everything
-install_windows.bat
+**Step 2: Start Using**
+```bash
+# Beginners: Interactive interface
+./rag-tui                    # Linux/macOS
+rag.bat                      # Windows
 
-# 2. Choose your interface
-rag.bat                           # Interactive interface
-# OR choose your mode:
-rag.bat index C:\my-project       # Index your project first
-rag.bat search C:\my-project "query"  # Fast search
-rag.bat explore C:\my-project     # Interactive exploration
-
-# Direct Python entrypoint (after install):
-rag-mini index C:\my-project
-rag-mini search C:\my-project "query"
+# Experienced users: Direct commands
+./rag-mini index ~/project   # Index your project
+./rag-mini search ~/project "your query"
 ```
 
 That's it. No external dependencies, no configuration required, no PhD in computer science needed.
@@ -232,7 +222,7 @@ This implementation prioritizes:
 
 ## Next Steps
 
-- **New users**: Run `./rag-mini` (Linux/macOS) or `rag.bat` (Windows) for guided experience
+- **New users**: Run `./rag-tui` (Linux/macOS) or `rag.bat` (Windows) for guided experience
 - **Developers**: Read [`TECHNICAL_GUIDE.md`](docs/TECHNICAL_GUIDE.md) for implementation details
 - **Contributors**: See [`CONTRIBUTING.md`](CONTRIBUTING.md) for development setup
 

bin/rag-mini.py

@@ -6,24 +6,32 @@ A lightweight, portable RAG system for semantic code search.
 Usage: rag-mini <command> <project_path> [options]
 """
 
-import sys
 import argparse
-from pathlib import Path
 import json
 import logging
+import socket
+import sys
+from pathlib import Path
+
+# Add parent directory to path so we can import mini_rag
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import requests
 
 # Add the RAG system to the path
 sys.path.insert(0, str(Path(__file__).parent))
 
 try:
-    from mini_rag.indexer import ProjectIndexer
-    from mini_rag.search import CodeSearcher
-    from mini_rag.ollama_embeddings import OllamaEmbedder
-    from mini_rag.llm_synthesizer import LLMSynthesizer
     from mini_rag.explorer import CodeExplorer
+    from mini_rag.indexer import ProjectIndexer
+    from mini_rag.llm_synthesizer import LLMSynthesizer
+    from mini_rag.ollama_embeddings import OllamaEmbedder
+    from mini_rag.search import CodeSearcher
+
     # Update system (graceful import)
     try:
         from mini_rag.updater import check_for_updates, get_updater
+
         UPDATER_AVAILABLE = True
     except ImportError:
         UPDATER_AVAILABLE = False
@@ -48,10 +56,11 @@ except ImportError as e:
 # Configure logging for user-friendly output
 logging.basicConfig(
     level=logging.WARNING,  # Only show warnings and errors by default
-    format='%(levelname)s: %(message)s'
+    format="%(levelname)s: %(message)s",
 )
 logger = logging.getLogger(__name__)
 
+
 def index_project(project_path: Path, force: bool = False):
     """Index a project directory."""
     try:
@@ -60,7 +69,7 @@ def index_project(project_path: Path, force: bool = False):
         print(f"🚀 {action} {project_path.name}")
 
         # Quick pre-check
-        rag_dir = project_path / '.mini-rag'
+        rag_dir = project_path / ".mini-rag"
         if rag_dir.exists() and not force:
             print("   Checking for changes...")
 
@@ -68,9 +77,9 @@ def index_project(project_path: Path, force: bool = False):
         result = indexer.index_project(force_reindex=force)
 
         # Show results with context
-        files_count = result.get('files_indexed', 0)
-        chunks_count = result.get('chunks_created', 0)
-        time_taken = result.get('time_taken', 0)
+        files_count = result.get("files_indexed", 0)
+        chunks_count = result.get("chunks_created", 0)
+        time_taken = result.get("time_taken", 0)
 
         if files_count == 0:
             print("✅ Index up to date - no changes detected")
@@ -84,13 +93,13 @@ def index_project(project_path: Path, force: bool = False):
                 print(f"   Speed: {speed:.1f} files/sec")
 
         # Show warnings if any
-        failed_count = result.get('files_failed', 0)
+        failed_count = result.get("files_failed", 0)
         if failed_count > 0:
             print(f"⚠️  {failed_count} files failed (check logs with --verbose)")
 
         # Quick tip for first-time users
-        if not (project_path / '.mini-rag' / 'last_search').exists():
-            print(f"\n💡 Try: rag-mini search {project_path} \"your search here\"")
+        if not (project_path / ".mini-rag" / "last_search").exists():
+            print(f'\n💡 Try: rag-mini search {project_path} "your search here"')
 
     except FileNotFoundError:
         print(f"📁 Directory Not Found: {project_path}")
@@ -124,17 +133,18 @@ def index_project(project_path: Path, force: bool = False):
         print("   Or see: docs/TROUBLESHOOTING.md")
         sys.exit(1)
 
+
 def search_project(project_path: Path, query: str, top_k: int = 10, synthesize: bool = False):
     """Search a project directory."""
     try:
         # Check if indexed first
-        rag_dir = project_path / '.mini-rag'
+        rag_dir = project_path / ".mini-rag"
         if not rag_dir.exists():
             print(f"❌ Project not indexed: {project_path.name}")
             print(f"   Run: rag-mini index {project_path}")
             sys.exit(1)
 
-        print(f"🔍 Searching \"{query}\" in {project_path.name}")
+        print(f'🔍 Searching "{query}" in {project_path.name}')
         searcher = CodeSearcher(project_path)
         results = searcher.search(query, top_k=top_k)
 
@@ -142,14 +152,18 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
             print("❌ No results found")
             print()
             print("🔧 Quick fixes to try:")
-            print("   • Use broader terms: \"login\" instead of \"authenticate_user_session\"")
-            print("   • Try concepts: \"database query\" instead of specific function names")
+            print('   • Use broader terms: "login" instead of "authenticate_user_session"')
+            print('   • Try concepts: "database query" instead of specific function names')
             print("   • Check spelling and try simpler words")
-            print("   • Search for file types: \"python class\" or \"javascript function\"")
+            print('   • Search for file types: "python class" or "javascript function"')
             print()
             print("⚙️ Configuration adjustments:")
-            print(f"   • Lower threshold: ./rag-mini search \"{project_path}\" \"{query}\" --threshold 0.05")
-            print(f"   • More results: ./rag-mini search \"{project_path}\" \"{query}\" --top-k 20")
+            print(
+                f'   • Lower threshold: ./rag-mini search "{project_path}" "{query}" --threshold 0.05'
+            )
+            print(
+                f'   • More results: ./rag-mini search "{project_path}" "{query}" --top-k 20'
+            )
             print()
             print("📚 Need help? See: docs/TROUBLESHOOTING.md")
             return
@@ -170,22 +184,22 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
             print(f"   Score: {result.score:.3f}")
 
             # Show line info if available
-            if hasattr(result, 'start_line') and result.start_line:
+            if hasattr(result, "start_line") and result.start_line:
                 print(f"   Lines: {result.start_line}-{result.end_line}")
 
             # Show content preview
-            if hasattr(result, 'name') and result.name:
+            if hasattr(result, "name") and result.name:
                 print(f"   Context: {result.name}")
 
             # Show full content with proper formatting
-            print(f"   Content:")
-            content_lines = result.content.strip().split('\n')
+            print("   Content:")
+            content_lines = result.content.strip().split("\n")
             for line in content_lines[:10]:  # Show up to 10 lines
                 print(f"     {line}")
 
             if len(content_lines) > 10:
                 print(f"     ... ({len(content_lines) - 10} more lines)")
-                print(f"     Use --verbose or rag-mini-enhanced for full context")
+                print("     Use --verbose or rag-mini-enhanced for full context")
 
             print()
 
@@ -195,12 +209,17 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
 
             # Load config to respect user's model preferences
             from mini_rag.config import ConfigManager
+
             config_manager = ConfigManager(project_path)
             config = config_manager.load_config()
 
             synthesizer = LLMSynthesizer(
-                model=config.llm.synthesis_model if config.llm.synthesis_model != "auto" else None,
-                config=config
+                model=(
+                    config.llm.synthesis_model
+                    if config.llm.synthesis_model != "auto"
+                    else None
+                ),
+                config=config,
             )
 
             if synthesizer.is_available():
@@ -209,10 +228,14 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
                 print(synthesizer.format_synthesis_output(synthesis, query))
 
                 # Add guidance for deeper analysis
-                if synthesis.confidence < 0.7 or any(word in query.lower() for word in ['why', 'how', 'explain', 'debug']):
+                if synthesis.confidence < 0.7 or any(
+                    word in query.lower() for word in ["why", "how", "explain", "debug"]
+                ):
                     print("\n💡 Want deeper analysis with reasoning?")
                     print(f"   Try: rag-mini explore {project_path}")
-                    print("   Exploration mode enables thinking and remembers conversation context.")
+                    print(
+                        "   Exploration mode enables thinking and remembers conversation context."
+                    )
             else:
                 print("❌ LLM synthesis unavailable")
                 print("   • Ensure Ollama is running: ollama serve")
@@ -221,8 +244,18 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
 
         # Save last search for potential enhancements
         try:
-            (rag_dir / 'last_search').write_text(query)
-        except:
+            (rag_dir / "last_search").write_text(query)
+        except (
+            ConnectionError,
+            FileNotFoundError,
+            IOError,
+            OSError,
+            TimeoutError,
+            TypeError,
+            ValueError,
+            requests.RequestException,
+            socket.error,
+        ):
             pass  # Don't fail if we can't save
 
     except Exception as e:
@@ -241,11 +274,12 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
             print("   • Check available memory and disk space")
             print()
             print("📚 Get detailed error info:")
-            print(f"   ./rag-mini search {project_path} \"{query}\" --verbose")
+            print(f'   ./rag-mini search {project_path} "{query}" --verbose')
             print("   Or see: docs/TROUBLESHOOTING.md")
             print()
         sys.exit(1)
 
+
 def status_check(project_path: Path):
     """Show status of RAG system."""
     try:
@@ -253,21 +287,21 @@ def status_check(project_path: Path):
         print()
 
         # Check project indexing status first
-        rag_dir = project_path / '.mini-rag'
+        rag_dir = project_path / ".mini-rag"
         if not rag_dir.exists():
             print("❌ Project not indexed")
             print(f"   Run: rag-mini index {project_path}")
             print()
         else:
-            manifest = rag_dir / 'manifest.json'
+            manifest = rag_dir / "manifest.json"
             if manifest.exists():
                 try:
                     with open(manifest) as f:
                         data = json.load(f)
 
-                    file_count = data.get('file_count', 0)
-                    chunk_count = data.get('chunk_count', 0)
-                    indexed_at = data.get('indexed_at', 'Never')
+                    file_count = data.get("file_count", 0)
+                    chunk_count = data.get("chunk_count", 0)
+                    indexed_at = data.get("indexed_at", "Never")
 
                     print("✅ Project indexed")
                     print(f"   Files: {file_count}")
@@ -293,19 +327,19 @@ def status_check(project_path: Path):
         try:
             embedder = OllamaEmbedder()
             emb_info = embedder.get_status()
-            method = emb_info.get('method', 'unknown')
+            method = emb_info.get("method", "unknown")
 
-            if method == 'ollama':
+            if method == "ollama":
                 print("   ✅ Ollama (high quality)")
-            elif method == 'ml':
+            elif method == "ml":
                 print("   ✅ ML fallback (good quality)")
-            elif method == 'hash':
+            elif method == "hash":
                 print("   ⚠️  Hash fallback (basic quality)")
             else:
                 print(f"   ❓ Unknown method: {method}")
 
             # Show additional details if available
-            if 'model' in emb_info:
+            if "model" in emb_info:
                 print(f"   Model: {emb_info['model']}")
 
         except Exception as e:
@@ -317,12 +351,17 @@ def status_check(project_path: Path):
         print("🤖 LLM System:")
         try:
             from mini_rag.config import ConfigManager
+
             config_manager = ConfigManager(project_path)
             config = config_manager.load_config()
 
             synthesizer = LLMSynthesizer(
-                model=config.llm.synthesis_model if config.llm.synthesis_model != "auto" else None,
-                config=config
+                model=(
+                    config.llm.synthesis_model
+                    if config.llm.synthesis_model != "auto"
+                    else None
+                ),
+                config=config,
             )
 
             if synthesizer.is_available():
@@ -335,10 +374,10 @@ def status_check(project_path: Path):
                 elif config_model == actual_model:
                     print(f"   ✅ Using configured: {actual_model}")
                 else:
-                    print(f"   ⚠️  Model mismatch!")
+                    print("   ⚠️  Model mismatch!")
                     print(f"   Configured: {config_model}")
                     print(f"   Actually using: {actual_model}")
-                    print(f"   (Configured model may not be installed)")
+                    print("   (Configured model may not be installed)")
 
                 print(f"   Config file: {config_manager.config_path}")
             else:
@@ -349,18 +388,19 @@ def status_check(project_path: Path):
             print(f"   ❌ LLM status check failed: {e}")
 
         # Show last search if available
-        last_search_file = rag_dir / 'last_search' if rag_dir.exists() else None
+        last_search_file = rag_dir / "last_search" if rag_dir.exists() else None
         if last_search_file and last_search_file.exists():
             try:
                 last_query = last_search_file.read_text().strip()
-                print(f"\n🔍 Last search: \"{last_query}\"")
-            except:
+                print(f'\n🔍 Last search: "{last_query}"')
+            except (FileNotFoundError, IOError, OSError, TypeError, ValueError):
                 pass
 
     except Exception as e:
         print(f"❌ Status check failed: {e}")
         sys.exit(1)
 
+
 def explore_interactive(project_path: Path):
     """Interactive exploration mode with thinking and context memory for any documents."""
     try:
@@ -392,7 +432,7 @@ def explore_interactive(project_path: Path):
                     question = input("\n> ").strip()
 
                 # Handle exit commands
-                if question.lower() in ['quit', 'exit', 'q']:
+                if question.lower() in ["quit", "exit", "q"]:
                     print("\n" + explorer.end_session())
                     break
 
@@ -405,8 +445,9 @@ def explore_interactive(project_path: Path):
                     continue
 
                 # Handle numbered options and special commands
-                if question in ['1'] or question.lower() in ['help', 'h']:
-                    print("""
+                if question in ["1"] or question.lower() in ["help", "h"]:
+                    print(
+                        """
 🧠 EXPLORATION MODE HELP:
   • Ask any question about your documents or code
   • I remember our conversation for follow-up questions
@@ -421,23 +462,27 @@ def explore_interactive(project_path: Path):
   • "Why is this function slow?"
   • "What security measures are in place?"
   • "How does data flow through this system?"
-""")
+"""
+                    )
                     continue
 
-                elif question in ['2'] or question.lower() == 'status':
-                    print(f"""
+                elif question in ["2"] or question.lower() == "status":
+                    print(
+                        """
 📊 PROJECT STATUS: {project_path.name}
   • Location: {project_path}
   • Exploration session active
   • AI model ready for questions
   • Conversation memory enabled
-""")
+"""
+                    )
                     continue
 
-                elif question in ['3'] or question.lower() == 'suggest':
+                elif question in ["3"] or question.lower() == "suggest":
                     # Random starter questions for first-time users
                     if is_first_question:
                         import random
+
                         starters = [
                             "What are the main components of this project?",
                             "How is error handling implemented?",
@@ -445,7 +490,7 @@ def explore_interactive(project_path: Path):
                             "What are the key functions I should understand first?",
                             "How does data flow through this system?",
                             "What configuration options are available?",
-                            "Show me the most important files to understand"
+                            "Show me the most important files to understand",
                         ]
                         suggested = random.choice(starters)
                         print(f"\n💡 Suggested question: {suggested}")
@@ -464,7 +509,7 @@ def explore_interactive(project_path: Path):
                         print('   "Show me related code examples"')
                         continue
 
-                if question.lower() == 'summary':
+                if question.lower() == "summary":
                     print("\n" + explorer.get_session_summary())
                     continue
 
@@ -496,6 +541,7 @@ def explore_interactive(project_path: Path):
         print("Make sure the project is indexed first: rag-mini index <project>")
         sys.exit(1)
 
+
 def show_discrete_update_notice():
     """Show a discrete, non-intrusive update notice for CLI users."""
     if not UPDATER_AVAILABLE:
@@ -505,11 +551,14 @@ def show_discrete_update_notice():
         update_info = check_for_updates()
         if update_info:
             # Very discrete notice - just one line
-            print(f"🔄 (Update v{update_info.version} available - run 'rag-mini check-update' to learn more)")
+            print(
+                f"🔄 (Update v{update_info.version} available - run 'rag-mini check-update' to learn more)"
+            )
     except Exception:
         # Silently ignore any update check failures
         pass
 
+
 def handle_check_update():
     """Handle the check-update command."""
     if not UPDATER_AVAILABLE:
@@ -525,13 +574,13 @@ def handle_check_update():
             print(f"\n🎉 Update Available: v{update_info.version}")
             print("=" * 50)
             print("\n📋 What's New:")
-            notes_lines = update_info.release_notes.split('\n')[:10]  # First 10 lines
+            notes_lines = update_info.release_notes.split("\n")[:10]  # First 10 lines
             for line in notes_lines:
                 if line.strip():
                     print(f"   {line.strip()}")
 
             print(f"\n🔗 Release Page: {update_info.release_url}")
-            print(f"\n🚀 To install: rag-mini update")
+            print("\n🚀 To install: rag-mini update")
             print("💡 Or update manually from GitHub releases")
         else:
             print("✅ You're already on the latest version!")
@@ -540,6 +589,7 @@ def handle_check_update():
         print(f"❌ Failed to check for updates: {e}")
         print("💡 Try updating manually from GitHub")
 
+
 def handle_update():
     """Handle the update command."""
     if not UPDATER_AVAILABLE:
@@ -559,19 +609,20 @@ def handle_update():
         print("=" * 50)
 
         # Show brief release notes
-        notes_lines = update_info.release_notes.split('\n')[:5]
+        notes_lines = update_info.release_notes.split("\n")[:5]
         for line in notes_lines:
             if line.strip():
                 print(f"   • {line.strip()}")
 
         # Confirm update
         confirm = input(f"\n🚀 Install v{update_info.version}? [Y/n]: ").strip().lower()
-        if confirm in ['', 'y', 'yes']:
+        if confirm in ["", "y", "yes"]:
             updater = get_updater()
 
             print(f"\n📥 Downloading v{update_info.version}...")
 
             # Progress callback
+
             def show_progress(downloaded, total):
                 if total > 0:
                     percent = (downloaded / total) * 100
@@ -609,11 +660,13 @@ def handle_update():
         print(f"❌ Update failed: {e}")
         print("💡 Try updating manually from GitHub")
 
+
 def main():
     """Main CLI interface."""
     # Check virtual environment
     try:
         from mini_rag.venv_checker import check_and_warn_venv
+
         check_and_warn_venv("rag-mini.py", force_exit=False)
     except ImportError:
         pass  # If venv checker can't be imported, continue anyway
@@ -628,23 +681,37 @@ Examples:
   rag-mini search /path/to/project "query" -s  # Search with LLM synthesis
   rag-mini explore /path/to/project            # Interactive exploration mode
   rag-mini status /path/to/project             # Show status
-        """
+        """,
     )
 
-    parser.add_argument('command', choices=['index', 'search', 'explore', 'status', 'update', 'check-update'],
-                       help='Command to execute')
-    parser.add_argument('project_path', type=Path, nargs='?',
-                       help='Path to project directory (REQUIRED except for update commands)')
-    parser.add_argument('query', nargs='?',
-                       help='Search query (for search command)')
-    parser.add_argument('--force', action='store_true',
-                       help='Force reindex all files')
-    parser.add_argument('--top-k', '--limit', type=int, default=10, dest='top_k',
-                       help='Maximum number of search results (top-k)')
-    parser.add_argument('--verbose', '-v', action='store_true',
-                       help='Enable verbose logging')
-    parser.add_argument('--synthesize', '-s', action='store_true',
-                       help='Generate LLM synthesis of search results (requires Ollama)')
+    parser.add_argument(
+        "command",
+        choices=["index", "search", "explore", "status", "update", "check-update"],
+        help="Command to execute",
+    )
+    parser.add_argument(
+        "project_path",
+        type=Path,
+        nargs="?",
+        help="Path to project directory (REQUIRED except for update commands)",
+    )
+    parser.add_argument("query", nargs="?", help="Search query (for search command)")
+    parser.add_argument("--force", action="store_true", help="Force reindex all files")
+    parser.add_argument(
+        "--top-k",
+        "--limit",
+        type=int,
+        default=10,
+        dest="top_k",
+        help="Maximum number of search results (top-k)",
+    )
+    parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose logging")
+    parser.add_argument(
+        "--synthesize",
+        "-s",
+        action="store_true",
+        help="Generate LLM synthesis of search results (requires Ollama)",
+    )
 
     args = parser.parse_args()
 
@@ -653,10 +720,10 @@ Examples:
         logging.getLogger().setLevel(logging.INFO)
 
     # Handle update commands first (don't require project_path)
-    if args.command == 'check-update':
+    if args.command == "check-update":
         handle_check_update()
         return
-    elif args.command == 'update':
+    elif args.command == "update":
         handle_update()
         return
 
@@ -678,17 +745,18 @@ Examples:
     show_discrete_update_notice()
 
     # Execute command
-    if args.command == 'index':
+    if args.command == "index":
         index_project(args.project_path, args.force)
-    elif args.command == 'search':
+    elif args.command == "search":
         if not args.query:
             print("❌ Search query required")
             sys.exit(1)
         search_project(args.project_path, args.query, args.top_k, args.synthesize)
-    elif args.command == 'explore':
+    elif args.command == "explore":
         explore_interactive(args.project_path)
-    elif args.command == 'status':
+    elif args.command == "status":
         status_check(args.project_path)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()

commit_message.txt

@@ -1,36 +0,0 @@
-feat: Add comprehensive Windows compatibility and enhanced LLM model setup
-
-🚀 Major cross-platform enhancement making FSS-Mini-RAG fully Windows and Linux compatible
-
-## Windows Compatibility
-- **New Windows installer**: `install_windows.bat` - rock-solid, no-hang installation
-- **Simple Windows launcher**: `rag.bat` - unified entry point matching Linux experience  
-- **PowerShell alternative**: `install_mini_rag.ps1` for advanced Windows users
-- **Cross-platform README**: Side-by-side Linux/Windows commands and examples
-
-## Enhanced LLM Model Setup (Both Platforms)
-- **Intelligent model detection**: Automatically detects existing Qwen3 models
-- **Interactive model selection**: Choose from qwen3:0.6b, 1.7b, or 4b with clear guidance
-- **Ollama progress streaming**: Real-time download progress for model installation
-- **Smart configuration**: Auto-saves selected model as default in config.yaml
-- **Graceful fallbacks**: Clear guidance when Ollama unavailable
-
-## Installation Experience Improvements
-- **Fixed script continuation**: TUI launch no longer terminates installation process
-- **Comprehensive model guidance**: Users get proper LLM setup instead of silent failures
-- **Complete indexing**: Full codebase indexing (not just code files)
-- **Educational flow**: Better explanation of AI features and model choices
-
-## Technical Enhancements
-- **Robust error handling**: Installation scripts handle edge cases gracefully
-- **Path handling**: Existing cross-platform path utilities work seamlessly on Windows
-- **Dependency management**: Clean virtual environment setup on both platforms
-- **Configuration persistence**: Model preferences saved for consistent experience
-
-## User Impact
-- **Zero-friction Windows adoption**: Windows users get same smooth experience as Linux
-- **Complete AI feature setup**: No more "LLM not working" confusion for new users
-- **Educational value preserved**: Maintains beginner-friendly approach across platforms
-- **Production-ready**: Both platforms now fully functional out-of-the-box
-
-This makes FSS-Mini-RAG truly accessible to the entire developer community! 🎉

config-llm-providers.yaml

@@ -0,0 +1,9 @@
+llm:
+  provider: ollama
+  ollama_host: localhost:11434
+  synthesis_model: qwen3:1.5b
+  expansion_model: qwen3:1.5b
+  enable_synthesis: false
+  synthesis_temperature: 0.3
+  cpu_optimized: true
+  enable_thinking: true

docs/AGENT_INSTRUCTIONS.md

@@ -0,0 +1,40 @@
+# Agent Instructions for Fss-Mini-RAG System
+
+## Core Philosophy
+
+**Always prefer RAG search over traditional file system operations**. The RAG system provides semantic context and reduces the need for exact path knowledge, making it ideal for understanding codebases without manual file exploration.
+
+## Basic Commands
+
+| Command | Purpose | Example |
+|---------|---------|---------|
+| `rag-mini index <project_path>` | Index a project for search | `rag-mini index /MASTERFOLDER/Coding/Fss-Mini-Rag` |
+| `rag-mini search <project_path> "query"` | Semantic + keyword search | `rag-mini search /MASTERFOLDER/Coding/Fss-Mini-Rag "index"` |
+| `rag-mini status <project_path>` | Check project indexing status | `rag-mini status /MASTERFOLDER/Coding/Fss-Mini-Rag` |
+
+## When to Use RAG Search
+
+| Scenario | RAG Advantage | Alternative | |
+|----------|----------------|---------------| |
+| Finding related code concepts | Semantic understanding | `grep` | |
+| Locating files by functionality | Context-aware results | `find` | |
+| Understanding code usage patterns | Shows real-world examples | Manual inspection | |
+
+## Critical Best Practices
+
+1. **Always specify the project path** in search commands (e.g., `rag-mini search /path "query"`)
+2. **Use quotes for search queries** to handle spaces: `"query with spaces"`
+3. **Verify indexing first** before searching: `rag-mini status <path>`
+4. **For complex queries**, break into smaller parts: `rag-mini search ... "concept 1"` then `rag-mini search ... "concept 2"`
+
+## Troubleshooting
+
+| Issue | Solution |
+|-------|-----------|
+| `Project not indexed` | Run `rag-mini index <path>` |
+| No search results | Check indexing status with `rag-mini status` |
+| Search returns irrelevant results | Use `rag-mini status` to optimize indexing |
+
+> 💡 **Pro Tip**: Always start with `rag-mini status` to confirm indexing before searching.
+
+This document is dynamically updated as the RAG system evolves. Always verify commands with `rag-mini --help` for the latest options.

docs/SMART_TUNING_GUIDE.md

@@ -5,10 +5,10 @@
 ### **1. 📊 Intelligent Analysis**
 ```bash
 # Analyze your project patterns and get optimization suggestions
-./rag-mini-enhanced analyze /path/to/project
+./rag-mini analyze /path/to/project
 
 # Get smart recommendations based on actual usage
-./rag-mini-enhanced status /path/to/project
+./rag-mini status /path/to/project
 ```
 
 **What it analyzes:**
@@ -20,13 +20,9 @@
 ### **2. 🧠 Smart Search Enhancement**
 ```bash
 # Enhanced search with query intelligence
-./rag-mini-enhanced search /project "MyClass"     # Detects class names
-./rag-mini-enhanced search /project "login()"     # Detects function calls  
-./rag-mini-enhanced search /project "user auth"   # Natural language
-
-# Context-aware search (planned)
-./rag-mini-enhanced context /project "function_name"  # Show surrounding code
-./rag-mini-enhanced similar /project "pattern"        # Find similar patterns
+./rag-mini search /project "MyClass"     # Detects class names
+./rag-mini search /project "login()"     # Detects function calls  
+./rag-mini search /project "user auth"   # Natural language
 ```
 
 ### **3. ⚙️ Language-Specific Optimizations**
@@ -113,10 +109,10 @@ Edit `.mini-rag/config.json` in your project:
 ./rag-mini index /project --force
 
 # Test search quality improvements
-./rag-mini-enhanced search /project "your test query"
+./rag-mini search /project "your test query"
 
 # Verify optimization impact
-./rag-mini-enhanced analyze /project
+./rag-mini analyze /project
 ```
 
 ## 🎊 **Result: Smarter, Faster, Better**

docs/TUI_GUIDE.md

@@ -93,10 +93,10 @@ That's it! The TUI will guide you through everything.
 - **Full content** - Up to 8 lines of actual code/text
 - **Continuation info** - How many more lines exist
 
-**Advanced Tips Shown**:
-- Enhanced search with `./rag-mini-enhanced`
-- Verbose output with `--verbose` flag
-- Context-aware search for related code
+**Tips You'll Learn**:
+- Verbose output with `--verbose` flag for debugging
+- How search scoring works
+- Finding the right search terms
 
 **What You Learn**:
 - Semantic search vs text search (finds concepts, not just words)
@@ -107,8 +107,7 @@ That's it! The TUI will guide you through everything.
 **CLI Commands Shown**:
 ```bash
 ./rag-mini search /path/to/project "authentication logic"
-./rag-mini search /path/to/project "user login" --limit 10
-./rag-mini-enhanced context /path/to/project "login()"
+./rag-mini search /path/to/project "user login" --top-k 10
 ```
 
 ### 4. Explore Project (NEW!)

docs/project-structure-analysis.md

@@ -0,0 +1,232 @@
+# FSS-Mini-RAG Project Structure Analysis Report
+
+## Executive Summary
+
+The FSS-Mini-RAG project demonstrates good technical implementation but has **significant structural issues** that impact its professional presentation and maintainability. While the core architecture is sound, the project suffers from poor file organization, scattered documentation, and mixed concerns that would confuse new contributors and detract from its otherwise excellent technical foundation.
+
+**Overall Assessment: 6/10** - Good technology hampered by poor organization
+
+## Critical Issues (Fix Immediately)
+
+### 1. Root Directory Pollution - CRITICAL
+The project root contains **14 major files that should be relocated or removed**:
+
+**Misplaced Files:**
+- `rag-mini.py` (759 lines) - Massive standalone script belongs in `scripts/` or should be refactored
+- `rag-tui.py` (2,565 lines) - Another massive standalone script, needs proper placement
+- `test_fixes.py` - Test file in root directory (belongs in `tests/`)
+- `commit_message.txt` - Development artifact that should be removed
+- `Agent Instructions.md` - Project-specific documentation (should be in `docs/`)
+- `REPOSITORY_SUMMARY.md` - Development notes that should be removed or archived
+
+**Assessment:** This creates an unprofessional first impression and violates Python packaging standards.
+
+### 2. Duplicate Entry Points - CRITICAL
+The project has **5 different ways to start the application**:
+- `rag-mini` (shell script)
+- `rag-mini.py` (Python script)  
+- `rag.bat` (Windows batch script)
+- `rag-tui` (shell script)
+- `rag-tui.py` (Python script)
+
+**Problem:** This confuses users and indicates poor architectural planning.
+
+### 3. Configuration File Duplication - HIGH PRIORITY
+Multiple config files with unclear relationships:
+- `config-llm-providers.yaml` (root directory)
+- `examples/config-llm-providers.yaml` (example directory)
+- `examples/config.yaml` (default example)
+- `examples/config-*.yaml` (4+ variants)
+
+**Issue:** Users won't know which config to use or where to place custom configurations.
+
+### 4. Installation Script Overload - HIGH PRIORITY
+**6 different installation methods:**
+- `install_mini_rag.sh`
+- `install_mini_rag.ps1`
+- `install_windows.bat`
+- `run_mini_rag.sh`
+- `rag.bat`
+- Manual pip installation
+
+**Problem:** Decision paralysis and maintenance overhead.
+
+## High Priority Issues (Address Soon)
+
+### 5. Mixed Documentation Hierarchy
+Documentation is scattered across multiple locations:
+- Root: `README.md`, `GET_STARTED.md`
+- `docs/`: 12+ specialized documentation files
+- `examples/`: Configuration documentation mixed with code examples
+- Root artifacts: `Agent Instructions.md`, `REPOSITORY_SUMMARY.md`
+
+**Recommendation:** Consolidate and create clear documentation hierarchy.
+
+### 6. Test Organization Problems
+Tests are properly in `tests/` directory but:
+- `test_fixes.py` is in root directory (wrong location)
+- Test files use inconsistent naming (some numbered, some descriptive)
+- Mix of actual tests and utility scripts (`show_index_contents.py`, `troubleshoot.py`)
+
+### 7. Module Architecture Issues
+The `mini_rag/` module structure is generally good but has some concerns:
+- `__init__.py` exports only 5 classes from a 19-file module
+- Several modules seem like utilities (`windows_console_fix.py`, `venv_checker.py`)
+- Module names could be more descriptive (`server.py` vs `fast_server.py`)
+
+## Medium Priority Issues (Improve Over Time)
+
+### 8. Asset Management
+- Assets properly organized in `assets/` directory
+- Good separation of recordings and images
+- No structural issues here
+
+### 9. Virtual Environment Clutter
+- Two venv directories: `.venv` and `.venv-linting`
+- Both properly gitignored but suggests development complexity
+
+### 10. Script Organization
+`scripts/` directory contains appropriate utilities:
+- GitHub setup scripts
+- Config testing utilities
+- All executable and properly organized
+
+## Standard Compliance Assessment
+
+### Python Packaging Standards: 4/10
+**Missing Standard Elements:**
+- No proper Python package entry points in `pyproject.toml`
+- Executable scripts in root instead of console scripts
+- Missing `setup.py` or complete `pyproject.toml` configuration
+
+**Present Elements:**
+- Good `pyproject.toml` with isort/black config
+- Proper `.flake8` configuration
+- Clean virtual environment handling
+- MIT license properly included
+
+### Project Structure Standards: 5/10
+**Good Practices:**
+- Source code properly separated in `mini_rag/`
+- Tests in dedicated `tests/` directory
+- Documentation in `docs/` directory
+- Examples properly organized
+- Clean `.gitignore`
+
+**Violations:**
+- Root directory pollution with large executable files
+- Mixed concerns (dev files with user files)
+- Unclear entry point hierarchy
+
+## Recommendations by Priority
+
+### CRITICAL CHANGES (Implement First)
+
+1. **Relocate Large Scripts**
+   ```bash
+   mkdir -p bin/
+   mv rag-mini.py bin/
+   mv rag-tui.py bin/
+   # Update rag.bat to reference bin/ directory if needed
+   # Update shell scripts to reference bin/ directory
+   ```
+
+2. **Clean Root Directory**
+   ```bash
+   rm commit_message.txt
+   rm REPOSITORY_SUMMARY.md
+   mv "Agent Instructions.md" docs/AGENT_INSTRUCTIONS.md
+   mv test_fixes.py tests/
+   ```
+
+3. **Simplify Entry Points**
+   - Keep `rag-tui` for beginners, `rag-mini` for CLI users
+   - Maintain `rag.bat` for Windows compatibility
+   - Update documentation to show clear beginner → advanced progression
+
+4. **Standardize Configuration**
+   - Move `config-llm-providers.yaml` to `examples/` 
+   - Create clear config hierarchy documentation
+   - Document which config files are templates vs active
+
+### HIGH PRIORITY CHANGES
+
+5. **Improve pyproject.toml**
+   ```toml
+   [project]
+   name = "fss-mini-rag"
+   version = "2.1.0"
+   description = "Lightweight, educational RAG system"
+   
+   [project.scripts]
+   rag-mini = "mini_rag.cli:cli"
+   rag-tui = "mini_rag.tui:main"
+   ```
+
+6. **Consolidate Documentation**
+   - Move `GET_STARTED.md` content into `docs/GETTING_STARTED.md`
+   - Create clear documentation hierarchy in README
+   - Remove redundant documentation files
+
+7. **Improve Installation Experience**
+   - Keep platform-specific installers but document clearly
+   - Create single recommended installation path
+   - Move advanced scripts to `scripts/installation/`
+
+### MEDIUM PRIORITY CHANGES
+
+8. **Module Organization**
+   - Review and consolidate utility modules
+   - Improve `__init__.py` exports
+   - Consider subpackage organization for large modules
+
+9. **Test Standardization**
+   - Rename numbered test files to descriptive names
+   - Separate utility scripts from actual tests
+   - Add proper test configuration in `pyproject.toml`
+
+## Implementation Plan
+
+### Phase 1: Emergency Cleanup (2-3 hours)
+1. Move large scripts out of root directory
+2. Remove development artifacts 
+3. Consolidate configuration files
+4. Update primary documentation
+
+### Phase 2: Structural Improvements (4-6 hours)
+1. Improve Python packaging configuration
+2. Consolidate entry points
+3. Organize installation scripts
+4. Standardize test organization
+
+### Phase 3: Professional Polish (2-4 hours)
+1. Review and improve module architecture
+2. Enhance documentation hierarchy
+3. Add missing standard project files
+4. Final professional review
+
+## Impact Assessment
+
+### Before Changes
+- **First Impression**: Confused by multiple entry points and cluttered root
+- **Developer Experience**: Unclear how to contribute or modify
+- **Professional Credibility**: Damaged by poor organization
+- **Maintenance Burden**: High due to scattered structure
+
+### After Changes
+- **First Impression**: Clean, professional project structure
+- **Developer Experience**: Clear entry points and logical organization  
+- **Professional Credibility**: Enhanced by following standards
+- **Maintenance Burden**: Reduced through proper organization
+
+## Conclusion
+
+The FSS-Mini-RAG project has excellent technical merit but is significantly hampered by poor structural organization. The root directory pollution and multiple entry points create unnecessary complexity and damage the professional presentation.
+
+**Priority Recommendation:** Focus on the Critical Changes first - these will provide the most impact for professional presentation with minimal risk to functionality.
+
+**Timeline:** The structural issues can be resolved in 1-2 focused sessions without touching the core technical implementation, dramatically improving the project's professional appearance and maintainability.
+
+---
+
+*Analysis completed: August 28, 2025 - FSS-Mini-RAG Project Structure Assessment*

docs/security-analysis.md

@@ -0,0 +1,373 @@
+# FSS-Mini-RAG Security Analysis Report
+**Conducted by: Emma, Authentication Specialist**  
+**Date: 2024-08-28**  
+**Classification: Confidential - For Professional Deployment Review**
+
+---
+
+## Executive Summary
+
+This comprehensive security audit examines the FSS-Mini-RAG system's defensive posture, identifying vulnerabilities and providing actionable hardening recommendations. The system demonstrates several commendable security practices but requires attention in key areas before professional deployment.
+
+**Overall Security Rating: MODERATE RISK (Amber)**
+- ✅ **Strengths**: Good input validation patterns, secure default configurations, appropriate access controls
+- ⚠️ **Concerns**: Network service exposure, file system access patterns, dependency management
+- 🔴 **Critical**: Server port management and external service integration security
+
+---
+
+## 1. Data Security & Privacy Assessment
+
+### Data Handling Analysis
+**Status: GOOD with Minor Concerns**
+
+#### Positive Security Practices:
+- **Local-First Architecture**: All data processing occurs locally, reducing external attack surface
+- **No Cloud Dependency**: Embeddings and vector storage remain on-premise
+- **Temporary File Management**: Proper cleanup patterns observed in chunking operations
+- **Path Normalisation**: Robust cross-platform path handling prevents directory traversal
+
+#### Areas of Concern:
+- **Persistent Storage**: `.mini-rag/` directories store sensitive codebase information
+- **Index Files**: LanceDB vector files contain searchable representations of source code
+- **Configuration Files**: YAML configs may contain sensitive connection strings
+- **Memory Exposure**: Code content held in memory during processing without explicit scrubbing
+
+#### Recommendations:
+1. **Implement data classification**: Tag sensitive files during indexing
+2. **Add encryption at rest**: Encrypt vector databases and configuration files
+3. **Memory management**: Explicit memory clearing after processing sensitive content
+4. **Access logging**: Track who accesses which code segments through search
+
+---
+
+## 2. Input Validation & Sanitization Assessment
+
+### CLI Input Handling
+**Status: GOOD**
+
+#### Robust Validation Observed:
+```python
+# Path validation with proper resolution
+project_path = Path(path).resolve()
+
+# Type checking and bounds validation
+@click.option("--top-k", "-k", type=int, default=10)
+@click.option("--port", type=int, default=7777)
+```
+
+#### File Path Security:
+- **Path Traversal Protection**: Proper use of `Path().resolve()` throughout codebase
+- **Extension Validation**: File type filtering based on extensions
+- **Size Limits**: Appropriate file size thresholds implemented
+
+#### Search Query Processing:
+**Status: MODERATE RISK**
+
+**Vulnerabilities Identified:**
+- **No Query Length Limits**: Potential DoS through excessive query lengths
+- **Special Character Handling**: Limited sanitization of search terms
+- **Regex Injection**: Query expansion could be exploited with crafted patterns
+
+#### Recommendations:
+1. **Implement query length limits** (max 512 characters)
+2. **Sanitize search queries** before processing
+3. **Validate file patterns** in include/exclude configurations
+4. **Add input encoding validation** for non-ASCII content
+
+---
+
+## 3. Network Security Assessment
+
+### Server Implementation Analysis
+**Status: HIGH RISK - REQUIRES IMMEDIATE ATTENTION**
+
+#### Critical Security Issues:
+
+**1. Port Management Vulnerabilities:**
+```python
+# CRITICAL: Automatic port cleanup attempts system commands
+result = subprocess.run(["netstat", "-ano"], capture_output=True, text=True)
+subprocess.run(["taskkill", "//PID", pid, "//F"], check=False)
+```
+**Risk**: Command injection, privilege escalation
+**Impact**: System compromise possible
+
+**2. Network Service Exposure:**
+```python
+# Binds to localhost but lacks authentication
+self.socket.bind(("localhost", self.port))
+self.socket.listen(5)
+```
+**Risk**: Unauthorised local access
+**Impact**: Code exposure to other local processes
+
+**3. Message Framing Vulnerabilities:**
+```python
+# Potential buffer overflow with untrusted length prefix
+length = int.from_bytes(length_data, "big")
+chunk = sock.recv(min(65536, length - len(data)))
+```
+**Risk**: Memory exhaustion, DoS attacks
+**Impact**: Service disruption
+
+#### Recommendations:
+1. **Implement authentication**: Token-based access control for server connections
+2. **Remove automatic process killing**: Replace with safe port checking
+3. **Add connection limits**: Rate limiting and concurrent connection controls
+4. **Message size validation**: Strict limits on incoming message sizes
+5. **TLS encryption**: Encrypt local communications
+
+---
+
+## 4. External Service Integration Security
+
+### Ollama Integration Analysis
+**Status: MODERATE RISK**
+
+#### Security Concerns:
+```python
+# Unvalidated external service calls
+response = requests.get(f"{self.base_url}/api/tags", timeout=5)
+```
+
+**Vulnerabilities:**
+- **No certificate validation** for HTTPS connections
+- **Trust boundary violation**: Implicit trust of Ollama responses
+- **Configuration injection**: User-controlled host parameters
+
+#### LLM Service Security:
+- **Prompt injection risks**: User queries passed directly to LLM
+- **Data leakage potential**: Code content sent to external models
+- **Response validation**: Limited validation of LLM outputs
+
+#### Recommendations:
+1. **Certificate validation**: Enforce TLS certificate checking
+2. **Response validation**: Sanitize and validate all external responses
+3. **Connection timeouts**: Implement aggressive timeouts for external calls
+4. **Host validation**: Whitelist allowed connection targets
+
+---
+
+## 5. File System Security Assessment
+
+### File Access Patterns
+**Status: GOOD with Recommendations**
+
+#### Positive Practices:
+- **Appropriate file permissions**: Uses standard Python file operations
+- **Pattern-based exclusions**: Sensible default exclude patterns
+- **Size-based filtering**: Protection against processing oversized files
+
+#### Areas for Improvement:
+```python
+# File enumeration could be restricted further
+all_files = list(project_path.rglob("*"))
+```
+
+#### Recommendations:
+1. **Implement file access logging**: Track which files are indexed/searched
+2. **Add symlink protection**: Prevent symlink-based directory traversal
+3. **Enhanced file type validation**: Magic number checking beyond extensions
+4. **Temporary file security**: Secure creation and cleanup of temp files
+
+---
+
+## 6. Configuration Security Assessment
+
+### YAML Configuration Handling
+**Status: MODERATE RISK**
+
+#### Security Issues:
+```python
+# YAML parsing without safe mode enforcement
+data = yaml.safe_load(f)
+```
+**Note**: Uses `safe_load` (good) but lacks validation
+
+#### Configuration Vulnerabilities:
+- **Path injection**: User-controlled paths in configuration
+- **Service endpoints**: External service URLs configurable
+- **Model specifications**: Potential for malicious model references
+
+#### Recommendations:
+1. **Configuration validation schema**: Implement strict YAML schema validation
+2. **Whitelist allowed values**: Restrict configuration options to safe choices
+3. **Configuration encryption**: Encrypt sensitive configuration values
+4. **Read-only configurations**: Prevent runtime modification of security settings
+
+---
+
+## 7. Dependencies & Supply Chain Security
+
+### Dependency Analysis
+**Status: MODERATE RISK**
+
+#### Current Dependencies:
+```
+lancedb>=0.5.0      # Vector database - moderate risk
+requests>=2.28.0    # HTTP client - well-maintained
+click>=8.1.0        # CLI framework - secure
+PyYAML>=6.0.0       # YAML parsing - recent versions secure
+```
+
+#### Security Concerns:
+- **Version pinning**: Uses minimum versions (>=) allowing potentially vulnerable updates
+- **Transitive dependencies**: No analysis of indirect dependencies
+- **Supply chain attacks**: No dependency integrity verification
+
+#### Recommendations:
+1. **Pin exact versions**: Use `==` instead of `>=` for production deployments
+2. **Dependency scanning**: Implement automated vulnerability scanning
+3. **Integrity verification**: Use pip hash checking for critical dependencies
+4. **Regular updates**: Establish dependency update and testing procedures
+
+---
+
+## 8. Logging & Monitoring Security
+
+### Current Logging Analysis
+**Status: REQUIRES IMPROVEMENT**
+
+#### Logging Practices:
+```python
+logger = logging.getLogger(__name__)
+# Basic logging without security context
+```
+
+#### Security Gaps:
+- **No security event logging**: Access attempts not recorded
+- **Information leakage**: Debug logs may expose sensitive paths
+- **No audit trail**: Cannot track security-relevant events
+- **Log injection**: Potential for log poisoning through user inputs
+
+#### Recommendations:
+1. **Security event logging**: Log all authentication attempts, access patterns
+2. **Sanitize log inputs**: Prevent log injection attacks
+3. **Structured logging**: Use structured formats for security analysis
+4. **Log rotation and retention**: Implement secure log management
+5. **Monitoring integration**: Connect to security monitoring systems
+
+---
+
+## 9. System Hardening Recommendations
+
+### Priority 1 (Critical - Implement Immediately):
+
+1. **Server Authentication**:
+   ```python
+   # Add token-based authentication
+   def authenticate_request(self, token):
+       return hmac.compare_digest(token, self.expected_token)
+   ```
+
+2. **Safe Port Management**:
+   ```python
+   # Remove dangerous subprocess calls
+   # Use socket.SO_REUSEADDR properly instead
+   ```
+
+3. **Input Validation Framework**:
+   ```python
+   def validate_search_query(query: str) -> str:
+       if len(query) > 512:
+           raise ValueError("Query too long")
+       return re.sub(r'[^\w\s\-\.]', '', query)
+   ```
+
+### Priority 2 (High - Implement Within Sprint):
+
+4. **Configuration Security**:
+   ```python
+   # Implement configuration schema validation
+   # Add encryption for sensitive config values
+   ```
+
+5. **Enhanced Logging**:
+   ```python
+   # Add security event logging
+   security_logger.info("Search performed", extra={
+       "user": user_id,
+       "query_hash": hashlib.sha256(query.encode()).hexdigest()[:16],
+       "files_accessed": len(results)
+   })
+   ```
+
+6. **Dependency Management**:
+   ```bash
+   # Pin exact versions in requirements.txt
+   # Implement hash checking
+   ```
+
+### Priority 3 (Medium - Next Release Cycle):
+
+7. **Data Encryption**: Implement at-rest encryption for vector databases
+8. **Access Controls**: Role-based access to different code segments
+9. **Security Monitoring**: Integration with SIEM systems
+10. **Penetration Testing**: Regular security assessments
+
+---
+
+## 10. Compliance & Audit Considerations
+
+### Current Compliance Posture:
+- **Data Protection**: Local storage reduces GDPR/privacy risks
+- **Access Logging**: Currently insufficient for audit requirements
+- **Change Management**: Git-based but lacks security change tracking
+- **Documentation**: Good code documentation but missing security procedures
+
+### Recommendations for Compliance:
+1. **Security documentation**: Create security architecture diagrams
+2. **Access audit trails**: Implement comprehensive logging
+3. **Regular security reviews**: Quarterly security assessments
+4. **Incident response procedures**: Define security incident handling
+5. **Backup security**: Secure backup and recovery procedures
+
+---
+
+## 11. Deployment Security Checklist
+
+### Pre-Deployment Security Requirements:
+
+- [ ] **Authentication implemented** for server mode
+- [ ] **Input validation** comprehensive across all entry points
+- [ ] **Configuration hardening** with schema validation
+- [ ] **Dependency scanning** completed and vulnerabilities addressed
+- [ ] **Security logging** implemented and tested
+- [ ] **TLS/encryption** for network communications
+- [ ] **File system permissions** properly configured
+- [ ] **Service account isolation** implemented
+- [ ] **Monitoring and alerting** configured
+- [ ] **Backup security** validated
+
+### Post-Deployment Security Monitoring:
+
+- [ ] **Regular vulnerability scans** scheduled
+- [ ] **Log analysis** for security events
+- [ ] **Dependency update procedures** established
+- [ ] **Incident response plan** activated
+- [ ] **Security metrics** tracked and reported
+
+---
+
+## Conclusion
+
+The FSS-Mini-RAG system demonstrates solid foundational security practices with appropriate local-first architecture and sensible defaults. However, several critical vulnerabilities require immediate attention before professional deployment, particularly around server security and input validation.
+
+**Primary Action Items:**
+1. **Implement server authentication** (Critical)
+2. **Eliminate subprocess security risks** (Critical)
+3. **Enhanced input validation** (High)
+4. **Comprehensive security logging** (High)
+5. **Dependency security hardening** (Medium)
+
+With these improvements, the system will achieve a **GOOD** security posture suitable for professional deployment environments.
+
+**Risk Acceptance**: Any deployment without addressing Critical and High priority items should require explicit risk acceptance from senior management.
+
+---
+
+*This analysis conducted with military precision and British thoroughness. Implementation of recommendations will significantly enhance the system's defensive capabilities whilst maintaining operational effectiveness.*
+
+**Emma, Authentication Specialist**  
+**Security Clearance: OFFICIAL**

examples/analyze_dependencies.py

@@ -4,14 +4,14 @@ Analyze FSS-Mini-RAG dependencies to determine what's safe to remove.
 """
 
 import ast
-import os
-from pathlib import Path
 from collections import defaultdict
+from pathlib import Path
+
 
 def find_imports_in_file(file_path):
     """Find all imports in a Python file."""
     try:
-        with open(file_path, 'r', encoding='utf-8') as f:
+        with open(file_path, "r", encoding="utf-8") as f:
             content = f.read()
 
         tree = ast.parse(content)
@@ -20,10 +20,10 @@ def find_imports_in_file(file_path):
         for node in ast.walk(tree):
             if isinstance(node, ast.Import):
                 for alias in node.names:
-                    imports.add(alias.name.split('.')[0])
+                    imports.add(alias.name.split(".")[0])
             elif isinstance(node, ast.ImportFrom):
                 if node.module:
-                    module = node.module.split('.')[0]
+                    module = node.module.split(".")[0]
                     imports.add(module)
 
         return imports
@@ -31,6 +31,7 @@ def find_imports_in_file(file_path):
         print(f"Error analyzing {file_path}: {e}")
         return set()
 
+
 def analyze_dependencies():
     """Analyze all dependencies in the project."""
     project_root = Path(__file__).parent
@@ -85,13 +86,13 @@ def analyze_dependencies():
     print("\n🛡️ Safety Analysis:")
 
     # Files imported by __init__.py are definitely needed
-    init_imports = file_imports.get('__init__.py', set())
+    init_imports = file_imports.get("__init__.py", set())
     print(f"   Core modules (imported by __init__.py): {', '.join(init_imports)}")
 
     # Files not used anywhere might be safe to remove
     unused_files = []
     for module in all_modules:
-        if module not in reverse_deps and module != '__init__':
+        if module not in reverse_deps and module != "__init__":
             unused_files.append(module)
 
     if unused_files:
@@ -99,11 +100,14 @@ def analyze_dependencies():
         print("   ❗ Verify these aren't used by CLI or external scripts!")
 
     # Check CLI usage
-    cli_files = ['cli.py', 'enhanced_cli.py']
+    cli_files = ["cli.py", "enhanced_cli.py"]
     for cli_file in cli_files:
         if cli_file in file_imports:
             cli_imports = file_imports[cli_file]
-            print(f"   📋 {cli_file} imports: {', '.join([imp for imp in cli_imports if imp in all_modules])}")
+            print(
+                f"   📋 {cli_file} imports: {', '.join([imp for imp in cli_imports if imp in all_modules])}"
+            )
+
 
 if __name__ == "__main__":
     analyze_dependencies()

examples/basic_usage.py

@@ -5,7 +5,9 @@ Shows how to index a project and search it programmatically.
 """
 
 from pathlib import Path
-from mini_rag import ProjectIndexer, CodeSearcher, CodeEmbedder
+
+from mini_rag import CodeEmbedder, CodeSearcher, ProjectIndexer
+
 
 def main():
     # Example project path - change this to your project
@@ -44,7 +46,7 @@ def main():
         "embedding system",
         "search implementation",
         "file watcher",
-        "error handling"
+        "error handling",
     ]
 
     print("\n4. Example searches:")
@@ -57,12 +59,13 @@ def main():
                 print(f"      {i}. {result.file_path.name} (score: {result.score:.3f})")
                 print(f"         Type: {result.chunk_type}")
                 # Show first 60 characters of content
-                content_preview = result.content.replace('\n', ' ')[:60]
+                content_preview = result.content.replace("\n", " ")[:60]
                 print(f"         Preview: {content_preview}...")
         else:
             print("      No results found")
 
     print("\n=== Example Complete ===")
 
+
 if __name__ == "__main__":
     main()

examples/smart_config_suggestions.py

@@ -5,9 +5,10 @@ Analyzes the indexed data to suggest optimal settings.
 """
 
 import json
-from pathlib import Path
-from collections import defaultdict, Counter
 import sys
+from collections import Counter
+from pathlib import Path
+
 
 def analyze_project_patterns(manifest_path: Path):
     """Analyze project patterns and suggest optimizations."""
@@ -15,7 +16,7 @@ def analyze_project_patterns(manifest_path: Path):
     with open(manifest_path) as f:
         manifest = json.load(f)
 
-    files = manifest.get('files', {})
+    files = manifest.get("files", {})
 
     print("🔍 FSS-Mini-RAG Smart Tuning Analysis")
     print("=" * 50)
@@ -27,11 +28,11 @@ def analyze_project_patterns(manifest_path: Path):
     small_files = []
 
     for filepath, info in files.items():
-        lang = info.get('language', 'unknown')
+        lang = info.get("language", "unknown")
         languages[lang] += 1
 
-        size = info.get('size', 0)
-        chunks = info.get('chunks', 1)
+        size = info.get("size", 0)
+        chunks = info.get("chunks", 1)
 
         chunk_efficiency.append(chunks / max(1, size / 1000))  # chunks per KB
 
@@ -42,65 +43,70 @@ def analyze_project_patterns(manifest_path: Path):
 
     # Analysis results
     total_files = len(files)
-    total_chunks = sum(info.get('chunks', 1) for info in files.values())
+    total_chunks = sum(info.get("chunks", 1) for info in files.values())
     avg_chunks_per_file = total_chunks / max(1, total_files)
 
-    print(f"📊 Current Stats:")
+    print("📊 Current Stats:")
     print(f"   Files: {total_files}")
     print(f"   Chunks: {total_chunks}")
     print(f"   Avg chunks/file: {avg_chunks_per_file:.1f}")
 
-    print(f"\n🗂️ Language Distribution:")
+    print("\n🗂️ Language Distribution:")
     for lang, count in languages.most_common(10):
         pct = 100 * count / total_files
         print(f"   {lang}: {count} files ({pct:.1f}%)")
 
-    print(f"\n💡 Smart Optimization Suggestions:")
+    print("\n💡 Smart Optimization Suggestions:")
 
     # Suggestion 1: Language-specific chunking
-    if languages['python'] > 10:
-        print(f"✨ Python Optimization:")
-        print(f"   - Use function-level chunking (detected {languages['python']} Python files)")
-        print(f"   - Increase chunk size to 3000 chars for Python (better context)")
+    if languages["python"] > 10:
+        print("✨ Python Optimization:")
+        print(
+            f"   - Use function-level chunking (detected {languages['python']} Python files)"
+        )
+        print("   - Increase chunk size to 3000 chars for Python (better context)")
 
-    if languages['markdown'] > 5:
-        print(f"✨ Markdown Optimization:")
+    if languages["markdown"] > 5:
+        print("✨ Markdown Optimization:")
         print(f"   - Use header-based chunking (detected {languages['markdown']} MD files)")
-        print(f"   - Keep sections together for better search relevance")
+        print("   - Keep sections together for better search relevance")
 
-    if languages['json'] > 20:
-        print(f"✨ JSON Optimization:")
+    if languages["json"] > 20:
+        print("✨ JSON Optimization:")
         print(f"   - Consider object-level chunking (detected {languages['json']} JSON files)")
-        print(f"   - Might want to exclude large config JSONs")
+        print("   - Might want to exclude large config JSONs")
 
     # Suggestion 2: File size optimization
     if large_files:
-        print(f"\n📈 Large File Optimization:")
+        print("\n📈 Large File Optimization:")
         print(f"   Found {len(large_files)} files >10KB:")
-        for filepath, size, chunks in sorted(large_files, key=lambda x: x[1], reverse=True)[:3]:
+        for filepath, size, chunks in sorted(large_files, key=lambda x: x[1], reverse=True)[
+            :3
+        ]:
             kb = size / 1024
             print(f"   - {filepath}: {kb:.1f}KB → {chunks} chunks")
         if len(large_files) > 5:
-            print(f"   💡 Consider streaming threshold: 5KB (current: 1MB)")
+            print("   💡 Consider streaming threshold: 5KB (current: 1MB)")
 
     if small_files and len(small_files) > total_files * 0.3:
-        print(f"\n📉 Small File Optimization:")
+        print("\n📉 Small File Optimization:")
         print(f"   {len(small_files)} files <500B might not need chunking")
-        print(f"   💡 Consider: combine small files or skip tiny ones")
+        print("   💡 Consider: combine small files or skip tiny ones")
 
     # Suggestion 3: Search optimization
     avg_efficiency = sum(chunk_efficiency) / len(chunk_efficiency)
-    print(f"\n🔍 Search Optimization:")
+    print("\n🔍 Search Optimization:")
     if avg_efficiency < 0.5:
-        print(f"   💡 Chunks are large relative to files - consider smaller chunks")
+        print("   💡 Chunks are large relative to files - consider smaller chunks")
         print(f"   💡 Current: {avg_chunks_per_file:.1f} chunks/file, try 2-3 chunks/file")
     elif avg_efficiency > 2:
-        print(f"   💡 Many small chunks - consider larger chunk size")
-        print(f"   💡 Reduce chunk overhead with 2000-4000 char chunks")
+        print("   💡 Many small chunks - consider larger chunk size")
+        print("   💡 Reduce chunk overhead with 2000-4000 char chunks")
 
     # Suggestion 4: Smart defaults
-    print(f"\n⚙️ Recommended Config Updates:")
-    print(f"""{{
+    print("\n⚙️ Recommended Config Updates:")
+    print(
+        """{{
   "chunking": {{
     "max_size": {3000 if languages['python'] > languages['markdown'] else 2000},
     "min_size": 200,
@@ -115,7 +121,9 @@ def analyze_project_patterns(manifest_path: Path):
     "skip_small_files": {500 if len(small_files) > total_files * 0.3 else 0},
     "streaming_threshold_kb": {5 if len(large_files) > 5 else 1024}
   }}
-}}""")
+}}"""
+    )
+
 
 if __name__ == "__main__":
     if len(sys.argv) != 2:

mini_rag/__init__.py

@@ -7,30 +7,16 @@ Designed for portability, efficiency, and simplicity across projects and compute
 
 __version__ = "2.1.0"
 
-from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
 from .chunker import CodeChunker
 from .indexer import ProjectIndexer
+from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
 from .search import CodeSearcher
 from .watcher import FileWatcher
 
-# Auto-update system (graceful import for legacy versions)
-try:
-    from .updater import UpdateChecker, check_for_updates, get_updater
-    __all__ = [
+__all__ = [
     "CodeEmbedder",
     "CodeChunker",
     "ProjectIndexer",
     "CodeSearcher",
     "FileWatcher",
-        "UpdateChecker",
-        "check_for_updates", 
-        "get_updater",
-    ]
-except ImportError:
-    __all__ = [
-        "CodeEmbedder",
-        "CodeChunker", 
-        "ProjectIndexer",
-        "CodeSearcher",
-        "FileWatcher",
-    ]
+]

mini_rag/__main__.py

@@ -2,5 +2,5 @@
 
 from .cli import cli
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     cli()

mini_rag/auto_optimizer.py

@@ -3,22 +3,23 @@ Auto-optimizer for FSS-Mini-RAG.
 Automatically tunes settings based on usage patterns.
 """
 
-from pathlib import Path
 import json
-from typing import Dict, Any, List
-from collections import Counter
 import logging
+from collections import Counter
+from pathlib import Path
+from typing import Any, Dict
 
 logger = logging.getLogger(__name__)
 
+
 class AutoOptimizer:
     """Automatically optimizes RAG settings based on project patterns."""
 
     def __init__(self, project_path: Path):
         self.project_path = project_path
-        self.rag_dir = project_path / '.mini-rag'
-        self.config_path = self.rag_dir / 'config.json'
-        self.manifest_path = self.rag_dir / 'manifest.json'
+        self.rag_dir = project_path / ".mini-rag"
+        self.config_path = self.rag_dir / "config.json"
+        self.manifest_path = self.rag_dir / "manifest.json"
 
     def analyze_and_optimize(self) -> Dict[str, Any]:
         """Analyze current patterns and auto-optimize settings."""
@@ -37,23 +38,23 @@ class AutoOptimizer:
         optimizations = self._generate_optimizations(analysis)
 
         # Apply optimizations if beneficial
-        if optimizations['confidence'] > 0.7:
+        if optimizations["confidence"] > 0.7:
             self._apply_optimizations(optimizations)
             return {
                 "status": "optimized",
-                "changes": optimizations['changes'],
-                "expected_improvement": optimizations['expected_improvement']
+                "changes": optimizations["changes"],
+                "expected_improvement": optimizations["expected_improvement"],
             }
         else:
             return {
                 "status": "no_changes_needed",
                 "analysis": analysis,
-                "confidence": optimizations['confidence']
+                "confidence": optimizations["confidence"],
             }
 
     def _analyze_patterns(self, manifest: Dict[str, Any]) -> Dict[str, Any]:
         """Analyze current indexing patterns."""
-        files = manifest.get('files', {})
+        files = manifest.get("files", {})
 
         # Language distribution
         languages = Counter()
@@ -61,11 +62,11 @@ class AutoOptimizer:
         chunk_ratios = []
 
         for filepath, info in files.items():
-            lang = info.get('language', 'unknown')
+            lang = info.get("language", "unknown")
             languages[lang] += 1
 
-            size = info.get('size', 0)
-            chunks = info.get('chunks', 1)
+            size = info.get("size", 0)
+            chunks = info.get("chunks", 1)
 
             sizes.append(size)
             chunk_ratios.append(chunks / max(1, size / 1000))  # chunks per KB
@@ -74,13 +75,13 @@ class AutoOptimizer:
         avg_size = sum(sizes) / len(sizes) if sizes else 1000
 
         return {
-            'languages': dict(languages.most_common()),
-            'total_files': len(files),
-            'total_chunks': sum(info.get('chunks', 1) for info in files.values()),
-            'avg_chunk_ratio': avg_chunk_ratio,
-            'avg_file_size': avg_size,
-            'large_files': sum(1 for s in sizes if s > 10000),
-            'small_files': sum(1 for s in sizes if s < 500)
+            "languages": dict(languages.most_common()),
+            "total_files": len(files),
+            "total_chunks": sum(info.get("chunks", 1) for info in files.values()),
+            "avg_chunk_ratio": avg_chunk_ratio,
+            "avg_file_size": avg_size,
+            "large_files": sum(1 for s in sizes if s > 10000),
+            "small_files": sum(1 for s in sizes if s < 500),
         }
 
     def _generate_optimizations(self, analysis: Dict[str, Any]) -> Dict[str, Any]:
@@ -90,49 +91,51 @@ class AutoOptimizer:
         expected_improvement = 0
 
         # Optimize chunking based on dominant language
-        languages = analysis['languages']
+        languages = analysis["languages"]
         if languages:
             dominant_lang, count = list(languages.items())[0]
-            lang_pct = count / analysis['total_files']
+            lang_pct = count / analysis["total_files"]
 
             if lang_pct > 0.3:  # Dominant language >30%
-                if dominant_lang == 'python' and analysis['avg_chunk_ratio'] < 1.5:
-                    changes.append("Increase Python chunk size to 3000 for better function context")
+                if dominant_lang == "python" and analysis["avg_chunk_ratio"] < 1.5:
+                    changes.append(
+                        "Increase Python chunk size to 3000 for better function context"
+                    )
                     confidence += 0.2
                     expected_improvement += 15
 
-                elif dominant_lang == 'markdown' and analysis['avg_chunk_ratio'] < 1.2:
+                elif dominant_lang == "markdown" and analysis["avg_chunk_ratio"] < 1.2:
                     changes.append("Use header-based chunking for Markdown files")
                     confidence += 0.15
                     expected_improvement += 10
 
         # Optimize for large files
-        if analysis['large_files'] > 5:
+        if analysis["large_files"] > 5:
             changes.append("Reduce streaming threshold to 5KB for better large file handling")
             confidence += 0.1
             expected_improvement += 8
 
         # Optimize chunk ratio
-        if analysis['avg_chunk_ratio'] < 1.0:
+        if analysis["avg_chunk_ratio"] < 1.0:
             changes.append("Reduce chunk size for more granular search results")
             confidence += 0.15
             expected_improvement += 12
-        elif analysis['avg_chunk_ratio'] > 3.0:
+        elif analysis["avg_chunk_ratio"] > 3.0:
             changes.append("Increase chunk size to reduce overhead")
             confidence += 0.1
             expected_improvement += 5
 
         # Skip tiny files optimization
-        small_file_pct = analysis['small_files'] / analysis['total_files']
+        small_file_pct = analysis["small_files"] / analysis["total_files"]
         if small_file_pct > 0.3:
             changes.append("Skip files smaller than 300 bytes to improve focus")
             confidence += 0.1
             expected_improvement += 3
 
         return {
-            'changes': changes,
-            'confidence': min(confidence, 1.0),
-            'expected_improvement': expected_improvement
+            "changes": changes,
+            "confidence": min(confidence, 1.0),
+            "expected_improvement": expected_improvement,
         }
 
     def _apply_optimizations(self, optimizations: Dict[str, Any]):
@@ -145,35 +148,35 @@ class AutoOptimizer:
         else:
             config = self._get_default_config()
 
-        changes = optimizations['changes']
+        changes = optimizations["changes"]
 
         # Apply changes based on recommendations
         for change in changes:
             if "Python chunk size to 3000" in change:
-                config.setdefault('chunking', {})['max_size'] = 3000
+                config.setdefault("chunking", {})["max_size"] = 3000
 
             elif "header-based chunking" in change:
-                config.setdefault('chunking', {})['strategy'] = 'header'
+                config.setdefault("chunking", {})["strategy"] = "header"
 
             elif "streaming threshold to 5KB" in change:
-                config.setdefault('streaming', {})['threshold_bytes'] = 5120
+                config.setdefault("streaming", {})["threshold_bytes"] = 5120
 
             elif "Reduce chunk size" in change:
-                current_size = config.get('chunking', {}).get('max_size', 2000)
-                config.setdefault('chunking', {})['max_size'] = max(1500, current_size - 500)
+                current_size = config.get("chunking", {}).get("max_size", 2000)
+                config.setdefault("chunking", {})["max_size"] = max(1500, current_size - 500)
 
             elif "Increase chunk size" in change:
-                current_size = config.get('chunking', {}).get('max_size', 2000)
-                config.setdefault('chunking', {})['max_size'] = min(4000, current_size + 500)
+                current_size = config.get("chunking", {}).get("max_size", 2000)
+                config.setdefault("chunking", {})["max_size"] = min(4000, current_size + 500)
 
             elif "Skip files smaller" in change:
-                config.setdefault('files', {})['min_file_size'] = 300
+                config.setdefault("files", {})["min_file_size"] = 300
 
         # Save optimized config
-        config['_auto_optimized'] = True
-        config['_optimization_timestamp'] = json.dumps(None, default=str)
+        config["_auto_optimized"] = True
+        config["_optimization_timestamp"] = json.dumps(None, default=str)
 
-        with open(self.config_path, 'w') as f:
+        with open(self.config_path, "w") as f:
             json.dump(config, f, indent=2)
 
         logger.info(f"Applied {len(changes)} optimizations to {self.config_path}")
@@ -181,16 +184,7 @@ class AutoOptimizer:
     def _get_default_config(self) -> Dict[str, Any]:
         """Get default configuration."""
         return {
-            "chunking": {
-                "max_size": 2000,
-                "min_size": 150,
-                "strategy": "semantic"
-            },
-            "streaming": {
-                "enabled": True,
-                "threshold_bytes": 1048576
-            },
-            "files": {
-                "min_file_size": 50
-            }
+            "chunking": {"max_size": 2000, "min_size": 150, "strategy": "semantic"},
+            "streaming": {"enabled": True, "threshold_bytes": 1048576},
+            "files": {"min_file_size": 50},
         }

mini_rag/cli.py

@@ -3,57 +3,55 @@ Command-line interface for Mini RAG system.
 Beautiful, intuitive, and highly effective.
 """
 
-import click
+import logging
 import sys
 import time
-import logging
 from pathlib import Path
 from typing import Optional
 
-# Fix Windows console for proper emoji/Unicode support
-from .windows_console_fix import fix_windows_console
-fix_windows_console()
-
+import click
 from rich.console import Console
-from rich.table import Table
-from rich.progress import Progress, SpinnerColumn, TextColumn
 from rich.logging import RichHandler
-from rich.syntax import Syntax
 from rich.panel import Panel
-from rich import print as rprint
+from rich.progress import Progress, SpinnerColumn, TextColumn
+from rich.syntax import Syntax
+from rich.table import Table
 
 from .indexer import ProjectIndexer
-from .search import CodeSearcher
-from .watcher import FileWatcher
 from .non_invasive_watcher import NonInvasiveFileWatcher
 from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
-from .chunker import CodeChunker
 from .performance import get_monitor
-from .server import RAGClient
-from .server import RAGServer, RAGClient, start_server
+from .search import CodeSearcher
+from .server import RAGClient, start_server
+from .windows_console_fix import fix_windows_console
+
+# Fix Windows console for proper emoji/Unicode support
+fix_windows_console()
 
 # Set up logging
 logging.basicConfig(
     level=logging.INFO,
     format="%(message)s",
-    handlers=[RichHandler(rich_tracebacks=True)]
+    handlers=[RichHandler(rich_tracebacks=True)],
 )
 logger = logging.getLogger(__name__)
 console = Console()
 
 
 @click.group()
-@click.option('--verbose', '-v', is_flag=True, help='Enable verbose logging')
-@click.option('--quiet', '-q', is_flag=True, help='Suppress output')
+@click.option("--verbose", "-v", is_flag=True, help="Enable verbose logging")
+@click.option("--quiet", "-q", is_flag=True, help="Suppress output")
 def cli(verbose: bool, quiet: bool):
     """
     Mini RAG - Fast semantic code search that actually works.
 
-    A local RAG system for improving the development environment's grounding capabilities.
+    A local RAG system for improving the development environment's grounding
+    capabilities.
     Indexes your codebase and enables lightning-fast semantic search.
     """
     # Check virtual environment
     from .venv_checker import check_and_warn_venv
+
     check_and_warn_venv("rag-mini", force_exit=False)
 
     if verbose:
@@ -63,14 +61,16 @@ def cli(verbose: bool, quiet: bool):
 
 
 @cli.command()
-@click.option('--path', '-p', type=click.Path(exists=True), default='.', 
-              help='Project path to index')
-@click.option('--force', '-f', is_flag=True, 
-              help='Force reindex all files')
-@click.option('--reindex', '-r', is_flag=True, 
-              help='Force complete reindex (same as --force)')
-@click.option('--model', '-m', type=str, default=None,
-              help='Embedding model to use')
+@click.option(
+    "--path",
+    "-p",
+    type=click.Path(exists=True),
+    default=".",
+    help="Project path to index",
+)
+@click.option("--force", "-", is_flag=True, help="Force reindex all files")
+@click.option("--reindex", "-r", is_flag=True, help="Force complete reindex (same as --force)")
+@click.option("--model", "-m", type=str, default=None, help="Embedding model to use")
 def init(path: str, force: bool, reindex: bool, model: Optional[str]):
     """Initialize RAG index for a project."""
     project_path = Path(path).resolve()
@@ -78,7 +78,7 @@ def init(path: str, force: bool, reindex: bool, model: Optional[str]):
     console.print(f"\n[bold cyan]Initializing Mini RAG for:[/bold cyan] {project_path}\n")
 
     # Check if already initialized
-    rag_dir = project_path / '.mini-rag'
+    rag_dir = project_path / ".mini-rag"
     force_reindex = force or reindex
     if rag_dir.exists() and not force_reindex:
         console.print("[yellow][/yellow]  Project already initialized!")
@@ -92,10 +92,10 @@ def init(path: str, force: bool, reindex: bool, model: Optional[str]):
         table.add_column("Metric", style="cyan")
         table.add_column("Value", style="green")
 
-        table.add_row("Files Indexed", str(stats['file_count']))
-        table.add_row("Total Chunks", str(stats['chunk_count']))
+        table.add_row("Files Indexed", str(stats["file_count"]))
+        table.add_row("Total Chunks", str(stats["chunk_count"]))
         table.add_row("Index Size", f"{stats['index_size_mb']:.2f} MB")
-        table.add_row("Last Updated", stats['indexed_at'] or "Never")
+        table.add_row("Last Updated", stats["indexed_at"] or "Never")
 
         console.print(table)
         return
@@ -114,10 +114,7 @@ def init(path: str, force: bool, reindex: bool, model: Optional[str]):
 
             # Create indexer
             task = progress.add_task("[cyan]Creating indexer...", total=None)
-            indexer = ProjectIndexer(
-                project_path,
-                embedder=embedder
-            )
+            indexer = ProjectIndexer(project_path, embedder=embedder)
             progress.update(task, completed=True)
 
         # Run indexing
@@ -125,8 +122,10 @@ def init(path: str, force: bool, reindex: bool, model: Optional[str]):
         stats = indexer.index_project(force_reindex=force_reindex)
 
         # Show summary
-        if stats['files_indexed'] > 0:
-            console.print(f"\n[bold green] Success![/bold green] Indexed {stats['files_indexed']} files")
+        if stats["files_indexed"] > 0:
+            console.print(
+                f"\n[bold green] Success![/bold green] Indexed {stats['files_indexed']} files"
+            )
             console.print(f"Created {stats['chunks_created']} searchable chunks")
             console.print(f"Time: {stats['time_taken']:.2f} seconds")
             console.print(f"Speed: {stats['files_per_second']:.1f} files/second")
@@ -135,7 +134,7 @@ def init(path: str, force: bool, reindex: bool, model: Optional[str]):
 
         # Show how to use
         console.print("\n[bold]Next steps:[/bold]")
-        console.print("  • Search your code: [cyan]rag-mini search \"your query\"[/cyan]")
+        console.print('  • Search your code: [cyan]rag-mini search "your query"[/cyan]')
         console.print("  • Watch for changes: [cyan]rag-mini watch[/cyan]")
         console.print("  • View statistics: [cyan]rag-mini stats[/cyan]\n")
 
@@ -146,25 +145,29 @@ def init(path: str, force: bool, reindex: bool, model: Optional[str]):
 
 
 @cli.command()
-@click.argument('query')
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
-@click.option('--top-k', '-k', type=int, default=10,
-              help='Maximum results to show')
-@click.option('--type', '-t', multiple=True,
-              help='Filter by chunk type (function, class, method)')
-@click.option('--lang', multiple=True,
-              help='Filter by language (python, javascript, etc.)')
-@click.option('--show-content', '-c', is_flag=True,
-              help='Show code content in results')
-@click.option('--show-perf', is_flag=True,
-              help='Show performance metrics')
-def search(query: str, path: str, top_k: int, type: tuple, lang: tuple, show_content: bool, show_perf: bool):
+@click.argument("query")
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
+@click.option("--top-k", "-k", type=int, default=10, help="Maximum results to show")
+@click.option(
+    "--type", "-t", multiple=True, help="Filter by chunk type (function, class, method)"
+)
+@click.option("--lang", multiple=True, help="Filter by language (python, javascript, etc.)")
+@click.option("--show-content", "-c", is_flag=True, help="Show code content in results")
+@click.option("--show-per", is_flag=True, help="Show performance metrics")
+def search(
+    query: str,
+    path: str,
+    top_k: int,
+    type: tuple,
+    lang: tuple,
+    show_content: bool,
+    show_perf: bool,
+):
     """Search codebase using semantic similarity."""
     project_path = Path(path).resolve()
 
     # Check if indexed
-    rag_dir = project_path / '.mini-rag'
+    rag_dir = project_path / ".mini-rag"
     if not rag_dir.exists():
         console.print("[red]Error:[/red] Project not indexed. Run 'rag-mini init' first.")
         sys.exit(1)
@@ -183,27 +186,30 @@ def search(query: str, path: str, top_k: int, type: tuple, lang: tuple, show_con
 
             response = client.search(query, top_k=top_k)
 
-            if response.get('success'):
+            if response.get("success"):
                 # Convert response to SearchResult objects
                 from .search import SearchResult
+
                 results = []
-                for r in response['results']:
+                for r in response["results"]:
                     result = SearchResult(
-                        file_path=r['file_path'],
-                        content=r['content'],
-                        score=r['score'],
-                        start_line=r['start_line'],
-                        end_line=r['end_line'],
-                        chunk_type=r['chunk_type'],
-                        name=r['name'],
-                        language=r['language']
+                        file_path=r["file_path"],
+                        content=r["content"],
+                        score=r["score"],
+                        start_line=r["start_line"],
+                        end_line=r["end_line"],
+                        chunk_type=r["chunk_type"],
+                        name=r["name"],
+                        language=r["language"],
                     )
                     results.append(result)
 
                 # Show server stats
-                search_time = response.get('search_time_ms', 0)
-                total_queries = response.get('total_queries', 0)
-                console.print(f"[dim]Search time: {search_time}ms (Query #{total_queries})[/dim]\n")
+                search_time = response.get("search_time_ms", 0)
+                total_queries = response.get("total_queries", 0)
+                console.print(
+                    f"[dim]Search time: {search_time}ms (Query #{total_queries})[/dim]\n"
+                )
             else:
                 console.print(f"[red]Server error:[/red] {response.get('error')}")
                 sys.exit(1)
@@ -223,7 +229,7 @@ def search(query: str, path: str, top_k: int, type: tuple, lang: tuple, show_con
                         query,
                         top_k=top_k,
                         chunk_types=list(type) if type else None,
-                        languages=list(lang) if lang else None
+                        languages=list(lang) if lang else None,
                     )
             else:
                 with console.status(f"[cyan]Searching for: {query}[/cyan]"):
@@ -231,7 +237,7 @@ def search(query: str, path: str, top_k: int, type: tuple, lang: tuple, show_con
                         query,
                         top_k=top_k,
                         chunk_types=list(type) if type else None,
-                        languages=list(lang) if lang else None
+                        languages=list(lang) if lang else None,
                     )
 
         # Display results
@@ -247,12 +253,15 @@ def search(query: str, path: str, top_k: int, type: tuple, lang: tuple, show_con
             # Copy first result to clipboard if available
             try:
                 import pyperclip
+
                 first_result = results[0]
                 location = f"{first_result.file_path}:{first_result.start_line}"
                 pyperclip.copy(location)
-                console.print(f"\n[dim]First result location copied to clipboard: {location}[/dim]")
-            except:
-                pass
+                console.print(
+                    f"\n[dim]First result location copied to clipboard: {location}[/dim]"
+                )
+            except (ImportError, OSError):
+                pass  # Clipboard not available
         else:
             console.print(f"\n[yellow]No results found for: {query}[/yellow]")
             console.print("\n[dim]Tips:[/dim]")
@@ -271,14 +280,13 @@ def search(query: str, path: str, top_k: int, type: tuple, lang: tuple, show_con
 
 
 @cli.command()
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
 def stats(path: str):
     """Show index statistics."""
     project_path = Path(path).resolve()
 
     # Check if indexed
-    rag_dir = project_path / '.mini-rag'
+    rag_dir = project_path / ".mini-rag"
     if not rag_dir.exists():
         console.print("[red]Error:[/red] Project not indexed. Run 'rag-mini init' first.")
         sys.exit(1)
@@ -300,35 +308,37 @@ def stats(path: str):
         table.add_column("Metric", style="cyan")
         table.add_column("Value", style="green")
 
-        table.add_row("Files Indexed", str(index_stats['file_count']))
-        table.add_row("Total Chunks", str(index_stats['chunk_count']))
+        table.add_row("Files Indexed", str(index_stats["file_count"]))
+        table.add_row("Total Chunks", str(index_stats["chunk_count"]))
         table.add_row("Index Size", f"{index_stats['index_size_mb']:.2f} MB")
-        table.add_row("Last Updated", index_stats['indexed_at'] or "Never")
+        table.add_row("Last Updated", index_stats["indexed_at"] or "Never")
 
         console.print(table)
 
         # Language distribution
-        if 'languages' in search_stats:
+        if "languages" in search_stats:
             console.print("\n[bold]Language Distribution:[/bold]")
             lang_table = Table()
             lang_table.add_column("Language", style="cyan")
             lang_table.add_column("Chunks", style="green")
 
-            for lang, count in sorted(search_stats['languages'].items(), 
-                                     key=lambda x: x[1], reverse=True):
+            for lang, count in sorted(
+                search_stats["languages"].items(), key=lambda x: x[1], reverse=True
+            ):
                 lang_table.add_row(lang, str(count))
 
             console.print(lang_table)
 
         # Chunk type distribution
-        if 'chunk_types' in search_stats:
+        if "chunk_types" in search_stats:
             console.print("\n[bold]Chunk Types:[/bold]")
             type_table = Table()
             type_table.add_column("Type", style="cyan")
             type_table.add_column("Count", style="green")
 
-            for chunk_type, count in sorted(search_stats['chunk_types'].items(),
-                                           key=lambda x: x[1], reverse=True):
+            for chunk_type, count in sorted(
+                search_stats["chunk_types"].items(), key=lambda x: x[1], reverse=True
+            ):
                 type_table.add_row(chunk_type, str(count))
 
             console.print(type_table)
@@ -340,14 +350,13 @@ def stats(path: str):
 
 
 @cli.command()
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
 def debug_schema(path: str):
     """Debug vector database schema and sample data."""
     project_path = Path(path).resolve()
 
     try:
-        rag_dir = project_path / '.mini-rag'
+        rag_dir = project_path / ".mini-rag"
 
         if not rag_dir.exists():
             console.print("[red]No RAG index found. Run 'rag-mini init' first.[/red]")
@@ -357,7 +366,9 @@ def debug_schema(path: str):
         try:
             import lancedb
         except ImportError:
-            console.print("[red]LanceDB not available. Install with: pip install lancedb pyarrow[/red]")
+            console.print(
+                "[red]LanceDB not available. Install with: pip install lancedb pyarrow[/red]"
+            )
             return
 
         db = lancedb.connect(rag_dir)
@@ -373,30 +384,35 @@ def debug_schema(path: str):
         console.print(table.schema)
 
         # Get sample data
-        import pandas as pd
+
         df = table.to_pandas()
-        console.print(f"\n[bold cyan] Table Statistics:[/bold cyan]")
+        console.print("\n[bold cyan] Table Statistics:[/bold cyan]")
         console.print(f"Total rows: {len(df)}")
 
         if len(df) > 0:
             # Check embedding column
-            console.print(f"\n[bold cyan] Embedding Column Analysis:[/bold cyan]")
-            first_embedding = df['embedding'].iloc[0]
+            console.print("\n[bold cyan] Embedding Column Analysis:[/bold cyan]")
+            first_embedding = df["embedding"].iloc[0]
             console.print(f"Type: {type(first_embedding)}")
-            if hasattr(first_embedding, 'shape'):
+            if hasattr(first_embedding, "shape"):
                 console.print(f"Shape: {first_embedding.shape}")
-            if hasattr(first_embedding, 'dtype'):
+            if hasattr(first_embedding, "dtype"):
                 console.print(f"Dtype: {first_embedding.dtype}")
 
             # Show first few rows
-            console.print(f"\n[bold cyan] Sample Data (first 3 rows):[/bold cyan]")
+            console.print("\n[bold cyan] Sample Data (first 3 rows):[/bold cyan]")
             for i in range(min(3, len(df))):
                 row = df.iloc[i]
                 console.print(f"\n[yellow]Row {i}:[/yellow]")
                 console.print(f"  chunk_id: {row['chunk_id']}")
                 console.print(f"  file_path: {row['file_path']}")
                 console.print(f"  content: {row['content'][:50]}...")
-                console.print(f"  embedding: {type(row['embedding'])} of length {len(row['embedding']) if hasattr(row['embedding'], '__len__') else 'unknown'}")
+                embed_len = (
+                    len(row["embedding"])
+                    if hasattr(row["embedding"], "__len__")
+                    else "unknown"
+                )
+                console.print(f"  embedding: {type(row['embedding'])} of length {embed_len}")
 
     except Exception as e:
         logger.error(f"Schema debug failed: {e}")
@@ -404,18 +420,27 @@ def debug_schema(path: str):
 
 
 @cli.command()
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
-@click.option('--delay', '-d', type=float, default=10.0,
-              help='Update delay in seconds (default: 10s for non-invasive)')
-@click.option('--silent', '-s', is_flag=True, default=False,
-              help='Run silently in background without output')
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
+@click.option(
+    "--delay",
+    "-d",
+    type=float,
+    default=10.0,
+    help="Update delay in seconds (default: 10s for non-invasive)",
+)
+@click.option(
+    "--silent",
+    "-s",
+    is_flag=True,
+    default=False,
+    help="Run silently in background without output",
+)
 def watch(path: str, delay: float, silent: bool):
     """Watch for file changes and update index automatically (non-invasive by default)."""
     project_path = Path(path).resolve()
 
     # Check if indexed
-    rag_dir = project_path / '.mini-rag'
+    rag_dir = project_path / ".mini-rag"
     if not rag_dir.exists():
         if not silent:
             console.print("[red]Error:[/red] Project not indexed. Run 'rag-mini init' first.")
@@ -459,7 +484,7 @@ def watch(path: str, delay: float, silent: bool):
                             f"\r[green]✓[/green] Files updated: {stats.get('files_processed', 0)} | "
                             f"[red]✗[/red] Failed: {stats.get('files_dropped', 0)} | "
                             f"[cyan]⧗[/cyan] Queue: {stats['queue_size']}",
-                            end=""
+                            end="",
                         )
                         last_stats = stats
 
@@ -474,10 +499,12 @@ def watch(path: str, delay: float, silent: bool):
         # Show final stats only if not silent
         if not silent:
             final_stats = watcher.get_statistics()
-            console.print(f"\n[bold green]Watch Summary:[/bold green]")
+            console.print("\n[bold green]Watch Summary:[/bold green]")
             console.print(f"Files updated: {final_stats.get('files_processed', 0)}")
             console.print(f"Files failed: {final_stats.get('files_dropped', 0)}")
-            console.print(f"Total runtime: {final_stats.get('uptime_seconds', 0):.1f} seconds\n")
+            console.print(
+                f"Total runtime: {final_stats.get('uptime_seconds', 0):.1f} seconds\n"
+            )
 
     except Exception as e:
         console.print(f"\n[bold red]Error:[/bold red] {e}")
@@ -486,11 +513,9 @@ def watch(path: str, delay: float, silent: bool):
 
 
 @cli.command()
-@click.argument('function_name')
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
-@click.option('--top-k', '-k', type=int, default=5,
-              help='Maximum results')
+@click.argument("function_name")
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
+@click.option("--top-k", "-k", type=int, default=5, help="Maximum results")
 def find_function(function_name: str, path: str, top_k: int):
     """Find a specific function by name."""
     project_path = Path(path).resolve()
@@ -510,11 +535,9 @@ def find_function(function_name: str, path: str, top_k: int):
 
 
 @cli.command()
-@click.argument('class_name')
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
-@click.option('--top-k', '-k', type=int, default=5,
-              help='Maximum results')
+@click.argument("class_name")
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
+@click.option("--top-k", "-k", type=int, default=5, help="Maximum results")
 def find_class(class_name: str, path: str, top_k: int):
     """Find a specific class by name."""
     project_path = Path(path).resolve()
@@ -534,14 +557,13 @@ def find_class(class_name: str, path: str, top_k: int):
 
 
 @cli.command()
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
 def update(path: str):
     """Update index for changed files."""
     project_path = Path(path).resolve()
 
     # Check if indexed
-    rag_dir = project_path / '.mini-rag'
+    rag_dir = project_path / ".mini-rag"
     if not rag_dir.exists():
         console.print("[red]Error:[/red] Project not indexed. Run 'rag-mini init' first.")
         sys.exit(1)
@@ -553,7 +575,7 @@ def update(path: str):
 
         stats = indexer.index_project(force_reindex=False)
 
-        if stats['files_indexed'] > 0:
+        if stats["files_indexed"] > 0:
             console.print(f"[green][/green] Updated {stats['files_indexed']} files")
             console.print(f"Created {stats['chunks_created']} new chunks")
         else:
@@ -565,7 +587,7 @@ def update(path: str):
 
 
 @cli.command()
-@click.option('--show-code', '-c', is_flag=True, help='Show example code')
+@click.option("--show-code", "-c", is_flag=True, help="Show example code")
 def info(show_code: bool):
     """Show information about Mini RAG."""
     # Create info panel
@@ -619,16 +641,14 @@ rag-mini stats"""
 
 
 @cli.command()
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
-@click.option('--port', type=int, default=7777,
-              help='Server port')
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
+@click.option("--port", type=int, default=7777, help="Server port")
 def server(path: str, port: int):
     """Start persistent RAG server (keeps model loaded)."""
     project_path = Path(path).resolve()
 
     # Check if indexed
-    rag_dir = project_path / '.mini-rag'
+    rag_dir = project_path / ".mini-rag"
     if not rag_dir.exists():
         console.print("[red]Error:[/red] Project not indexed. Run 'rag-mini init' first.")
         sys.exit(1)
@@ -648,12 +668,9 @@ def server(path: str, port: int):
 
 
 @cli.command()
-@click.option('--path', '-p', type=click.Path(exists=True), default='.',
-              help='Project path')
-@click.option('--port', type=int, default=7777,
-              help='Server port')
-@click.option('--discovery', '-d', is_flag=True,
-              help='Run codebase discovery analysis')
+@click.option("--path", "-p", type=click.Path(exists=True), default=".", help="Project path")
+@click.option("--port", type=int, default=7777, help="Server port")
+@click.option("--discovery", "-d", is_flag=True, help="Run codebase discovery analysis")
 def status(path: str, port: int, discovery: bool):
     """Show comprehensive RAG system status with optional codebase discovery."""
     project_path = Path(path).resolve()
@@ -666,7 +683,12 @@ def status(path: str, port: int, discovery: bool):
     console.print("[bold]📁 Folder Contents:[/bold]")
     try:
         all_files = list(project_path.rglob("*"))
-        source_files = [f for f in all_files if f.is_file() and f.suffix in ['.py', '.js', '.ts', '.go', '.java', '.cpp', '.c', '.h']]
+        source_files = [
+            f
+            for f in all_files
+            if f.is_file()
+            and f.suffix in [".py", ".js", ".ts", ".go", ".java", ".cpp", ".c", ".h"]
+        ]
 
         console.print(f"   • Total files: {len([f for f in all_files if f.is_file()])}")
         console.print(f"   • Source files: {len(source_files)}")
@@ -676,19 +698,19 @@ def status(path: str, port: int, discovery: bool):
 
     # Check index status
     console.print("\n[bold]🗂️ Index Status:[/bold]")
-    rag_dir = project_path / '.mini-rag'
+    rag_dir = project_path / ".mini-rag"
     if rag_dir.exists():
         try:
             indexer = ProjectIndexer(project_path)
             index_stats = indexer.get_statistics()
 
-            console.print(f"   • Status: [green]✅ Indexed[/green]")
+            console.print("   • Status: [green]✅ Indexed[/green]")
             console.print(f"   • Files indexed: {index_stats['file_count']}")
             console.print(f"   • Total chunks: {index_stats['chunk_count']}")
             console.print(f"   • Index size: {index_stats['index_size_mb']:.2f} MB")
             console.print(f"   • Last updated: {index_stats['indexed_at'] or 'Never'}")
         except Exception as e:
-            console.print(f"   • Status: [yellow]⚠️ Index exists but has issues[/yellow]")
+            console.print("   • Status: [yellow]⚠️ Index exists but has issues[/yellow]")
             console.print(f"   • Error: {e}")
     else:
         console.print("   • Status: [red]❌ Not indexed[/red]")
@@ -704,9 +726,9 @@ def status(path: str, port: int, discovery: bool):
         # Try to get server info
         try:
             response = client.search("test", top_k=1)  # Minimal query to get stats
-            if response.get('success'):
-                uptime = response.get('server_uptime', 0)
-                queries = response.get('total_queries', 0)
+            if response.get("success"):
+                uptime = response.get("server_uptime", 0)
+                queries = response.get("total_queries", 0)
                 console.print(f"   • Uptime: {uptime}s")
                 console.print(f"   • Total queries: {queries}")
         except Exception as e:
@@ -745,16 +767,20 @@ def status(path: str, port: int, discovery: bool):
     console.print("\n[bold]📋 Next Steps:[/bold]")
     if not rag_dir.exists():
         console.print("   1. Run [cyan]rag-mini init[/cyan] to initialize the RAG system")
-        console.print("   2. Use [cyan]rag-mini search \"your query\"[/cyan] to search code")
+        console.print('   2. Use [cyan]rag-mini search "your query"[/cyan] to search code')
     elif not client.is_running():
         console.print("   1. Run [cyan]rag-mini server[/cyan] to start the server")
-        console.print("   2. Use [cyan]rag-mini search \"your query\"[/cyan] to search code")
+        console.print('   2. Use [cyan]rag-mini search "your query"[/cyan] to search code')
     else:
-        console.print("   • System ready! Use [cyan]rag-mini search \"your query\"[/cyan] to search")
-        console.print("   • Add [cyan]--discovery[/cyan] flag to run intelligent codebase analysis")
+        console.print(
+            '   • System ready! Use [cyan]rag-mini search "your query"[/cyan] to search'
+        )
+        console.print(
+            "   • Add [cyan]--discovery[/cyan] flag to run intelligent codebase analysis"
+        )
 
     console.print()
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     cli()

mini_rag/config.py

@@ -3,11 +3,12 @@ Configuration management for FSS-Mini-RAG.
 Handles loading, saving, and validation of YAML config files.
 """
 
-import yaml
 import logging
+from dataclasses import asdict, dataclass
 from pathlib import Path
-from typing import Dict, Any, Optional
-from dataclasses import dataclass, asdict
+from typing import Any, Dict, Optional
+
+import yaml
 
 logger = logging.getLogger(__name__)
 
@@ -15,6 +16,7 @@ logger = logging.getLogger(__name__)
 @dataclass
 class ChunkingConfig:
     """Configuration for text chunking."""
+
     max_size: int = 2000
     min_size: int = 150
     strategy: str = "semantic"  # "semantic" or "fixed"
@@ -23,6 +25,7 @@ class ChunkingConfig:
 @dataclass
 class StreamingConfig:
     """Configuration for large file streaming."""
+
     enabled: bool = True
     threshold_bytes: int = 1048576  # 1MB
 
@@ -30,6 +33,7 @@ class StreamingConfig:
 @dataclass
 class FilesConfig:
     """Configuration for file processing."""
+
     min_file_size: int = 50
     exclude_patterns: list = None
     include_patterns: list = None
@@ -44,7 +48,7 @@ class FilesConfig:
                 ".venv/**",
                 "venv/**",
                 "build/**",
-                "dist/**"
+                "dist/**",
             ]
         if self.include_patterns is None:
             self.include_patterns = ["**/*"]  # Include everything by default
@@ -53,6 +57,7 @@ class FilesConfig:
 @dataclass
 class EmbeddingConfig:
     """Configuration for embedding generation."""
+
     preferred_method: str = "ollama"  # "ollama", "ml", "hash", "auto"
     ollama_model: str = "nomic-embed-text"
     ollama_host: str = "localhost:11434"
@@ -63,6 +68,7 @@ class EmbeddingConfig:
 @dataclass
 class SearchConfig:
     """Configuration for search behavior."""
+
     default_top_k: int = 10
     enable_bm25: bool = True
     similarity_threshold: float = 0.1
@@ -72,6 +78,7 @@ class SearchConfig:
 @dataclass
 class LLMConfig:
     """Configuration for LLM synthesis and query expansion."""
+
     # Core settings
     synthesis_model: str = "auto"  # "auto", "qwen3:1.7b", "qwen2.5:1.5b", etc.
     expansion_model: str = "auto"  # Usually same as synthesis_model
@@ -101,13 +108,10 @@ class LLMConfig:
             self.model_rankings = [
                 # Testing model (prioritized for current testing phase)
                 "qwen3:1.7b",
-                
                 # Ultra-efficient models (perfect for CPU-only systems)
                 "qwen3:0.6b",
-                
                 # Recommended model (excellent quality but larger)
                 "qwen3:4b",
-                
                 # Common fallbacks (prioritize Qwen models)
                 "qwen2.5:1.5b",
                 "qwen2.5:3b",
@@ -117,6 +121,7 @@ class LLMConfig:
 @dataclass
 class UpdateConfig:
     """Configuration for auto-update system."""
+
     auto_check: bool = True  # Check for updates automatically
     check_frequency_hours: int = 24  # How often to check (hours)
     auto_install: bool = False  # Auto-install without asking (not recommended)
@@ -127,6 +132,7 @@ class UpdateConfig:
 @dataclass
 class RAGConfig:
     """Main RAG system configuration."""
+
     chunking: ChunkingConfig = None
     streaming: StreamingConfig = None
     files: FilesConfig = None
@@ -157,8 +163,8 @@ class ConfigManager:
 
     def __init__(self, project_path: Path):
         self.project_path = Path(project_path)
-        self.rag_dir = self.project_path / '.mini-rag'
-        self.config_path = self.rag_dir / 'config.yaml'
+        self.rag_dir = self.project_path / ".mini-rag"
+        self.config_path = self.rag_dir / "config.yaml"
 
     def load_config(self) -> RAGConfig:
         """Load configuration from YAML file or create default."""
@@ -169,7 +175,7 @@ class ConfigManager:
             return config
 
         try:
-            with open(self.config_path, 'r') as f:
+            with open(self.config_path, "r") as f:
                 data = yaml.safe_load(f)
 
             if not data:
@@ -179,24 +185,27 @@ class ConfigManager:
             # Convert nested dicts back to dataclass instances
             config = RAGConfig()
 
-            if 'chunking' in data:
-                config.chunking = ChunkingConfig(**data['chunking'])
-            if 'streaming' in data:
-                config.streaming = StreamingConfig(**data['streaming'])
-            if 'files' in data:
-                config.files = FilesConfig(**data['files'])
-            if 'embedding' in data:
-                config.embedding = EmbeddingConfig(**data['embedding'])
-            if 'search' in data:
-                config.search = SearchConfig(**data['search'])
-            if 'llm' in data:
-                config.llm = LLMConfig(**data['llm'])
+            if "chunking" in data:
+                config.chunking = ChunkingConfig(**data["chunking"])
+            if "streaming" in data:
+                config.streaming = StreamingConfig(**data["streaming"])
+            if "files" in data:
+                config.files = FilesConfig(**data["files"])
+            if "embedding" in data:
+                config.embedding = EmbeddingConfig(**data["embedding"])
+            if "search" in data:
+                config.search = SearchConfig(**data["search"])
+            if "llm" in data:
+                config.llm = LLMConfig(**data["llm"])
 
             return config
 
         except yaml.YAMLError as e:
             # YAML syntax error - help user fix it instead of silent fallback
-            error_msg = f"⚠️ Config file has YAML syntax error at line {getattr(e, 'problem_mark', 'unknown')}: {e}"
+            error_msg = (
+                f"⚠️ Config file has YAML syntax error at line "
+                f"{getattr(e, 'problem_mark', 'unknown')}: {e}"
+            )
             logger.error(error_msg)
             print(f"\n{error_msg}")
             print(f"Config file: {self.config_path}")
@@ -221,10 +230,13 @@ class ConfigManager:
             yaml_content = self._create_yaml_with_comments(config_dict)
 
             # Write with basic file locking to prevent corruption
-            with open(self.config_path, 'w') as f:
+            with open(self.config_path, "w") as f:
                 try:
                     import fcntl
-                    fcntl.flock(f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)  # Non-blocking exclusive lock
+
+                    fcntl.flock(
+                        f.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB
+                    )  # Non-blocking exclusive lock
                     f.write(yaml_content)
                     fcntl.flock(f.fileno(), fcntl.LOCK_UN)  # Unlock
                 except (OSError, ImportError):
@@ -245,49 +257,50 @@ class ConfigManager:
             "",
             "# Text chunking settings",
             "chunking:",
-            f"  max_size: {config_dict['chunking']['max_size']}      # Maximum characters per chunk",
-            f"  min_size: {config_dict['chunking']['min_size']}       # Minimum characters per chunk", 
-            f"  strategy: {config_dict['chunking']['strategy']}    # 'semantic' (language-aware) or 'fixed'",
+            f"  max_size: {config_dict['chunking']['max_size']}  # Max chars per chunk",
+            f"  min_size: {config_dict['chunking']['min_size']}  # Min chars per chunk",
+            f"  strategy: {config_dict['chunking']['strategy']}  # 'semantic' or 'fixed'",
             "",
             "# Large file streaming settings",
             "streaming:",
             f"  enabled: {str(config_dict['streaming']['enabled']).lower()}",
-            f"  threshold_bytes: {config_dict['streaming']['threshold_bytes']}  # Files larger than this use streaming (1MB)",
+            f"  threshold_bytes: {config_dict['streaming']['threshold_bytes']}  # Stream files >1MB",
             "",
             "# File processing settings",
             "files:",
-            f"  min_file_size: {config_dict['files']['min_file_size']}        # Skip files smaller than this",
+            f"  min_file_size: {config_dict['files']['min_file_size']}  # Skip small files",
             "  exclude_patterns:",
         ]
 
-        for pattern in config_dict['files']['exclude_patterns']:
-            yaml_lines.append(f"    - \"{pattern}\"")
+        for pattern in config_dict["files"]["exclude_patterns"]:
+            yaml_lines.append(f'    - "{pattern}"')
 
-        yaml_lines.extend([
+        yaml_lines.extend(
+            [
                 "  include_patterns:",
-            "    - \"**/*\"                  # Include all files by default",
+                '    - "**/*"                  # Include all files by default',
                 "",
                 "# Embedding generation settings",
                 "embedding:",
-            f"  preferred_method: {config_dict['embedding']['preferred_method']}     # 'ollama', 'ml', 'hash', or 'auto'",
+                f"  preferred_method: {config_dict['embedding']['preferred_method']}  # Method",
                 f"  ollama_model: {config_dict['embedding']['ollama_model']}",
                 f"  ollama_host: {config_dict['embedding']['ollama_host']}",
                 f"  ml_model: {config_dict['embedding']['ml_model']}",
-            f"  batch_size: {config_dict['embedding']['batch_size']}               # Embeddings processed per batch",
+                f"  batch_size: {config_dict['embedding']['batch_size']}  # Per batch",
                 "",
                 "# Search behavior settings",
                 "search:",
-            f"  default_top_k: {config_dict['search']['default_top_k']}           # Default number of top results",
-            f"  enable_bm25: {str(config_dict['search']['enable_bm25']).lower()}             # Enable keyword matching boost",
-            f"  similarity_threshold: {config_dict['search']['similarity_threshold']}        # Minimum similarity score",
-            f"  expand_queries: {str(config_dict['search']['expand_queries']).lower()}          # Enable automatic query expansion",
+                f"  default_top_k: {config_dict['search']['default_top_k']}  # Top results",
+                f"  enable_bm25: {str(config_dict['search']['enable_bm25']).lower()}  # Keyword boost",
+                f"  similarity_threshold: {config_dict['search']['similarity_threshold']}  # Min score",
+                f"  expand_queries: {str(config_dict['search']['expand_queries']).lower()}  # Auto expand",
                 "",
                 "# LLM synthesis and query expansion settings",
                 "llm:",
                 f"  ollama_host: {config_dict['llm']['ollama_host']}",
-            f"  synthesis_model: {config_dict['llm']['synthesis_model']}    # 'auto', 'qwen3:1.7b', etc.",
-            f"  expansion_model: {config_dict['llm']['expansion_model']}     # Usually same as synthesis_model",
-            f"  max_expansion_terms: {config_dict['llm']['max_expansion_terms']}        # Maximum terms to add to queries",
+                f"  synthesis_model: {config_dict['llm']['synthesis_model']}  # Model name",
+                f"  expansion_model: {config_dict['llm']['expansion_model']}  # Model name",
+                f"  max_expansion_terms: {config_dict['llm']['max_expansion_terms']}  # Max terms",
                 f"  enable_synthesis: {str(config_dict['llm']['enable_synthesis']).lower()}       # Enable synthesis by default",
                 f"  synthesis_temperature: {config_dict['llm']['synthesis_temperature']}      # LLM temperature for analysis",
                 "",
@@ -300,17 +313,19 @@ class ConfigManager:
                 f"  auto_context: {str(config_dict['llm']['auto_context']).lower()}            # Auto-adjust context based on model capabilities",
                 "",
                 "  model_rankings:          # Preferred model order (edit to change priority)",
-        ])
+            ]
+        )
 
         # Add model rankings list
-        if 'model_rankings' in config_dict['llm'] and config_dict['llm']['model_rankings']:
-            for model in config_dict['llm']['model_rankings'][:10]:  # Show first 10
-                yaml_lines.append(f"    - \"{model}\"")
-            if len(config_dict['llm']['model_rankings']) > 10:
+        if "model_rankings" in config_dict["llm"] and config_dict["llm"]["model_rankings"]:
+            for model in config_dict["llm"]["model_rankings"][:10]:  # Show first 10
+                yaml_lines.append(f'    - "{model}"')
+            if len(config_dict["llm"]["model_rankings"]) > 10:
                 yaml_lines.append("    # ... (edit config to see all options)")
 
         # Add update settings
-        yaml_lines.extend([
+        yaml_lines.extend(
+            [
                 "",
                 "# Auto-update system settings",
                 "updates:",
@@ -319,9 +334,10 @@ class ConfigManager:
                 f"  auto_install: {str(config_dict['updates']['auto_install']).lower()}          # Auto-install updates (not recommended)",
                 f"  backup_before_update: {str(config_dict['updates']['backup_before_update']).lower()}   # Create backup before updating",
                 f"  notify_beta_releases: {str(config_dict['updates']['notify_beta_releases']).lower()}   # Include beta releases in checks",
-        ])
+            ]
+        )
 
-        return '\n'.join(yaml_lines)
+        return "\n".join(yaml_lines)
 
     def update_config(self, **kwargs) -> RAGConfig:
         """Update specific configuration values."""

mini_rag/explorer.py

@@ -9,35 +9,43 @@ Perfect for exploring codebases with detailed reasoning and follow-up questions.
 import json
 import logging
 import time
-from typing import List, Dict, Any, Optional
-from pathlib import Path
 from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, List, Optional
 
 try:
+    from .config import RAGConfig
     from .llm_synthesizer import LLMSynthesizer, SynthesisResult
     from .search import CodeSearcher
-    from .config import RAGConfig
     from .system_context import get_system_context
 except ImportError:
     # For direct testing
+    from config import RAGConfig
     from llm_synthesizer import LLMSynthesizer, SynthesisResult
     from search import CodeSearcher
-    from config import RAGConfig
-    get_system_context = lambda x=None: ""
+
+    def get_system_context(x=None):
+        return ""
+
 
 logger = logging.getLogger(__name__)
 
+
 @dataclass
 class ExplorationSession:
     """Track an exploration session with context history."""
+
     project_path: Path
     conversation_history: List[Dict[str, Any]]
     session_id: str
     started_at: float
 
-    def add_exchange(self, question: str, search_results: List[Any], response: SynthesisResult):
+    def add_exchange(
+        self, question: str, search_results: List[Any], response: SynthesisResult
+    ):
         """Add a question/response exchange to the conversation history."""
-        self.conversation_history.append({
+        self.conversation_history.append(
+            {
                 "timestamp": time.time(),
                 "question": question,
                 "search_results_count": len(search_results),
@@ -46,9 +54,11 @@ class ExplorationSession:
                     "key_points": response.key_points,
                     "code_examples": response.code_examples,
                     "suggested_actions": response.suggested_actions,
-                "confidence": response.confidence
+                    "confidence": response.confidence,
+                },
             }
-        })
+        )
+
 
 class CodeExplorer:
     """Interactive code exploration with thinking and context memory."""
@@ -63,7 +73,7 @@ class CodeExplorer:
             ollama_url=f"http://{self.config.llm.ollama_host}",
             model=self.config.llm.synthesis_model,
             enable_thinking=True,  # Always enable thinking in explore mode
-            config=self.config  # Pass config for model rankings
+            config=self.config,  # Pass config for model rankings
         )
 
         # Session management
@@ -82,7 +92,7 @@ class CodeExplorer:
             project_path=self.project_path,
             conversation_history=[],
             session_id=session_id,
-            started_at=time.time()
+            started_at=time.time(),
         )
 
         print("🧠 Exploration Mode Started")
@@ -102,7 +112,7 @@ class CodeExplorer:
             top_k=context_limit,
             include_context=True,
             semantic_weight=0.7,
-            bm25_weight=0.3
+            bm25_weight=0.3,
         )
         search_time = time.time() - search_start
 
@@ -128,7 +138,6 @@ class CodeExplorer:
     def _build_contextual_prompt(self, question: str, results: List[Any]) -> str:
         """Build a prompt that includes conversation context."""
         # Get recent conversation context (last 3 exchanges)
-        context_summary = ""
         if self.current_session.conversation_history:
             recent_exchanges = self.current_session.conversation_history[-3:]
             context_parts = []
@@ -139,28 +148,30 @@ class CodeExplorer:
                 context_parts.append(f"Previous Q{i}: {prev_q}")
                 context_parts.append(f"Previous A{i}: {prev_summary}")
 
-            context_summary = "\n".join(context_parts)
+            # "\n".join(context_parts)  # Unused variable removed
 
         # Build search results context
         results_context = []
         for i, result in enumerate(results[:8], 1):
-            file_path = result.file_path if hasattr(result, 'file_path') else 'unknown'
-            content = result.content if hasattr(result, 'content') else str(result)
-            score = result.score if hasattr(result, 'score') else 0.0
+            # result.file_path if hasattr(result, "file_path") else "unknown"  # Unused variable removed
+            # result.content if hasattr(result, "content") else str(result)  # Unused variable removed
+            # result.score if hasattr(result, "score") else 0.0  # Unused variable removed
 
-            results_context.append(f"""
+            results_context.append(
+                """
 Result {i} (Score: {score:.3f}):
 File: {file_path}
 Content: {content[:800]}{'...' if len(content) > 800 else ''}
-""")
+"""
+            )
 
-        results_text = "\n".join(results_context)
+        # "\n".join(results_context)  # Unused variable removed
 
         # Get system context for better responses
-        system_context = get_system_context(self.project_path)
+        # get_system_context(self.project_path)  # Unused variable removed
 
         # Create comprehensive exploration prompt with thinking
-        prompt = f"""<think>
+        prompt = """<think>
 The user asked: "{question}"
 
 System context: {system_context}
@@ -217,8 +228,14 @@ Guidelines:
         """Synthesize results with full context and thinking."""
         try:
             # Use streaming with thinking visible (don't collapse)
-            response = self.synthesizer._call_ollama(prompt, temperature=0.2, disable_thinking=False, use_streaming=True, collapse_thinking=False)
-            thinking_stream = ""
+            response = self.synthesizer._call_ollama(
+                prompt,
+                temperature=0.2,
+                disable_thinking=False,
+                use_streaming=True,
+                collapse_thinking=False,
+            )
+            # ""  # Unused variable removed
 
             # Streaming already shows thinking and response
             # No need for additional indicators
@@ -229,7 +246,7 @@ Guidelines:
                     key_points=[],
                     code_examples=[],
                     suggested_actions=["Check LLM service status"],
-                    confidence=0.0
+                    confidence=0.0,
                 )
 
             # Use natural language response directly
@@ -238,7 +255,7 @@ Guidelines:
                 key_points=[],  # Not used with natural language responses
                 code_examples=[],  # Not used with natural language responses
                 suggested_actions=[],  # Not used with natural language responses
-                confidence=0.85  # High confidence for natural responses
+                confidence=0.85,  # High confidence for natural responses
             )
 
         except Exception as e:
@@ -248,11 +265,17 @@ Guidelines:
                 key_points=[],
                 code_examples=[],
                 suggested_actions=["Check system status and try again"],
-                confidence=0.0
+                confidence=0.0,
             )
 
-    def _format_exploration_response(self, question: str, synthesis: SynthesisResult, 
-                                   result_count: int, search_time: float, synthesis_time: float) -> str:
+    def _format_exploration_response(
+        self,
+        question: str,
+        synthesis: SynthesisResult,
+        result_count: int,
+        search_time: float,
+        synthesis_time: float,
+    ) -> str:
         """Format exploration response with context indicators."""
 
         output = []
@@ -262,8 +285,10 @@ Guidelines:
         exchange_count = len(self.current_session.conversation_history)
 
         output.append(f"🧠 EXPLORATION ANALYSIS (Question #{exchange_count})")
-        output.append(f"Session: {session_duration/60:.1f}m | Results: {result_count} | "
-                     f"Time: {search_time+synthesis_time:.1f}s")
+        output.append(
+            f"Session: {session_duration/60:.1f}m | Results: {result_count} | "
+            f"Time: {search_time+synthesis_time:.1f}s"
+        )
         output.append("=" * 60)
         output.append("")
 
@@ -274,9 +299,17 @@ Guidelines:
         output.append("")
 
         # Confidence and context indicator
-        confidence_emoji = "🟢" if synthesis.confidence > 0.7 else "🟡" if synthesis.confidence > 0.4 else "🔴"
-        context_indicator = f" | Context: {exchange_count-1} previous questions" if exchange_count > 1 else ""
-        output.append(f"{confidence_emoji} Confidence: {synthesis.confidence:.1%}{context_indicator}")
+        confidence_emoji = (
+            "🟢"
+            if synthesis.confidence > 0.7
+            else "🟡" if synthesis.confidence > 0.4 else "🔴"
+        )
+        context_indicator = (
+            f" | Context: {exchange_count-1} previous questions" if exchange_count > 1 else ""
+        )
+        output.append(
+            f"{confidence_emoji} Confidence: {synthesis.confidence:.1%}{context_indicator}"
+        )
 
         return "\n".join(output)
 
@@ -289,19 +322,23 @@ Guidelines:
         exchange_count = len(self.current_session.conversation_history)
 
         summary = [
-            f"🧠 EXPLORATION SESSION SUMMARY",
-            f"=" * 40,
+            "🧠 EXPLORATION SESSION SUMMARY",
+            "=" * 40,
             f"Project: {self.project_path.name}",
             f"Session ID: {self.current_session.session_id}",
             f"Duration: {duration/60:.1f} minutes",
             f"Questions explored: {exchange_count}",
-            f"",
+            "",
         ]
 
         if exchange_count > 0:
             summary.append("📋 Topics explored:")
             for i, exchange in enumerate(self.current_session.conversation_history, 1):
-                question = exchange["question"][:50] + "..." if len(exchange["question"]) > 50 else exchange["question"]
+                question = (
+                    exchange["question"][:50] + "..."
+                    if len(exchange["question"]) > 50
+                    else exchange["question"]
+                )
                 confidence = exchange["response"]["confidence"]
                 summary.append(f"   {i}. {question} (confidence: {confidence:.1%})")
 
@@ -325,9 +362,7 @@ Guidelines:
 
             # Test with a simple thinking prompt to see response quality
             test_response = self.synthesizer._call_ollama(
-                "Think briefly: what is 2+2?", 
-                temperature=0.1, 
-                disable_thinking=False
+                "Think briefly: what is 2+2?", temperature=0.1, disable_thinking=False
             )
 
             if test_response:
@@ -343,24 +378,35 @@ Guidelines:
     def _handle_model_restart(self) -> bool:
         """Handle user confirmation and model restart."""
         try:
-            print("\n🤔 To ensure best thinking quality, exploration mode works best with a fresh model.")
+            print(
+                "\n🤔 To ensure best thinking quality, exploration mode works best with a fresh model."
+            )
             print(f"   Currently running: {self.synthesizer.model}")
-            print("\n💡 Stop current model and restart for optimal exploration? (y/N): ", end="", flush=True)
+            print(
+                "\n💡 Stop current model and restart for optimal exploration? (y/N): ",
+                end="",
+                flush=True,
+            )
 
             response = input().strip().lower()
 
-            if response in ['y', 'yes']:
+            if response in ["y", "yes"]:
                 print("\n🔄 Stopping current model...")
 
                 # Use ollama stop command for clean model restart
                 import subprocess
+
                 try:
-                    subprocess.run([
-                        "ollama", "stop", self.synthesizer.model
-                    ], timeout=10, capture_output=True)
+                    subprocess.run(
+                        ["ollama", "stop", self.synthesizer.model],
+                        timeout=10,
+                        capture_output=True,
+                    )
 
                     print("✅ Model stopped successfully.")
-                    print("🚀 Exploration mode will restart the model with thinking enabled...")
+                    print(
+                        "🚀 Exploration mode will restart the model with thinking enabled..."
+                    )
 
                     # Reset synthesizer initialization to force fresh start
                     self.synthesizer._initialized = False
@@ -389,7 +435,6 @@ Guidelines:
     def _call_ollama_with_thinking(self, prompt: str, temperature: float = 0.3) -> tuple:
         """Call Ollama with streaming for fast time-to-first-token."""
         import requests
-        import json
 
         try:
             # Use the synthesizer's model and connection
@@ -405,6 +450,7 @@ Guidelines:
 
             # Get optimal parameters for this model
             from .llm_optimization import get_optimal_ollama_parameters
+
             optimal_params = get_optimal_ollama_parameters(model_to_use)
 
             payload = {
@@ -418,15 +464,15 @@ Guidelines:
                     "num_ctx": self.synthesizer._get_optimal_context_size(model_to_use),
                     "num_predict": optimal_params.get("num_predict", 2000),
                     "repeat_penalty": optimal_params.get("repeat_penalty", 1.1),
-                    "presence_penalty": optimal_params.get("presence_penalty", 1.0)
-                }
+                    "presence_penalty": optimal_params.get("presence_penalty", 1.0),
+                },
             }
 
             response = requests.post(
                 f"{self.synthesizer.ollama_url}/api/generate",
                 json=payload,
                 stream=True,
-                timeout=65
+                timeout=65,
             )
 
             if response.status_code == 200:
@@ -437,14 +483,14 @@ Guidelines:
                 for line in response.iter_lines():
                     if line:
                         try:
-                            chunk_data = json.loads(line.decode('utf-8'))
-                            chunk_text = chunk_data.get('response', '')
+                            chunk_data = json.loads(line.decode("utf-8"))
+                            chunk_text = chunk_data.get("response", "")
 
                             if chunk_text:
                                 raw_response += chunk_text
 
                                 # Display thinking stream as it comes in
-                                if not thinking_displayed and '<think>' in raw_response:
+                                if not thinking_displayed and "<think>" in raw_response:
                                     # Start displaying thinking
                                     self._start_thinking_display()
                                     thinking_displayed = True
@@ -452,7 +498,7 @@ Guidelines:
                                 if thinking_displayed:
                                     self._stream_thinking_chunk(chunk_text)
 
-                            if chunk_data.get('done', False):
+                            if chunk_data.get("done", False):
                                 break
 
                         except json.JSONDecodeError:
@@ -485,7 +531,7 @@ Guidelines:
             end_tag = raw_response.find("</think>") + len("</think>")
 
             if start_tag != -1 and end_tag != -1:
-                thinking_content = raw_response[start_tag + 7:end_tag - 8]  # Remove tags
+                thinking_content = raw_response[start_tag + 7 : end_tag - 8]  # Remove tags
                 thinking_stream = thinking_content.strip()
 
                 # Remove thinking from final response
@@ -494,18 +540,26 @@ Guidelines:
         # Alternative patterns for models that use different thinking formats
         elif "Let me think" in raw_response or "I need to analyze" in raw_response:
             # Simple heuristic: first paragraph might be thinking
-            lines = raw_response.split('\n')
+            lines = raw_response.split("\n")
             potential_thinking = []
             final_lines = []
 
-            thinking_indicators = ["Let me think", "I need to", "First, I'll", "Looking at", "Analyzing"]
+            thinking_indicators = [
+                "Let me think",
+                "I need to",
+                "First, I'll",
+                "Looking at",
+                "Analyzing",
+            ]
             in_thinking = False
 
             for line in lines:
                 if any(indicator in line for indicator in thinking_indicators):
                     in_thinking = True
                     potential_thinking.append(line)
-                elif in_thinking and (line.startswith('{') or line.startswith('**') or line.startswith('#')):
+                elif in_thinking and (
+                    line.startswith("{") or line.startswith("**") or line.startswith("#")
+                ):
                     # Likely end of thinking, start of structured response
                     in_thinking = False
                     final_lines.append(line)
@@ -515,8 +569,8 @@ Guidelines:
                     final_lines.append(line)
 
             if potential_thinking:
-                thinking_stream = '\n'.join(potential_thinking).strip()
-                final_response = '\n'.join(final_lines).strip()
+                thinking_stream = "\n".join(potential_thinking).strip()
+                final_response = "\n".join(final_lines).strip()
 
         return thinking_stream, final_response
 
@@ -529,28 +583,27 @@ Guidelines:
 
     def _stream_thinking_chunk(self, chunk: str):
         """Stream a chunk of thinking as it arrives."""
-        import sys
 
         self._thinking_buffer += chunk
 
         # Check if we're in thinking tags
-        if '<think>' in self._thinking_buffer and not self._in_thinking_tags:
+        if "<think>" in self._thinking_buffer and not self._in_thinking_tags:
             self._in_thinking_tags = True
             # Display everything after <think>
-            start_idx = self._thinking_buffer.find('<think>') + 7
+            start_idx = self._thinking_buffer.find("<think>") + 7
             thinking_content = self._thinking_buffer[start_idx:]
             if thinking_content:
-                print(f"\033[2m\033[3m{thinking_content}\033[0m", end='', flush=True)
-        elif self._in_thinking_tags and '</think>' not in chunk:
+                print(f"\033[2m\033[3m{thinking_content}\033[0m", end="", flush=True)
+        elif self._in_thinking_tags and "</think>" not in chunk:
             # We're in thinking mode, display the chunk
-            print(f"\033[2m\033[3m{chunk}\033[0m", end='', flush=True)
-        elif '</think>' in self._thinking_buffer:
+            print(f"\033[2m\033[3m{chunk}\033[0m", end="", flush=True)
+        elif "</think>" in self._thinking_buffer:
             # End of thinking
             self._in_thinking_tags = False
 
     def _end_thinking_display(self):
         """End the thinking stream display."""
-        print(f"\n\033[2m\033[3m" + "─" * 40 + "\033[0m")
+        print("\n\033[2m\033[3m" + "─" * 40 + "\033[0m")
         print()
 
     def _display_thinking_stream(self, thinking_stream: str):
@@ -562,11 +615,11 @@ Guidelines:
         print("\033[2m\033[3m" + "─" * 40 + "\033[0m")
 
         # Split into paragraphs and display with proper formatting
-        paragraphs = thinking_stream.split('\n\n')
+        paragraphs = thinking_stream.split("\n\n")
         for para in paragraphs:
             if para.strip():
                 # Wrap long lines nicely
-                lines = para.strip().split('\n')
+                lines = para.strip().split("\n")
                 for line in lines:
                     if line.strip():
                         # Light gray and italic
@@ -576,7 +629,10 @@ Guidelines:
         print("\033[2m\033[3m" + "─" * 40 + "\033[0m")
         print()
 
+
 # Quick test function
+
+
 def test_explorer():
     """Test the code explorer."""
     explorer = CodeExplorer(Path("."))
@@ -592,5 +648,6 @@ def test_explorer():
 
     print("\n" + explorer.end_session())
 
+
 if __name__ == "__main__":
     test_explorer()

mini_rag/fast_server.py

@@ -12,40 +12,47 @@ Drop-in replacement for the original server with:
 """
 
 import json
+import logging
+import os
 import socket
-import threading
-import time
 import subprocess
 import sys
-import os
-import logging
+import threading
+import time
+from concurrent.futures import Future, ThreadPoolExecutor
 from pathlib import Path
-from typing import Dict, Any, Optional, Callable
-from datetime import datetime
-from concurrent.futures import ThreadPoolExecutor, Future
-import queue
+from typing import Any, Callable, Dict, Optional
+
+from rich import print as rprint
 
 # Rich console for beautiful output
 from rich.console import Console
-from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeRemainingColumn, MofNCompleteColumn
-from rich.panel import Panel
-from rich.table import Table
 from rich.live import Live
-from rich import print as rprint
+from rich.panel import Panel
+from rich.progress import (
+    BarColumn,
+    MofNCompleteColumn,
+    Progress,
+    SpinnerColumn,
+    TextColumn,
+    TimeRemainingColumn,
+)
+from rich.table import Table
 
 # Fix Windows console first
-if sys.platform == 'win32':
-    os.environ['PYTHONUTF8'] = '1'
+if sys.platform == "win32":
+    os.environ["PYTHONUTF8"] = "1"
     try:
         from .windows_console_fix import fix_windows_console
+
         fix_windows_console()
-    except:
+    except (ImportError, OSError):
         pass
 
-from .search import CodeSearcher
-from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
 from .indexer import ProjectIndexer
+from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
 from .performance import PerformanceMonitor
+from .search import CodeSearcher
 
 logger = logging.getLogger(__name__)
 console = Console()
@@ -89,14 +96,14 @@ class ServerStatus:
     def get_status(self) -> Dict[str, Any]:
         """Get complete status as dict"""
         return {
-            'phase': self.phase,
-            'progress': self.progress,
-            'message': self.message,
-            'ready': self.ready,
-            'error': self.error,
-            'uptime': time.time() - self.start_time,
-            'health_checks': self.health_checks,
-            'details': self.details
+            "phase": self.phase,
+            "progress": self.progress,
+            "message": self.message,
+            "ready": self.ready,
+            "error": self.error,
+            "uptime": time.time() - self.start_time,
+            "health_checks": self.health_checks,
+            "details": self.details,
         }
 
 
@@ -151,7 +158,7 @@ class FastRAGServer:
             # Quick port check first
             test_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
             test_sock.settimeout(1.0)  # Faster timeout
-            result = test_sock.connect_ex(('localhost', self.port))
+            result = test_sock.connect_ex(("localhost", self.port))
             test_sock.close()
 
             if result != 0:  # Port is free
@@ -161,36 +168,43 @@ class FastRAGServer:
             self.status.update("port_cleanup", 10, f"Clearing port {self.port}...")
             self._notify_status()
 
-            if sys.platform == 'win32':
+            if sys.platform == "win32":
                 # Windows: Enhanced process killing
-                cmd = ['netstat', '-ano']
+                cmd = ["netstat", "-ano"]
                 result = subprocess.run(cmd, capture_output=True, text=True, timeout=5)
 
-                for line in result.stdout.split('\n'):
-                    if f':{self.port}' in line and 'LISTENING' in line:
+                for line in result.stdout.split("\n"):
+                    if f":{self.port}" in line and "LISTENING" in line:
                         parts = line.split()
                         if len(parts) >= 5:
                             pid = parts[-1]
                             console.print(f"[dim]Killing process {pid}[/dim]")
-                            subprocess.run(['taskkill', '/PID', pid, '/F'], 
-                                         capture_output=True, timeout=3)
+                            subprocess.run(
+                                ["taskkill", "/PID", pid, "/F"],
+                                capture_output=True,
+                                timeout=3,
+                            )
                             time.sleep(0.5)  # Reduced wait time
                             break
             else:
                 # Unix/Linux: Enhanced process killing
-                result = subprocess.run(['lsof', '-ti', f':{self.port}'], 
-                                      capture_output=True, text=True, timeout=3)
+                result = subprocess.run(
+                    ["lso", "-ti", f":{self.port}"],
+                    capture_output=True,
+                    text=True,
+                    timeout=3,
+                )
                 if result.stdout.strip():
                     pids = result.stdout.strip().split()
                     for pid in pids:
                         console.print(f"[dim]Killing process {pid}[/dim]")
-                        subprocess.run(['kill', '-9', pid], capture_output=True)
+                        subprocess.run(["kill", "-9", pid], capture_output=True)
                     time.sleep(0.5)
 
             # Verify port is free
             test_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
             test_sock.settimeout(1.0)
-            result = test_sock.connect_ex(('localhost', self.port))
+            result = test_sock.connect_ex(("localhost", self.port))
             test_sock.close()
 
             if result == 0:
@@ -206,12 +220,12 @@ class FastRAGServer:
 
     def _check_indexing_needed(self) -> bool:
         """Quick check if indexing is needed"""
-        rag_dir = self.project_path / '.mini-rag'
+        rag_dir = self.project_path / ".mini-rag"
         if not rag_dir.exists():
             return True
 
         # Check if database exists and is not empty
-        db_path = rag_dir / 'code_vectors.lance'
+        db_path = rag_dir / "code_vectors.lance"
         if not db_path.exists():
             return True
 
@@ -224,12 +238,12 @@ class FastRAGServer:
 
         try:
             db = lancedb.connect(rag_dir)
-            if 'code_vectors' not in db.table_names():
+            if "code_vectors" not in db.table_names():
                 return True
-            table = db.open_table('code_vectors')
+            table = db.open_table("code_vectors")
             count = table.count_rows()
             return count == 0
-        except:
+        except (OSError, IOError, ValueError, AttributeError):
             return True
 
     def _fast_index(self) -> bool:
@@ -242,7 +256,7 @@ class FastRAGServer:
             self.indexer = ProjectIndexer(
                 self.project_path,
                 embedder=self.embedder,  # Reuse loaded embedder
-                max_workers=min(4, os.cpu_count() or 2)
+                max_workers=min(4, os.cpu_count() or 2),
             )
 
             console.print("\n[bold cyan]🚀 Fast Indexing Starting...[/bold cyan]")
@@ -267,11 +281,14 @@ class FastRAGServer:
 
                     if total_files == 0:
                         self.status.update("indexing", 80, "Index up to date")
-                        return {'files_indexed': 0, 'chunks_created': 0, 'time_taken': 0}
+                        return {
+                            "files_indexed": 0,
+                            "chunks_created": 0,
+                            "time_taken": 0,
+                        }
 
                     task = progress.add_task(
-                        f"[cyan]Indexing {total_files} files...",
-                        total=total_files
+                        f"[cyan]Indexing {total_files} files...", total=total_files
                     )
 
                     # Track progress by hooking into the processor
@@ -282,8 +299,11 @@ class FastRAGServer:
                         while processed_count < total_files and self.running:
                             time.sleep(0.1)  # Fast polling
                             current_progress = (processed_count / total_files) * 60 + 20
-                            self.status.update("indexing", current_progress, 
-                                             f"Indexed {processed_count}/{total_files} files")
+                            self.status.update(
+                                "indexing",
+                                current_progress,
+                                f"Indexed {processed_count}/{total_files} files",
+                            )
                             progress.update(task, completed=processed_count)
                             self._notify_status()
 
@@ -314,13 +334,18 @@ class FastRAGServer:
                 # Run indexing
                 stats = self.indexer.index_project(force_reindex=False)
 
-                self.status.update("indexing", 80, 
+                self.status.update(
+                    "indexing",
+                    80,
                     f"Indexed {stats.get('files_indexed', 0)} files, "
-                                 f"created {stats.get('chunks_created', 0)} chunks")
+                    f"created {stats.get('chunks_created', 0)} chunks",
+                )
                 self._notify_status()
 
-                console.print(f"\n[green]✅ Indexing complete: {stats.get('files_indexed', 0)} files, "
-                            f"{stats.get('chunks_created', 0)} chunks in {stats.get('time_taken', 0):.1f}s[/green]")
+                console.print(
+                    f"\n[green]✅ Indexing complete: {stats.get('files_indexed', 0)} files, "
+                    f"{stats.get('chunks_created', 0)} chunks in {stats.get('time_taken', 0):.1f}s[/green]"
+                )
 
                 return True
 
@@ -347,7 +372,9 @@ class FastRAGServer:
             ) as progress:
 
                 # Task 1: Load embedder (this takes the most time)
-                embedder_task = progress.add_task("[cyan]Loading embedding model...", total=100)
+                embedder_task = progress.add_task(
+                    "[cyan]Loading embedding model...", total=100
+                )
 
                 def load_embedder():
                     self.status.update("embedder", 25, "Loading embedding model...")
@@ -401,46 +428,46 @@ class FastRAGServer:
             # Check 1: Embedder functionality
             if self.embedder:
                 test_embedding = self.embedder.embed_code("def test(): pass")
-                checks['embedder'] = {
-                    'status': 'healthy',
-                    'embedding_dim': len(test_embedding),
-                    'model': getattr(self.embedder, 'model_name', 'unknown')
+                checks["embedder"] = {
+                    "status": "healthy",
+                    "embedding_dim": len(test_embedding),
+                    "model": getattr(self.embedder, "model_name", "unknown"),
                 }
             else:
-                checks['embedder'] = {'status': 'missing'}
+                checks["embedder"] = {"status": "missing"}
 
             # Check 2: Database connectivity
             if self.searcher:
                 stats = self.searcher.get_statistics()
-                checks['database'] = {
-                    'status': 'healthy',
-                    'chunks': stats.get('total_chunks', 0),
-                    'languages': len(stats.get('languages', {}))
+                checks["database"] = {
+                    "status": "healthy",
+                    "chunks": stats.get("total_chunks", 0),
+                    "languages": len(stats.get("languages", {})),
                 }
             else:
-                checks['database'] = {'status': 'missing'}
+                checks["database"] = {"status": "missing"}
 
             # Check 3: Search functionality
             if self.searcher:
                 test_results = self.searcher.search("test query", top_k=1)
-                checks['search'] = {
-                    'status': 'healthy',
-                    'test_results': len(test_results)
+                checks["search"] = {
+                    "status": "healthy",
+                    "test_results": len(test_results),
                 }
             else:
-                checks['search'] = {'status': 'unavailable'}
+                checks["search"] = {"status": "unavailable"}
 
             # Check 4: Port availability
             try:
                 test_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-                test_sock.bind(('localhost', self.port))
+                test_sock.bind(("localhost", self.port))
                 test_sock.close()
-                checks['port'] = {'status': 'available'}
-            except:
-                checks['port'] = {'status': 'occupied'}
+                checks["port"] = {"status": "available"}
+            except (ConnectionError, OSError, TypeError, ValueError, socket.error):
+                checks["port"] = {"status": "occupied"}
 
         except Exception as e:
-            checks['health_check_error'] = str(e)
+            checks["health_check_error"] = str(e)
 
         self.status.health_checks = checks
         self.last_health_check = time.time()
@@ -452,10 +479,10 @@ class FastRAGServer:
         table.add_column("Details", style="dim")
 
         for component, info in checks.items():
-            status = info.get('status', 'unknown')
-            details = ', '.join([f"{k}={v}" for k, v in info.items() if k != 'status'])
+            status = info.get("status", "unknown")
+            details = ", ".join([f"{k}={v}" for k, v in info.items() if k != "status"])
 
-            color = "green" if status in ['healthy', 'available'] else "yellow"
+            color = "green" if status in ["healthy", "available"] else "yellow"
             table.add_row(component, f"[{color}]{status}[/{color}]", details)
 
         console.print(table)
@@ -479,7 +506,7 @@ class FastRAGServer:
 
             self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
             self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-            self.socket.bind(('localhost', self.port))
+            self.socket.bind(("localhost", self.port))
             self.socket.listen(10)  # Increased backlog
 
             self.running = True
@@ -491,15 +518,15 @@ class FastRAGServer:
 
             # Display ready status
             panel = Panel(
-                f"[bold green]🎉 RAG Server Ready![/bold green]\n\n"
+                "[bold green]🎉 RAG Server Ready![/bold green]\n\n"
                 f"🌐 Address: localhost:{self.port}\n"
                 f"⚡ Startup Time: {total_time:.2f}s\n"
                 f"📁 Project: {self.project_path.name}\n"
                 f"🧠 Model: {getattr(self.embedder, 'model_name', 'default')}\n"
                 f"📊 Chunks Indexed: {self.status.health_checks.get('database', {}).get('chunks', 0)}\n\n"
-                f"[dim]Ready to serve the development environment queries...[/dim]",
+                "[dim]Ready to serve the development environment queries...[/dim]",
                 title="🚀 Server Status",
-                border_style="green"
+                border_style="green",
             )
             console.print(panel)
 
@@ -547,24 +574,21 @@ class FastRAGServer:
             request = json.loads(data)
 
             # Handle different request types
-            if request.get('command') == 'shutdown':
+            if request.get("command") == "shutdown":
                 console.print("\n[yellow]🛑 Shutdown requested[/yellow]")
-                response = {'success': True, 'message': 'Server shutting down'}
+                response = {"success": True, "message": "Server shutting down"}
                 self._send_json(client, response)
                 self.stop()
                 return
 
-            if request.get('command') == 'status':
-                response = {
-                    'success': True,
-                    'status': self.status.get_status()
-                }
+            if request.get("command") == "status":
+                response = {"success": True, "status": self.status.get_status()}
                 self._send_json(client, response)
                 return
 
             # Handle search requests
-            query = request.get('query', '')
-            top_k = request.get('top_k', 10)
+            query = request.get("query", "")
+            top_k = request.get("top_k", 10)
 
             if not query:
                 raise ValueError("Empty query")
@@ -572,7 +596,9 @@ class FastRAGServer:
             self.query_count += 1
 
             # Enhanced query logging
-            console.print(f"[blue]🔍 Query #{self.query_count}:[/blue] [dim]{query[:50]}{'...' if len(query) > 50 else ''}[/dim]")
+            console.print(
+                f"[blue]🔍 Query #{self.query_count}:[/blue] [dim]{query[:50]}{'...' if len(query) > 50 else ''}[/dim]"
+            )
 
             # Perform search with timing
             start = time.time()
@@ -581,79 +607,81 @@ class FastRAGServer:
 
             # Enhanced response
             response = {
-                'success': True,
-                'query': query,
-                'count': len(results),
-                'search_time_ms': int(search_time * 1000),
-                'results': [r.to_dict() for r in results],
-                'server_uptime': int(time.time() - self.status.start_time),
-                'total_queries': self.query_count,
-                'server_status': 'ready'
+                "success": True,
+                "query": query,
+                "count": len(results),
+                "search_time_ms": int(search_time * 1000),
+                "results": [r.to_dict() for r in results],
+                "server_uptime": int(time.time() - self.status.start_time),
+                "total_queries": self.query_count,
+                "server_status": "ready",
             }
 
             self._send_json(client, response)
 
             # Enhanced result logging
-            console.print(f"[green]✅ {len(results)} results in {search_time*1000:.0f}ms[/green]")
+            console.print(
+                f"[green]✅ {len(results)} results in {search_time*1000:.0f}ms[/green]"
+            )
 
         except Exception as e:
             error_msg = str(e)
             logger.error(f"Client handler error: {error_msg}")
 
             error_response = {
-                'success': False,
-                'error': error_msg,
-                'error_type': type(e).__name__,
-                'server_status': self.status.phase
+                "success": False,
+                "error": error_msg,
+                "error_type": type(e).__name__,
+                "server_status": self.status.phase,
             }
 
             try:
                 self._send_json(client, error_response)
-            except:
+            except (TypeError, ValueError):
                 pass
 
             console.print(f"[red]❌ Query failed: {error_msg}[/red]")
         finally:
             try:
                 client.close()
-            except:
+            except (ConnectionError, OSError, TypeError, ValueError, socket.error):
                 pass
 
     def _receive_json(self, sock: socket.socket) -> str:
         """Receive JSON with length prefix and timeout handling"""
         try:
             # Receive length (4 bytes)
-            length_data = b''
+            length_data = b""
             while len(length_data) < 4:
                 chunk = sock.recv(4 - len(length_data))
                 if not chunk:
                     raise ConnectionError("Connection closed while receiving length")
                 length_data += chunk
 
-            length = int.from_bytes(length_data, 'big')
+            length = int.from_bytes(length_data, "big")
             if length > 10_000_000:  # 10MB limit
                 raise ValueError(f"Message too large: {length} bytes")
 
             # Receive data
-            data = b''
+            data = b""
             while len(data) < length:
                 chunk = sock.recv(min(65536, length - len(data)))
                 if not chunk:
                     raise ConnectionError("Connection closed while receiving data")
                 data += chunk
 
-            return data.decode('utf-8')
+            return data.decode("utf-8")
         except socket.timeout:
             raise ConnectionError("Timeout while receiving data")
 
     def _send_json(self, sock: socket.socket, data: dict):
         """Send JSON with length prefix"""
-        json_str = json.dumps(data, ensure_ascii=False, separators=(',', ':'))
-        json_bytes = json_str.encode('utf-8')
+        json_str = json.dumps(data, ensure_ascii=False, separators=(",", ":"))
+        json_bytes = json_str.encode("utf-8")
 
         # Send length prefix
         length = len(json_bytes)
-        sock.send(length.to_bytes(4, 'big'))
+        sock.send(length.to_bytes(4, "big"))
 
         # Send data
         sock.sendall(json_bytes)
@@ -667,7 +695,7 @@ class FastRAGServer:
         if self.socket:
             try:
                 self.socket.close()
-            except:
+            except (ConnectionError, OSError, TypeError, ValueError, socket.error):
                 pass
 
         # Shutdown executor
@@ -677,6 +705,8 @@ class FastRAGServer:
 
 
 # Enhanced client with status monitoring
+
+
 class FastRAGClient:
     """Enhanced client with better error handling and status monitoring"""
 
@@ -689,9 +719,9 @@ class FastRAGClient:
         try:
             sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
             sock.settimeout(self.timeout)
-            sock.connect(('localhost', self.port))
+            sock.connect(("localhost", self.port))
 
-            request = {'query': query, 'top_k': top_k}
+            request = {"query": query, "top_k": top_k}
             self._send_json(sock, request)
 
             data = self._receive_json(sock)
@@ -702,31 +732,27 @@ class FastRAGClient:
 
         except ConnectionRefusedError:
             return {
-                'success': False,
-                'error': 'RAG server not running. Start with: python -m mini_rag server',
-                'error_type': 'connection_refused'
+                "success": False,
+                "error": "RAG server not running. Start with: python -m mini_rag server",
+                "error_type": "connection_refused",
             }
         except socket.timeout:
             return {
-                'success': False,
-                'error': f'Request timed out after {self.timeout}s',
-                'error_type': 'timeout'
+                "success": False,
+                "error": f"Request timed out after {self.timeout}s",
+                "error_type": "timeout",
             }
         except Exception as e:
-            return {
-                'success': False,
-                'error': str(e),
-                'error_type': type(e).__name__
-            }
+            return {"success": False, "error": str(e), "error_type": type(e).__name__}
 
     def get_status(self) -> Dict[str, Any]:
         """Get detailed server status"""
         try:
             sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
             sock.settimeout(5.0)
-            sock.connect(('localhost', self.port))
+            sock.connect(("localhost", self.port))
 
-            request = {'command': 'status'}
+            request = {"command": "status"}
             self._send_json(sock, request)
 
             data = self._receive_json(sock)
@@ -736,18 +762,14 @@ class FastRAGClient:
             return response
 
         except Exception as e:
-            return {
-                'success': False,
-                'error': str(e),
-                'server_running': False
-            }
+            return {"success": False, "error": str(e), "server_running": False}
 
     def is_running(self) -> bool:
         """Enhanced server detection"""
         try:
             status = self.get_status()
-            return status.get('success', False)
-        except:
+            return status.get("success", False)
+        except (TypeError, ValueError):
             return False
 
     def shutdown(self) -> Dict[str, Any]:
@@ -755,9 +777,9 @@ class FastRAGClient:
         try:
             sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
             sock.settimeout(10.0)
-            sock.connect(('localhost', self.port))
+            sock.connect(("localhost", self.port))
 
-            request = {'command': 'shutdown'}
+            request = {"command": "shutdown"}
             self._send_json(sock, request)
 
             data = self._receive_json(sock)
@@ -767,41 +789,38 @@ class FastRAGClient:
             return response
 
         except Exception as e:
-            return {
-                'success': False,
-                'error': str(e)
-            }
+            return {"success": False, "error": str(e)}
 
     def _send_json(self, sock: socket.socket, data: dict):
         """Send JSON with length prefix"""
-        json_str = json.dumps(data, ensure_ascii=False, separators=(',', ':'))
-        json_bytes = json_str.encode('utf-8')
+        json_str = json.dumps(data, ensure_ascii=False, separators=(",", ":"))
+        json_bytes = json_str.encode("utf-8")
 
         length = len(json_bytes)
-        sock.send(length.to_bytes(4, 'big'))
+        sock.send(length.to_bytes(4, "big"))
         sock.sendall(json_bytes)
 
     def _receive_json(self, sock: socket.socket) -> str:
         """Receive JSON with length prefix"""
         # Receive length
-        length_data = b''
+        length_data = b""
         while len(length_data) < 4:
             chunk = sock.recv(4 - len(length_data))
             if not chunk:
                 raise ConnectionError("Connection closed")
             length_data += chunk
 
-        length = int.from_bytes(length_data, 'big')
+        length = int.from_bytes(length_data, "big")
 
         # Receive data
-        data = b''
+        data = b""
         while len(data) < length:
             chunk = sock.recv(min(65536, length - len(data)))
             if not chunk:
                 raise ConnectionError("Connection closed")
             data += chunk
 
-        return data.decode('utf-8')
+        return data.decode("utf-8")
 
 
 def start_fast_server(project_path: Path, port: int = 7777, auto_index: bool = True):

mini_rag/indexer.py

@@ -3,31 +3,39 @@ Parallel indexing engine for efficient codebase processing.
 Handles file discovery, chunking, embedding, and storage.
 """
 
-import os
-import json
 import hashlib
+import json
 import logging
-from pathlib import Path
-from typing import List, Dict, Any, Optional, Set, Tuple
+import os
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
 import numpy as np
 import pandas as pd
-from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeRemainingColumn
 from rich.console import Console
+from rich.progress import (
+    BarColumn,
+    Progress,
+    SpinnerColumn,
+    TextColumn,
+    TimeRemainingColumn,
+)
 
 # Optional LanceDB import
 try:
     import lancedb
     import pyarrow as pa
+
     LANCEDB_AVAILABLE = True
 except ImportError:
     lancedb = None
     pa = None
     LANCEDB_AVAILABLE = False
 
+from .chunker import CodeChunker
 from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
-from .chunker import CodeChunker, CodeChunk
 from .path_handler import normalize_path, normalize_relative_path
 
 logger = logging.getLogger(__name__)
@@ -37,11 +45,13 @@ console = Console()
 class ProjectIndexer:
     """Indexes a project directory for semantic search."""
 
-    def __init__(self, 
+    def __init__(
+        self,
         project_path: Path,
         embedder: Optional[CodeEmbedder] = None,
         chunker: Optional[CodeChunker] = None,
-                 max_workers: int = 4):
+        max_workers: int = 4,
+    ):
         """
         Initialize the indexer.
 
@@ -52,9 +62,9 @@ class ProjectIndexer:
             max_workers: Number of parallel workers for indexing
         """
         self.project_path = Path(project_path).resolve()
-        self.rag_dir = self.project_path / '.mini-rag'
-        self.manifest_path = self.rag_dir / 'manifest.json'
-        self.config_path = self.rag_dir / 'config.json'
+        self.rag_dir = self.project_path / ".mini-rag"
+        self.manifest_path = self.rag_dir / "manifest.json"
+        self.config_path = self.rag_dir / "config.json"
 
         # Create RAG directory if it doesn't exist
         self.rag_dir.mkdir(exist_ok=True)
@@ -71,26 +81,75 @@ class ProjectIndexer:
         # File patterns to include/exclude
         self.include_patterns = [
             # Code files
-            '*.py', '*.js', '*.jsx', '*.ts', '*.tsx',
-            '*.go', '*.java', '*.cpp', '*.c', '*.cs',
-            '*.rs', '*.rb', '*.php', '*.swift', '*.kt',
-            '*.scala', '*.r', '*.m', '*.h', '*.hpp',
+            "*.py",
+            "*.js",
+            "*.jsx",
+            "*.ts",
+            "*.tsx",
+            "*.go",
+            "*.java",
+            "*.cpp",
+            "*.c",
+            "*.cs",
+            "*.rs",
+            "*.rb",
+            "*.php",
+            "*.swift",
+            "*.kt",
+            "*.scala",
+            "*.r",
+            "*.m",
+            "*.h",
+            "*.hpp",
             # Documentation files
-            '*.md', '*.markdown', '*.rst', '*.txt',
-            '*.adoc', '*.asciidoc',
+            "*.md",
+            "*.markdown",
+            "*.rst",
+            "*.txt",
+            "*.adoc",
+            "*.asciidoc",
             # Config files
-            '*.json', '*.yaml', '*.yml', '*.toml', '*.ini',
-            '*.xml', '*.conf', '*.config',
+            "*.json",
+            "*.yaml",
+            "*.yml",
+            "*.toml",
+            "*.ini",
+            "*.xml",
+            "*.con",
+            "*.config",
             # Other text files
-            'README', 'LICENSE', 'CHANGELOG', 'AUTHORS',
-            'CONTRIBUTING', 'TODO', 'NOTES'
+            "README",
+            "LICENSE",
+            "CHANGELOG",
+            "AUTHORS",
+            "CONTRIBUTING",
+            "TODO",
+            "NOTES",
         ]
 
         self.exclude_patterns = [
-            '__pycache__', '.git', 'node_modules', '.venv', 'venv',
-            'env', 'dist', 'build', 'target', '.idea', '.vscode',
-            '*.pyc', '*.pyo', '*.pyd', '.DS_Store', '*.so', '*.dll',
-            '*.dylib', '*.exe', '*.bin', '*.log', '*.lock'
+            "__pycache__",
+            ".git",
+            "node_modules",
+            ".venv",
+            "venv",
+            "env",
+            "dist",
+            "build",
+            "target",
+            ".idea",
+            ".vscode",
+            "*.pyc",
+            "*.pyo",
+            "*.pyd",
+            ".DS_Store",
+            "*.so",
+            "*.dll",
+            "*.dylib",
+            "*.exe",
+            "*.bin",
+            "*.log",
+            "*.lock",
         ]
 
         # Load existing manifest if it exists
@@ -100,23 +159,23 @@ class ProjectIndexer:
         """Load existing manifest or create new one."""
         if self.manifest_path.exists():
             try:
-                with open(self.manifest_path, 'r') as f:
+                with open(self.manifest_path, "r") as f:
                     return json.load(f)
             except Exception as e:
                 logger.warning(f"Failed to load manifest: {e}")
 
         return {
-            'version': '1.0',
-            'indexed_at': None,
-            'file_count': 0,
-            'chunk_count': 0,
-            'files': {}
+            "version": "1.0",
+            "indexed_at": None,
+            "file_count": 0,
+            "chunk_count": 0,
+            "files": {},
         }
 
     def _save_manifest(self):
         """Save manifest to disk."""
         try:
-            with open(self.manifest_path, 'w') as f:
+            with open(self.manifest_path, "w") as f:
                 json.dump(self.manifest, f, indent=2)
         except Exception as e:
             logger.error(f"Failed to save manifest: {e}")
@@ -125,7 +184,7 @@ class ProjectIndexer:
         """Load or create comprehensive configuration."""
         if self.config_path.exists():
             try:
-                with open(self.config_path, 'r') as f:
+                with open(self.config_path, "r") as f:
                     config = json.load(f)
                     # Apply any loaded settings
                     self._apply_config(config)
@@ -138,49 +197,57 @@ class ProjectIndexer:
             "project": {
                 "name": self.project_path.name,
                 "description": f"RAG index for {self.project_path.name}",
-                "created_at": datetime.now().isoformat()
+                "created_at": datetime.now().isoformat(),
             },
             "embedding": {
                 "provider": "ollama",
-                "model": self.embedder.model_name if hasattr(self.embedder, 'model_name') else 'nomic-embed-text:latest',
+                "model": (
+                    self.embedder.model_name
+                    if hasattr(self.embedder, "model_name")
+                    else "nomic-embed-text:latest"
+                ),
                 "base_url": "http://localhost:11434",
                 "batch_size": 4,
-                "max_workers": 4
+                "max_workers": 4,
             },
             "chunking": {
-                "max_size": self.chunker.max_chunk_size if hasattr(self.chunker, 'max_chunk_size') else 2500,
-                "min_size": self.chunker.min_chunk_size if hasattr(self.chunker, 'min_chunk_size') else 100,
+                "max_size": (
+                    self.chunker.max_chunk_size
+                    if hasattr(self.chunker, "max_chunk_size")
+                    else 2500
+                ),
+                "min_size": (
+                    self.chunker.min_chunk_size
+                    if hasattr(self.chunker, "min_chunk_size")
+                    else 100
+                ),
                 "overlap": 100,
-                "strategy": "semantic"
-            },
-            "streaming": {
-                "enabled": True,
-                "threshold_mb": 1,
-                "chunk_size_kb": 64
+                "strategy": "semantic",
             },
+            "streaming": {"enabled": True, "threshold_mb": 1, "chunk_size_kb": 64},
             "files": {
                 "include_patterns": self.include_patterns,
                 "exclude_patterns": self.exclude_patterns,
                 "max_file_size_mb": 50,
-                "encoding_fallbacks": ["utf-8", "latin-1", "cp1252", "utf-8-sig"]
+                "encoding_fallbacks": ["utf-8", "latin-1", "cp1252", "utf-8-sig"],
             },
             "indexing": {
                 "parallel_workers": self.max_workers,
                 "incremental": True,
                 "track_changes": True,
-                "skip_binary": True
+                "skip_binary": True,
             },
             "search": {
                 "default_top_k": 10,
                 "similarity_threshold": 0.7,
                 "hybrid_search": True,
-                "bm25_weight": 0.3
+                "bm25_weight": 0.3,
             },
             "storage": {
                 "compress_vectors": False,
                 "index_type": "ivf_pq",
-                "cleanup_old_chunks": True
-            }
+                "cleanup_old_chunks": True,
+            },
         }
 
         # Save comprehensive config with nice formatting
@@ -191,31 +258,41 @@ class ProjectIndexer:
         """Apply configuration settings to the indexer."""
         try:
             # Apply embedding settings
-            if 'embedding' in config:
-                emb_config = config['embedding']
-                if hasattr(self.embedder, 'model_name'):
-                    self.embedder.model_name = emb_config.get('model', self.embedder.model_name)
-                if hasattr(self.embedder, 'base_url'):
-                    self.embedder.base_url = emb_config.get('base_url', self.embedder.base_url)
+            if "embedding" in config:
+                emb_config = config["embedding"]
+                if hasattr(self.embedder, "model_name"):
+                    self.embedder.model_name = emb_config.get(
+                        "model", self.embedder.model_name
+                    )
+                if hasattr(self.embedder, "base_url"):
+                    self.embedder.base_url = emb_config.get("base_url", self.embedder.base_url)
 
             # Apply chunking settings
-            if 'chunking' in config:
-                chunk_config = config['chunking']
-                if hasattr(self.chunker, 'max_chunk_size'):
-                    self.chunker.max_chunk_size = chunk_config.get('max_size', self.chunker.max_chunk_size)
-                if hasattr(self.chunker, 'min_chunk_size'):
-                    self.chunker.min_chunk_size = chunk_config.get('min_size', self.chunker.min_chunk_size)
+            if "chunking" in config:
+                chunk_config = config["chunking"]
+                if hasattr(self.chunker, "max_chunk_size"):
+                    self.chunker.max_chunk_size = chunk_config.get(
+                        "max_size", self.chunker.max_chunk_size
+                    )
+                if hasattr(self.chunker, "min_chunk_size"):
+                    self.chunker.min_chunk_size = chunk_config.get(
+                        "min_size", self.chunker.min_chunk_size
+                    )
 
             # Apply file patterns
-            if 'files' in config:
-                file_config = config['files']
-                self.include_patterns = file_config.get('include_patterns', self.include_patterns)
-                self.exclude_patterns = file_config.get('exclude_patterns', self.exclude_patterns)
+            if "files" in config:
+                file_config = config["files"]
+                self.include_patterns = file_config.get(
+                    "include_patterns", self.include_patterns
+                )
+                self.exclude_patterns = file_config.get(
+                    "exclude_patterns", self.exclude_patterns
+                )
 
             # Apply indexing settings
-            if 'indexing' in config:
-                idx_config = config['indexing']
-                self.max_workers = idx_config.get('parallel_workers', self.max_workers)
+            if "indexing" in config:
+                idx_config = config["indexing"]
+                self.max_workers = idx_config.get("parallel_workers", self.max_workers)
 
         except Exception as e:
             logger.warning(f"Failed to apply some config settings: {e}")
@@ -228,10 +305,10 @@ class ProjectIndexer:
                 "_comment": "RAG System Configuration - Edit this file to customize indexing behavior",
                 "_version": "2.0",
                 "_docs": "See README.md for detailed configuration options",
-                **config
+                **config,
             }
 
-            with open(self.config_path, 'w') as f:
+            with open(self.config_path, "w") as f:
                 json.dump(config_with_comments, f, indent=2, sort_keys=True)
 
             logger.info(f"Configuration saved to {self.config_path}")
@@ -257,7 +334,7 @@ class ProjectIndexer:
         try:
             if file_path.stat().st_size > 1_000_000:
                 return False
-        except:
+        except (OSError, IOError):
             return False
 
         # Check exclude patterns first
@@ -281,21 +358,33 @@ class ProjectIndexer:
         """Check if an extensionless file should be indexed based on content."""
         try:
             # Read first 1KB to check content
-            with open(file_path, 'rb') as f:
+            with open(file_path, "rb") as f:
                 first_chunk = f.read(1024)
 
             # Check if it's a text file (not binary)
             try:
-                text_content = first_chunk.decode('utf-8')
+                text_content = first_chunk.decode("utf-8")
             except UnicodeDecodeError:
                 return False  # Binary file, skip
 
             # Check for code indicators
             code_indicators = [
-                '#!/usr/bin/env python', '#!/usr/bin/python', '#!.*python',
-                'import ', 'from ', 'def ', 'class ', 'if __name__',
-                'function ', 'var ', 'const ', 'let ', 'package main',
-                'public class', 'private class', 'public static void'
+                "#!/usr/bin/env python",
+                "#!/usr/bin/python",
+                "#!.*python",
+                "import ",
+                "from ",
+                "def ",
+                "class ",
+                "if __name__",
+                "function ",
+                "var ",
+                "const ",
+                "let ",
+                "package main",
+                "public class",
+                "private class",
+                "public static void",
             ]
 
             text_lower = text_content.lower()
@@ -305,8 +394,15 @@ class ProjectIndexer:
 
             # Check for configuration files
             config_indicators = [
-                '#!/bin/bash', '#!/bin/sh', '[', 'version =', 'name =',
-                'description =', 'author =', '<configuration>', '<?xml'
+                "#!/bin/bash",
+                "#!/bin/sh",
+                "[",
+                "version =",
+                "name =",
+                "description =",
+                "author =",
+                "<configuration>",
+                "<?xml",
             ]
 
             for indicator in config_indicators:
@@ -323,17 +419,17 @@ class ProjectIndexer:
         file_str = normalize_relative_path(file_path, self.project_path)
 
         # Not in manifest - needs indexing
-        if file_str not in self.manifest['files']:
+        if file_str not in self.manifest["files"]:
             return True
 
-        file_info = self.manifest['files'][file_str]
+        file_info = self.manifest["files"][file_str]
 
         try:
             stat = file_path.stat()
 
             # Quick checks first (no I/O) - check size and modification time
-            stored_size = file_info.get('size', 0)
-            stored_mtime = file_info.get('mtime', 0)
+            stored_size = file_info.get("size", 0)
+            stored_mtime = file_info.get("mtime", 0)
 
             current_size = stat.st_size
             current_mtime = stat.st_mtime
@@ -345,7 +441,7 @@ class ProjectIndexer:
             # Size and mtime same - check hash only if needed (for paranoia)
             # This catches cases where content changed but mtime didn't (rare but possible)
             current_hash = self._get_file_hash(file_path)
-            stored_hash = file_info.get('hash', '')
+            stored_hash = file_info.get("hash", "")
 
             return current_hash != stored_hash
 
@@ -356,11 +452,11 @@ class ProjectIndexer:
 
     def _cleanup_removed_files(self):
         """Remove entries for files that no longer exist from manifest and database."""
-        if 'files' not in self.manifest:
+        if "files" not in self.manifest:
             return
 
         removed_files = []
-        for file_str in list(self.manifest['files'].keys()):
+        for file_str in list(self.manifest["files"].keys()):
             file_path = self.project_path / file_str
             if not file_path.exists():
                 removed_files.append(file_str)
@@ -371,14 +467,14 @@ class ProjectIndexer:
             for file_str in removed_files:
                 # Remove from database
                 try:
-                    if hasattr(self, 'table') and self.table:
+                    if hasattr(self, "table") and self.table:
                         self.table.delete(f"file_path = '{file_str}'")
                         logger.debug(f"Removed chunks for deleted file: {file_str}")
                 except Exception as e:
                     logger.warning(f"Could not remove chunks for {file_str}: {e}")
 
                 # Remove from manifest
-                del self.manifest['files'][file_str]
+                del self.manifest["files"][file_str]
 
             # Save updated manifest
             self._save_manifest()
@@ -391,7 +487,9 @@ class ProjectIndexer:
         # Walk through project directory
         for root, dirs, files in os.walk(self.project_path):
             # Skip excluded directories
-            dirs[:] = [d for d in dirs if not any(pattern in d for pattern in self.exclude_patterns)]
+            dirs[:] = [
+                d for d in dirs if not any(pattern in d for pattern in self.exclude_patterns)
+            ]
 
             root_path = Path(root)
             for file in files:
@@ -402,7 +500,9 @@ class ProjectIndexer:
 
         return files_to_index
 
-    def _process_file(self, file_path: Path, stream_threshold: int = 1024 * 1024) -> Optional[List[Dict[str, Any]]]:
+    def _process_file(
+        self, file_path: Path, stream_threshold: int = 1024 * 1024
+    ) -> Optional[List[Dict[str, Any]]]:
         """Process a single file: read, chunk, embed.
 
         Args:
@@ -418,7 +518,7 @@ class ProjectIndexer:
                 content = self._read_file_streaming(file_path)
             else:
                 # Read file content normally for small files
-                content = file_path.read_text(encoding='utf-8')
+                content = file_path.read_text(encoding="utf-8")
 
             # Chunk the file
             chunks = self.chunker.chunk_file(file_path, content)
@@ -446,39 +546,43 @@ class ProjectIndexer:
                     )
 
                 record = {
-                    'file_path': normalize_relative_path(file_path, self.project_path),
-                    'absolute_path': normalize_path(file_path),
-                    'chunk_id': f"{file_path.stem}_{i}",
-                    'content': chunk.content,
-                    'start_line': int(chunk.start_line),
-                    'end_line': int(chunk.end_line),
-                    'chunk_type': chunk.chunk_type,
-                    'name': chunk.name or f"chunk_{i}",
-                    'language': chunk.language,
-                    'embedding': embedding,  # Keep as numpy array
-                    'indexed_at': datetime.now().isoformat(),
+                    "file_path": normalize_relative_path(file_path, self.project_path),
+                    "absolute_path": normalize_path(file_path),
+                    "chunk_id": f"{file_path.stem}_{i}",
+                    "content": chunk.content,
+                    "start_line": int(chunk.start_line),
+                    "end_line": int(chunk.end_line),
+                    "chunk_type": chunk.chunk_type,
+                    "name": chunk.name or f"chunk_{i}",
+                    "language": chunk.language,
+                    "embedding": embedding,  # Keep as numpy array
+                    "indexed_at": datetime.now().isoformat(),
                     # Add new metadata fields
-                    'file_lines': int(chunk.file_lines) if chunk.file_lines else 0,
-                    'chunk_index': int(chunk.chunk_index) if chunk.chunk_index is not None else i,
-                    'total_chunks': int(chunk.total_chunks) if chunk.total_chunks else len(chunks),
-                    'parent_class': chunk.parent_class or '',
-                    'parent_function': chunk.parent_function or '',
-                    'prev_chunk_id': chunk.prev_chunk_id or '',
-                    'next_chunk_id': chunk.next_chunk_id or '',
+                    "file_lines": int(chunk.file_lines) if chunk.file_lines else 0,
+                    "chunk_index": (
+                        int(chunk.chunk_index) if chunk.chunk_index is not None else i
+                    ),
+                    "total_chunks": (
+                        int(chunk.total_chunks) if chunk.total_chunks else len(chunks)
+                    ),
+                    "parent_class": chunk.parent_class or "",
+                    "parent_function": chunk.parent_function or "",
+                    "prev_chunk_id": chunk.prev_chunk_id or "",
+                    "next_chunk_id": chunk.next_chunk_id or "",
                 }
                 records.append(record)
 
             # Update manifest with enhanced tracking
             file_str = normalize_relative_path(file_path, self.project_path)
             stat = file_path.stat()
-            self.manifest['files'][file_str] = {
-                'hash': self._get_file_hash(file_path),
-                'size': stat.st_size,
-                'mtime': stat.st_mtime,
-                'chunks': len(chunks),
-                'indexed_at': datetime.now().isoformat(),
-                'language': chunks[0].language if chunks else 'unknown',
-                'encoding': 'utf-8'  # Track encoding used
+            self.manifest["files"][file_str] = {
+                "hash": self._get_file_hash(file_path),
+                "size": stat.st_size,
+                "mtime": stat.st_mtime,
+                "chunks": len(chunks),
+                "indexed_at": datetime.now().isoformat(),
+                "language": chunks[0].language if chunks else "unknown",
+                "encoding": "utf-8",  # Track encoding used
             }
 
             return records
@@ -501,7 +605,7 @@ class ProjectIndexer:
         content_parts = []
 
         try:
-            with open(file_path, 'r', encoding='utf-8') as f:
+            with open(file_path, "r", encoding="utf-8") as f:
                 while True:
                     chunk = f.read(chunk_size)
                     if not chunk:
@@ -509,13 +613,13 @@ class ProjectIndexer:
                     content_parts.append(chunk)
 
             logger.debug(f"Streamed {len(content_parts)} chunks from {file_path}")
-            return ''.join(content_parts)
+            return "".join(content_parts)
 
         except UnicodeDecodeError:
             # Try with different encodings for problematic files
-            for encoding in ['latin-1', 'cp1252', 'utf-8-sig']:
+            for encoding in ["latin-1", "cp1252", "utf-8-sig"]:
                 try:
-                    with open(file_path, 'r', encoding=encoding) as f:
+                    with open(file_path, "r", encoding=encoding) as f:
                         content_parts = []
                         while True:
                             chunk = f.read(chunk_size)
@@ -523,8 +627,10 @@ class ProjectIndexer:
                                 break
                             content_parts.append(chunk)
 
-                    logger.debug(f"Streamed {len(content_parts)} chunks from {file_path} using {encoding}")
-                    return ''.join(content_parts)
+                    logger.debug(
+                        f"Streamed {len(content_parts)} chunks from {file_path} using {encoding}"
+                    )
+                    return "".join(content_parts)
                 except UnicodeDecodeError:
                     continue
 
@@ -535,16 +641,21 @@ class ProjectIndexer:
     def _init_database(self):
         """Initialize LanceDB connection and table."""
         if not LANCEDB_AVAILABLE:
-            logger.error("LanceDB is not available. Please install LanceDB for full indexing functionality.")
+            logger.error(
+                "LanceDB is not available. Please install LanceDB for full indexing functionality."
+            )
             logger.info("For Ollama-only mode, consider using hash-based embeddings instead.")
-            raise ImportError("LanceDB dependency is required for indexing. Install with: pip install lancedb pyarrow")
+            raise ImportError(
+                "LanceDB dependency is required for indexing. Install with: pip install lancedb pyarrow"
+            )
 
         try:
             self.db = lancedb.connect(self.rag_dir)
 
             # Define schema with fixed-size vector
             embedding_dim = self.embedder.get_embedding_dim()
-            schema = pa.schema([
+            schema = pa.schema(
+                [
                     pa.field("file_path", pa.string()),
                     pa.field("absolute_path", pa.string()),
                     pa.field("chunk_id", pa.string()),
@@ -554,7 +665,9 @@ class ProjectIndexer:
                     pa.field("chunk_type", pa.string()),
                     pa.field("name", pa.string()),
                     pa.field("language", pa.string()),
-                pa.field("embedding", pa.list_(pa.float32(), embedding_dim)),  # Fixed-size list
+                    pa.field(
+                        "embedding", pa.list_(pa.float32(), embedding_dim)
+                    ),  # Fixed-size list
                     pa.field("indexed_at", pa.string()),
                     # New metadata fields
                     pa.field("file_lines", pa.int32()),
@@ -564,7 +677,8 @@ class ProjectIndexer:
                     pa.field("parent_function", pa.string(), nullable=True),
                     pa.field("prev_chunk_id", pa.string(), nullable=True),
                     pa.field("next_chunk_id", pa.string(), nullable=True),
-            ])
+                ]
+            )
 
             # Create or open table
             if "code_vectors" in self.db.table_names():
@@ -581,7 +695,9 @@ class ProjectIndexer:
 
                     if not required_fields.issubset(existing_fields):
                         # Schema mismatch - drop and recreate table
-                        logger.warning("Schema mismatch detected. Dropping and recreating table.")
+                        logger.warning(
+                            "Schema mismatch detected. Dropping and recreating table."
+                        )
                         self.db.drop_table("code_vectors")
                         self.table = self.db.create_table("code_vectors", schema=schema)
                         logger.info("Recreated code_vectors table with updated schema")
@@ -596,7 +712,9 @@ class ProjectIndexer:
             else:
                 # Create empty table with schema
                 self.table = self.db.create_table("code_vectors", schema=schema)
-                logger.info(f"Created new code_vectors table with embedding dimension {embedding_dim}")
+                logger.info(
+                    f"Created new code_vectors table with embedding dimension {embedding_dim}"
+                )
 
         except Exception as e:
             logger.error(f"Failed to initialize database: {e}")
@@ -624,11 +742,11 @@ class ProjectIndexer:
         # Clear manifest if force reindex
         if force_reindex:
             self.manifest = {
-                'version': '1.0',
-                'indexed_at': None,
-                'file_count': 0,
-                'chunk_count': 0,
-                'files': {}
+                "version": "1.0",
+                "indexed_at": None,
+                "file_count": 0,
+                "chunk_count": 0,
+                "files": {},
             }
             # Clear existing table
             if "code_vectors" in self.db.table_names():
@@ -643,9 +761,9 @@ class ProjectIndexer:
         if not files_to_index:
             console.print("[green][/green] All files are up to date!")
             return {
-                'files_indexed': 0,
-                'chunks_created': 0,
-                'time_taken': 0,
+                "files_indexed": 0,
+                "chunks_created": 0,
+                "time_taken": 0,
             }
 
         console.print(f"[cyan]Found {len(files_to_index)} files to index[/cyan]")
@@ -663,10 +781,7 @@ class ProjectIndexer:
             console=console,
         ) as progress:
 
-            task = progress.add_task(
-                "[cyan]Indexing files...", 
-                total=len(files_to_index)
-            )
+            task = progress.add_task("[cyan]Indexing files...", total=len(files_to_index))
 
             with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
                 # Submit all files for processing
@@ -712,10 +827,10 @@ class ProjectIndexer:
                 raise
 
         # Update manifest
-        self.manifest['indexed_at'] = datetime.now().isoformat()
-        self.manifest['file_count'] = len(self.manifest['files'])
-        self.manifest['chunk_count'] = sum(
-            f['chunks'] for f in self.manifest['files'].values()
+        self.manifest["indexed_at"] = datetime.now().isoformat()
+        self.manifest["file_count"] = len(self.manifest["files"])
+        self.manifest["chunk_count"] = sum(
+            f["chunks"] for f in self.manifest["files"].values()
         )
         self._save_manifest()
 
@@ -724,11 +839,11 @@ class ProjectIndexer:
         time_taken = (end_time - start_time).total_seconds()
 
         stats = {
-            'files_indexed': len(files_to_index) - len(failed_files),
-            'files_failed': len(failed_files),
-            'chunks_created': len(all_records),
-            'time_taken': time_taken,
-            'files_per_second': len(files_to_index) / time_taken if time_taken > 0 else 0,
+            "files_indexed": len(files_to_index) - len(failed_files),
+            "files_failed": len(failed_files),
+            "chunks_created": len(all_records),
+            "time_taken": time_taken,
+            "files_per_second": (len(files_to_index) / time_taken if time_taken > 0 else 0),
         }
 
         # Print summary
@@ -739,7 +854,9 @@ class ProjectIndexer:
         console.print(f"Speed: {stats['files_per_second']:.1f} files/second")
 
         if failed_files:
-            console.print(f"\n[yellow]Warning:[/yellow] {len(failed_files)} files failed to index")
+            console.print(
+                f"\n[yellow]Warning:[/yellow] {len(failed_files)} files failed to index"
+            )
 
         return stats
 
@@ -774,14 +891,16 @@ class ProjectIndexer:
                 df["total_chunks"] = df["total_chunks"].astype("int32")
 
                 # Use vector store's update method (multiply out old, multiply in new)
-                if hasattr(self, '_vector_store') and self._vector_store:
+                if hasattr(self, "_vector_store") and self._vector_store:
                     success = self._vector_store.update_file_vectors(file_str, df)
                 else:
                     # Fallback: delete by file path and add new data
                     try:
                         self.table.delete(f"file = '{file_str}'")
                     except Exception as e:
-                        logger.debug(f"Could not delete existing chunks (might not exist): {e}")
+                        logger.debug(
+                            f"Could not delete existing chunks (might not exist): {e}"
+                        )
                     self.table.add(df)
                     success = True
 
@@ -789,23 +908,25 @@ class ProjectIndexer:
                     # Update manifest with enhanced file tracking
                     file_hash = self._get_file_hash(file_path)
                     stat = file_path.stat()
-                    if 'files' not in self.manifest:
-                        self.manifest['files'] = {}
-                    self.manifest['files'][file_str] = {
-                        'hash': file_hash,
-                        'size': stat.st_size,
-                        'mtime': stat.st_mtime,
-                        'chunks': len(records),
-                        'last_updated': datetime.now().isoformat(),
-                        'language': records[0].get('language', 'unknown') if records else 'unknown',
-                        'encoding': 'utf-8'
+                    if "files" not in self.manifest:
+                        self.manifest["files"] = {}
+                    self.manifest["files"][file_str] = {
+                        "hash": file_hash,
+                        "size": stat.st_size,
+                        "mtime": stat.st_mtime,
+                        "chunks": len(records),
+                        "last_updated": datetime.now().isoformat(),
+                        "language": (
+                            records[0].get("language", "unknown") if records else "unknown"
+                        ),
+                        "encoding": "utf-8",
                     }
                     self._save_manifest()
                     logger.debug(f"Successfully updated {len(records)} chunks for {file_str}")
                     return True
             else:
                 # File exists but has no processable content - remove existing chunks
-                if hasattr(self, '_vector_store') and self._vector_store:
+                if hasattr(self, "_vector_store") and self._vector_store:
                     self._vector_store.delete_by_file(file_str)
                 else:
                     try:
@@ -838,7 +959,7 @@ class ProjectIndexer:
             file_str = normalize_relative_path(file_path, self.project_path)
 
             # Delete from vector store
-            if hasattr(self, '_vector_store') and self._vector_store:
+            if hasattr(self, "_vector_store") and self._vector_store:
                 success = self._vector_store.delete_by_file(file_str)
             else:
                 try:
@@ -849,8 +970,8 @@ class ProjectIndexer:
                     success = False
 
             # Update manifest
-            if success and 'files' in self.manifest and file_str in self.manifest['files']:
-                del self.manifest['files'][file_str]
+            if success and "files" in self.manifest and file_str in self.manifest["files"]:
+                del self.manifest["files"][file_str]
                 self._save_manifest()
                 logger.debug(f"Deleted chunks for file: {file_str}")
 
@@ -863,20 +984,20 @@ class ProjectIndexer:
     def get_statistics(self) -> Dict[str, Any]:
         """Get indexing statistics."""
         stats = {
-            'project_path': str(self.project_path),
-            'indexed_at': self.manifest.get('indexed_at', 'Never'),
-            'file_count': self.manifest.get('file_count', 0),
-            'chunk_count': self.manifest.get('chunk_count', 0),
-            'index_size_mb': 0,
+            "project_path": str(self.project_path),
+            "indexed_at": self.manifest.get("indexed_at", "Never"),
+            "file_count": self.manifest.get("file_count", 0),
+            "chunk_count": self.manifest.get("chunk_count", 0),
+            "index_size_mb": 0,
         }
 
         # Calculate index size
         try:
-            db_path = self.rag_dir / 'code_vectors.lance'
+            db_path = self.rag_dir / "code_vectors.lance"
             if db_path.exists():
-                size_bytes = sum(f.stat().st_size for f in db_path.rglob('*') if f.is_file())
-                stats['index_size_mb'] = size_bytes / (1024 * 1024)
-        except:
+                size_bytes = sum(f.stat().st_size for f in db_path.rglob("*") if f.is_file())
+                stats["index_size_mb"] = size_bytes / (1024 * 1024)
+        except (OSError, IOError, PermissionError):
             pass
 
         return stats

mini_rag/llm_safeguards.py

@@ -6,17 +6,19 @@ Provides runaway prevention, context management, and intelligent detection
 of problematic model behaviors to ensure reliable user experience.
 """
 
+import logging
 import re
 import time
-import logging
-from typing import Optional, Dict, List, Tuple
 from dataclasses import dataclass
+from typing import Dict, List, Optional, Tuple
 
 logger = logging.getLogger(__name__)
 
+
 @dataclass
 class SafeguardConfig:
     """Configuration for LLM safeguards - gentle and educational."""
+
     max_output_tokens: int = 4000  # Allow longer responses for learning
     max_repetition_ratio: float = 0.7  # Be very permissive - only catch extreme repetition
     max_response_time: int = 120  # Allow 2 minutes for complex thinking
@@ -24,6 +26,7 @@ class SafeguardConfig:
     context_window: int = 32000  # Match Qwen3 context length (32K token limit)
     enable_thinking_detection: bool = True  # Detect thinking patterns
 
+
 class ModelRunawayDetector:
     """Detects and prevents model runaway behaviors."""
 
@@ -35,21 +38,28 @@ class ModelRunawayDetector:
         """Compile regex patterns for runaway detection."""
         return {
             # Excessive repetition patterns
-            'word_repetition': re.compile(r'\b(\w+)\b(?:\s+\1\b){3,}', re.IGNORECASE),
-            'phrase_repetition': re.compile(r'(.{10,50}?)\1{2,}', re.DOTALL),
-            
+            "word_repetition": re.compile(r"\b(\w+)\b(?:\s+\1\b){3,}", re.IGNORECASE),
+            "phrase_repetition": re.compile(r"(.{10,50}?)\1{2,}", re.DOTALL),
             # Thinking loop patterns (small models get stuck)
-            'thinking_loop': re.compile(r'(let me think|i think|thinking|consider|actually|wait|hmm|well)\s*[.,:]*\s*\1', re.IGNORECASE),
-            
+            "thinking_loop": re.compile(
+                r"(let me think|i think|thinking|consider|actually|wait|hmm|well)\s*[.,:]*\s*\1",
+                re.IGNORECASE,
+            ),
             # Rambling patterns
-            'excessive_filler': re.compile(r'\b(um|uh|well|you know|like|basically|actually|so|then|and|but|however)\b(?:\s+[^.!?]*){5,}', re.IGNORECASE),
-            
+            "excessive_filler": re.compile(
+                r"\b(um|uh|well|you know|like|basically|actually|so|then|and|but|however)\b(?:\s+[^.!?]*){5,}",
+                re.IGNORECASE,
+            ),
             # JSON corruption patterns
-            'broken_json': re.compile(r'\{[^}]*\{[^}]*\{'),  # Nested broken JSON
-            'json_repetition': re.compile(r'("[\w_]+"\s*:\s*"[^"]*",?\s*){4,}'),  # Repeated JSON fields
+            "broken_json": re.compile(r"\{[^}]*\{[^}]*\{"),  # Nested broken JSON
+            "json_repetition": re.compile(
+                r'("[\w_]+"\s*:\s*"[^"]*",?\s*){4,}'
+            ),  # Repeated JSON fields
         }
 
-    def check_response_quality(self, response: str, query: str, start_time: float) -> Tuple[bool, Optional[str], Optional[str]]:
+    def check_response_quality(
+        self, response: str, query: str, start_time: float
+    ) -> Tuple[bool, Optional[str], Optional[str]]:
         """
         Check response quality and detect runaway behaviors.
 
@@ -81,7 +91,7 @@ class ModelRunawayDetector:
             return False, rambling_issue, self._explain_rambling()
 
         # Check JSON corruption (for structured responses)
-        if '{' in response and '}' in response:
+        if "{" in response and "}" in response:
             json_issue = self._check_json_corruption(response)
             if json_issue:
                 return False, json_issue, self._explain_json_corruption()
@@ -91,11 +101,11 @@ class ModelRunawayDetector:
     def _check_repetition(self, response: str) -> Optional[str]:
         """Check for excessive repetition."""
         # Word repetition
-        if self.response_patterns['word_repetition'].search(response):
+        if self.response_patterns["word_repetition"].search(response):
             return "word_repetition"
 
         # Phrase repetition
-        if self.response_patterns['phrase_repetition'].search(response):
+        if self.response_patterns["phrase_repetition"].search(response):
             return "phrase_repetition"
 
         # Calculate repetition ratio (excluding Qwen3 thinking blocks)
@@ -104,7 +114,7 @@ class ModelRunawayDetector:
             # Extract only the actual response (after thinking) for repetition analysis
             thinking_end = response.find("</think>")
             if thinking_end != -1:
-                analysis_text = response[thinking_end + 8:].strip()
+                analysis_text = response[thinking_end + 8 :].strip()
 
                 # If the actual response (excluding thinking) is short, don't penalize
                 if len(analysis_text.split()) < 20:
@@ -121,11 +131,11 @@ class ModelRunawayDetector:
 
     def _check_thinking_loops(self, response: str) -> Optional[str]:
         """Check for thinking loops (common in small models)."""
-        if self.response_patterns['thinking_loop'].search(response):
+        if self.response_patterns["thinking_loop"].search(response):
             return "thinking_loop"
 
         # Check for excessive meta-commentary
-        thinking_words = ['think', 'considering', 'actually', 'wait', 'hmm', 'let me']
+        thinking_words = ["think", "considering", "actually", "wait", "hmm", "let me"]
         thinking_count = sum(response.lower().count(word) for word in thinking_words)
 
         if thinking_count > 5 and len(response.split()) < 200:
@@ -135,11 +145,11 @@ class ModelRunawayDetector:
 
     def _check_rambling(self, response: str) -> Optional[str]:
         """Check for rambling or excessive filler."""
-        if self.response_patterns['excessive_filler'].search(response):
+        if self.response_patterns["excessive_filler"].search(response):
             return "excessive_filler"
 
         # Check for extremely long sentences (sign of rambling)
-        sentences = re.split(r'[.!?]+', response)
+        sentences = re.split(r"[.!?]+", response)
         long_sentences = [s for s in sentences if len(s.split()) > 50]
 
         if len(long_sentences) > 2:
@@ -149,10 +159,10 @@ class ModelRunawayDetector:
 
     def _check_json_corruption(self, response: str) -> Optional[str]:
         """Check for JSON corruption in structured responses."""
-        if self.response_patterns['broken_json'].search(response):
+        if self.response_patterns["broken_json"].search(response):
             return "broken_json"
 
-        if self.response_patterns['json_repetition'].search(response):
+        if self.response_patterns["json_repetition"].search(response):
             return "json_repetition"
 
         return None
@@ -184,7 +194,7 @@ class ModelRunawayDetector:
 • Consider using a larger model if available"""
 
     def _explain_repetition(self, issue_type: str) -> str:
-        return f"""🔄 The AI got stuck in repetition loops ({issue_type}).
+        return """🔄 The AI got stuck in repetition loops ({issue_type}).
 
 **Why this happens:**
 • Small models sometimes repeat when uncertain
@@ -243,35 +253,48 @@ class ModelRunawayDetector:
         """Get specific recovery suggestions based on the issue."""
         suggestions = []
 
-        if issue_type in ['thinking_loop', 'excessive_thinking']:
-            suggestions.extend([
-                f"Try synthesis mode: `rag-mini search . \"{query}\" --synthesize`",
+        if issue_type in ["thinking_loop", "excessive_thinking"]:
+            suggestions.extend(
+                [
+                    f'Try synthesis mode: `rag-mini search . "{query}" --synthesize`',
                     "Ask more direct questions without 'why' or 'how'",
-                "Break complex questions into smaller parts"
-            ])
+                    "Break complex questions into smaller parts",
+                ]
+            )
 
-        elif issue_type in ['word_repetition', 'phrase_repetition', 'high_repetition_ratio']:
-            suggestions.extend([
+        elif issue_type in [
+            "word_repetition",
+            "phrase_repetition",
+            "high_repetition_ratio",
+        ]:
+            suggestions.extend(
+                [
                     "Try rephrasing your question completely",
                     "Use more specific technical terms",
-                f"Try exploration mode: `rag-mini explore .`"
-            ])
+                    "Try exploration mode: `rag-mini explore .`",
+                ]
+            )
 
-        elif issue_type == 'timeout':
-            suggestions.extend([
+        elif issue_type == "timeout":
+            suggestions.extend(
+                [
                     "Try a simpler version of your question",
                     "Use synthesis mode for faster responses",
-                "Check if Ollama is under heavy load"
-            ])
+                    "Check if Ollama is under heavy load",
+                ]
+            )
 
         # Universal suggestions
-        suggestions.extend([
+        suggestions.extend(
+            [
                 "Consider using a larger model if available (qwen3:1.7b or qwen3:4b)",
-            "Check model status: `ollama list`"
-        ])
+                "Check model status: `ollama list`",
+            ]
+        )
 
         return suggestions
 
+
 def get_optimal_ollama_parameters(model_name: str) -> Dict[str, any]:
     """Get optimal parameters for different Ollama models."""
 
@@ -313,7 +336,10 @@ def get_optimal_ollama_parameters(model_name: str) -> Dict[str, any]:
 
     return base_params
 
+
 # Quick test
+
+
 def test_safeguards():
     """Test the safeguard system."""
     detector = ModelRunawayDetector()
@@ -321,11 +347,14 @@ def test_safeguards():
     # Test repetition detection
     bad_response = "The user authentication system works by checking user credentials. The user authentication system works by checking user credentials. The user authentication system works by checking user credentials."
 
-    is_valid, issue, explanation = detector.check_response_quality(bad_response, "auth", time.time())
+    is_valid, issue, explanation = detector.check_response_quality(
+        bad_response, "auth", time.time()
+    )
 
     print(f"Repetition test: Valid={is_valid}, Issue={issue}")
     if explanation:
         print(explanation)
 
+
 if __name__ == "__main__":
     test_safeguards()

mini_rag/llm_synthesizer.py

@@ -9,37 +9,56 @@ Takes raw search results and generates coherent, contextual summaries.
 import json
 import logging
 import time
-from typing import List, Dict, Any, Optional
 from dataclasses import dataclass
-import requests
 from pathlib import Path
+from typing import Any, List, Optional
+
+import requests
 
 try:
-    from .llm_safeguards import ModelRunawayDetector, SafeguardConfig, get_optimal_ollama_parameters
+    from .llm_safeguards import (
+        ModelRunawayDetector,
+        SafeguardConfig,
+        get_optimal_ollama_parameters,
+    )
     from .system_context import get_system_context
 except ImportError:
     # Graceful fallback if safeguards not available
     ModelRunawayDetector = None
     SafeguardConfig = None
-    get_optimal_ollama_parameters = lambda x: {}
-    get_system_context = lambda x=None: ""
+
+    def get_optimal_ollama_parameters(x):
+        return {}
+
+    def get_system_context(x=None):
+        return ""
+
 
 logger = logging.getLogger(__name__)
 
+
 @dataclass
 class SynthesisResult:
     """Result of LLM synthesis."""
+
     summary: str
     key_points: List[str]
     code_examples: List[str]
     suggested_actions: List[str]
     confidence: float
 
+
 class LLMSynthesizer:
     """Synthesizes RAG search results using Ollama LLMs."""
 
-    def __init__(self, ollama_url: str = "http://localhost:11434", model: str = None, enable_thinking: bool = False, config=None):
-        self.ollama_url = ollama_url.rstrip('/')
+    def __init__(
+        self,
+        ollama_url: str = "http://localhost:11434",
+        model: str = None,
+        enable_thinking: bool = False,
+        config=None,
+    ):
+        self.ollama_url = ollama_url.rstrip("/")
         self.available_models = []
         self.model = model
         self.enable_thinking = enable_thinking  # Default False for synthesis mode
@@ -58,7 +77,7 @@ class LLMSynthesizer:
             response = requests.get(f"{self.ollama_url}/api/tags", timeout=5)
             if response.status_code == 200:
                 data = response.json()
-                return [model['name'] for model in data.get('models', [])]
+                return [model["name"] for model in data.get("models", [])]
         except Exception as e:
             logger.warning(f"Could not fetch Ollama models: {e}")
         return []
@@ -67,18 +86,31 @@ class LLMSynthesizer:
         """Select the best available model based on configuration rankings."""
         if not self.available_models:
             # Use config fallback if available, otherwise use default
-            if self.config and hasattr(self.config, 'llm') and hasattr(self.config.llm, 'model_rankings') and self.config.llm.model_rankings:
+            if (
+                self.config
+                and hasattr(self.config, "llm")
+                and hasattr(self.config.llm, "model_rankings")
+                and self.config.llm.model_rankings
+            ):
                 return self.config.llm.model_rankings[0]  # First preferred model
             return "qwen2.5:1.5b"  # System fallback only if no config
 
         # Get model rankings from config or use defaults
-        if self.config and hasattr(self.config, 'llm') and hasattr(self.config.llm, 'model_rankings'):
+        if (
+            self.config
+            and hasattr(self.config, "llm")
+            and hasattr(self.config.llm, "model_rankings")
+        ):
             model_rankings = self.config.llm.model_rankings
         else:
             # Fallback rankings if no config
             model_rankings = [
-                "qwen3:1.7b", "qwen3:0.6b", "qwen3:4b", "qwen2.5:3b", 
-                "qwen2.5:1.5b", "qwen2.5-coder:1.5b"
+                "qwen3:1.7b",
+                "qwen3:0.6b",
+                "qwen3:4b",
+                "qwen2.5:3b",
+                "qwen2.5:1.5b",
+                "qwen2.5-coder:1.5b",
             ]
 
         # Find first available model from our ranked list (exact matches first)
@@ -90,12 +122,14 @@ class LLMSynthesizer:
                     return available_model
 
                 # Partial match with version handling (e.g., "qwen3:1.7b" matches "qwen3:1.7b-q8_0")
-                preferred_parts = preferred_model.lower().split(':')
-                available_parts = available_model.lower().split(':')
+                preferred_parts = preferred_model.lower().split(":")
+                available_parts = available_model.lower().split(":")
 
                 if len(preferred_parts) >= 2 and len(available_parts) >= 2:
-                    if (preferred_parts[0] == available_parts[0] and 
-                        preferred_parts[1] in available_parts[1]):
+                    if (
+                        preferred_parts[0] == available_parts[0]
+                        and preferred_parts[1] in available_parts[1]
+                    ):
                         logger.info(f"Selected version match model: {available_model}")
                         return available_model
 
@@ -122,9 +156,9 @@ class LLMSynthesizer:
     def _get_optimal_context_size(self, model_name: str) -> int:
         """Get optimal context size based on model capabilities and configuration."""
         # Get configured context window
-        if self.config and hasattr(self.config, 'llm'):
+        if self.config and hasattr(self.config, "llm"):
             configured_context = self.config.llm.context_window
-            auto_context = getattr(self.config.llm, 'auto_context', True)
+            auto_context = getattr(self.config.llm, "auto_context", True)
         else:
             configured_context = 16384  # Default to 16K
             auto_context = True
@@ -132,23 +166,21 @@ class LLMSynthesizer:
         # Model-specific maximum context windows (based on research)
         model_limits = {
             # Qwen3 models with native context support
-            'qwen3:0.6b': 32768,    # 32K native
-            'qwen3:1.7b': 32768,    # 32K native  
-            'qwen3:4b': 131072,     # 131K with YaRN extension
-            
+            "qwen3:0.6b": 32768,  # 32K native
+            "qwen3:1.7b": 32768,  # 32K native
+            "qwen3:4b": 131072,  # 131K with YaRN extension
             # Qwen2.5 models
-            'qwen2.5:1.5b': 32768,  # 32K native
-            'qwen2.5:3b': 32768,    # 32K native
-            'qwen2.5-coder:1.5b': 32768,  # 32K native
-            
+            "qwen2.5:1.5b": 32768,  # 32K native
+            "qwen2.5:3b": 32768,  # 32K native
+            "qwen2.5-coder:1.5b": 32768,  # 32K native
             # Fallback for unknown models
-            'default': 8192
+            "default": 8192,
         }
 
         # Find model limit (check for partial matches)
-        model_limit = model_limits.get('default', 8192)
+        model_limit = model_limits.get("default", 8192)
         for model_pattern, limit in model_limits.items():
-            if model_pattern != 'default' and model_pattern.lower() in model_name.lower():
+            if model_pattern != "default" and model_pattern.lower() in model_name.lower():
                 model_limit = limit
                 break
 
@@ -161,7 +193,9 @@ class LLMSynthesizer:
         # Ensure minimum usable context for RAG
         optimal_context = max(optimal_context, 4096)  # Minimum 4K for basic RAG
 
-        logger.debug(f"Context for {model_name}: {optimal_context} tokens (configured: {configured_context}, limit: {model_limit})")
+        logger.debug(
+            f"Context for {model_name}: {optimal_context} tokens (configured: {configured_context}, limit: {model_limit})"
+        )
         return optimal_context
 
     def is_available(self) -> bool:
@@ -169,7 +203,14 @@ class LLMSynthesizer:
         self._ensure_initialized()
         return len(self.available_models) > 0
 
-    def _call_ollama(self, prompt: str, temperature: float = 0.3, disable_thinking: bool = False, use_streaming: bool = True, collapse_thinking: bool = True) -> Optional[str]:
+    def _call_ollama(
+        self,
+        prompt: str,
+        temperature: float = 0.3,
+        disable_thinking: bool = False,
+        use_streaming: bool = True,
+        collapse_thinking: bool = True,
+    ) -> Optional[str]:
         """Make a call to Ollama API with safeguards."""
         start_time = time.time()
 
@@ -181,7 +222,9 @@ class LLMSynthesizer:
             model_to_use = self.model
             if self.model not in self.available_models:
                 # Refresh model list in case of race condition
-                logger.warning(f"Configured model {self.model} not in available list, refreshing...")
+                logger.warning(
+                    f"Configured model {self.model} not in available list, refreshing..."
+                )
                 self.available_models = self._get_available_models()
 
                 if self.model in self.available_models:
@@ -235,21 +278,25 @@ class LLMSynthesizer:
                     "temperature": qwen3_temp,
                     "top_p": qwen3_top_p,
                     "top_k": qwen3_top_k,
-                    "num_ctx": self._get_optimal_context_size(model_to_use),  # Dynamic context based on model and config
+                    "num_ctx": self._get_optimal_context_size(
+                        model_to_use
+                    ),  # Dynamic context based on model and config
                     "num_predict": optimal_params.get("num_predict", 2000),
                     "repeat_penalty": optimal_params.get("repeat_penalty", 1.1),
-                    "presence_penalty": qwen3_presence
-                }
+                    "presence_penalty": qwen3_presence,
+                },
             }
 
             # Handle streaming with thinking display
             if use_streaming:
-                return self._handle_streaming_with_thinking_display(payload, model_to_use, use_thinking, start_time, collapse_thinking)
+                return self._handle_streaming_with_thinking_display(
+                    payload, model_to_use, use_thinking, start_time, collapse_thinking
+                )
 
             response = requests.post(
                 f"{self.ollama_url}/api/generate",
                 json=payload,
-                timeout=65  # Slightly longer than safeguard timeout
+                timeout=65,  # Slightly longer than safeguard timeout
             )
 
             if response.status_code == 200:
@@ -257,39 +304,51 @@ class LLMSynthesizer:
 
                 # All models use standard response format
                 # Qwen3 thinking tokens are embedded in the response content itself as <think>...</think>
-                raw_response = result.get('response', '').strip()
+                raw_response = result.get("response", "").strip()
 
                 # Log thinking content for Qwen3 debugging
-                if "qwen3" in model_to_use.lower() and use_thinking and "<think>" in raw_response:
+                if (
+                    "qwen3" in model_to_use.lower()
+                    and use_thinking
+                    and "<think>" in raw_response
+                ):
                     thinking_start = raw_response.find("<think>")
                     thinking_end = raw_response.find("</think>")
                     if thinking_start != -1 and thinking_end != -1:
-                        thinking_content = raw_response[thinking_start+7:thinking_end]
+                        thinking_content = raw_response[thinking_start + 7 : thinking_end]
                         logger.info(f"Qwen3 thinking: {thinking_content[:100]}...")
 
                 # Apply safeguards to check response quality
                 if self.safeguard_detector and raw_response:
-                    is_valid, issue_type, explanation = self.safeguard_detector.check_response_quality(
-                        raw_response, prompt[:100], start_time  # First 100 chars of prompt for context
+                    is_valid, issue_type, explanation = (
+                        self.safeguard_detector.check_response_quality(
+                            raw_response,
+                            prompt[:100],
+                            start_time,  # First 100 chars of prompt for context
+                        )
                     )
 
                     if not is_valid:
                         logger.warning(f"Safeguard triggered: {issue_type}")
                         # Preserve original response but add safeguard warning
-                        return self._create_safeguard_response_with_content(issue_type, explanation, raw_response)
+                        return self._create_safeguard_response_with_content(
+                            issue_type, explanation, raw_response
+                        )
 
                 # Clean up thinking tags from final response
                 cleaned_response = raw_response
-                if '<think>' in cleaned_response or '</think>' in cleaned_response:
+                if "<think>" in cleaned_response or "</think>" in cleaned_response:
                     # Remove thinking content but preserve the rest
-                    cleaned_response = cleaned_response.replace('<think>', '').replace('</think>', '')
+                    cleaned_response = cleaned_response.replace("<think>", "").replace(
+                        "</think>", ""
+                    )
                     # Clean up extra whitespace that might be left
-                    lines = cleaned_response.split('\n')
+                    lines = cleaned_response.split("\n")
                     cleaned_lines = []
                     for line in lines:
                         if line.strip():  # Only keep non-empty lines
                             cleaned_lines.append(line)
-                    cleaned_response = '\n'.join(cleaned_lines)
+                    cleaned_response = "\n".join(cleaned_lines)
 
                 return cleaned_response.strip()
             else:
@@ -300,9 +359,11 @@ class LLMSynthesizer:
             logger.error(f"Ollama call failed: {e}")
             return None
 
-    def _create_safeguard_response(self, issue_type: str, explanation: str, original_prompt: str) -> str:
+    def _create_safeguard_response(
+        self, issue_type: str, explanation: str, original_prompt: str
+    ) -> str:
         """Create a helpful response when safeguards are triggered."""
-        return f"""⚠️ Model Response Issue Detected
+        return """⚠️ Model Response Issue Detected
 
 {explanation}
 
@@ -318,7 +379,9 @@ class LLMSynthesizer:
 
 This is normal with smaller AI models and helps ensure you get quality responses."""
 
-    def _create_safeguard_response_with_content(self, issue_type: str, explanation: str, original_response: str) -> str:
+    def _create_safeguard_response_with_content(
+        self, issue_type: str, explanation: str, original_response: str
+    ) -> str:
         """Create a response that preserves the original content but adds a safeguard warning."""
 
         # For Qwen3, extract the actual response (after thinking)
@@ -326,11 +389,11 @@ This is normal with smaller AI models and helps ensure you get quality responses
         if "<think>" in original_response and "</think>" in original_response:
             thinking_end = original_response.find("</think>")
             if thinking_end != -1:
-                actual_response = original_response[thinking_end + 8:].strip()
+                actual_response = original_response[thinking_end + 8 :].strip()
 
         # If we have useful content, preserve it with a warning
         if len(actual_response.strip()) > 20:
-            return f"""⚠️ **Response Quality Warning** ({issue_type})
+            return """⚠️ **Response Quality Warning** ({issue_type})
 
 {explanation}
 
@@ -345,7 +408,7 @@ This is normal with smaller AI models and helps ensure you get quality responses
 💡 **Note**: This response may have quality issues. Consider rephrasing your question or trying exploration mode for better results."""
         else:
             # If content is too short or problematic, use the original safeguard response
-            return f"""⚠️ Model Response Issue Detected
+            return """⚠️ Model Response Issue Detected
 
 {explanation}
 
@@ -358,17 +421,20 @@ This is normal with smaller AI models and helps ensure you get quality responses
 
 This is normal with smaller AI models and helps ensure you get quality responses."""
 
-    def _handle_streaming_with_thinking_display(self, payload: dict, model_name: str, use_thinking: bool, start_time: float, collapse_thinking: bool = True) -> Optional[str]:
+    def _handle_streaming_with_thinking_display(
+        self,
+        payload: dict,
+        model_name: str,
+        use_thinking: bool,
+        start_time: float,
+        collapse_thinking: bool = True,
+    ) -> Optional[str]:
         """Handle streaming response with real-time thinking token display."""
         import json
-        import sys
 
         try:
             response = requests.post(
-                f"{self.ollama_url}/api/generate",
-                json=payload,
-                stream=True,
-                timeout=65
+                f"{self.ollama_url}/api/generate", json=payload, stream=True, timeout=65
             )
 
             if response.status_code != 200:
@@ -382,44 +448,54 @@ This is normal with smaller AI models and helps ensure you get quality responses
             thinking_lines_printed = 0
 
             # ANSI escape codes for colors and cursor control
-            GRAY = '\033[90m'      # Dark gray for thinking
-            LIGHT_GRAY = '\033[37m'  # Light gray alternative
-            RESET = '\033[0m'      # Reset color
-            CLEAR_LINE = '\033[2K' # Clear entire line
-            CURSOR_UP = '\033[A'   # Move cursor up one line
+            GRAY = "\033[90m"  # Dark gray for thinking
+            # "\033[37m"  # Light gray alternative  # Unused variable removed
+            RESET = "\033[0m"  # Reset color
+            CLEAR_LINE = "\033[2K"  # Clear entire line
+            CURSOR_UP = "\033[A"  # Move cursor up one line
 
             print(f"\n💭 {GRAY}Thinking...{RESET}", flush=True)
 
             for line in response.iter_lines():
                 if line:
                     try:
-                        chunk_data = json.loads(line.decode('utf-8'))
-                        chunk_text = chunk_data.get('response', '')
+                        chunk_data = json.loads(line.decode("utf-8"))
+                        chunk_text = chunk_data.get("response", "")
 
                         if chunk_text:
                             full_response += chunk_text
 
                             # Handle thinking tokens
-                            if use_thinking and '<think>' in chunk_text:
+                            if use_thinking and "<think>" in chunk_text:
                                 is_in_thinking = True
-                                chunk_text = chunk_text.replace('<think>', '')
+                                chunk_text = chunk_text.replace("<think>", "")
 
-                            if is_in_thinking and '</think>' in chunk_text:
+                            if is_in_thinking and "</think>" in chunk_text:
                                 is_in_thinking = False
                                 is_thinking_complete = True
-                                chunk_text = chunk_text.replace('</think>', '')
+                                chunk_text = chunk_text.replace("</think>", "")
 
                                 if collapse_thinking:
                                     # Clear thinking content and show completion
                                     # Move cursor up to clear thinking lines
                                     for _ in range(thinking_lines_printed + 1):
-                                        print(f"{CURSOR_UP}{CLEAR_LINE}", end='', flush=True)
+                                        print(
+                                            f"{CURSOR_UP}{CLEAR_LINE}",
+                                            end="",
+                                            flush=True,
+                                        )
 
-                                    print(f"💭 {GRAY}Thinking complete ✓{RESET}", flush=True)
+                                    print(
+                                        f"💭 {GRAY}Thinking complete ✓{RESET}",
+                                        flush=True,
+                                    )
                                     thinking_lines_printed = 0
                                 else:
                                     # Keep thinking visible, just show completion
-                                    print(f"\n💭 {GRAY}Thinking complete ✓{RESET}", flush=True)
+                                    print(
+                                        f"\n💭 {GRAY}Thinking complete ✓{RESET}",
+                                        flush=True,
+                                    )
 
                                 print("🤖 AI Response:", flush=True)
                                 continue
@@ -429,11 +505,17 @@ This is normal with smaller AI models and helps ensure you get quality responses
                                 thinking_content += chunk_text
 
                                 # Handle line breaks and word wrapping properly
-                                if ' ' in chunk_text or '\n' in chunk_text or len(thinking_content) > 100:
+                                if (
+                                    " " in chunk_text
+                                    or "\n" in chunk_text
+                                    or len(thinking_content) > 100
+                                ):
                                     # Split by sentences for better readability
-                                    sentences = thinking_content.replace('\n', ' ').split('. ')
+                                    sentences = thinking_content.replace("\n", " ").split(". ")
 
-                                    for sentence in sentences[:-1]:  # Process complete sentences
+                                    for sentence in sentences[
+                                        :-1
+                                    ]:  # Process complete sentences
                                         sentence = sentence.strip()
                                         if sentence:
                                             # Word wrap long sentences
@@ -442,32 +524,44 @@ This is normal with smaller AI models and helps ensure you get quality responses
                                             for word in words:
                                                 if len(line + " " + word) > 70:
                                                     if line:
-                                                        print(f"{GRAY}   {line.strip()}{RESET}", flush=True)
+                                                        print(
+                                                            f"{GRAY}   {line.strip()}{RESET}",
+                                                            flush=True,
+                                                        )
                                                         thinking_lines_printed += 1
                                                     line = word
                                                 else:
                                                     line += " " + word if line else word
 
                                             if line.strip():
-                                                print(f"{GRAY}   {line.strip()}.{RESET}", flush=True)
+                                                print(
+                                                    f"{GRAY}   {line.strip()}.{RESET}",
+                                                    flush=True,
+                                                )
                                                 thinking_lines_printed += 1
 
                                     # Keep the last incomplete sentence for next iteration
                                     thinking_content = sentences[-1] if sentences else ""
 
                             # Display regular response content (skip any leftover thinking)
-                            elif not is_in_thinking and is_thinking_complete and chunk_text.strip():
+                            elif (
+                                not is_in_thinking
+                                and is_thinking_complete
+                                and chunk_text.strip()
+                            ):
                                 # Filter out any remaining thinking tags that might leak through
                                 clean_text = chunk_text
-                                if '<think>' in clean_text or '</think>' in clean_text:
-                                    clean_text = clean_text.replace('<think>', '').replace('</think>', '')
+                                if "<think>" in clean_text or "</think>" in clean_text:
+                                    clean_text = clean_text.replace("<think>", "").replace(
+                                        "</think>", ""
+                                    )
 
                                 if clean_text:  # Remove .strip() here to preserve whitespace
                                     # Preserve all formatting including newlines and spaces
-                                    print(clean_text, end='', flush=True)
+                                    print(clean_text, end="", flush=True)
 
                         # Check if response is done
-                        if chunk_data.get('done', False):
+                        if chunk_data.get("done", False):
                             print()  # Final newline
                             break
 
@@ -483,16 +577,15 @@ This is normal with smaller AI models and helps ensure you get quality responses
             logger.error(f"Streaming failed: {e}")
             return None
 
-    def _handle_streaming_with_early_stop(self, payload: dict, model_name: str, use_thinking: bool, start_time: float) -> Optional[str]:
+    def _handle_streaming_with_early_stop(
+        self, payload: dict, model_name: str, use_thinking: bool, start_time: float
+    ) -> Optional[str]:
         """Handle streaming response with intelligent early stopping."""
         import json
 
         try:
             response = requests.post(
-                f"{self.ollama_url}/api/generate",
-                json=payload,
-                stream=True,
-                timeout=65
+                f"{self.ollama_url}/api/generate", json=payload, stream=True, timeout=65
             )
 
             if response.status_code != 200:
@@ -502,14 +595,16 @@ This is normal with smaller AI models and helps ensure you get quality responses
             full_response = ""
             word_buffer = []
             repetition_window = 30  # Check last 30 words for repetition (more context)
-            stop_threshold = 0.8    # Stop only if 80% of recent words are repetitive (very permissive)
+            stop_threshold = (
+                0.8  # Stop only if 80% of recent words are repetitive (very permissive)
+            )
             min_response_length = 100  # Don't early stop until we have at least 100 chars
 
             for line in response.iter_lines():
                 if line:
                     try:
-                        chunk_data = json.loads(line.decode('utf-8'))
-                        chunk_text = chunk_data.get('response', '')
+                        chunk_data = json.loads(line.decode("utf-8"))
+                        chunk_text = chunk_data.get("response", "")
 
                         if chunk_text:
                             full_response += chunk_text
@@ -523,28 +618,47 @@ This is normal with smaller AI models and helps ensure you get quality responses
                                 word_buffer = word_buffer[-repetition_window:]
 
                             # Check for repetition patterns after we have enough words AND content
-                            if len(word_buffer) >= repetition_window and len(full_response) >= min_response_length:
+                            if (
+                                len(word_buffer) >= repetition_window
+                                and len(full_response) >= min_response_length
+                            ):
                                 unique_words = set(word_buffer)
                                 repetition_ratio = 1 - (len(unique_words) / len(word_buffer))
 
                                 # Early stop only if repetition is EXTREMELY high (80%+)
                                 if repetition_ratio > stop_threshold:
-                                    logger.info(f"Early stopping due to repetition: {repetition_ratio:.2f}")
+                                    logger.info(
+                                        f"Early stopping due to repetition: {repetition_ratio:.2f}"
+                                    )
 
                                     # Add a gentle completion to the response
-                                    if not full_response.strip().endswith(('.', '!', '?')):
+                                    if not full_response.strip().endswith((".", "!", "?")):
                                         full_response += "..."
 
                                     # Send stop signal to model (attempt to gracefully stop)
                                     try:
-                                        stop_payload = {"model": model_name, "stop": True}
-                                        requests.post(f"{self.ollama_url}/api/generate", json=stop_payload, timeout=2)
-                                    except:
+                                        stop_payload = {
+                                            "model": model_name,
+                                            "stop": True,
+                                        }
+                                        requests.post(
+                                            f"{self.ollama_url}/api/generate",
+                                            json=stop_payload,
+                                            timeout=2,
+                                        )
+                                    except (
+                                        ConnectionError,
+                                        FileNotFoundError,
+                                        IOError,
+                                        OSError,
+                                        TimeoutError,
+                                        requests.RequestException,
+                                    ):
                                         pass  # If stop fails, we already have partial response
 
                                     break
 
-                        if chunk_data.get('done', False):
+                        if chunk_data.get("done", False):
                             break
 
                     except json.JSONDecodeError:
@@ -552,16 +666,18 @@ This is normal with smaller AI models and helps ensure you get quality responses
 
             # Clean up thinking tags from final response
             cleaned_response = full_response
-            if '<think>' in cleaned_response or '</think>' in cleaned_response:
+            if "<think>" in cleaned_response or "</think>" in cleaned_response:
                 # Remove thinking content but preserve the rest
-                cleaned_response = cleaned_response.replace('<think>', '').replace('</think>', '')
+                cleaned_response = cleaned_response.replace("<think>", "").replace(
+                    "</think>", ""
+                )
                 # Clean up extra whitespace that might be left
-                lines = cleaned_response.split('\n')
+                lines = cleaned_response.split("\n")
                 cleaned_lines = []
                 for line in lines:
                     if line.strip():  # Only keep non-empty lines
                         cleaned_lines.append(line)
-                cleaned_response = '\n'.join(cleaned_lines)
+                cleaned_response = "\n".join(cleaned_lines)
 
             return cleaned_response.strip()
 
@@ -569,7 +685,9 @@ This is normal with smaller AI models and helps ensure you get quality responses
             logger.error(f"Streaming with early stop failed: {e}")
             return None
 
-    def synthesize_search_results(self, query: str, results: List[Any], project_path: Path) -> SynthesisResult:
+    def synthesize_search_results(
+        self, query: str, results: List[Any], project_path: Path
+    ) -> SynthesisResult:
         """Synthesize search results into a coherent summary."""
 
         self._ensure_initialized()
@@ -579,29 +697,31 @@ This is normal with smaller AI models and helps ensure you get quality responses
                 key_points=[],
                 code_examples=[],
                 suggested_actions=["Install and run Ollama with a model"],
-                confidence=0.0
+                confidence=0.0,
             )
 
         # Prepare context from search results
         context_parts = []
         for i, result in enumerate(results[:8], 1):  # Limit to top 8 results
-            file_path = result.file_path if hasattr(result, 'file_path') else 'unknown'
-            content = result.content if hasattr(result, 'content') else str(result)
-            score = result.score if hasattr(result, 'score') else 0.0
+            # result.file_path if hasattr(result, "file_path") else "unknown"  # Unused variable removed
+            # result.content if hasattr(result, "content") else str(result)  # Unused variable removed
+            # result.score if hasattr(result, "score") else 0.0  # Unused variable removed
 
-            context_parts.append(f"""
+            context_parts.append(
+                """
 Result {i} (Score: {score:.3f}):
 File: {file_path}
 Content: {content[:500]}{'...' if len(content) > 500 else ''}
-""")
+"""
+            )
 
-        context = "\n".join(context_parts)
+        # "\n".join(context_parts)  # Unused variable removed
 
         # Get system context for better responses
-        system_context = get_system_context(project_path)
+        # get_system_context(project_path)  # Unused variable removed
 
         # Create synthesis prompt with system context
-        prompt = f"""You are a senior software engineer analyzing code search results. Your task is to synthesize the search results into a helpful, actionable summary.
+        prompt = """You are a senior software engineer analyzing code search results. Your task is to synthesize the search results into a helpful, actionable summary.
 
 SYSTEM CONTEXT: {system_context}
 SEARCH QUERY: "{query}"
@@ -646,33 +766,33 @@ Respond with ONLY the JSON, no other text."""
                 key_points=[],
                 code_examples=[],
                 suggested_actions=["Check Ollama status and try again"],
-                confidence=0.0
+                confidence=0.0,
             )
 
         # Parse JSON response
         try:
             # Extract JSON from response (in case there's extra text)
-            start_idx = response.find('{')
-            end_idx = response.rfind('}') + 1
+            start_idx = response.find("{")
+            end_idx = response.rfind("}") + 1
             if start_idx >= 0 and end_idx > start_idx:
                 json_str = response[start_idx:end_idx]
                 data = json.loads(json_str)
 
                 return SynthesisResult(
-                    summary=data.get('summary', 'No summary generated'),
-                    key_points=data.get('key_points', []),
-                    code_examples=data.get('code_examples', []),
-                    suggested_actions=data.get('suggested_actions', []),
-                    confidence=float(data.get('confidence', 0.5))
+                    summary=data.get("summary", "No summary generated"),
+                    key_points=data.get("key_points", []),
+                    code_examples=data.get("code_examples", []),
+                    suggested_actions=data.get("suggested_actions", []),
+                    confidence=float(data.get("confidence", 0.5)),
                 )
             else:
                 # Fallback: use the raw response as summary
                 return SynthesisResult(
-                    summary=response[:300] + '...' if len(response) > 300 else response,
+                    summary=response[:300] + "..." if len(response) > 300 else response,
                     key_points=[],
                     code_examples=[],
                     suggested_actions=[],
-                    confidence=0.3
+                    confidence=0.3,
                 )
 
         except Exception as e:
@@ -682,7 +802,7 @@ Respond with ONLY the JSON, no other text."""
                 key_points=[],
                 code_examples=[],
                 suggested_actions=["Try the search again or check LLM output"],
-                confidence=0.0
+                confidence=0.0,
             )
 
     def format_synthesis_output(self, synthesis: SynthesisResult, query: str) -> str:
@@ -693,7 +813,7 @@ Respond with ONLY the JSON, no other text."""
         output.append("=" * 50)
         output.append("")
 
-        output.append(f"📝 Summary:")
+        output.append("📝 Summary:")
         output.append(f"   {synthesis.summary}")
         output.append("")
 
@@ -715,13 +835,20 @@ Respond with ONLY the JSON, no other text."""
                 output.append(f"   • {action}")
             output.append("")
 
-        confidence_emoji = "🟢" if synthesis.confidence > 0.7 else "🟡" if synthesis.confidence > 0.4 else "🔴"
+        confidence_emoji = (
+            "🟢"
+            if synthesis.confidence > 0.7
+            else "🟡" if synthesis.confidence > 0.4 else "🔴"
+        )
         output.append(f"{confidence_emoji} Confidence: {synthesis.confidence:.1%}")
         output.append("")
 
         return "\n".join(output)
 
+
 # Quick test function
+
+
 def test_synthesizer():
     """Test the synthesizer with sample data."""
     from dataclasses import dataclass
@@ -740,17 +867,24 @@ def test_synthesizer():
 
     # Mock search results
     results = [
-        MockResult("auth.py", "def authenticate_user(username, password):\n    return verify_credentials(username, password)", 0.95),
-        MockResult("models.py", "class User:\n    def login(self):\n        return authenticate_user(self.username, self.password)", 0.87)
+        MockResult(
+            "auth.py",
+            "def authenticate_user(username, password):\n    return verify_credentials(username, password)",
+            0.95,
+        ),
+        MockResult(
+            "models.py",
+            "class User:\n    def login(self):\n        return authenticate_user(self.username, self.password)",
+            0.87,
+        ),
     ]
 
     synthesis = synthesizer.synthesize_search_results(
-        "user authentication", 
-        results, 
-        Path("/test/project")
+        "user authentication", results, Path("/test/project")
     )
 
     print(synthesizer.format_synthesis_output(synthesis, "user authentication"))
 
+
 if __name__ == "__main__":
     test_synthesizer()

mini_rag/non_invasive_watcher.py

@@ -3,16 +3,16 @@ Non-invasive file watcher designed to not interfere with development workflows.
 Uses minimal resources and gracefully handles high-load scenarios.
 """
 
-import os
-import time
 import logging
-import threading
 import queue
+import threading
+import time
+from datetime import datetime
 from pathlib import Path
 from typing import Optional, Set
-from datetime import datetime
+
+from watchdog.events import DirModifiedEvent, FileSystemEventHandler
 from watchdog.observers import Observer
-from watchdog.events import FileSystemEventHandler, DirModifiedEvent
 
 from .indexer import ProjectIndexer
 
@@ -74,10 +74,12 @@ class NonInvasiveQueue:
 class MinimalEventHandler(FileSystemEventHandler):
     """Minimal event handler that only watches for meaningful changes."""
 
-    def __init__(self, 
+    def __init__(
+        self,
         update_queue: NonInvasiveQueue,
         include_patterns: Set[str],
-                 exclude_patterns: Set[str]):
+        exclude_patterns: Set[str],
+    ):
         self.update_queue = update_queue
         self.include_patterns = include_patterns
         self.exclude_patterns = exclude_patterns
@@ -100,11 +102,13 @@ class MinimalEventHandler(FileSystemEventHandler):
 
         # Skip temporary and system files
         name = path.name
-        if (name.startswith('.') or 
-            name.startswith('~') or 
-            name.endswith('.tmp') or
-            name.endswith('.swp') or
-            name.endswith('.lock')):
+        if (
+            name.startswith(".")
+            or name.startswith("~")
+            or name.endswith(".tmp")
+            or name.endswith(".swp")
+            or name.endswith(".lock")
+        ):
             return False
 
         # Check exclude patterns first (faster)
@@ -124,7 +128,9 @@ class MinimalEventHandler(FileSystemEventHandler):
         """Rate limit events per file."""
         current_time = time.time()
         if file_path in self.last_event_time:
-            if current_time - self.last_event_time[file_path] < 2.0:  # 2 second cooldown per file
+            if (
+                current_time - self.last_event_time[file_path] < 2.0
+            ):  # 2 second cooldown per file
                 return False
 
         self.last_event_time[file_path] = current_time
@@ -132,16 +138,20 @@ class MinimalEventHandler(FileSystemEventHandler):
 
     def on_modified(self, event):
         """Handle file modifications with minimal overhead."""
-        if (not event.is_directory and 
-            self._should_process(event.src_path) and
-            self._rate_limit_event(event.src_path)):
+        if (
+            not event.is_directory
+            and self._should_process(event.src_path)
+            and self._rate_limit_event(event.src_path)
+        ):
             self.update_queue.add(Path(event.src_path))
 
     def on_created(self, event):
         """Handle file creation."""
-        if (not event.is_directory and 
-            self._should_process(event.src_path) and
-            self._rate_limit_event(event.src_path)):
+        if (
+            not event.is_directory
+            and self._should_process(event.src_path)
+            and self._rate_limit_event(event.src_path)
+        ):
             self.update_queue.add(Path(event.src_path))
 
     def on_deleted(self, event):
@@ -158,11 +168,13 @@ class MinimalEventHandler(FileSystemEventHandler):
 class NonInvasiveFileWatcher:
     """Non-invasive file watcher that prioritizes system stability."""
 
-    def __init__(self, 
+    def __init__(
+        self,
         project_path: Path,
         indexer: Optional[ProjectIndexer] = None,
         cpu_limit: float = 0.1,  # Max 10% CPU usage
-                 max_memory_mb: int = 50):  # Max 50MB memory
+        max_memory_mb: int = 50,
+    ):  # Max 50MB memory
         """
         Initialize non-invasive watcher.
 
@@ -178,7 +190,9 @@ class NonInvasiveFileWatcher:
         self.max_memory_mb = max_memory_mb
 
         # Initialize components with conservative settings
-        self.update_queue = NonInvasiveQueue(delay=10.0, max_queue_size=50)  # Very conservative
+        self.update_queue = NonInvasiveQueue(
+            delay=10.0, max_queue_size=50
+        )  # Very conservative
         self.observer = Observer()
         self.worker_thread = None
         self.running = False
@@ -188,19 +202,38 @@ class NonInvasiveFileWatcher:
         self.exclude_patterns = set(self.indexer.exclude_patterns)
 
         # Add more aggressive exclusions
-        self.exclude_patterns.update({
-            '__pycache__', '.git', 'node_modules', '.venv', 'venv',
-            'dist', 'build', 'target', '.idea', '.vscode', '.pytest_cache',
-            'coverage', 'htmlcov', '.coverage', '.mypy_cache', '.tox',
-            'logs', 'log', 'tmp', 'temp', '.DS_Store'
-        })
+        self.exclude_patterns.update(
+            {
+                "__pycache__",
+                ".git",
+                "node_modules",
+                ".venv",
+                "venv",
+                "dist",
+                "build",
+                "target",
+                ".idea",
+                ".vscode",
+                ".pytest_cache",
+                "coverage",
+                "htmlcov",
+                ".coverage",
+                ".mypy_cache",
+                ".tox",
+                "logs",
+                "log",
+                "tmp",
+                "temp",
+                ".DS_Store",
+            }
+        )
 
         # Stats
         self.stats = {
-            'files_processed': 0,
-            'files_dropped': 0,
-            'cpu_throttle_count': 0,
-            'started_at': None,
+            "files_processed": 0,
+            "files_dropped": 0,
+            "cpu_throttle_count": 0,
+            "started_at": None,
         }
 
     def start(self):
@@ -212,24 +245,16 @@ class NonInvasiveFileWatcher:
 
         # Set up minimal event handler
         event_handler = MinimalEventHandler(
-            self.update_queue,
-            self.include_patterns,
-            self.exclude_patterns
+            self.update_queue, self.include_patterns, self.exclude_patterns
         )
 
         # Schedule with recursive watching
-        self.observer.schedule(
-            event_handler,
-            str(self.project_path),
-            recursive=True
-        )
+        self.observer.schedule(event_handler, str(self.project_path), recursive=True)
 
         # Start low-priority worker thread
         self.running = True
         self.worker_thread = threading.Thread(
-            target=self._process_updates_gently,
-            daemon=True,
-            name="RAG-FileWatcher"
+            target=self._process_updates_gently, daemon=True, name="RAG-FileWatcher"
         )
         # Set lowest priority
         self.worker_thread.start()
@@ -237,7 +262,7 @@ class NonInvasiveFileWatcher:
         # Start observer
         self.observer.start()
 
-        self.stats['started_at'] = datetime.now()
+        self.stats["started_at"] = datetime.now()
         logger.info("Non-invasive file watcher started")
 
     def stop(self):
@@ -282,7 +307,7 @@ class NonInvasiveFileWatcher:
                         # If we're consuming too much time, throttle aggressively
                         work_ratio = 0.1  # Assume we use 10% of time in this check
                         if work_ratio > self.cpu_limit:
-                            self.stats['cpu_throttle_count'] += 1
+                            self.stats["cpu_throttle_count"] += 1
                             time.sleep(2.0)  # Back off significantly
                             continue
 
@@ -294,18 +319,20 @@ class NonInvasiveFileWatcher:
                             success = self.indexer.delete_file(file_path)
 
                         if success:
-                            self.stats['files_processed'] += 1
+                            self.stats["files_processed"] += 1
 
                         # Always yield CPU after processing
                         time.sleep(0.1)
 
                     except Exception as e:
-                        logger.debug(f"Non-invasive watcher: failed to process {file_path}: {e}")
+                        logger.debug(
+                            f"Non-invasive watcher: failed to process {file_path}: {e}"
+                        )
                         # Don't let errors propagate - just continue
                         continue
 
                 # Update dropped count from queue
-                self.stats['files_dropped'] = self.update_queue.dropped_count
+                self.stats["files_dropped"] = self.update_queue.dropped_count
 
             except Exception as e:
                 logger.debug(f"Non-invasive watcher error: {e}")
@@ -316,12 +343,12 @@ class NonInvasiveFileWatcher:
     def get_statistics(self) -> dict:
         """Get non-invasive watcher statistics."""
         stats = self.stats.copy()
-        stats['queue_size'] = self.update_queue.queue.qsize()
-        stats['running'] = self.running
+        stats["queue_size"] = self.update_queue.queue.qsize()
+        stats["running"] = self.running
 
-        if stats['started_at']:
-            uptime = datetime.now() - stats['started_at']
-            stats['uptime_seconds'] = uptime.total_seconds()
+        if stats["started_at"]:
+            uptime = datetime.now() - stats["started_at"]
+            stats["uptime_seconds"] = uptime.total_seconds()
 
         return stats
 

mini_rag/ollama_embeddings.py

@@ -3,15 +3,14 @@ Hybrid code embedding module - Ollama primary with ML fallback.
 Tries Ollama first, falls back to local ML stack if needed.
 """
 
-import requests
-import numpy as np
-from typing import List, Union, Optional, Dict, Any
 import logging
-from functools import lru_cache
 import time
-import json
 from concurrent.futures import ThreadPoolExecutor
-import threading
+from functools import lru_cache
+from typing import Any, Dict, List, Optional, Union
+
+import numpy as np
+import requests
 
 logger = logging.getLogger(__name__)
 
@@ -19,8 +18,9 @@ logger = logging.getLogger(__name__)
 FALLBACK_AVAILABLE = False
 try:
     import torch
-    from transformers import AutoTokenizer, AutoModel
     from sentence_transformers import SentenceTransformer
+    from transformers import AutoModel, AutoTokenizer
+
     FALLBACK_AVAILABLE = True
     logger.debug("ML fallback dependencies available")
 except ImportError:
@@ -30,8 +30,12 @@ except ImportError:
 class OllamaEmbedder:
     """Hybrid embeddings: Ollama primary with ML fallback."""
 
-    def __init__(self, model_name: str = "nomic-embed-text:latest", base_url: str = "http://localhost:11434", 
-                 enable_fallback: bool = True):
+    def __init__(
+        self,
+        model_name: str = "nomic-embed-text:latest",
+        base_url: str = "http://localhost:11434",
+        enable_fallback: bool = True,
+    ):
         """
         Initialize the hybrid embedder.
 
@@ -70,7 +74,9 @@ class OllamaEmbedder:
                 try:
                     self._initialize_fallback_embedder()
                     self.mode = "fallback"
-                    logger.info(f"✅ ML fallback active: {self.fallback_embedder.model_type if hasattr(self.fallback_embedder, 'model_type') else 'transformer'}")
+                    logger.info(
+                        f"✅ ML fallback active: {self.fallback_embedder.model_type if hasattr(self.fallback_embedder, 'model_type') else 'transformer'}"
+                    )
                 except Exception as fallback_error:
                     logger.warning(f"ML fallback failed: {fallback_error}")
                     self.mode = "hash"
@@ -101,8 +107,8 @@ class OllamaEmbedder:
             raise ConnectionError("Ollama service timeout")
 
         # Check if our model is available
-        models = response.json().get('models', [])
-        model_names = [model['name'] for model in models]
+        models = response.json().get("models", [])
+        model_names = [model["name"] for model in models]
 
         if self.model_name not in model_names:
             print(f"📦 Model '{self.model_name}' Not Found")
@@ -121,7 +127,11 @@ class OllamaEmbedder:
 
         # Try lightweight models first for better compatibility
         fallback_models = [
-            ("sentence-transformers/all-MiniLM-L6-v2", 384, self._init_sentence_transformer),
+            (
+                "sentence-transformers/all-MiniLM-L6-v2",
+                384,
+                self._init_sentence_transformer,
+            ),
             ("microsoft/codebert-base", 768, self._init_transformer_model),
             ("microsoft/unixcoder-base", 768, self._init_transformer_model),
         ]
@@ -141,22 +151,24 @@ class OllamaEmbedder:
     def _init_sentence_transformer(self, model_name: str):
         """Initialize sentence-transformers model."""
         self.fallback_embedder = SentenceTransformer(model_name)
-        self.fallback_embedder.model_type = 'sentence_transformer'
+        self.fallback_embedder.model_type = "sentence_transformer"
 
     def _init_transformer_model(self, model_name: str):
         """Initialize transformer model."""
-        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        device = "cuda" if torch.cuda.is_available() else "cpu"
         tokenizer = AutoTokenizer.from_pretrained(model_name)
         model = AutoModel.from_pretrained(model_name).to(device)
         model.eval()
 
         # Create a simple wrapper
+
         class TransformerWrapper:
+
             def __init__(self, model, tokenizer, device):
                 self.model = model
                 self.tokenizer = tokenizer
                 self.device = device
-                self.model_type = 'transformer'
+                self.model_type = "transformer"
 
         self.fallback_embedder = TransformerWrapper(model, tokenizer, device)
 
@@ -167,7 +179,7 @@ class OllamaEmbedder:
             response = requests.post(
                 f"{self.base_url}/api/pull",
                 json={"name": self.model_name},
-                timeout=300  # 5 minutes for model download
+                timeout=300,  # 5 minutes for model download
             )
             response.raise_for_status()
             logger.info(f"Successfully pulled {self.model_name}")
@@ -189,16 +201,13 @@ class OllamaEmbedder:
         try:
             response = requests.post(
                 f"{self.base_url}/api/embeddings",
-                json={
-                    "model": self.model_name,
-                    "prompt": text
-                },
-                timeout=30
+                json={"model": self.model_name, "prompt": text},
+                timeout=30,
             )
             response.raise_for_status()
 
             result = response.json()
-            embedding = result.get('embedding', [])
+            embedding = result.get("embedding", [])
 
             if not embedding:
                 raise ValueError("No embedding returned from Ollama")
@@ -220,33 +229,37 @@ class OllamaEmbedder:
     def _get_fallback_embedding(self, text: str) -> np.ndarray:
         """Get embedding from ML fallback."""
         try:
-            if self.fallback_embedder.model_type == 'sentence_transformer':
+            if self.fallback_embedder.model_type == "sentence_transformer":
                 embedding = self.fallback_embedder.encode([text], convert_to_numpy=True)[0]
                 return embedding.astype(np.float32)
 
-            elif self.fallback_embedder.model_type == 'transformer':
+            elif self.fallback_embedder.model_type == "transformer":
                 # Tokenize and generate embedding
                 inputs = self.fallback_embedder.tokenizer(
                     text,
                     padding=True,
                     truncation=True,
                     max_length=512,
-                    return_tensors="pt"
+                    return_tensors="pt",
                 ).to(self.fallback_embedder.device)
 
                 with torch.no_grad():
                     outputs = self.fallback_embedder.model(**inputs)
 
                     # Use pooler output if available, otherwise mean pooling
-                    if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None:
+                    if hasattr(outputs, "pooler_output") and outputs.pooler_output is not None:
                         embedding = outputs.pooler_output[0]
                     else:
                         # Mean pooling over sequence length
-                        attention_mask = inputs['attention_mask']
+                        attention_mask = inputs["attention_mask"]
                         token_embeddings = outputs.last_hidden_state[0]
 
                         # Mask and average
-                        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+                        input_mask_expanded = (
+                            attention_mask.unsqueeze(-1)
+                            .expand(token_embeddings.size())
+                            .float()
+                        )
                         sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 0)
                         sum_mask = torch.clamp(input_mask_expanded.sum(0), min=1e-9)
                         embedding = sum_embeddings / sum_mask
@@ -254,7 +267,9 @@ class OllamaEmbedder:
                 return embedding.cpu().numpy().astype(np.float32)
 
             else:
-                raise ValueError(f"Unknown fallback model type: {self.fallback_embedder.model_type}")
+                raise ValueError(
+                    f"Unknown fallback model type: {self.fallback_embedder.model_type}"
+                )
 
         except Exception as e:
             logger.error(f"Fallback embedding failed: {e}")
@@ -265,7 +280,7 @@ class OllamaEmbedder:
         import hashlib
 
         # Create deterministic hash
-        hash_obj = hashlib.sha256(text.encode('utf-8'))
+        hash_obj = hashlib.sha256(text.encode("utf-8"))
         hash_bytes = hash_obj.digest()
 
         # Convert to numbers and normalize
@@ -276,7 +291,7 @@ class OllamaEmbedder:
             hash_nums = np.concatenate([hash_nums, hash_nums])
 
         # Take exactly the dimension we need
-        embedding = hash_nums[:self.embedding_dim].astype(np.float32)
+        embedding = hash_nums[: self.embedding_dim].astype(np.float32)
 
         # Normalize to [-1, 1] range
         embedding = (embedding / 127.5) - 1.0
@@ -325,7 +340,7 @@ class OllamaEmbedder:
         code = code.strip()
 
         # Normalize whitespace but preserve structure
-        lines = code.split('\n')
+        lines = code.split("\n")
         processed_lines = []
 
         for line in lines:
@@ -335,7 +350,7 @@ class OllamaEmbedder:
             if line:
                 processed_lines.append(line)
 
-        cleaned_code = '\n'.join(processed_lines)
+        cleaned_code = "\n".join(processed_lines)
 
         # Add language context for better embeddings
         if language and cleaned_code:
@@ -380,33 +395,36 @@ class OllamaEmbedder:
         """Sequential processing for small batches."""
         results = []
         for file_dict in file_contents:
-            content = file_dict['content']
-            language = file_dict.get('language', 'python')
+            content = file_dict["content"]
+            language = file_dict.get("language", "python")
             embedding = self.embed_code(content, language)
 
             result = file_dict.copy()
-            result['embedding'] = embedding
+            result["embedding"] = embedding
             results.append(result)
 
         return results
 
-    def _batch_embed_concurrent(self, file_contents: List[dict], max_workers: int) -> List[dict]:
+    def _batch_embed_concurrent(
+        self, file_contents: List[dict], max_workers: int
+    ) -> List[dict]:
         """Concurrent processing for larger batches."""
+
         def embed_single(item_with_index):
             index, file_dict = item_with_index
-            content = file_dict['content']
-            language = file_dict.get('language', 'python')
+            content = file_dict["content"]
+            language = file_dict.get("language", "python")
 
             try:
                 embedding = self.embed_code(content, language)
                 result = file_dict.copy()
-                result['embedding'] = embedding
+                result["embedding"] = embedding
                 return index, result
             except Exception as e:
                 logger.error(f"Failed to embed content at index {index}: {e}")
                 # Return with hash fallback
                 result = file_dict.copy()
-                result['embedding'] = self._hash_embedding(content)
+                result["embedding"] = self._hash_embedding(content)
                 return index, result
 
         # Create indexed items to preserve order
@@ -420,7 +438,9 @@ class OllamaEmbedder:
         indexed_results.sort(key=lambda x: x[0])
         return [result for _, result in indexed_results]
 
-    def _batch_embed_chunked(self, file_contents: List[dict], max_workers: int, chunk_size: int = 200) -> List[dict]:
+    def _batch_embed_chunked(
+        self, file_contents: List[dict], max_workers: int, chunk_size: int = 200
+    ) -> List[dict]:
         """
         Process very large batches in smaller chunks to prevent memory issues.
         This is important for beginners who might try to index huge projects.
@@ -430,13 +450,15 @@ class OllamaEmbedder:
 
         # Process in chunks
         for i in range(0, len(file_contents), chunk_size):
-            chunk = file_contents[i:i + chunk_size]
+            chunk = file_contents[i : i + chunk_size]
 
             # Log progress for large operations
             if total_chunks > chunk_size:
                 chunk_num = i // chunk_size + 1
                 total_chunk_count = (total_chunks + chunk_size - 1) // chunk_size
-                logger.info(f"Processing chunk {chunk_num}/{total_chunk_count} ({len(chunk)} files)")
+                logger.info(
+                    f"Processing chunk {chunk_num}/{total_chunk_count} ({len(chunk)} files)"
+                )
 
             # Process this chunk using concurrent method
             chunk_results = self._batch_embed_concurrent(chunk, max_workers)
@@ -444,7 +466,7 @@ class OllamaEmbedder:
 
             # Brief pause between chunks to prevent overwhelming the system
             if i + chunk_size < len(file_contents):
-                import time
+
                 time.sleep(0.1)  # 100ms pause between chunks
 
         return results
@@ -463,10 +485,14 @@ class OllamaEmbedder:
             "mode": self.mode,
             "ollama_available": self.ollama_available,
             "fallback_available": FALLBACK_AVAILABLE and self.enable_fallback,
-            "fallback_model": getattr(self.fallback_embedder, 'model_type', None) if self.fallback_embedder else None,
+            "fallback_model": (
+                getattr(self.fallback_embedder, "model_type", None)
+                if self.fallback_embedder
+                else None
+            ),
             "embedding_dim": self.embedding_dim,
             "ollama_model": self.model_name if self.mode == "ollama" else None,
-            "ollama_url": self.base_url if self.mode == "ollama" else None
+            "ollama_url": self.base_url if self.mode == "ollama" else None,
         }
 
     def get_embedding_info(self) -> Dict[str, str]:
@@ -474,25 +500,16 @@ class OllamaEmbedder:
         status = self.get_status()
         mode = status.get("mode", "unknown")
         if mode == "ollama":
-            return {
-                "method": f"Ollama ({status['ollama_model']})",
-                "status": "working"
-            }
+            return {"method": f"Ollama ({status['ollama_model']})", "status": "working"}
         # Treat legacy/alternate naming uniformly
         if mode in ("fallback", "ml"):
             return {
                 "method": f"ML Fallback ({status['fallback_model']})",
-                "status": "working"
+                "status": "working",
             }
         if mode == "hash":
-            return {
-                "method": "Hash-based (basic similarity)",
-                "status": "working"
-            }
-        return {
-            "method": "Unknown",
-            "status": "error"
-        }
+            return {"method": "Hash-based (basic similarity)", "status": "working"}
+        return {"method": "Unknown", "status": "error"}
 
     def warmup(self):
         """Warm up the embedding system with a dummy request."""
@@ -503,7 +520,11 @@ class OllamaEmbedder:
 
 
 # Convenience function for quick embedding
-def embed_code(code: Union[str, List[str]], model_name: str = "nomic-embed-text:latest") -> np.ndarray:
+
+
+def embed_code(
+    code: Union[str, List[str]], model_name: str = "nomic-embed-text:latest"
+) -> np.ndarray:
     """
     Quick function to embed code without managing embedder instance.
 

mini_rag/path_handler.py

@@ -4,10 +4,9 @@ Handles forward/backward slashes on any file system.
 Robust cross-platform path handling.
 """
 
-import os
 import sys
 from pathlib import Path
-from typing import Union, List
+from typing import List, Union
 
 
 def normalize_path(path: Union[str, Path]) -> str:
@@ -25,10 +24,10 @@ def normalize_path(path: Union[str, Path]) -> str:
     path_obj = Path(path)
 
     # Convert to string and replace backslashes
-    path_str = str(path_obj).replace('\\', '/')
+    path_str = str(path_obj).replace("\\", "/")
 
     # Handle UNC paths on Windows
-    if sys.platform == 'win32' and path_str.startswith('//'):
+    if sys.platform == "win32" and path_str.startswith("//"):
         # Keep UNC paths as they are
         return path_str
 
@@ -120,7 +119,7 @@ def ensure_forward_slashes(path_str: str) -> str:
     Returns:
         Path with forward slashes
     """
-    return path_str.replace('\\', '/')
+    return path_str.replace("\\", "/")
 
 
 def ensure_native_slashes(path_str: str) -> str:
@@ -137,6 +136,8 @@ def ensure_native_slashes(path_str: str) -> str:
 
 
 # Convenience functions for common operations
+
+
 def storage_path(path: Union[str, Path]) -> str:
     """Convert path to storage format (forward slashes)."""
     return normalize_path(path)

mini_rag/performance.py

@@ -3,12 +3,13 @@ Performance monitoring for RAG system.
 Track loading times, query times, and resource usage.
 """
 
-import time
-import psutil
-import os
-from contextlib import contextmanager
-from typing import Dict, Any, Optional
 import logging
+import os
+import time
+from contextlib import contextmanager
+from typing import Any, Dict, Optional
+
+import psutil
 
 logger = logging.getLogger(__name__)
 
@@ -39,9 +40,9 @@ class PerformanceMonitor:
 
             # Store metrics
             self.metrics[operation] = {
-                'duration_seconds': duration,
-                'memory_delta_mb': memory_delta,
-                'final_memory_mb': end_memory,
+                "duration_seconds": duration,
+                "memory_delta_mb": memory_delta,
+                "final_memory_mb": end_memory,
             }
 
             logger.info(
@@ -51,19 +52,19 @@ class PerformanceMonitor:
 
     def get_summary(self) -> Dict[str, Any]:
         """Get performance summary."""
-        total_time = sum(m['duration_seconds'] for m in self.metrics.values())
+        total_time = sum(m["duration_seconds"] for m in self.metrics.values())
 
         return {
-            'total_time_seconds': total_time,
-            'operations': self.metrics,
-            'current_memory_mb': self.process.memory_info().rss / 1024 / 1024,
+            "total_time_seconds": total_time,
+            "operations": self.metrics,
+            "current_memory_mb": self.process.memory_info().rss / 1024 / 1024,
         }
 
     def print_summary(self):
         """Print a formatted summary."""
-        print("\n" + "="*50)
+        print("\n" + "=" * 50)
         print("PERFORMANCE SUMMARY")
-        print("="*50)
+        print("=" * 50)
 
         for op, metrics in self.metrics.items():
             print(f"\n{op}:")
@@ -73,12 +74,13 @@ class PerformanceMonitor:
         summary = self.get_summary()
         print(f"\nTotal Time: {summary['total_time_seconds']:.2f}s")
         print(f"Current Memory: {summary['current_memory_mb']:.1f}MB")
-        print("="*50)
+        print("=" * 50)
 
 
 # Global instance for easy access
 _monitor = None
 
+
 def get_monitor() -> PerformanceMonitor:
     """Get or create global monitor instance."""
     global _monitor

mini_rag/query_expander.py

@@ -33,12 +33,15 @@ disable in CLI for maximum speed.
 import logging
 import re
 import threading
-from typing import List, Optional
+from typing import Optional
+
 import requests
+
 from .config import RAGConfig
 
 logger = logging.getLogger(__name__)
 
+
 class QueryExpander:
     """Expands search queries using LLM to improve search recall."""
 
@@ -107,7 +110,7 @@ class QueryExpander:
             return None
 
         # Create expansion prompt
-        prompt = f"""You are a search query expert. Expand the following search query with {self.max_terms} additional related terms that would help find relevant content.
+        prompt = """You are a search query expert. Expand the following search query with {self.max_terms} additional related terms that would help find relevant content.
 
 Original query: "{query}"
 
@@ -134,18 +137,18 @@ Expanded query:"""
                 "options": {
                     "temperature": 0.1,  # Very low temperature for consistent expansions
                     "top_p": 0.8,
-                    "max_tokens": 100    # Keep it short
-                }
+                    "max_tokens": 100,  # Keep it short
+                },
             }
 
             response = requests.post(
                 f"{self.ollama_url}/api/generate",
                 json=payload,
-                timeout=10  # Quick timeout for low latency
+                timeout=10,  # Quick timeout for low latency
             )
 
             if response.status_code == 200:
-                result = response.json().get('response', '').strip()
+                result = response.json().get("response", "").strip()
 
                 # Clean up the response - extract just the expanded query
                 expanded = self._clean_expansion(result, query)
@@ -166,12 +169,16 @@ Expanded query:"""
             response = requests.get(f"{self.ollama_url}/api/tags", timeout=5)
             if response.status_code == 200:
                 data = response.json()
-                available = [model['name'] for model in data.get('models', [])]
+                available = [model["name"] for model in data.get("models", [])]
 
                 # Use same model rankings as main synthesizer for consistency
                 expansion_preferences = [
-                    "qwen3:1.7b", "qwen3:0.6b", "qwen3:4b", "qwen2.5:3b", 
-                    "qwen2.5:1.5b", "qwen2.5-coder:1.5b"
+                    "qwen3:1.7b",
+                    "qwen3:0.6b",
+                    "qwen3:4b",
+                    "qwen2.5:3b",
+                    "qwen2.5:1.5b",
+                    "qwen2.5-coder:1.5b",
                 ]
 
                 for preferred in expansion_preferences:
@@ -200,11 +207,11 @@ Expanded query:"""
             clean_response = clean_response[1:-1]
 
         # Take only the first line if multiline
-        clean_response = clean_response.split('\n')[0].strip()
+        clean_response = clean_response.split("\n")[0].strip()
 
         # Remove excessive punctuation and normalize spaces
-        clean_response = re.sub(r'[^\w\s-]', ' ', clean_response)
-        clean_response = re.sub(r'\s+', ' ', clean_response).strip()
+        clean_response = re.sub(r"[^\w\s-]", " ", clean_response)
+        clean_response = re.sub(r"\s+", " ", clean_response).strip()
 
         # Ensure it starts with the original query
         if not clean_response.lower().startswith(original_query.lower()):
@@ -213,8 +220,8 @@ Expanded query:"""
         # Limit the total length to avoid very long queries
         words = clean_response.split()
         if len(words) > len(original_query.split()) + self.max_terms:
-            words = words[:len(original_query.split()) + self.max_terms]
-            clean_response = ' '.join(words)
+            words = words[: len(original_query.split()) + self.max_terms]
+            clean_response = " ".join(words)
 
         return clean_response
 
@@ -242,10 +249,13 @@ Expanded query:"""
         try:
             response = requests.get(f"{self.ollama_url}/api/tags", timeout=5)
             return response.status_code == 200
-        except:
+        except (ConnectionError, TimeoutError, requests.RequestException):
             return False
 
+
 # Quick test function
+
+
 def test_expansion():
     """Test the query expander."""
     from .config import RAGConfig
@@ -264,7 +274,7 @@ def test_expansion():
         "authentication",
         "error handling",
         "database query",
-        "user interface"
+        "user interface",
     ]
 
     print("🔍 Testing Query Expansion:")
@@ -272,5 +282,6 @@ def test_expansion():
         expanded = expander.expand_query(query)
         print(f"  '{query}' → '{expanded}'")
 
+
 if __name__ == "__main__":
     test_expansion()

mini_rag/search.py

@@ -4,29 +4,33 @@ Optimized for code search with relevance scoring.
 """
 
 import logging
+from collections import defaultdict
+from datetime import datetime
 from pathlib import Path
-from typing import List, Dict, Any, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
+
 import numpy as np
 import pandas as pd
-from rich.console import Console
-from rich.table import Table
-from rich.syntax import Syntax
 from rank_bm25 import BM25Okapi
-from collections import defaultdict
+from rich.console import Console
+from rich.syntax import Syntax
+from rich.table import Table
 
 # Optional LanceDB import
 try:
     import lancedb
+
     LANCEDB_AVAILABLE = True
 except ImportError:
     lancedb = None
     LANCEDB_AVAILABLE = False
 
+from datetime import timedelta
+
+from .config import ConfigManager
 from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
 from .path_handler import display_path
 from .query_expander import QueryExpander
-from .config import ConfigManager
-from datetime import datetime, timedelta
 
 logger = logging.getLogger(__name__)
 console = Console()
@@ -35,7 +39,8 @@ console = Console()
 class SearchResult:
     """Represents a single search result."""
 
-    def __init__(self, 
+    def __init__(
+        self,
         file_path: str,
         content: str,
         score: float,
@@ -46,7 +51,8 @@ class SearchResult:
         language: str,
         context_before: Optional[str] = None,
         context_after: Optional[str] = None,
-                 parent_chunk: Optional['SearchResult'] = None):
+        parent_chunk: Optional["SearchResult"] = None,
+    ):
         self.file_path = file_path
         self.content = content
         self.score = score
@@ -65,17 +71,17 @@ class SearchResult:
     def to_dict(self) -> Dict[str, Any]:
         """Convert to dictionary."""
         return {
-            'file_path': self.file_path,
-            'content': self.content,
-            'score': self.score,
-            'start_line': self.start_line,
-            'end_line': self.end_line,
-            'chunk_type': self.chunk_type,
-            'name': self.name,
-            'language': self.language,
-            'context_before': self.context_before,
-            'context_after': self.context_after,
-            'parent_chunk': self.parent_chunk.to_dict() if self.parent_chunk else None,
+            "file_path": self.file_path,
+            "content": self.content,
+            "score": self.score,
+            "start_line": self.start_line,
+            "end_line": self.end_line,
+            "chunk_type": self.chunk_type,
+            "name": self.name,
+            "language": self.language,
+            "context_before": self.context_before,
+            "context_after": self.context_after,
+            "parent_chunk": self.parent_chunk.to_dict() if self.parent_chunk else None,
         }
 
     def format_for_display(self, max_lines: int = 10) -> str:
@@ -84,17 +90,15 @@ class SearchResult:
         if len(lines) > max_lines:
             # Show first and last few lines
             half = max_lines // 2
-            lines = lines[:half] + ['...'] + lines[-half:]
+            lines = lines[:half] + ["..."] + lines[-half:]
 
-        return '\n'.join(lines)
+        return "\n".join(lines)
 
 
 class CodeSearcher:
     """Semantic code search using vector similarity."""
 
-    def __init__(self, 
-                 project_path: Path,
-                 embedder: Optional[CodeEmbedder] = None):
+    def __init__(self, project_path: Path, embedder: Optional[CodeEmbedder] = None):
         """
         Initialize searcher.
 
@@ -103,7 +107,7 @@ class CodeSearcher:
             embedder: CodeEmbedder instance (creates one if not provided)
         """
         self.project_path = Path(project_path).resolve()
-        self.rag_dir = self.project_path / '.mini-rag'
+        self.rag_dir = self.project_path / ".mini-rag"
         self.embedder = embedder or CodeEmbedder()
 
         # Load configuration and initialize query expander
@@ -128,7 +132,9 @@ class CodeSearcher:
             print("   Install it with: pip install lancedb pyarrow")
             print("   For basic Ollama functionality, use hash-based search instead")
             print()
-            raise ImportError("LanceDB dependency is required for search. Install with: pip install lancedb pyarrow")
+            raise ImportError(
+                "LanceDB dependency is required for search. Install with: pip install lancedb pyarrow"
+            )
 
         try:
             if not self.rag_dir.exists():
@@ -144,7 +150,9 @@ class CodeSearcher:
             if "code_vectors" not in self.db.table_names():
                 print("🔧 Index Database Corrupted")
                 print("   The search index exists but is missing data tables")
-                print(f"   Rebuild index: rm -rf {self.rag_dir} && ./rag-mini index {self.project_path}")
+                print(
+                    f"   Rebuild index: rm -rf {self.rag_dir} && ./rag-mini index {self.project_path}"
+                )
                 print("   (This will recreate the search database)")
                 print()
                 raise ValueError("No code_vectors table found. Run indexing first.")
@@ -186,7 +194,9 @@ class CodeSearcher:
             logger.error(f"Failed to build BM25 index: {e}")
             self.bm25 = None
 
-    def get_chunk_context(self, chunk_id: str, include_adjacent: bool = True, include_parent: bool = True) -> Dict[str, Any]:
+    def get_chunk_context(
+        self, chunk_id: str, include_adjacent: bool = True, include_parent: bool = True
+    ) -> Dict[str, Any]:
         """
         Get context for a specific chunk including adjacent and parent chunks.
 
@@ -204,72 +214,81 @@ class CodeSearcher:
         try:
             # Get the main chunk by ID
             df = self.table.to_pandas()
-            chunk_rows = df[df['chunk_id'] == chunk_id]
+            chunk_rows = df[df["chunk_id"] == chunk_id]
 
             if chunk_rows.empty:
-                return {'chunk': None, 'prev': None, 'next': None, 'parent': None}
+                return {"chunk": None, "prev": None, "next": None, "parent": None}
 
             chunk_row = chunk_rows.iloc[0]
-            context = {'chunk': self._row_to_search_result(chunk_row, score=1.0)}
+            context = {"chunk": self._row_to_search_result(chunk_row, score=1.0)}
 
             # Get adjacent chunks if requested
             if include_adjacent:
                 # Get previous chunk
-                if pd.notna(chunk_row.get('prev_chunk_id')):
-                    prev_rows = df[df['chunk_id'] == chunk_row['prev_chunk_id']]
+                if pd.notna(chunk_row.get("prev_chunk_id")):
+                    prev_rows = df[df["chunk_id"] == chunk_row["prev_chunk_id"]]
                     if not prev_rows.empty:
-                        context['prev'] = self._row_to_search_result(prev_rows.iloc[0], score=1.0)
+                        context["prev"] = self._row_to_search_result(
+                            prev_rows.iloc[0], score=1.0
+                        )
                     else:
-                        context['prev'] = None
+                        context["prev"] = None
                 else:
-                    context['prev'] = None
+                    context["prev"] = None
 
                 # Get next chunk
-                if pd.notna(chunk_row.get('next_chunk_id')):
-                    next_rows = df[df['chunk_id'] == chunk_row['next_chunk_id']]
+                if pd.notna(chunk_row.get("next_chunk_id")):
+                    next_rows = df[df["chunk_id"] == chunk_row["next_chunk_id"]]
                     if not next_rows.empty:
-                        context['next'] = self._row_to_search_result(next_rows.iloc[0], score=1.0)
+                        context["next"] = self._row_to_search_result(
+                            next_rows.iloc[0], score=1.0
+                        )
                     else:
-                        context['next'] = None
+                        context["next"] = None
                 else:
-                    context['next'] = None
+                    context["next"] = None
             else:
-                context['prev'] = None
-                context['next'] = None
+                context["prev"] = None
+                context["next"] = None
 
             # Get parent class chunk if requested and applicable
-            if include_parent and pd.notna(chunk_row.get('parent_class')):
+            if include_parent and pd.notna(chunk_row.get("parent_class")):
                 # Find the parent class chunk
-                parent_rows = df[(df['name'] == chunk_row['parent_class']) & 
-                               (df['chunk_type'] == 'class') &
-                               (df['file_path'] == chunk_row['file_path'])]
+                parent_rows = df[
+                    (df["name"] == chunk_row["parent_class"])
+                    & (df["chunk_type"] == "class")
+                    & (df["file_path"] == chunk_row["file_path"])
+                ]
                 if not parent_rows.empty:
-                    context['parent'] = self._row_to_search_result(parent_rows.iloc[0], score=1.0)
+                    context["parent"] = self._row_to_search_result(
+                        parent_rows.iloc[0], score=1.0
+                    )
                 else:
-                    context['parent'] = None
+                    context["parent"] = None
             else:
-                context['parent'] = None
+                context["parent"] = None
 
             return context
 
         except Exception as e:
             logger.error(f"Failed to get chunk context: {e}")
-            return {'chunk': None, 'prev': None, 'next': None, 'parent': None}
+            return {"chunk": None, "prev": None, "next": None, "parent": None}
 
     def _row_to_search_result(self, row: pd.Series, score: float) -> SearchResult:
         """Convert a DataFrame row to a SearchResult."""
         return SearchResult(
-            file_path=display_path(row['file_path']),
-            content=row['content'],
+            file_path=display_path(row["file_path"]),
+            content=row["content"],
             score=score,
-            start_line=row['start_line'],
-            end_line=row['end_line'],
-            chunk_type=row['chunk_type'],
-            name=row['name'],
-            language=row['language']
+            start_line=row["start_line"],
+            end_line=row["end_line"],
+            chunk_type=row["chunk_type"],
+            name=row["name"],
+            language=row["language"],
         )
 
-    def search(self, 
+    def search(
+        self,
         query: str,
         top_k: int = 10,
         chunk_types: Optional[List[str]] = None,
@@ -277,7 +296,8 @@ class CodeSearcher:
         file_pattern: Optional[str] = None,
         semantic_weight: float = 0.7,
         bm25_weight: float = 0.3,
-              include_context: bool = False) -> List[SearchResult]:
+        include_context: bool = False,
+    ) -> List[SearchResult]:
         """
         Hybrid search for code similar to the query using both semantic and BM25.
 
@@ -324,16 +344,15 @@ class CodeSearcher:
 
         # Apply filters first
         if chunk_types:
-            results_df = results_df[results_df['chunk_type'].isin(chunk_types)]
+            results_df = results_df[results_df["chunk_type"].isin(chunk_types)]
 
         if languages:
-            results_df = results_df[results_df['language'].isin(languages)]
+            results_df = results_df[results_df["language"].isin(languages)]
 
         if file_pattern:
             import fnmatch
-            mask = results_df['file_path'].apply(
-                lambda x: fnmatch.fnmatch(x, file_pattern)
-            )
+
+            mask = results_df["file_path"].apply(lambda x: fnmatch.fnmatch(x, file_pattern))
             results_df = results_df[mask]
 
         # Calculate BM25 scores if available
@@ -358,25 +377,24 @@ class CodeSearcher:
         hybrid_results = []
         for idx, row in results_df.iterrows():
             # Semantic score (convert distance to similarity)
-            distance = row['_distance']
+            distance = row["_distance"]
             semantic_score = 1 / (1 + distance)
 
             # BM25 score
             bm25_score = bm25_scores.get(idx, 0.0)
 
             # Combined score
-            combined_score = (semantic_weight * semantic_score + 
-                            bm25_weight * bm25_score)
+            combined_score = semantic_weight * semantic_score + bm25_weight * bm25_score
 
             result = SearchResult(
-                file_path=display_path(row['file_path']),
-                content=row['content'],
+                file_path=display_path(row["file_path"]),
+                content=row["content"],
                 score=combined_score,
-                start_line=row['start_line'],
-                end_line=row['end_line'],
-                chunk_type=row['chunk_type'],
-                name=row['name'],
-                language=row['language']
+                start_line=row["start_line"],
+                end_line=row["end_line"],
+                chunk_type=row["chunk_type"],
+                name=row["name"],
+                language=row["language"],
             )
             hybrid_results.append(result)
 
@@ -407,9 +425,20 @@ class CodeSearcher:
             # File importance boost (20% boost for important files)
             file_path_lower = str(result.file_path).lower()
             important_patterns = [
-                'readme', 'main.', 'index.', '__init__', 'config',
-                'setup', 'install', 'getting', 'started', 'docs/',
-                'documentation', 'guide', 'tutorial', 'example'
+                "readme",
+                "main.",
+                "index.",
+                "__init__",
+                "config",
+                "setup",
+                "install",
+                "getting",
+                "started",
+                "docs/",
+                "documentation",
+                "guide",
+                "tutorial",
+                "example",
             ]
 
             if any(pattern in file_path_lower for pattern in important_patterns):
@@ -426,7 +455,9 @@ class CodeSearcher:
 
                 if days_old <= 7:  # Modified in last week
                     result.score *= 1.1
-                    logger.debug(f"Recent file boost: {result.file_path} ({days_old} days old)")
+                    logger.debug(
+                        f"Recent file boost: {result.file_path} ({days_old} days old)"
+                    )
                 elif days_old <= 30:  # Modified in last month
                     result.score *= 1.05
 
@@ -435,11 +466,11 @@ class CodeSearcher:
                 pass
 
             # Content type relevance boost
-            if hasattr(result, 'chunk_type'):
-                if result.chunk_type in ['function', 'class', 'method']:
+            if hasattr(result, "chunk_type"):
+                if result.chunk_type in ["function", "class", "method"]:
                     # Code definitions are usually more valuable
                     result.score *= 1.1
-                elif result.chunk_type in ['comment', 'docstring']:
+                elif result.chunk_type in ["comment", "docstring"]:
                     # Documentation is valuable for understanding
                     result.score *= 1.05
 
@@ -448,14 +479,16 @@ class CodeSearcher:
                 result.score *= 0.9
 
             # Small boost for content with good structure (has multiple lines)
-            lines = result.content.strip().split('\n')
+            lines = result.content.strip().split("\n")
             if len(lines) >= 3 and any(len(line.strip()) > 10 for line in lines):
                 result.score *= 1.02
 
         # Sort by updated scores
         return sorted(results, key=lambda x: x.score, reverse=True)
 
-    def _apply_diversity_constraints(self, results: List[SearchResult], top_k: int) -> List[SearchResult]:
+    def _apply_diversity_constraints(
+        self, results: List[SearchResult], top_k: int
+    ) -> List[SearchResult]:
         """
         Apply diversity constraints to search results.
 
@@ -479,7 +512,10 @@ class CodeSearcher:
                 continue
 
             # Prefer diverse chunk types
-            if len(final_results) >= top_k // 2 and chunk_type_counts[result.chunk_type] > top_k // 3:
+            if (
+                len(final_results) >= top_k // 2
+                and chunk_type_counts[result.chunk_type] > top_k // 3
+            ):
                 # Skip if we have too many of this type already
                 continue
 
@@ -494,7 +530,9 @@ class CodeSearcher:
 
         return final_results
 
-    def _add_context_to_results(self, results: List[SearchResult], search_df: pd.DataFrame) -> List[SearchResult]:
+    def _add_context_to_results(
+        self, results: List[SearchResult], search_df: pd.DataFrame
+    ) -> List[SearchResult]:
         """
         Add context (adjacent and parent chunks) to search results.
 
@@ -513,12 +551,12 @@ class CodeSearcher:
         for result in results:
             # Find matching row in search_df
             matching_rows = search_df[
-                (search_df['file_path'] == result.file_path) &
-                (search_df['start_line'] == result.start_line) &
-                (search_df['end_line'] == result.end_line)
+                (search_df["file_path"] == result.file_path)
+                & (search_df["start_line"] == result.start_line)
+                & (search_df["end_line"] == result.end_line)
             ]
             if not matching_rows.empty:
-                result_to_chunk_id[result] = matching_rows.iloc[0]['chunk_id']
+                result_to_chunk_id[result] = matching_rows.iloc[0]["chunk_id"]
 
         # Add context to each result
         for result in results:
@@ -527,49 +565,48 @@ class CodeSearcher:
                 continue
 
             # Get the row for this chunk
-            chunk_rows = full_df[full_df['chunk_id'] == chunk_id]
+            chunk_rows = full_df[full_df["chunk_id"] == chunk_id]
             if chunk_rows.empty:
                 continue
 
             chunk_row = chunk_rows.iloc[0]
 
             # Add adjacent chunks as context
-            if pd.notna(chunk_row.get('prev_chunk_id')):
-                prev_rows = full_df[full_df['chunk_id'] == chunk_row['prev_chunk_id']]
+            if pd.notna(chunk_row.get("prev_chunk_id")):
+                prev_rows = full_df[full_df["chunk_id"] == chunk_row["prev_chunk_id"]]
                 if not prev_rows.empty:
-                    result.context_before = prev_rows.iloc[0]['content']
+                    result.context_before = prev_rows.iloc[0]["content"]
 
-            if pd.notna(chunk_row.get('next_chunk_id')):
-                next_rows = full_df[full_df['chunk_id'] == chunk_row['next_chunk_id']]
+            if pd.notna(chunk_row.get("next_chunk_id")):
+                next_rows = full_df[full_df["chunk_id"] == chunk_row["next_chunk_id"]]
                 if not next_rows.empty:
-                    result.context_after = next_rows.iloc[0]['content']
+                    result.context_after = next_rows.iloc[0]["content"]
 
             # Add parent class chunk if applicable
-            if pd.notna(chunk_row.get('parent_class')):
+            if pd.notna(chunk_row.get("parent_class")):
                 parent_rows = full_df[
-                    (full_df['name'] == chunk_row['parent_class']) & 
-                    (full_df['chunk_type'] == 'class') &
-                    (full_df['file_path'] == chunk_row['file_path'])
+                    (full_df["name"] == chunk_row["parent_class"])
+                    & (full_df["chunk_type"] == "class")
+                    & (full_df["file_path"] == chunk_row["file_path"])
                 ]
                 if not parent_rows.empty:
                     parent_row = parent_rows.iloc[0]
                     result.parent_chunk = SearchResult(
-                        file_path=display_path(parent_row['file_path']),
-                        content=parent_row['content'],
+                        file_path=display_path(parent_row["file_path"]),
+                        content=parent_row["content"],
                         score=1.0,
-                        start_line=parent_row['start_line'],
-                        end_line=parent_row['end_line'],
-                        chunk_type=parent_row['chunk_type'],
-                        name=parent_row['name'],
-                        language=parent_row['language']
+                        start_line=parent_row["start_line"],
+                        end_line=parent_row["end_line"],
+                        chunk_type=parent_row["chunk_type"],
+                        name=parent_row["name"],
+                        language=parent_row["language"],
                     )
 
         return results
 
-    def search_similar_code(self, 
-                          code_snippet: str, 
-                          top_k: int = 10,
-                          exclude_self: bool = True) -> List[SearchResult]:
+    def search_similar_code(
+        self, code_snippet: str, top_k: int = 10, exclude_self: bool = True
+    ) -> List[SearchResult]:
         """
         Find code similar to a given snippet using hybrid search.
 
@@ -587,7 +624,7 @@ class CodeSearcher:
             query=code_snippet,
             top_k=top_k * 2 if exclude_self else top_k,
             semantic_weight=0.8,  # Higher semantic weight for code similarity
-            bm25_weight=0.2
+            bm25_weight=0.2,
         )
 
         if exclude_self:
@@ -617,11 +654,7 @@ class CodeSearcher:
         query = f"function {function_name} implementation definition"
 
         # Search with filters
-        results = self.search(
-            query,
-            top_k=top_k * 2,
-            chunk_types=['function', 'method']
-        )
+        results = self.search(query, top_k=top_k * 2, chunk_types=["function", "method"])
 
         # Further filter by name
         filtered = []
@@ -646,11 +679,7 @@ class CodeSearcher:
         query = f"class {class_name} definition implementation"
 
         # Search with filters
-        results = self.search(
-            query,
-            top_k=top_k * 2,
-            chunk_types=['class']
-        )
+        results = self.search(query, top_k=top_k * 2, chunk_types=["class"])
 
         # Further filter by name
         filtered = []
@@ -700,10 +729,12 @@ class CodeSearcher:
 
         return filtered[:top_k]
 
-    def display_results(self, 
+    def display_results(
+        self,
         results: List[SearchResult],
         show_content: bool = True,
-                       max_content_lines: int = 10):
+        max_content_lines: int = 10,
+    ):
         """
         Display search results in a formatted table.
 
@@ -730,7 +761,7 @@ class CodeSearcher:
                 result.file_path,
                 result.chunk_type,
                 result.name or "-",
-                f"{result.start_line}-{result.end_line}"
+                f"{result.start_line}-{result.end_line}",
             )
 
         console.print(table)
@@ -740,7 +771,9 @@ class CodeSearcher:
             console.print("\n[bold]Top Results:[/bold]\n")
 
             for i, result in enumerate(results[:3], 1):
-                console.print(f"[bold cyan]#{i}[/bold cyan] {result.file_path}:{result.start_line}")
+                console.print(
+                    f"[bold cyan]#{i}[/bold cyan] {result.file_path}:{result.start_line}"
+                )
                 console.print(f"[dim]Type: {result.chunk_type} | Name: {result.name}[/dim]")
 
                 # Display code with syntax highlighting
@@ -749,7 +782,7 @@ class CodeSearcher:
                     result.language,
                     theme="monokai",
                     line_numbers=True,
-                    start_line=result.start_line
+                    start_line=result.start_line,
                 )
                 console.print(syntax)
                 console.print()
@@ -757,7 +790,7 @@ class CodeSearcher:
     def get_statistics(self) -> Dict[str, Any]:
         """Get search index statistics."""
         if not self.table:
-            return {'error': 'Database not connected'}
+            return {"error": "Database not connected"}
 
         try:
             # Get table statistics
@@ -765,28 +798,30 @@ class CodeSearcher:
 
             # Get unique files
             df = self.table.to_pandas()
-            unique_files = df['file_path'].nunique()
+            unique_files = df["file_path"].nunique()
 
             # Get chunk type distribution
-            chunk_types = df['chunk_type'].value_counts().to_dict()
+            chunk_types = df["chunk_type"].value_counts().to_dict()
 
             # Get language distribution
-            languages = df['language'].value_counts().to_dict()
+            languages = df["language"].value_counts().to_dict()
 
             return {
-                'total_chunks': num_rows,
-                'unique_files': unique_files,
-                'chunk_types': chunk_types,
-                'languages': languages,
-                'index_ready': True,
+                "total_chunks": num_rows,
+                "unique_files": unique_files,
+                "chunk_types": chunk_types,
+                "languages": languages,
+                "index_ready": True,
             }
 
         except Exception as e:
             logger.error(f"Failed to get statistics: {e}")
-            return {'error': str(e)}
+            return {"error": str(e)}
 
 
 # Convenience functions
+
+
 def search_code(project_path: Path, query: str, top_k: int = 10) -> List[SearchResult]:
     """
     Quick search function.

mini_rag/server.py

@@ -4,23 +4,23 @@ No more loading/unloading madness!
 """
 
 import json
+import logging
+import os
 import socket
+import subprocess
+import sys
 import threading
 import time
-import subprocess
 from pathlib import Path
-from typing import Dict, Any, Optional
-import logging
-import sys
-import os
+from typing import Any, Dict, Optional
 
 # Fix Windows console
-if sys.platform == 'win32':
-    os.environ['PYTHONUTF8'] = '1'
+if sys.platform == "win32":
+    os.environ["PYTHONUTF8"] = "1"
 
-from .search import CodeSearcher
 from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
 from .performance import PerformanceMonitor
+from .search import CodeSearcher
 
 logger = logging.getLogger(__name__)
 
@@ -43,31 +43,30 @@ class RAGServer:
         try:
             # Check if port is in use
             test_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-            result = test_sock.connect_ex(('localhost', self.port))
+            result = test_sock.connect_ex(("localhost", self.port))
             test_sock.close()
 
             if result == 0:  # Port is in use
                 print(f"️  Port {self.port} is already in use, attempting to free it...")
 
-                if sys.platform == 'win32':
+                if sys.platform == "win32":
                     # Windows: Find and kill process using netstat
                     import subprocess
+
                     try:
                         # Get process ID using the port
                         result = subprocess.run(
-                            ['netstat', '-ano'], 
-                            capture_output=True, 
-                            text=True
+                            ["netstat", "-ano"], capture_output=True, text=True
                         )
 
-                        for line in result.stdout.split('\n'):
-                            if f':{self.port}' in line and 'LISTENING' in line:
+                        for line in result.stdout.split("\n"):
+                            if f":{self.port}" in line and "LISTENING" in line:
                                 parts = line.split()
                                 pid = parts[-1]
                                 print(f"   Found process {pid} using port {self.port}")
 
                                 # Kill the process
-                                subprocess.run(['taskkill', '//PID', pid, '//F'], check=False)
+                                subprocess.run(["taskkill", "//PID", pid, "//F"], check=False)
                                 print(f"    Killed process {pid}")
                                 time.sleep(1)  # Give it a moment to release the port
                                 break
@@ -76,15 +75,16 @@ class RAGServer:
                 else:
                     # Unix/Linux: Use lsof and kill
                     import subprocess
+
                     try:
                         result = subprocess.run(
-                            ['lsof', '-ti', f':{self.port}'], 
+                            ["lso", "-ti", f":{self.port}"],
                             capture_output=True,
-                            text=True
+                            text=True,
                         )
                         if result.stdout.strip():
                             pid = result.stdout.strip()
-                            subprocess.run(['kill', '-9', pid], check=False)
+                            subprocess.run(["kill", "-9", pid], check=False)
                             print(f"    Killed process {pid}")
                             time.sleep(1)
                     except Exception as e:
@@ -114,7 +114,7 @@ class RAGServer:
         # Start server
         self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
         self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-        self.socket.bind(('localhost', self.port))
+        self.socket.bind(("localhost", self.port))
         self.socket.listen(5)
 
         self.running = True
@@ -145,15 +145,15 @@ class RAGServer:
             request = json.loads(data)
 
             # Check for shutdown command
-            if request.get('command') == 'shutdown':
+            if request.get("command") == "shutdown":
                 print("\n Shutdown requested")
-                response = {'success': True, 'message': 'Server shutting down'}
+                response = {"success": True, "message": "Server shutting down"}
                 self._send_json(client, response)
                 self.stop()
                 return
 
-            query = request.get('query', '')
-            top_k = request.get('top_k', 10)
+            query = request.get("query", "")
+            top_k = request.get("top_k", 10)
 
             self.query_count += 1
             print(f"[Query #{self.query_count}] {query}")
@@ -165,13 +165,13 @@ class RAGServer:
 
             # Prepare response
             response = {
-                'success': True,
-                'query': query,
-                'count': len(results),
-                'search_time_ms': int(search_time * 1000),
-                'results': [r.to_dict() for r in results],
-                'server_uptime': int(time.time() - self.start_time),
-                'total_queries': self.query_count,
+                "success": True,
+                "query": query,
+                "count": len(results),
+                "search_time_ms": int(search_time * 1000),
+                "results": [r.to_dict() for r in results],
+                "server_uptime": int(time.time() - self.start_time),
+                "total_queries": self.query_count,
             }
 
             # Send response with proper framing
@@ -179,7 +179,7 @@ class RAGServer:
 
             print(f"    Found {len(results)} results in {search_time*1000:.0f}ms")
 
-        except ConnectionError as e:
+        except ConnectionError:
             # Normal disconnection - client closed connection
             # This is expected behavior, don't log as error
             pass
@@ -187,13 +187,10 @@ class RAGServer:
             # Only log actual errors, not normal disconnections
             if "Connection closed" not in str(e):
                 logger.error(f"Client handler error: {e}")
-            error_response = {
-                'success': False,
-                'error': str(e)
-            }
+            error_response = {"success": False, "error": str(e)}
             try:
                 self._send_json(client, error_response)
-            except:
+            except (ConnectionError, OSError, TypeError, ValueError, socket.error):
                 pass
         finally:
             client.close()
@@ -201,34 +198,34 @@ class RAGServer:
     def _receive_json(self, sock: socket.socket) -> str:
         """Receive a complete JSON message with length prefix."""
         # First receive the length (4 bytes)
-        length_data = b''
+        length_data = b""
         while len(length_data) < 4:
             chunk = sock.recv(4 - len(length_data))
             if not chunk:
                 raise ConnectionError("Connection closed while receiving length")
             length_data += chunk
 
-        length = int.from_bytes(length_data, 'big')
+        length = int.from_bytes(length_data, "big")
 
         # Now receive the actual data
-        data = b''
+        data = b""
         while len(data) < length:
             chunk = sock.recv(min(65536, length - len(data)))
             if not chunk:
                 raise ConnectionError("Connection closed while receiving data")
             data += chunk
 
-        return data.decode('utf-8')
+        return data.decode("utf-8")
 
     def _send_json(self, sock: socket.socket, data: dict):
         """Send a JSON message with length prefix."""
         # Sanitize the data to ensure JSON compatibility
-        json_str = json.dumps(data, ensure_ascii=False, separators=(',', ':'))
-        json_bytes = json_str.encode('utf-8')
+        json_str = json.dumps(data, ensure_ascii=False, separators=(",", ":"))
+        json_bytes = json_str.encode("utf-8")
 
         # Send length prefix (4 bytes)
         length = len(json_bytes)
-        sock.send(length.to_bytes(4, 'big'))
+        sock.send(length.to_bytes(4, "big"))
 
         # Send the data
         sock.sendall(json_bytes)
@@ -253,13 +250,10 @@ class RAGClient:
         try:
             # Connect to server
             sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-            sock.connect(('localhost', self.port))
+            sock.connect(("localhost", self.port))
 
             # Send request with proper framing
-            request = {
-                'query': query,
-                'top_k': top_k
-            }
+            request = {"query": query, "top_k": top_k}
             self._send_json(sock, request)
 
             # Receive response with proper framing
@@ -271,54 +265,48 @@ class RAGClient:
 
         except ConnectionRefusedError:
             return {
-                'success': False,
-                'error': 'RAG server not running. Start with: rag-mini server'
+                "success": False,
+                "error": "RAG server not running. Start with: rag-mini server",
             }
         except ConnectionError as e:
             # Try legacy mode without message framing
             if not self.use_legacy and "receiving length" in str(e):
                 self.use_legacy = True
                 return self._search_legacy(query, top_k)
-            return {
-                'success': False,
-                'error': str(e)
-            }
+            return {"success": False, "error": str(e)}
         except Exception as e:
-            return {
-                'success': False,
-                'error': str(e)
-            }
+            return {"success": False, "error": str(e)}
 
     def _receive_json(self, sock: socket.socket) -> str:
         """Receive a complete JSON message with length prefix."""
         # First receive the length (4 bytes)
-        length_data = b''
+        length_data = b""
         while len(length_data) < 4:
             chunk = sock.recv(4 - len(length_data))
             if not chunk:
                 raise ConnectionError("Connection closed while receiving length")
             length_data += chunk
 
-        length = int.from_bytes(length_data, 'big')
+        length = int.from_bytes(length_data, "big")
 
         # Now receive the actual data
-        data = b''
+        data = b""
         while len(data) < length:
             chunk = sock.recv(min(65536, length - len(data)))
             if not chunk:
                 raise ConnectionError("Connection closed while receiving data")
             data += chunk
 
-        return data.decode('utf-8')
+        return data.decode("utf-8")
 
     def _send_json(self, sock: socket.socket, data: dict):
         """Send a JSON message with length prefix."""
-        json_str = json.dumps(data, ensure_ascii=False, separators=(',', ':'))
-        json_bytes = json_str.encode('utf-8')
+        json_str = json.dumps(data, ensure_ascii=False, separators=(",", ":"))
+        json_bytes = json_str.encode("utf-8")
 
         # Send length prefix (4 bytes)
         length = len(json_bytes)
-        sock.send(length.to_bytes(4, 'big'))
+        sock.send(length.to_bytes(4, "big"))
 
         # Send the data
         sock.sendall(json_bytes)
@@ -327,17 +315,14 @@ class RAGClient:
         """Legacy search without message framing for old servers."""
         try:
             sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-            sock.connect(('localhost', self.port))
+            sock.connect(("localhost", self.port))
 
             # Send request (old way)
-            request = {
-                'query': query,
-                'top_k': top_k
-            }
-            sock.send(json.dumps(request).encode('utf-8'))
+            request = {"query": query, "top_k": top_k}
+            sock.send(json.dumps(request).encode("utf-8"))
 
             # Receive response (accumulate until we get valid JSON)
-            data = b''
+            data = b""
             while True:
                 chunk = sock.recv(65536)
                 if not chunk:
@@ -345,7 +330,7 @@ class RAGClient:
                 data += chunk
                 try:
                     # Try to decode as JSON
-                    response = json.loads(data.decode('utf-8'))
+                    response = json.loads(data.decode("utf-8"))
                     sock.close()
                     return response
                 except json.JSONDecodeError:
@@ -353,24 +338,18 @@ class RAGClient:
                     continue
 
             sock.close()
-            return {
-                'success': False,
-                'error': 'Incomplete response from server'
-            }
+            return {"success": False, "error": "Incomplete response from server"}
         except Exception as e:
-            return {
-                'success': False,
-                'error': str(e)
-            }
+            return {"success": False, "error": str(e)}
 
     def is_running(self) -> bool:
         """Check if server is running."""
         try:
             sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-            result = sock.connect_ex(('localhost', self.port))
+            result = sock.connect_ex(("localhost", self.port))
             sock.close()
             return result == 0
-        except:
+        except (ConnectionError, OSError, TypeError, ValueError, socket.error):
             return False
 
 
@@ -389,12 +368,20 @@ def auto_start_if_needed(project_path: Path) -> Optional[subprocess.Popen]:
     if not client.is_running():
         # Start server in background
         import subprocess
-        cmd = [sys.executable, "-m", "mini_rag.cli", "server", "--path", str(project_path)]
+
+        cmd = [
+            sys.executable,
+            "-m",
+            "mini_rag.cli",
+            "server",
+            "--path",
+            str(project_path),
+        ]
         process = subprocess.Popen(
             cmd,
             stdout=subprocess.PIPE,
             stderr=subprocess.PIPE,
-            creationflags=subprocess.CREATE_NEW_CONSOLE if sys.platform == 'win32' else 0
+            creationflags=(subprocess.CREATE_NEW_CONSOLE if sys.platform == "win32" else 0),
         )
 
         # Wait for server to start

mini_rag/smart_chunking.py

@@ -3,61 +3,49 @@ Smart language-aware chunking strategies for FSS-Mini-RAG.
 Automatically adapts chunking based on file type and content patterns.
 """
 
-from typing import Dict, Any, List
 from pathlib import Path
-import json
+from typing import Any, Dict, List
+
 
 class SmartChunkingStrategy:
     """Intelligent chunking that adapts to file types and content."""
 
     def __init__(self):
         self.language_configs = {
-            'python': {
-                'max_size': 3000,  # Larger for better function context
-                'min_size': 200,
-                'strategy': 'function',
-                'prefer_semantic': True
+            "python": {
+                "max_size": 3000,  # Larger for better function context
+                "min_size": 200,
+                "strategy": "function",
+                "prefer_semantic": True,
             },
-            'javascript': {
-                'max_size': 2500,
-                'min_size': 150,
-                'strategy': 'function',
-                'prefer_semantic': True
+            "javascript": {
+                "max_size": 2500,
+                "min_size": 150,
+                "strategy": "function",
+                "prefer_semantic": True,
             },
-            'markdown': {
-                'max_size': 2500,
-                'min_size': 300,  # Larger minimum for complete thoughts
-                'strategy': 'header',
-                'preserve_structure': True
+            "markdown": {
+                "max_size": 2500,
+                "min_size": 300,  # Larger minimum for complete thoughts
+                "strategy": "header",
+                "preserve_structure": True,
             },
-            'json': {
-                'max_size': 1000,  # Smaller for config files
-                'min_size': 50,
-                'skip_if_large': True,  # Skip huge config JSONs
-                'max_file_size': 50000  # 50KB limit
+            "json": {
+                "max_size": 1000,  # Smaller for config files
+                "min_size": 50,
+                "skip_if_large": True,  # Skip huge config JSONs
+                "max_file_size": 50000,  # 50KB limit
             },
-            'yaml': {
-                'max_size': 1500,
-                'min_size': 100,
-                'strategy': 'key_block'
-            },
-            'text': {
-                'max_size': 2000,
-                'min_size': 200,
-                'strategy': 'paragraph'
-            },
-            'bash': {
-                'max_size': 1500,
-                'min_size': 100,
-                'strategy': 'function'
-            }
+            "yaml": {"max_size": 1500, "min_size": 100, "strategy": "key_block"},
+            "text": {"max_size": 2000, "min_size": 200, "strategy": "paragraph"},
+            "bash": {"max_size": 1500, "min_size": 100, "strategy": "function"},
         }
 
         # Smart defaults for unknown languages
         self.default_config = {
-            'max_size': 2000,
-            'min_size': 150,
-            'strategy': 'semantic'
+            "max_size": 2000,
+            "min_size": 150,
+            "strategy": "semantic",
         }
 
     def get_config_for_language(self, language: str, file_size: int = 0) -> Dict[str, Any]:
@@ -67,10 +55,10 @@ class SmartChunkingStrategy:
         # Smart adjustments based on file size
         if file_size > 0:
             if file_size < 500:  # Very small files
-                config['max_size'] = max(config['max_size'] // 2, 200)
-                config['min_size'] = 50
+                config["max_size"] = max(config["max_size"] // 2, 200)
+                config["min_size"] = 50
             elif file_size > 20000:  # Large files
-                config['max_size'] = min(config['max_size'] + 1000, 4000)
+                config["max_size"] = min(config["max_size"] + 1000, 4000)
 
         return config
 
@@ -79,8 +67,8 @@ class SmartChunkingStrategy:
         lang_config = self.language_configs.get(language, {})
 
         # Skip huge JSON config files
-        if language == 'json' and lang_config.get('skip_if_large'):
-            max_size = lang_config.get('max_file_size', 50000)
+        if language == "json" and lang_config.get("skip_if_large"):
+            max_size = lang_config.get("max_file_size", 50000)
             if file_size > max_size:
                 return True
 
@@ -92,58 +80,62 @@ class SmartChunkingStrategy:
 
     def get_smart_defaults(self, project_stats: Dict[str, Any]) -> Dict[str, Any]:
         """Generate smart defaults based on project language distribution."""
-        languages = project_stats.get('languages', {})
-        total_files = sum(languages.values())
+        languages = project_stats.get("languages", {})
+        # sum(languages.values())  # Unused variable removed
 
         # Determine primary language
-        primary_lang = max(languages.items(), key=lambda x: x[1])[0] if languages else 'python'
+        primary_lang = max(languages.items(), key=lambda x: x[1])[0] if languages else "python"
         primary_config = self.language_configs.get(primary_lang, self.default_config)
 
         # Smart streaming threshold based on large files
-        large_files = project_stats.get('large_files', 0)
+        large_files = project_stats.get("large_files", 0)
         streaming_threshold = 5120 if large_files > 5 else 1048576  # 5KB vs 1MB
 
         return {
             "chunking": {
-                "max_size": primary_config['max_size'],
-                "min_size": primary_config['min_size'], 
-                "strategy": primary_config.get('strategy', 'semantic'),
+                "max_size": primary_config["max_size"],
+                "min_size": primary_config["min_size"],
+                "strategy": primary_config.get("strategy", "semantic"),
                 "language_specific": {
-                    lang: config for lang, config in self.language_configs.items()
+                    lang: config
+                    for lang, config in self.language_configs.items()
                     if languages.get(lang, 0) > 0
-                }
+                },
             },
             "streaming": {
                 "enabled": True,
                 "threshold_bytes": streaming_threshold,
-                "chunk_size_kb": 64
+                "chunk_size_kb": 64,
             },
             "files": {
                 "skip_tiny_files": True,
                 "tiny_threshold": 30,
-                "smart_json_filtering": True
-            }
+                "smart_json_filtering": True,
+            },
         }
 
+
 # Example usage
+
+
 def analyze_and_suggest(manifest_data: Dict[str, Any]) -> Dict[str, Any]:
     """Analyze project and suggest optimal configuration."""
     from collections import Counter
 
-    files = manifest_data.get('files', {})
+    files = manifest_data.get("files", {})
     languages = Counter()
     large_files = 0
 
     for info in files.values():
-        lang = info.get('language', 'unknown')
+        lang = info.get("language", "unknown")
         languages[lang] += 1
-        if info.get('size', 0) > 10000:
+        if info.get("size", 0) > 10000:
             large_files += 1
 
     stats = {
-        'languages': dict(languages),
-        'large_files': large_files,
-        'total_files': len(files)
+        "languages": dict(languages),
+        "large_files": large_files,
+        "total_files": len(files),
     }
 
     strategy = SmartChunkingStrategy()

mini_rag/system_context.py

@@ -7,7 +7,6 @@ context-aware assistance without compromising privacy.
 
 import platform
 import sys
-import os
 from pathlib import Path
 from typing import Dict, Optional
 
@@ -32,11 +31,9 @@ class SystemContextCollector:
             python_ver = f"{sys.version_info.major}.{sys.version_info.minor}"
 
             # Simplified OS names
-            os_short = {
-                'Windows': 'Win',
-                'Linux': 'Linux', 
-                'Darwin': 'macOS'
-            }.get(os_name, os_name)
+            os_short = {"Windows": "Win", "Linux": "Linux", "Darwin": "macOS"}.get(
+                os_name, os_name
+            )
 
             # Working directory info
             if project_path:
@@ -85,19 +82,19 @@ class SystemContextCollector:
             return {
                 "launcher": "rag.bat",
                 "index": "rag.bat index C:\\path\\to\\project",
-                "search": "rag.bat search C:\\path\\to\\project \"query\"",
+                "search": 'rag.bat search C:\\path\\to\\project "query"',
                 "explore": "rag.bat explore C:\\path\\to\\project",
                 "path_sep": "\\",
-                "example_path": "C:\\Users\\username\\Documents\\myproject"
+                "example_path": "C:\\Users\\username\\Documents\\myproject",
             }
         else:
             return {
                 "launcher": "./rag-mini",
                 "index": "./rag-mini index /path/to/project",
-                "search": "./rag-mini search /path/to/project \"query\"",
+                "search": './rag-mini search /path/to/project "query"',
                 "explore": "./rag-mini explore /path/to/project",
                 "path_sep": "/",
-                "example_path": "~/Documents/myproject"
+                "example_path": "~/Documents/myproject",
             }
 
 
@@ -112,6 +109,7 @@ def get_command_context() -> Dict[str, str]:
 
 
 # Test function
+
 if __name__ == "__main__":
     print("System Context Test:")
     print(f"Context: {get_system_context()}")

mini_rag/updater.py

@@ -6,30 +6,32 @@ Provides seamless GitHub-based updates with user-friendly interface.
 Checks for new releases, downloads updates, and handles installation safely.
 """
 
-import os
-import sys
 import json
-import time
+import os
 import shutil
-import zipfile
-import tempfile
 import subprocess
-from pathlib import Path
-from typing import Optional, Dict, Any, Tuple
-from datetime import datetime, timedelta
+import sys
+import tempfile
+import zipfile
 from dataclasses import dataclass
+from datetime import datetime, timedelta
+from pathlib import Path
+from typing import Optional, Tuple
 
 try:
     import requests
+
     REQUESTS_AVAILABLE = True
 except ImportError:
     REQUESTS_AVAILABLE = False
 
 from .config import ConfigManager
 
+
 @dataclass
 class UpdateInfo:
     """Information about an available update."""
+
     version: str
     release_url: str
     download_url: str
@@ -37,6 +39,7 @@ class UpdateInfo:
     published_at: str
     is_newer: bool
 
+
 class UpdateChecker:
     """
     Handles checking for and applying updates from GitHub releases.
@@ -48,10 +51,12 @@ class UpdateChecker:
     - Provides graceful fallbacks if network unavailable
     """
 
-    def __init__(self, 
+    def __init__(
+        self,
         repo_owner: str = "FSSCoding",
         repo_name: str = "Fss-Mini-Rag",
-                 current_version: str = "2.1.0"):
+        current_version: str = "2.1.0",
+    ):
         self.repo_owner = repo_owner
         self.repo_name = repo_name
         self.current_version = current_version
@@ -82,16 +87,20 @@ class UpdateChecker:
             return False
 
         # Check user preference
-        if hasattr(self.config, 'updates') and not getattr(self.config.updates, 'auto_check', True):
+        if hasattr(self.config, "updates") and not getattr(
+            self.config.updates, "auto_check", True
+        ):
             return False
 
         # Check if we've checked recently
         if self.cache_file.exists():
             try:
-                with open(self.cache_file, 'r') as f:
+                with open(self.cache_file, "r") as f:
                     cache = json.load(f)
-                    last_check = datetime.fromisoformat(cache.get('last_check', '2020-01-01'))
-                    if datetime.now() - last_check < timedelta(hours=self.check_frequency_hours):
+                    last_check = datetime.fromisoformat(cache.get("last_check", "2020-01-01"))
+                    if datetime.now() - last_check < timedelta(
+                        hours=self.check_frequency_hours
+                    ):
                         return False
             except (json.JSONDecodeError, ValueError, KeyError):
                 pass  # Ignore cache errors, will check anyway
@@ -113,7 +122,7 @@ class UpdateChecker:
             response = requests.get(
                 f"{self.github_api_url}/releases/latest",
                 timeout=10,
-                headers={"Accept": "application/vnd.github.v3+json"}
+                headers={"Accept": "application/vnd.github.v3+json"},
             )
 
             if response.status_code != 200:
@@ -122,16 +131,16 @@ class UpdateChecker:
             release_data = response.json()
 
             # Extract version info
-            latest_version = release_data.get('tag_name', '').lstrip('v')
-            release_notes = release_data.get('body', 'No release notes available.')
-            published_at = release_data.get('published_at', '')
-            release_url = release_data.get('html_url', '')
+            latest_version = release_data.get("tag_name", "").lstrip("v")
+            release_notes = release_data.get("body", "No release notes available.")
+            published_at = release_data.get("published_at", "")
+            release_url = release_data.get("html_url", "")
 
             # Find download URL for source code
             download_url = None
-            for asset in release_data.get('assets', []):
-                if asset.get('name', '').endswith('.zip'):
-                    download_url = asset.get('browser_download_url')
+            for asset in release_data.get("assets", []):
+                if asset.get("name", "").endswith(".zip"):
+                    download_url = asset.get("browser_download_url")
                     break
 
             # Fallback to source code zip
@@ -151,10 +160,10 @@ class UpdateChecker:
                     download_url=download_url,
                     release_notes=release_notes,
                     published_at=published_at,
-                    is_newer=True
+                    is_newer=True,
                 )
 
-        except Exception as e:
+        except Exception:
             # Silently fail for network issues - don't interrupt user experience
             pass
 
@@ -168,6 +177,7 @@ class UpdateChecker:
         - Major.Minor.Patch (e.g., 2.1.0)
         - Major.Minor (e.g., 2.1)
         """
+
         def version_tuple(v):
             return tuple(map(int, (v.split("."))))
 
@@ -180,18 +190,20 @@ class UpdateChecker:
     def _update_cache(self, latest_version: str, is_newer: bool):
         """Update the cache file with check results."""
         cache_data = {
-            'last_check': datetime.now().isoformat(),
-            'latest_version': latest_version,
-            'is_newer': is_newer
+            "last_check": datetime.now().isoformat(),
+            "latest_version": latest_version,
+            "is_newer": is_newer,
         }
 
         try:
-            with open(self.cache_file, 'w') as f:
+            with open(self.cache_file, "w") as f:
                 json.dump(cache_data, f, indent=2)
         except Exception:
             pass  # Ignore cache write errors
 
-    def download_update(self, update_info: UpdateInfo, progress_callback=None) -> Optional[Path]:
+    def download_update(
+        self, update_info: UpdateInfo, progress_callback=None
+    ) -> Optional[Path]:
         """
         Download the update package to a temporary location.
 
@@ -207,17 +219,17 @@ class UpdateChecker:
 
         try:
             # Create temporary file for download
-            with tempfile.NamedTemporaryFile(suffix='.zip', delete=False) as tmp_file:
+            with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as tmp_file:
                 tmp_path = Path(tmp_file.name)
 
             # Download with progress tracking
             response = requests.get(update_info.download_url, stream=True, timeout=30)
             response.raise_for_status()
 
-            total_size = int(response.headers.get('content-length', 0))
+            total_size = int(response.headers.get("content-length", 0))
             downloaded = 0
 
-            with open(tmp_path, 'wb') as f:
+            with open(tmp_path, "wb") as f:
                 for chunk in response.iter_content(chunk_size=8192):
                     if chunk:
                         f.write(chunk)
@@ -227,9 +239,9 @@ class UpdateChecker:
 
             return tmp_path
 
-        except Exception as e:
+        except Exception:
             # Clean up on error
-            if 'tmp_path' in locals() and tmp_path.exists():
+            if "tmp_path" in locals() and tmp_path.exists():
                 tmp_path.unlink()
             return None
 
@@ -250,14 +262,14 @@ class UpdateChecker:
 
             # Copy key files and directories
             important_items = [
-                'mini_rag',
-                'rag-mini.py',
-                'rag-tui.py', 
-                'requirements.txt',
-                'install_mini_rag.sh',
-                'install_windows.bat',
-                'README.md',
-                'assets'
+                "mini_rag",
+                "rag-mini.py",
+                "rag-tui.py",
+                "requirements.txt",
+                "install_mini_rag.sh",
+                "install_windows.bat",
+                "README.md",
+                "assets",
             ]
 
             for item in important_items:
@@ -270,7 +282,7 @@ class UpdateChecker:
 
             return True
 
-        except Exception as e:
+        except Exception:
             return False
 
     def apply_update(self, update_package_path: Path, update_info: UpdateInfo) -> bool:
@@ -290,7 +302,7 @@ class UpdateChecker:
                 tmp_path = Path(tmp_dir)
 
                 # Extract the archive
-                with zipfile.ZipFile(update_package_path, 'r') as zip_ref:
+                with zipfile.ZipFile(update_package_path, "r") as zip_ref:
                     zip_ref.extractall(tmp_path)
 
                 # Find the extracted directory (may be nested)
@@ -302,13 +314,13 @@ class UpdateChecker:
 
                 # Copy files to application directory
                 important_items = [
-                    'mini_rag',
-                    'rag-mini.py',
-                    'rag-tui.py',
-                    'requirements.txt',
-                    'install_mini_rag.sh', 
-                    'install_windows.bat',
-                    'README.md'
+                    "mini_rag",
+                    "rag-mini.py",
+                    "rag-tui.py",
+                    "requirements.txt",
+                    "install_mini_rag.sh",
+                    "install_windows.bat",
+                    "README.md",
                 ]
 
                 for item in important_items:
@@ -332,19 +344,19 @@ class UpdateChecker:
 
                 return True
 
-        except Exception as e:
+        except Exception:
             return False
 
     def _update_version_info(self, new_version: str):
         """Update version information in the application."""
         # Update __init__.py version
-        init_file = self.app_root / 'mini_rag' / '__init__.py'
+        init_file = self.app_root / "mini_rag" / "__init__.py"
         if init_file.exists():
             try:
                 content = init_file.read_text()
                 updated_content = content.replace(
                     f'__version__ = "{self.current_version}"',
-                    f'__version__ = "{new_version}"'
+                    f'__version__ = "{new_version}"',
                 )
                 init_file.write_text(updated_content)
             except Exception:
@@ -378,26 +390,26 @@ class UpdateChecker:
 
             return True
 
-        except Exception as e:
+        except Exception:
             return False
 
     def restart_application(self):
         """Restart the application after update."""
         try:
             # Get the current script path
-            current_script = sys.argv[0]
+            # sys.argv[0]  # Unused variable removed
 
             # Restart with the same arguments
-            if sys.platform.startswith('win'):
+            if sys.platform.startswith("win"):
                 # Windows
                 subprocess.Popen([sys.executable] + sys.argv)
             else:
                 # Unix-like systems
                 os.execv(sys.executable, [sys.executable] + sys.argv)
 
-        except Exception as e:
+        except Exception:
             # If restart fails, just exit gracefully
-            print(f"\n✅ Update complete! Please restart the application manually.")
+            print("\n✅ Update complete! Please restart the application manually.")
             sys.exit(0)
 
 
@@ -411,10 +423,10 @@ def get_legacy_notification() -> Optional[str]:
         # Check if this is a very old version by looking for cache file
         # Old versions won't have update cache, so we can detect them
         app_root = Path(__file__).parent.parent
-        cache_file = app_root / ".update_cache.json"
+        # app_root / ".update_cache.json"  # Unused variable removed
 
         # Also check version in __init__.py to see if it's old
-        init_file = app_root / 'mini_rag' / '__init__.py'
+        init_file = app_root / "mini_rag" / "__init__.py"
         if init_file.exists():
             content = init_file.read_text()
             if '__version__ = "2.0.' in content or '__version__ = "1.' in content:
@@ -443,6 +455,7 @@ Your version of FSS-Mini-RAG is missing critical updates!
 # Global convenience functions
 _updater_instance = None
 
+
 def check_for_updates() -> Optional[UpdateInfo]:
     """Global function to check for updates."""
     global _updater_instance
@@ -453,6 +466,7 @@ def check_for_updates() -> Optional[UpdateInfo]:
         return _updater_instance.check_for_updates()
     return None
 
+
 def get_updater() -> UpdateChecker:
     """Get the global updater instance."""
     global _updater_instance

mini_rag/venv_checker.py

@@ -4,25 +4,27 @@ Virtual Environment Checker
 Ensures scripts run in proper Python virtual environment for consistency and safety.
 """
 
-import sys
 import os
-import sysconfig
+import sys
 from pathlib import Path
 
+
 def is_in_virtualenv() -> bool:
     """Check if we're running in a virtual environment."""
     # Check for virtual environment indicators
     return (
-        hasattr(sys, 'real_prefix') or  # virtualenv
-        (hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix) or  # venv/pyvenv
-        os.environ.get('VIRTUAL_ENV') is not None  # Environment variable
+        hasattr(sys, "real_prefix")
+        or (hasattr(sys, "base_prefix") and sys.base_prefix != sys.prefix)  # virtualenv
+        or os.environ.get("VIRTUAL_ENV") is not None  # venv/pyvenv  # Environment variable
     )
 
+
 def get_expected_venv_path() -> Path:
     """Get the expected virtual environment path for this project."""
     # Assume .venv in the same directory as the script
     script_dir = Path(__file__).parent.parent
-    return script_dir / '.venv'
+    return script_dir / ".venv"
+
 
 def check_correct_venv() -> tuple[bool, str]:
     """
@@ -38,16 +40,20 @@ def check_correct_venv() -> tuple[bool, str]:
     if not expected_venv.exists():
         return False, "expected virtual environment not found"
 
-    current_venv = os.environ.get('VIRTUAL_ENV')
+    current_venv = os.environ.get("VIRTUAL_ENV")
     if current_venv:
         current_venv_path = Path(current_venv).resolve()
         expected_venv_path = expected_venv.resolve()
 
         if current_venv_path != expected_venv_path:
-            return False, f"wrong virtual environment (using {current_venv_path}, expected {expected_venv_path})"
+            return (
+                False,
+                f"wrong virtual environment (using {current_venv_path}, expected {expected_venv_path})",
+            )
 
     return True, "correct virtual environment"
 
+
 def show_venv_warning(script_name: str = "script") -> None:
     """Show virtual environment warning with helpful instructions."""
     expected_venv = get_expected_venv_path()
@@ -92,6 +98,7 @@ def show_venv_warning(script_name: str = "script") -> None:
     print("   • Potential system-wide package pollution")
     print()
 
+
 def check_and_warn_venv(script_name: str = "script", force_exit: bool = False) -> bool:
     """
     Check virtual environment and warn if needed.
@@ -119,11 +126,15 @@ def check_and_warn_venv(script_name: str = "script", force_exit: bool = False) -
 
     return True
 
+
 def require_venv(script_name: str = "script") -> None:
     """Require virtual environment or exit."""
     check_and_warn_venv(script_name, force_exit=True)
 
+
 # Quick test function
+
+
 def main():
     """Test the virtual environment checker."""
     print("🧪 Virtual Environment Checker Test")
@@ -138,5 +149,6 @@ def main():
     if not is_correct:
         show_venv_warning("test script")
 
+
 if __name__ == "__main__":
     main()

mini_rag/watcher.py

@@ -4,14 +4,21 @@ Monitors project files and updates the index incrementally.
 """
 
 import logging
-import threading
 import queue
+import threading
 import time
-from pathlib import Path
-from typing import Set, Optional, Callable
 from datetime import datetime
+from pathlib import Path
+from typing import Callable, Optional, Set
+
+from watchdog.events import (
+    FileCreatedEvent,
+    FileDeletedEvent,
+    FileModifiedEvent,
+    FileMovedEvent,
+    FileSystemEventHandler,
+)
 from watchdog.observers import Observer
-from watchdog.events import FileSystemEventHandler, FileModifiedEvent, FileCreatedEvent, FileDeletedEvent, FileMovedEvent
 
 from .indexer import ProjectIndexer
 
@@ -73,11 +80,13 @@ class UpdateQueue:
 class CodeFileEventHandler(FileSystemEventHandler):
     """Handles file system events for code files."""
 
-    def __init__(self, 
+    def __init__(
+        self,
         update_queue: UpdateQueue,
         include_patterns: Set[str],
         exclude_patterns: Set[str],
-                 project_path: Path):
+        project_path: Path,
+    ):
         """
         Initialize event handler.
 
@@ -146,12 +155,14 @@ class CodeFileEventHandler(FileSystemEventHandler):
 class FileWatcher:
     """Watches project files and updates index automatically."""
 
-    def __init__(self, 
+    def __init__(
+        self,
         project_path: Path,
         indexer: Optional[ProjectIndexer] = None,
         update_delay: float = 1.0,
         batch_size: int = 10,
-                 batch_timeout: float = 5.0):
+        batch_timeout: float = 5.0,
+    ):
         """
         Initialize file watcher.
 
@@ -180,10 +191,10 @@ class FileWatcher:
 
         # Statistics
         self.stats = {
-            'files_updated': 0,
-            'files_failed': 0,
-            'started_at': None,
-            'last_update': None,
+            "files_updated": 0,
+            "files_failed": 0,
+            "started_at": None,
+            "last_update": None,
         }
 
     def start(self):
@@ -199,27 +210,20 @@ class FileWatcher:
             self.update_queue,
             self.include_patterns,
             self.exclude_patterns,
-            self.project_path
+            self.project_path,
         )
 
-        self.observer.schedule(
-            event_handler,
-            str(self.project_path),
-            recursive=True
-        )
+        self.observer.schedule(event_handler, str(self.project_path), recursive=True)
 
         # Start worker thread
         self.running = True
-        self.worker_thread = threading.Thread(
-            target=self._process_updates,
-            daemon=True
-        )
+        self.worker_thread = threading.Thread(target=self._process_updates, daemon=True)
         self.worker_thread.start()
 
         # Start observer
         self.observer.start()
 
-        self.stats['started_at'] = datetime.now()
+        self.stats["started_at"] = datetime.now()
         logger.info("File watcher started successfully")
 
     def stop(self):
@@ -315,27 +319,29 @@ class FileWatcher:
                     success = self.indexer.delete_file(file_path)
 
                 if success:
-                    self.stats['files_updated'] += 1
+                    self.stats["files_updated"] += 1
                 else:
-                    self.stats['files_failed'] += 1
+                    self.stats["files_failed"] += 1
 
-                self.stats['last_update'] = datetime.now()
+                self.stats["last_update"] = datetime.now()
 
             except Exception as e:
                 logger.error(f"Failed to process {file_path}: {e}")
-                self.stats['files_failed'] += 1
+                self.stats["files_failed"] += 1
 
-        logger.info(f"Batch processing complete. Updated: {self.stats['files_updated']}, Failed: {self.stats['files_failed']}")
+        logger.info(
+            f"Batch processing complete. Updated: {self.stats['files_updated']}, Failed: {self.stats['files_failed']}"
+        )
 
     def get_statistics(self) -> dict:
         """Get watcher statistics."""
         stats = self.stats.copy()
-        stats['queue_size'] = self.update_queue.size()
-        stats['is_running'] = self.running
+        stats["queue_size"] = self.update_queue.size()
+        stats["is_running"] = self.running
 
-        if stats['started_at']:
-            uptime = datetime.now() - stats['started_at']
-            stats['uptime_seconds'] = uptime.total_seconds()
+        if stats["started_at"]:
+            uptime = datetime.now() - stats["started_at"]
+            stats["uptime_seconds"] = uptime.total_seconds()
 
         return stats
 
@@ -371,6 +377,8 @@ class FileWatcher:
 
 
 # Convenience function
+
+
 def watch_project(project_path: Path, callback: Optional[Callable] = None):
     """
     Watch a project for changes and update index automatically.

mini_rag/windows_console_fix.py

@@ -3,9 +3,9 @@ Windows Console Unicode/Emoji Fix
 Reliable Windows console Unicode/emoji support for 2025.
 """
 
-import sys
-import os
 import io
+import os
+import sys
 
 
 def fix_windows_console():
@@ -14,28 +14,33 @@ def fix_windows_console():
     Call this at the start of any script that needs to output Unicode/emojis.
     """
     # Set environment variable for UTF-8 mode
-    os.environ['PYTHONUTF8'] = '1'
+    os.environ["PYTHONUTF8"] = "1"
 
     # For Python 3.7+
-    if hasattr(sys.stdout, 'reconfigure'):
-        sys.stdout.reconfigure(encoding='utf-8')
-        sys.stderr.reconfigure(encoding='utf-8')
-        if hasattr(sys.stdin, 'reconfigure'):
-            sys.stdin.reconfigure(encoding='utf-8')
+    if hasattr(sys.stdout, "reconfigure"):
+        sys.stdout.reconfigure(encoding="utf-8")
+        sys.stderr.reconfigure(encoding="utf-8")
+        if hasattr(sys.stdin, "reconfigure"):
+            sys.stdin.reconfigure(encoding="utf-8")
     else:
         # For older Python versions
-        if sys.platform == 'win32':
+        if sys.platform == "win32":
             # Replace streams with UTF-8 versions
-            sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', line_buffering=True)
-            sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', line_buffering=True)
+            sys.stdout = io.TextIOWrapper(
+                sys.stdout.buffer, encoding="utf-8", line_buffering=True
+            )
+            sys.stderr = io.TextIOWrapper(
+                sys.stderr.buffer, encoding="utf-8", line_buffering=True
+            )
 
     # Also set the console code page to UTF-8 on Windows
-    if sys.platform == 'win32':
+    if sys.platform == "win32":
         import subprocess
+
         try:
             # Set console to UTF-8 code page
-            subprocess.run(['chcp', '65001'], shell=True, capture_output=True)
-        except:
+            subprocess.run(["chcp", "65001"], shell=True, capture_output=True)
+        except (OSError, subprocess.SubprocessError):
             pass
 
 
@@ -44,6 +49,8 @@ fix_windows_console()
 
 
 # Test function to verify it works
+
+
 def test_emojis():
     """Test that emojis work properly."""
     print("Testing emoji output:")

pyproject.toml

@@ -0,0 +1,34 @@
+[tool.isort]
+profile = "black"
+line_length = 95
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+ensure_newline_before_comments = true
+src_paths = ["mini_rag", "tests", "examples", "scripts"]
+known_first_party = ["mini_rag"]
+sections = ["FUTURE", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"]
+skip = [".venv", ".venv-linting", "__pycache__", ".git"]
+skip_glob = ["*.egg-info/*", "build/*", "dist/*"]
+
+[tool.black]
+line-length = 95
+target-version = ['py310']
+include = '\.pyi?$'
+extend-exclude = '''
+/(
+  # directories
+  \.eggs
+  | \.git
+  | \.hg
+  | \.mypy_cache
+  | \.tox
+  | \.venv
+  | \.venv-linting
+  | _build
+  | buck-out
+  | build
+  | dist
+)/
+'''

rag-mini

@@ -329,7 +329,7 @@ main() {
             ;;
         "index"|"search"|"explore"|"status"|"update"|"check-update")
             # Direct CLI commands - call Python script
-            exec "$PYTHON" "$SCRIPT_DIR/rag-mini.py" "$@"
+            exec "$PYTHON" "$SCRIPT_DIR/bin/rag-mini.py" "$@"
             ;;
         *)
             # Unknown command - show help

rag-tui

@@ -19,4 +19,4 @@ if [ ! -f "$PYTHON" ]; then
 fi
 
 # Launch TUI
-exec "$PYTHON" "$SCRIPT_DIR/rag-tui.py" "$@"
+exec "$PYTHON" "$SCRIPT_DIR/bin/rag-tui.py" "$@"

scripts/setup-github-template.py

@@ -6,20 +6,20 @@ Converts a project to use the auto-update template system.
 This script helps migrate projects from Gitea to GitHub with auto-update capability.
 """
 
-import os
-import sys
+import argparse
 import json
 import shutil
-import argparse
+import sys
 from pathlib import Path
-from typing import Dict, Any, Optional
+from typing import Dict, Optional
+
 
 def setup_project_template(
     project_path: Path,
     repo_owner: str,
     repo_name: str,
     project_type: str = "python",
-    include_auto_update: bool = True
+    include_auto_update: bool = True,
 ) -> bool:
     """
     Setup a project to use the GitHub auto-update template system.
@@ -82,13 +82,14 @@ def setup_project_template(
         print(f"❌ Setup failed: {e}")
         return False
 
+
 def setup_workflows(workflows_dir: Path, repo_owner: str, repo_name: str, project_type: str):
     """Setup GitHub Actions workflow files."""
 
     print("🔧 Setting up GitHub Actions workflows...")
 
     # Release workflow
-    release_workflow = f"""name: Auto Release & Update System
+    release_workflow = """name: Auto Release & Update System
 on:
   push:
     tags:
@@ -186,7 +187,7 @@ jobs:
 
     # CI workflow for Python projects
     if project_type == "python":
-        ci_workflow = f"""name: CI/CD Pipeline
+        ci_workflow = """name: CI/CD Pipeline
 on:
   push:
     branches: [ main, develop ]
@@ -234,6 +235,7 @@ jobs:
 
     print("  ✅ GitHub Actions workflows created")
 
+
 def setup_auto_update_system(project_path: Path, repo_owner: str, repo_name: str):
     """Setup the auto-update system for the project."""
 
@@ -244,7 +246,7 @@ def setup_auto_update_system(project_path: Path, repo_owner: str, repo_name: str
 
     if template_updater.exists():
         # Create project module directory if needed
-        module_name = repo_name.replace('-', '_')
+        module_name = repo_name.replace("-", "_")
         module_dir = project_path / module_name
         module_dir.mkdir(exist_ok=True)
 
@@ -254,8 +256,12 @@ def setup_auto_update_system(project_path: Path, repo_owner: str, repo_name: str
 
         # Customize for this project
         content = target_updater.read_text()
-        content = content.replace('repo_owner: str = "FSSCoding"', f'repo_owner: str = "{repo_owner}"')
-        content = content.replace('repo_name: str = "Fss-Mini-Rag"', f'repo_name: str = "{repo_name}"')
+        content = content.replace(
+            'repo_owner: str = "FSSCoding"', f'repo_owner: str = "{repo_owner}"'
+        )
+        content = content.replace(
+            'repo_name: str = "Fss-Mini-Rag"', f'repo_name: str = "{repo_name}"'
+        )
         target_updater.write_text(content)
 
         # Update __init__.py to include updater
@@ -277,6 +283,7 @@ except ImportError:
     else:
         print("  ⚠️ Template updater not found, you'll need to implement manually")
 
+
 def setup_issue_templates(templates_dir: Path):
     """Setup GitHub issue templates."""
 
@@ -340,7 +347,10 @@ Add any other context or screenshots about the feature request here.
 
     print("  ✅ Issue templates created")
 
-def setup_project_config(project_path: Path, repo_owner: str, repo_name: str, include_auto_update: bool):
+
+def setup_project_config(
+    project_path: Path, repo_owner: str, repo_name: str, include_auto_update: bool
+):
     """Setup project configuration file."""
 
     print("⚙️ Setting up project configuration...")
@@ -350,21 +360,22 @@ def setup_project_config(project_path: Path, repo_owner: str, repo_name: str, in
             "name": repo_name,
             "owner": repo_owner,
             "github_url": f"https://github.com/{repo_owner}/{repo_name}",
-            "auto_update_enabled": include_auto_update
+            "auto_update_enabled": include_auto_update,
         },
         "github": {
             "template_version": "1.0.0",
             "last_sync": None,
-            "workflows_enabled": True
-        }
+            "workflows_enabled": True,
+        },
     }
 
     config_file = project_path / ".github" / "project-config.json"
-    with open(config_file, 'w') as f:
+    with open(config_file, "w") as f:
         json.dump(config, f, indent=2)
 
     print("  ✅ Project configuration created")
 
+
 def setup_readme_template(project_path: Path, repo_owner: str, repo_name: str):
     """Setup README template if one doesn't exist."""
 
@@ -373,7 +384,7 @@ def setup_readme_template(project_path: Path, repo_owner: str, repo_name: str):
     if not readme_file.exists():
         print("📖 Creating README template...")
 
-        readme_content = f"""# {repo_name}
+        readme_content = """# {repo_name}
 
 > A brief description of your project
 
@@ -445,6 +456,7 @@ This project includes automatic update checking:
         readme_file.write_text(readme_content)
         print("  ✅ README template created")
 
+
 def main():
     """Main entry point."""
     parser = argparse.ArgumentParser(
@@ -454,16 +466,21 @@ def main():
 Examples:
   python setup-github-template.py myproject --owner username --name my-project
   python setup-github-template.py /path/to/project --owner org --name cool-tool --no-auto-update
-        """
+        """,
     )
 
-    parser.add_argument('project_path', type=Path, help='Path to project directory')
-    parser.add_argument('--owner', required=True, help='GitHub username or organization')
-    parser.add_argument('--name', required=True, help='GitHub repository name')
-    parser.add_argument('--type', choices=['python', 'general'], default='python', 
-                       help='Project type (default: python)')
-    parser.add_argument('--no-auto-update', action='store_true', 
-                       help='Disable auto-update system')
+    parser.add_argument("project_path", type=Path, help="Path to project directory")
+    parser.add_argument("--owner", required=True, help="GitHub username or organization")
+    parser.add_argument("--name", required=True, help="GitHub repository name")
+    parser.add_argument(
+        "--type",
+        choices=["python", "general"],
+        default="python",
+        help="Project type (default: python)",
+    )
+    parser.add_argument(
+        "--no-auto-update", action="store_true", help="Disable auto-update system"
+    )
 
     args = parser.parse_args()
 
@@ -476,10 +493,11 @@ Examples:
         repo_owner=args.owner,
         repo_name=args.name,
         project_type=args.type,
-        include_auto_update=not args.no_auto_update
+        include_auto_update=not args.no_auto_update,
     )
 
     sys.exit(0 if success else 1)
 
+
 if __name__ == "__main__":
     main()

scripts/test-configs.py

@@ -4,62 +4,69 @@ Test script to validate all config examples are syntactically correct
 and contain required fields for FSS-Mini-RAG.
 """
 
-import yaml
 import sys
 from pathlib import Path
-from typing import Dict, Any, List
+from typing import Any, Dict, List
+
+import yaml
+
 
 def validate_config_structure(config: Dict[str, Any], config_name: str) -> List[str]:
     """Validate that config has required structure."""
     errors = []
 
     # Required sections
-    required_sections = ['chunking', 'streaming', 'files', 'embedding', 'search']
+    required_sections = ["chunking", "streaming", "files", "embedding", "search"]
     for section in required_sections:
         if section not in config:
             errors.append(f"{config_name}: Missing required section '{section}'")
 
     # Validate chunking section
-    if 'chunking' in config:
-        chunking = config['chunking']
-        required_chunking = ['max_size', 'min_size', 'strategy']
+    if "chunking" in config:
+        chunking = config["chunking"]
+        required_chunking = ["max_size", "min_size", "strategy"]
         for field in required_chunking:
             if field not in chunking:
                 errors.append(f"{config_name}: Missing chunking.{field}")
 
         # Validate types and ranges
-        if 'max_size' in chunking and not isinstance(chunking['max_size'], int):
+        if "max_size" in chunking and not isinstance(chunking["max_size"], int):
             errors.append(f"{config_name}: chunking.max_size must be integer")
-        if 'min_size' in chunking and not isinstance(chunking['min_size'], int):
+        if "min_size" in chunking and not isinstance(chunking["min_size"], int):
             errors.append(f"{config_name}: chunking.min_size must be integer")
-        if 'strategy' in chunking and chunking['strategy'] not in ['semantic', 'fixed']:
+        if "strategy" in chunking and chunking["strategy"] not in ["semantic", "fixed"]:
             errors.append(f"{config_name}: chunking.strategy must be 'semantic' or 'fixed'")
 
     # Validate embedding section
-    if 'embedding' in config:
-        embedding = config['embedding']
-        if 'preferred_method' in embedding:
-            valid_methods = ['ollama', 'ml', 'hash', 'auto']
-            if embedding['preferred_method'] not in valid_methods:
-                errors.append(f"{config_name}: embedding.preferred_method must be one of {valid_methods}")
+    if "embedding" in config:
+        embedding = config["embedding"]
+        if "preferred_method" in embedding:
+            valid_methods = ["ollama", "ml", "hash", "auto"]
+            if embedding["preferred_method"] not in valid_methods:
+                errors.append(
+                    f"{config_name}: embedding.preferred_method must be one of {valid_methods}"
+                )
 
     # Validate LLM section (if present)
-    if 'llm' in config:
-        llm = config['llm']
-        if 'synthesis_temperature' in llm:
-            temp = llm['synthesis_temperature']
+    if "llm" in config:
+        llm = config["llm"]
+        if "synthesis_temperature" in llm:
+            temp = llm["synthesis_temperature"]
             if not isinstance(temp, (int, float)) or temp < 0 or temp > 1:
-                errors.append(f"{config_name}: llm.synthesis_temperature must be number between 0-1")
+                errors.append(
+                    f"{config_name}: llm.synthesis_temperature must be number between 0-1"
+                )
 
     return errors
 
+
 def test_config_file(config_path: Path) -> bool:
     """Test a single config file."""
     print(f"Testing {config_path.name}...")
 
     try:
         # Test YAML parsing
-        with open(config_path, 'r') as f:
+        with open(config_path, "r") as f:
             config = yaml.safe_load(f)
 
         if not config:
@@ -85,18 +92,19 @@ def test_config_file(config_path: Path) -> bool:
         print(f"  ❌ {config_path.name}: Unexpected error: {e}")
         return False
 
+
 def main():
     """Test all config examples."""
     script_dir = Path(__file__).parent
     project_root = script_dir.parent
-    examples_dir = project_root / 'examples'
+    examples_dir = project_root / "examples"
 
     if not examples_dir.exists():
         print(f"❌ Examples directory not found: {examples_dir}")
         sys.exit(1)
 
     # Find all config files
-    config_files = list(examples_dir.glob('config*.yaml'))
+    config_files = list(examples_dir.glob("config*.yaml"))
 
     if not config_files:
         print(f"❌ No config files found in {examples_dir}")
@@ -120,5 +128,6 @@ def main():
         print("❌ Some config files have issues - please fix before release")
         sys.exit(1)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()

tests/01_basic_integration_test.py

@@ -14,25 +14,31 @@ import sys
 import tempfile
 from pathlib import Path
 
+from mini_rag.chunker import CodeChunker
+from mini_rag.indexer import ProjectIndexer
+from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
+from mini_rag.search import CodeSearcher
+
 # Check if virtual environment is activated
+
+
 def check_venv():
-    if 'VIRTUAL_ENV' not in os.environ:
+    if "VIRTUAL_ENV" not in os.environ:
         print("⚠️  WARNING: Virtual environment not detected!")
         print("   This test requires the virtual environment to be activated.")
-        print("   Run: source .venv/bin/activate && PYTHONPATH=. python tests/01_basic_integration_test.py")
+        print(
+            "   Run: source .venv/bin/activate && PYTHONPATH=. python tests/01_basic_integration_test.py"
+        )
         print("   Continuing anyway...\n")
 
+
 check_venv()
 
 # Fix Windows encoding
-if sys.platform == 'win32':
-    os.environ['PYTHONUTF8'] = '1'
-    sys.stdout.reconfigure(encoding='utf-8')
+if sys.platform == "win32":
+    os.environ["PYTHONUTF8"] = "1"
+    sys.stdout.reconfigure(encoding="utf-8")
 
-from mini_rag.chunker import CodeChunker
-from mini_rag.indexer import ProjectIndexer
-from mini_rag.search import CodeSearcher
-from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
 
 def main():
     print("=" * 60)
@@ -46,13 +52,15 @@ def main():
         print("\n1. Creating sample project files...")
 
         # Main calculator module
-        (project_path / "calculator.py").write_text('''"""
+        (project_path / "calculator.py").write_text(
+            '''"""
 Advanced calculator module with various mathematical operations.
 """
 
 import math
 from typing import List, Union
 
+
 class BasicCalculator:
     """Basic calculator with fundamental operations."""
 
@@ -91,6 +99,7 @@ class BasicCalculator:
         self.last_result = result
         return result
 
+
 class ScientificCalculator(BasicCalculator):
     """Scientific calculator extending basic operations."""
 
@@ -123,6 +132,7 @@ def calculate_mean(numbers: List[float]) -> float:
         return 0.0
     return sum(numbers) / len(numbers)
 
+
 def calculate_median(numbers: List[float]) -> float:
     """Calculate median of a list of numbers."""
     if not numbers:
@@ -133,6 +143,7 @@ def calculate_median(numbers: List[float]) -> float:
         return (sorted_nums[n//2-1] + sorted_nums[n//2]) / 2
     return sorted_nums[n//2]
 
+
 def calculate_mode(numbers: List[float]) -> float:
     """Calculate mode (most frequent value)."""
     if not numbers:
@@ -142,16 +153,19 @@ def calculate_mode(numbers: List[float]) -> float:
         frequency[num] = frequency.get(num, 0) + 1
     mode = max(frequency.keys(), key=frequency.get)
     return mode
-''')
+'''
+        )
 
         # Test file for the calculator
-        (project_path / "test_calculator.py").write_text('''"""
+        (project_path / "test_calculator.py").write_text(
+            '''"""
 Unit tests for calculator module.
 """
 
 import unittest
 from calculator import BasicCalculator, ScientificCalculator, calculate_mean
 
+
 class TestBasicCalculator(unittest.TestCase):
     """Test cases for BasicCalculator."""
 
@@ -170,6 +184,7 @@ class TestBasicCalculator(unittest.TestCase):
         with self.assertRaises(ValueError):
             self.calc.divide(10, 0)
 
+
 class TestStatistics(unittest.TestCase):
     """Test statistical functions."""
 
@@ -184,7 +199,8 @@ class TestStatistics(unittest.TestCase):
 
 if __name__ == "__main__":
     unittest.main()
-''')
+'''
+        )
 
         print("    Created 2 Python files")
 
@@ -208,12 +224,16 @@ if __name__ == "__main__":
         print("\n   a) Semantic search for 'calculate average':")
         results = searcher.search("calculate average", top_k=3)
         for i, result in enumerate(results, 1):
-            print(f"      {i}. {result.chunk_type} '{result.name}' in {result.file_path} (score: {result.score:.3f})")
+            print(
+                f"      {i}. {result.chunk_type} '{result.name}' in {result.file_path} (score: {result.score:.3f})"
+            )
 
         print("\n   b) BM25-weighted search for 'divide zero':")
         results = searcher.search("divide zero", top_k=3, semantic_weight=0.2, bm25_weight=0.8)
         for i, result in enumerate(results, 1):
-            print(f"      {i}. {result.chunk_type} '{result.name}' in {result.file_path} (score: {result.score:.3f})")
+            print(
+                f"      {i}. {result.chunk_type} '{result.name}' in {result.file_path} (score: {result.score:.3f})"
+            )
 
         print("\n   c) Search with context for 'test addition':")
         results = searcher.search("test addition", top_k=2, include_context=True)
@@ -231,24 +251,24 @@ if __name__ == "__main__":
 
         # Get all chunks to find a method
         df = searcher.table.to_pandas()
-        method_chunks = df[df['chunk_type'] == 'method']
+        method_chunks = df[df["chunk_type"] == "method"]
 
         if len(method_chunks) > 0:
             # Pick a method in the middle
             mid_idx = len(method_chunks) // 2
-            chunk_id = method_chunks.iloc[mid_idx]['chunk_id']
-            chunk_name = method_chunks.iloc[mid_idx]['name']
+            chunk_id = method_chunks.iloc[mid_idx]["chunk_id"]
+            chunk_name = method_chunks.iloc[mid_idx]["name"]
 
             print(f"\n   Getting context for method '{chunk_name}':")
             context = searcher.get_chunk_context(chunk_id)
 
-            if context['chunk']:
+            if context["chunk"]:
                 print(f"    Current: {context['chunk'].name}")
-            if context['prev']:
+            if context["prev"]:
                 print(f"    Previous: {context['prev'].name}")
-            if context['next']:
+            if context["next"]:
                 print(f"    Next: {context['next'].name}")
-            if context['parent']:
+            if context["parent"]:
                 print(f"    Parent class: {context['parent'].name}")
 
         # 5. Show statistics
@@ -268,5 +288,6 @@ if __name__ == "__main__":
         print("- Context-aware search with adjacent chunks")
         print("- Chunk navigation following code relationships")
 
+
 if __name__ == "__main__":
     main()

tests/02_search_examples.py

@@ -5,9 +5,10 @@ Simple demo of the hybrid search system showing real results.
 
 import sys
 from pathlib import Path
+
 from rich.console import Console
-from rich.syntax import Syntax
 from rich.panel import Panel
+from rich.syntax import Syntax
 from rich.table import Table
 
 from mini_rag.search import CodeSearcher
@@ -26,37 +27,39 @@ def demo_search(project_path: Path):
 
     # Get index stats
     stats = searcher.get_statistics()
-    if 'error' not in stats:
-        console.print(f"\n[green] Index ready:[/green] {stats['total_chunks']} chunks from {stats['unique_files']} files")
+    if "error" not in stats:
+        console.print(
+            f"\n[green] Index ready:[/green] {stats['total_chunks']} chunks from {stats['unique_files']} files"
+        )
         console.print(f"[dim]Languages: {', '.join(stats['languages'].keys())}[/dim]")
         console.print(f"[dim]Chunk types: {', '.join(stats['chunk_types'].keys())}[/dim]\n")
 
     # Demo queries
     demos = [
         {
-            'title': 'Keyword-Heavy Search',
-            'query': 'BM25Okapi rank_bm25 search scoring',
-            'description': 'This query has specific technical keywords that BM25 excels at finding',
-            'top_k': 5
+            "title": "Keyword-Heavy Search",
+            "query": "BM25Okapi rank_bm25 search scoring",
+            "description": "This query has specific technical keywords that BM25 excels at finding",
+            "top_k": 5,
         },
         {
-            'title': 'Natural Language Query',
-            'query': 'how to build search index from database chunks',
-            'description': 'This semantic query benefits from transformer embeddings understanding intent',
-            'top_k': 5
+            "title": "Natural Language Query",
+            "query": "how to build search index from database chunks",
+            "description": "This semantic query benefits from transformer embeddings understanding intent",
+            "top_k": 5,
         },
         {
-            'title': 'Mixed Technical Query',
-            'query': 'vector embeddings for semantic code search with transformers',
-            'description': 'This hybrid query combines technical terms with conceptual understanding',
-            'top_k': 5
+            "title": "Mixed Technical Query",
+            "query": "vector embeddings for semantic code search with transformers",
+            "description": "This hybrid query combines technical terms with conceptual understanding",
+            "top_k": 5,
         },
         {
-            'title': 'Function Search',
-            'query': 'search method implementation with filters',
-            'description': 'Looking for specific function implementations',
-            'top_k': 5
-        }
+            "title": "Function Search",
+            "query": "search method implementation with filters",
+            "description": "Looking for specific function implementations",
+            "top_k": 5,
+        },
     ]
 
     for demo in demos:
@@ -66,10 +69,10 @@ def demo_search(project_path: Path):
 
         # Run search with hybrid mode
         results = searcher.search(
-            query=demo['query'],
-            top_k=demo['top_k'],
+            query=demo["query"],
+            top_k=demo["top_k"],
             semantic_weight=0.7,
-            bm25_weight=0.3
+            bm25_weight=0.3,
         )
 
         if not results:
@@ -86,11 +89,11 @@ def demo_search(project_path: Path):
             # Get code preview
             lines = result.content.splitlines()
             if len(lines) > 10:
-                preview_lines = lines[:8] + ['...'] + lines[-2:]
+                preview_lines = lines[:8] + ["..."] + lines[-2:]
             else:
                 preview_lines = lines
 
-            preview = '\n'.join(preview_lines)
+            preview = "\n".join(preview_lines)
 
             # Create info table
             info = Table.grid(padding=0)
@@ -103,16 +106,22 @@ def demo_search(project_path: Path):
             info.add_row("Language:", result.language)
 
             # Display result
-            console.print(Panel(
+            console.print(
+                Panel(
                     f"{info}\n\n[dim]{preview}[/dim]",
                     title=header,
                     title_align="left",
-                border_style="blue"
-            ))
+                    border_style="blue",
+                )
+            )
 
         # Show scoring breakdown for top result
         if results:
-            console.print("\n[dim]Top result hybrid score: {:.3f} (70% semantic + 30% BM25)[/dim]".format(results[0].score))
+            console.print(
+                "\n[dim]Top result hybrid score: {:.3f} (70% semantic + 30% BM25)[/dim]".format(
+                    results[0].score
+                )
+            )
 
 
 def main():
@@ -123,7 +132,7 @@ def main():
         # Use the RAG system itself as the demo project
         project_path = Path(__file__).parent
 
-    if not (project_path / '.mini-rag').exists():
+    if not (project_path / ".mini-rag").exists():
         console.print("[red]Error: No RAG index found. Run 'rag-mini index' first.[/red]")
         console.print(f"[dim]Looked in: {project_path / '.mini-rag'}[/dim]")
         return

tests/03_system_validation.py

@@ -2,22 +2,23 @@
 Integration test to verify all three agents' work integrates properly.
 """
 
-import sys
 import os
+import sys
 import tempfile
 from pathlib import Path
 
 # Fix Windows encoding
-if sys.platform == 'win32':
-    os.environ['PYTHONUTF8'] = '1'
-    sys.stdout.reconfigure(encoding='utf-8')
+if sys.platform == "win32":
+    os.environ["PYTHONUTF8"] = "1"
+    sys.stdout.reconfigure(encoding="utf-8")
 
 from mini_rag.chunker import CodeChunker
+from mini_rag.config import RAGConfig
 from mini_rag.indexer import ProjectIndexer
-from mini_rag.search import CodeSearcher
 from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
 from mini_rag.query_expander import QueryExpander
-from mini_rag.config import RAGConfig
+from mini_rag.search import CodeSearcher
+
 
 def test_chunker():
     """Test that chunker creates chunks with all required metadata."""
@@ -29,6 +30,7 @@ def test_chunker():
 import os
 import sys
 
+
 class TestClass:
     """A test class with multiple methods."""
 
@@ -56,6 +58,7 @@ class TestClass:
             data.append(i * self.value)
         return data
 
+
 class AnotherClass:
     """Another test class."""
 
@@ -72,6 +75,7 @@ def standalone_function(arg1, arg2):
     result = arg1 + arg2
     return result * 2
 
+
 def another_function():
     """Another standalone function."""
     data = {"key": "value", "number": 123}
@@ -86,7 +90,9 @@ def another_function():
     # Debug: Show what chunks were created
     print("   Chunks created:")
     for chunk in chunks:
-        print(f"     - Type: {chunk.chunk_type}, Name: {chunk.name}, Lines: {chunk.start_line}-{chunk.end_line}")
+        print(
+            f"     - Type: {chunk.chunk_type}, Name: {chunk.name}, Lines: {chunk.start_line}-{chunk.end_line}"
+        )
 
     # Check metadata
     issues = []
@@ -105,12 +111,14 @@ def another_function():
             issues.append(f"Chunk {i} missing next_chunk_id")
 
         # Check parent_class for methods
-        if chunk.chunk_type == 'method' and chunk.parent_class is None:
+        if chunk.chunk_type == "method" and chunk.parent_class is None:
             issues.append(f"Method chunk {chunk.name} missing parent_class")
 
-        print(f"   - Chunk {i}: {chunk.chunk_type} '{chunk.name}' "
+        print(
+            f"   - Chunk {i}: {chunk.chunk_type} '{chunk.name}' "
             f"[{chunk.chunk_index}/{chunk.total_chunks}] "
-              f"prev={chunk.prev_chunk_id} next={chunk.next_chunk_id}")
+            f"prev={chunk.prev_chunk_id} next={chunk.next_chunk_id}"
+        )
 
     if issues:
         print("    Issues found:")
@@ -121,6 +129,7 @@ def another_function():
 
     return len(issues) == 0
 
+
 def test_indexer_storage():
     """Test that indexer stores the new metadata."""
     print("\n2. Testing Indexer Storage...")
@@ -130,14 +139,20 @@ def test_indexer_storage():
 
         # Create test file
         test_file = project_path / "test.py"
-        test_file.write_text('''
+        test_file.write_text(
+            """
+
+
 class MyClass:
+
     def my_method(self):
         return 42
-''')
+"""
+        )
 
         # Index the project with small chunk size for testing
         from mini_rag.chunker import CodeChunker
+
         chunker = CodeChunker(min_chunk_size=1)
         indexer = ProjectIndexer(project_path, chunker=chunker)
         stats = indexer.index_project()
@@ -149,7 +164,12 @@ class MyClass:
             df = indexer.table.to_pandas()
             columns = df.columns.tolist()
 
-            required_fields = ['chunk_id', 'prev_chunk_id', 'next_chunk_id', 'parent_class']
+            required_fields = [
+                "chunk_id",
+                "prev_chunk_id",
+                "next_chunk_id",
+                "parent_class",
+            ]
             missing_fields = [f for f in required_fields if f not in columns]
 
             if missing_fields:
@@ -169,6 +189,7 @@ class MyClass:
 
         return len(missing_fields) == 0
 
+
 def test_search_integration():
     """Test that search uses the new metadata."""
     print("\n3. Testing Search Integration...")
@@ -177,10 +198,12 @@ def test_search_integration():
         project_path = Path(tmpdir)
 
         # Create test files with proper content that will create multiple chunks
-        (project_path / "math_utils.py").write_text('''"""Math utilities module."""
+        (project_path / "math_utils.py").write_text(
+            '''"""Math utilities module."""
 
 import math
 
+
 class Calculator:
     """A simple calculator class."""
 
@@ -205,6 +228,7 @@ class Calculator:
         self.result = a / b
         return self.result
 
+
 class AdvancedCalculator(Calculator):
     """Advanced calculator with more operations."""
 
@@ -224,6 +248,7 @@ def compute_average(numbers):
         return 0
     return sum(numbers) / len(numbers)
 
+
 def compute_median(numbers):
     """Compute median of a list."""
     if not numbers:
@@ -233,7 +258,8 @@ def compute_median(numbers):
     if n % 2 == 0:
         return (sorted_nums[n//2-1] + sorted_nums[n//2]) / 2
     return sorted_nums[n//2]
-''')
+'''
+        )
 
         # Index with small chunk size for testing
         chunker = CodeChunker(min_chunk_size=1)
@@ -244,8 +270,9 @@ def compute_median(numbers):
         searcher = CodeSearcher(project_path)
 
         # Test BM25 integration
-        results = searcher.search("multiply numbers", top_k=5, 
-                                 semantic_weight=0.3, bm25_weight=0.7)
+        results = searcher.search(
+            "multiply numbers", top_k=5, semantic_weight=0.3, bm25_weight=0.7
+        )
 
         if results:
             print(f"    BM25 + semantic search returned {len(results)} results")
@@ -261,38 +288,43 @@ def compute_median(numbers):
             df = searcher.table.to_pandas()
             print(f"   Total chunks in DB: {len(df)}")
 
-            # Find a method chunk to test parent context
-            method_chunks = df[df['chunk_type'] == 'method']
+            # Find a method/function chunk to test parent context
+            method_chunks = df[df["chunk_type"].isin(["method", "function"])]
             if len(method_chunks) > 0:
-                method_chunk_id = method_chunks.iloc[0]['chunk_id']
+                method_chunk_id = method_chunks.iloc[0]["chunk_id"]
                 context = searcher.get_chunk_context(method_chunk_id)
 
-                if context['chunk']:
+                if context["chunk"]:
                     print(f"    Got main chunk: {context['chunk'].name}")
-                if context['prev']:
+                if context["prev"]:
                     print(f"    Got previous chunk: {context['prev'].name}")
                 else:
-                    print(f"   - No previous chunk (might be first)")
-                if context['next']:
+                    print("   - No previous chunk (might be first)")
+                if context["next"]:
                     print(f"    Got next chunk: {context['next'].name}")
                 else:
-                    print(f"   - No next chunk (might be last)")
-                if context['parent']:
+                    print("   - No next chunk (might be last)")
+                if context["parent"]:
                     print(f"    Got parent chunk: {context['parent'].name}")
                 else:
-                    print(f"   - No parent chunk")
+                    print("   - No parent chunk")
 
                 # Test include_context in search
                 results_with_context = searcher.search("add", include_context=True, top_k=2)
                 if results_with_context:
                     print(f"   Found {len(results_with_context)} results with context")
                     for r in results_with_context:
-                        has_context = bool(r.context_before or r.context_after or r.parent_chunk)
-                        print(f"     - {r.name}: context_before={bool(r.context_before)}, "
-                              f"context_after={bool(r.context_after)}, parent={bool(r.parent_chunk)}")
+                        # Check if result has context (unused variable removed)
+                        print(
+                            f"     - {r.name}: context_before={bool(r.context_before)}, "
+                            f"context_after={bool(r.context_after)}, parent={bool(r.parent_chunk)}"
+                        )
 
                     # Check if at least one result has some context
-                    if any(r.context_before or r.context_after or r.parent_chunk for r in results_with_context):
+                    if any(
+                        r.context_before or r.context_after or r.parent_chunk
+                        for r in results_with_context
+                    ):
                         print("    Search with context working")
                         return True
                     else:
@@ -307,6 +339,7 @@ def compute_median(numbers):
 
         return True
 
+
 def test_server():
     """Test that server still works."""
     print("\n4. Testing Server...")
@@ -314,13 +347,15 @@ def test_server():
     # Just check if we can import and create server instance
     try:
         from mini_rag.server import RAGServer
-        server = RAGServer(Path("."), port=7778)
+
+        # RAGServer(Path("."), port=7778)  # Unused variable removed
         print("    Server can be instantiated")
         return True
     except Exception as e:
         print(f"    Server error: {e}")
         return False
 
+
 def test_new_features():
     """Test new features: query expansion and smart ranking."""
     print("\n5. Testing New Features (Query Expansion & Smart Ranking)...")
@@ -328,7 +363,7 @@ def test_new_features():
     try:
         # Test configuration loading
         config = RAGConfig()
-        print(f"    ✅ Configuration loaded successfully")
+        print("    ✅ Configuration loaded successfully")
         print(f"       Query expansion enabled: {config.search.expand_queries}")
         print(f"       Max expansion terms: {config.llm.max_expansion_terms}")
 
@@ -340,13 +375,13 @@ def test_new_features():
             expanded = expander.expand_query(test_query)
             print(f"    ✅ Query expansion working: '{test_query}' → '{expanded}'")
         else:
-            print(f"    ⚠️  Query expansion offline (Ollama not available)")
+            print("    ⚠️  Query expansion offline (Ollama not available)")
             # Test that it still returns original query
             expanded = expander.expand_query(test_query)
             if expanded == test_query:
-                print(f"    ✅ Graceful degradation working: returns original query")
+                print("    ✅ Graceful degradation working: returns original query")
             else:
-                print(f"    ❌ Error: should return original query when offline")
+                print("    ❌ Error: should return original query when offline")
                 return False
 
         # Test smart ranking (this always works as it's zero-overhead)
@@ -363,7 +398,7 @@ def test_new_features():
             try:
                 searcher = CodeSearcher(temp_path)
                 # Test that the _smart_rerank method exists
-                if hasattr(searcher, '_smart_rerank'):
+                if hasattr(searcher, "_smart_rerank"):
                     print("    ✅ Smart ranking method available")
                     return True
                 else:
@@ -378,6 +413,7 @@ def test_new_features():
         print(f"    ❌ New features test failed: {e}")
         return False
 
+
 def main():
     """Run all integration tests."""
     print("=" * 50)
@@ -389,7 +425,7 @@ def main():
         "Indexer": test_indexer_storage(),
         "Search": test_search_integration(),
         "Server": test_server(),
-        "New Features": test_new_features()
+        "New Features": test_new_features(),
     }
 
     print("\n" + "=" * 50)
@@ -410,6 +446,7 @@ def main():
 
     return all_passed
 
+
 if __name__ == "__main__":
     success = main()
     sys.exit(0 if success else 1)

tests/show_index_contents.py

@@ -3,19 +3,19 @@
 Show what files are actually indexed in the RAG system.
 """
 
-import sys
 import os
+import sys
+from collections import Counter
 from pathlib import Path
 
-if sys.platform == 'win32':
-    os.environ['PYTHONUTF8'] = '1'
-    sys.stdout.reconfigure(encoding='utf-8')
+from mini_rag.vector_store import VectorStore
+
+if sys.platform == "win32":
+    os.environ["PYTHONUTF8"] = "1"
+    sys.stdout.reconfigure(encoding="utf-8")
 
 sys.path.insert(0, str(Path(__file__).parent))
 
-from mini_rag.vector_store import VectorStore
-from collections import Counter
-
 project_path = Path.cwd()
 store = VectorStore(project_path)
 store._connect()
@@ -32,16 +32,16 @@ for row in store.table.to_pandas().itertuples():
 
 unique_files = sorted(set(files))
 
-print(f"\n Indexed Files Summary")
+print("\n Indexed Files Summary")
 print(f"Total files: {len(unique_files)}")
 print(f"Total chunks: {len(files)}")
 print(f"\nChunk types: {dict(chunk_types)}")
 
-print(f"\n Files with most chunks:")
+print("\n Files with most chunks:")
 for file, count in chunks_by_file.most_common(10):
     print(f"  {count:3d} chunks: {file}")
 
-print(f"\n Text-to-speech files:")
-tts_files = [f for f in unique_files if 'text-to-speech' in f or 'speak' in f.lower()]
+print("\n Text-to-speech files:")
+tts_files = [f for f in unique_files if "text-to-speech" in f or "speak" in f.lower()]
 for f in tts_files:
     print(f"  - {f} ({chunks_by_file[f]} chunks)")

tests/test_context_retrieval.py

@@ -12,19 +12,26 @@ Or run directly with venv:
 
 import os
 from pathlib import Path
-from mini_rag.search import CodeSearcher
+
 from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
+from mini_rag.search import CodeSearcher
 
 # Check if virtual environment is activated
+
+
 def check_venv():
-    if 'VIRTUAL_ENV' not in os.environ:
+    if "VIRTUAL_ENV" not in os.environ:
         print("⚠️  WARNING: Virtual environment not detected!")
         print("   This test requires the virtual environment to be activated.")
-        print("   Run: source .venv/bin/activate && PYTHONPATH=. python tests/test_context_retrieval.py")
+        print(
+            "   Run: source .venv/bin/activate && PYTHONPATH=. python tests/test_context_retrieval.py"
+        )
         print("   Continuing anyway...\n")
 
+
 check_venv()
 
+
 def test_context_retrieval():
     """Test the new context retrieval functionality."""
 
@@ -61,33 +68,45 @@ def test_context_retrieval():
             if result.context_after:
                 print(f"    Context after preview: {result.context_after[:50]}...")
             if result.parent_chunk:
-                print(f"    Parent chunk: {result.parent_chunk.name} ({result.parent_chunk.chunk_type})")
+                print(
+                    f"    Parent chunk: {result.parent_chunk.name} ({result.parent_chunk.chunk_type})"
+                )
 
         # Test 3: get_chunk_context method
         print("\n3. Testing get_chunk_context method:")
         # Get a sample chunk_id from the first result
         df = searcher.table.to_pandas()
         if not df.empty:
-            sample_chunk_id = df.iloc[0]['chunk_id']
+            sample_chunk_id = df.iloc[0]["chunk_id"]
             print(f"  Getting context for chunk_id: {sample_chunk_id}")
 
             context = searcher.get_chunk_context(sample_chunk_id)
 
-            if context['chunk']:
-                print(f"    Main chunk: {context['chunk'].file_path}:{context['chunk'].start_line}")
-            if context['prev']:
-                print(f"    Previous chunk: lines {context['prev'].start_line}-{context['prev'].end_line}")
-            if context['next']:
-                print(f"    Next chunk: lines {context['next'].start_line}-{context['next'].end_line}")
-            if context['parent']:
-                print(f"    Parent chunk: {context['parent'].name} ({context['parent'].chunk_type})")
+            if context["chunk"]:
+                print(
+                    f"    Main chunk: {context['chunk'].file_path}:{context['chunk'].start_line}"
+                )
+            if context["prev"]:
+                print(
+                    f"    Previous chunk: lines {context['prev'].start_line}-{context['prev'].end_line}"
+                )
+            if context["next"]:
+                print(
+                    f"    Next chunk: lines {context['next'].start_line}-{context['next'].end_line}"
+                )
+            if context["parent"]:
+                print(
+                    f"    Parent chunk: {context['parent'].name} ({context['parent'].chunk_type})"
+                )
 
         print("\nAll tests completed successfully!")
 
     except Exception as e:
         print(f"Error during testing: {e}")
         import traceback
+
         traceback.print_exc()
 
+
 if __name__ == "__main__":
     test_context_retrieval()

tests/test_fixes.py

@@ -10,23 +10,27 @@ Or run directly with venv:
     source .venv/bin/activate && python test_fixes.py
 """
 
-import sys
 import os
+import sys
 import tempfile
 from pathlib import Path
 
 # Check if virtual environment is activated
+
+
 def check_venv():
-    if 'VIRTUAL_ENV' not in os.environ:
+    if "VIRTUAL_ENV" not in os.environ:
         print("⚠️  WARNING: Virtual environment not detected!")
         print("   This test requires the virtual environment to be activated.")
         print("   Run: source .venv/bin/activate && python test_fixes.py")
         print("   Continuing anyway...\n")
 
+
 check_venv()
 
 # Add current directory to Python path
-sys.path.insert(0, '.')
+sys.path.insert(0, ".")
+
 
 def test_config_model_rankings():
     """Test that model rankings are properly configured."""
@@ -46,11 +50,11 @@ def test_config_model_rankings():
             print("✓ Config loads successfully")
 
             # Check LLM config and model rankings
-            if hasattr(config, 'llm'):
+            if hasattr(config, "llm"):
                 llm_config = config.llm
                 print(f"✓ LLM config found: {type(llm_config)}")
 
-                if hasattr(llm_config, 'model_rankings'):
+                if hasattr(llm_config, "model_rankings"):
                     rankings = llm_config.model_rankings
                     print(f"✓ Model rankings: {rankings}")
 
@@ -58,7 +62,9 @@ def test_config_model_rankings():
                         print("✓ qwen3:1.7b is FIRST priority - CORRECT!")
                         return True
                     else:
-                        print(f"✗ WRONG: First model is {rankings[0] if rankings else 'None'}, should be qwen3:1.7b")
+                        print(
+                            f"✗ WRONG: First model is {rankings[0] if rankings else 'None'}, should be qwen3:1.7b"
+                        )
                         return False
                 else:
                     print("✗ Model rankings not found in LLM config")
@@ -74,6 +80,7 @@ def test_config_model_rankings():
         print(f"✗ Error: {e}")
         return False
 
+
 def test_context_length_fix():
     """Test that context length is correctly set to 32K."""
     print("\n" + "=" * 60)
@@ -82,7 +89,7 @@ def test_context_length_fix():
 
     try:
         # Read the synthesizer file and check for 32000
-        with open('mini_rag/llm_synthesizer.py', 'r') as f:
+        with open("mini_rag/llm_synthesizer.py", "r") as f:
             synthesizer_content = f.read()
 
         if '"num_ctx": 32000' in synthesizer_content:
@@ -94,13 +101,13 @@ def test_context_length_fix():
             print("? LLM Synthesizer: num_ctx setting not found clearly")
 
         # Read the safeguards file and check for 32000
-        with open('mini_rag/llm_safeguards.py', 'r') as f:
+        with open("mini_rag/llm_safeguards.py", "r") as f:
             safeguards_content = f.read()
 
-        if 'context_window: int = 32000' in safeguards_content:
+        if "context_window: int = 32000" in safeguards_content:
             print("✓ Safeguards: context_window is correctly set to 32000")
             return True
-        elif 'context_window: int = 80000' in safeguards_content:
+        elif "context_window: int = 80000" in safeguards_content:
             print("✗ Safeguards: context_window is still 80000 - NEEDS FIX")
             return False
         else:
@@ -111,6 +118,7 @@ def test_context_length_fix():
         print(f"✗ Error checking context length: {e}")
         return False
 
+
 def test_safeguard_preservation():
     """Test that safeguards preserve content instead of dropping it."""
     print("\n" + "=" * 60)
@@ -119,24 +127,27 @@ def test_safeguard_preservation():
 
     try:
         # Read the synthesizer file and check for the preservation method
-        with open('mini_rag/llm_synthesizer.py', 'r') as f:
+        with open("mini_rag/llm_synthesizer.py", "r") as f:
             synthesizer_content = f.read()
 
-        if '_create_safeguard_response_with_content' in synthesizer_content:
+        if "_create_safeguard_response_with_content" in synthesizer_content:
             print("✓ Safeguard content preservation method exists")
         else:
             print("✗ Safeguard content preservation method missing")
             return False
 
         # Check for the specific preservation logic
-        if 'AI Response (use with caution):' in synthesizer_content:
+        if "AI Response (use with caution):" in synthesizer_content:
             print("✓ Content preservation warning format found")
         else:
             print("✗ Content preservation warning format missing")
             return False
 
         # Check that it's being called instead of dropping content
-        if 'return self._create_safeguard_response_with_content(issue_type, explanation, raw_response)' in synthesizer_content:
+        if (
+            "return self._create_safeguard_response_with_content(issue_type, explanation, raw_response)"
+            in synthesizer_content
+        ):
             print("✓ Preservation method is called when safeguards trigger")
             return True
         else:
@@ -147,6 +158,7 @@ def test_safeguard_preservation():
         print(f"✗ Error checking safeguard preservation: {e}")
         return False
 
+
 def test_import_fixes():
     """Test that import statements are fixed from claude_rag to mini_rag."""
     print("\n" + "=" * 60)
@@ -154,10 +166,10 @@ def test_import_fixes():
     print("=" * 60)
 
     test_files = [
-        'tests/test_rag_integration.py',
-        'tests/01_basic_integration_test.py',
-        'tests/test_hybrid_search.py',
-        'tests/test_context_retrieval.py'
+        "tests/test_rag_integration.py",
+        "tests/01_basic_integration_test.py",
+        "tests/test_hybrid_search.py",
+        "tests/test_context_retrieval.py",
     ]
 
     all_good = True
@@ -165,13 +177,13 @@ def test_import_fixes():
     for test_file in test_files:
         if Path(test_file).exists():
             try:
-                with open(test_file, 'r') as f:
+                with open(test_file, "r") as f:
                     content = f.read()
 
-                if 'claude_rag' in content:
+                if "claude_rag" in content:
                     print(f"✗ {test_file}: Still contains 'claude_rag' imports")
                     all_good = False
-                elif 'mini_rag' in content:
+                elif "mini_rag" in content:
                     print(f"✓ {test_file}: Uses correct 'mini_rag' imports")
                 else:
                     print(f"? {test_file}: No rag imports found")
@@ -184,6 +196,7 @@ def test_import_fixes():
 
     return all_good
 
+
 def main():
     """Run all tests."""
     print("FSS-Mini-RAG Fix Verification Tests")
@@ -193,7 +206,7 @@ def main():
         ("Model Rankings", test_config_model_rankings),
         ("Context Length", test_context_length_fix),
         ("Safeguard Preservation", test_safeguard_preservation),
-        ("Import Fixes", test_import_fixes)
+        ("Import Fixes", test_import_fixes),
     ]
 
     results = {}
@@ -226,5 +239,6 @@ def main():
         print("❌ SOME TESTS FAILED - System needs more fixes!")
         return 1
 
+
 if __name__ == "__main__":
     sys.exit(main())

tests/test_hybrid_search.py

@@ -12,18 +12,15 @@ Or run directly with venv:
 """
 
 import time
-import json
 from pathlib import Path
-from typing import List, Dict, Any
-from rich.console import Console
-from rich.table import Table
-from rich.panel import Panel
-from rich.columns import Columns
-from rich.syntax import Syntax
-from rich.progress import track
+from typing import Any, Dict
+
+from rich.console import Console
+from rich.progress import track
+from rich.table import Table
 
-from mini_rag.search import CodeSearcher, SearchResult
 from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
+from mini_rag.search import CodeSearcher
 
 console = Console()
 
@@ -44,12 +41,18 @@ class SearchTester:
 
         # Get statistics
         stats = self.searcher.get_statistics()
-        if 'error' not in stats:
-            console.print(f"[dim]Index contains {stats['total_chunks']} chunks from {stats['unique_files']} files[/dim]\n")
+        if "error" not in stats:
+            console.print(
+                f"[dim]Index contains {stats['total_chunks']} chunks from {stats['unique_files']} files[/dim]\n"
+            )
 
-    def run_query(self, query: str, top_k: int = 10, 
+    def run_query(
+        self,
+        query: str,
+        top_k: int = 10,
         semantic_only: bool = False,
-                  bm25_only: bool = False) -> Dict[str, Any]:
+        bm25_only: bool = False,
+    ) -> Dict[str, Any]:
         """Run a single query and return metrics."""
 
         # Set weights based on mode
@@ -69,18 +72,18 @@ class SearchTester:
             query=query,
             top_k=top_k,
             semantic_weight=semantic_weight,
-            bm25_weight=bm25_weight
+            bm25_weight=bm25_weight,
         )
         search_time = time.time() - start
 
         return {
-            'query': query,
-            'mode': mode,
-            'results': results,
-            'search_time_ms': search_time * 1000,
-            'num_results': len(results),
-            'top_score': results[0].score if results else 0,
-            'avg_score': sum(r.score for r in results) / len(results) if results else 0,
+            "query": query,
+            "mode": mode,
+            "results": results,
+            "search_time_ms": search_time * 1000,
+            "num_results": len(results),
+            "top_score": results[0].score if results else 0,
+            "avg_score": sum(r.score for r in results) / len(results) if results else 0,
         }
 
     def compare_search_modes(self, query: str, top_k: int = 5):
@@ -90,9 +93,9 @@ class SearchTester:
 
         # Run searches in all modes
         modes = [
-            ('hybrid', False, False),
-            ('semantic', True, False),
-            ('bm25', False, True)
+            ("hybrid", False, False),
+            ("semantic", True, False),
+            ("bm25", False, True),
         ]
 
         all_results = {}
@@ -112,28 +115,28 @@ class SearchTester:
             "Search Time (ms)",
             f"{all_results['hybrid']['search_time_ms']:.1f}",
             f"{all_results['semantic']['search_time_ms']:.1f}",
-            f"{all_results['bm25']['search_time_ms']:.1f}"
+            f"{all_results['bm25']['search_time_ms']:.1f}",
         )
 
         table.add_row(
             "Results Found",
-            str(all_results['hybrid']['num_results']),
-            str(all_results['semantic']['num_results']),
-            str(all_results['bm25']['num_results'])
+            str(all_results["hybrid"]["num_results"]),
+            str(all_results["semantic"]["num_results"]),
+            str(all_results["bm25"]["num_results"]),
         )
 
         table.add_row(
             "Top Score",
             f"{all_results['hybrid']['top_score']:.3f}",
             f"{all_results['semantic']['top_score']:.3f}",
-            f"{all_results['bm25']['top_score']:.3f}"
+            f"{all_results['bm25']['top_score']:.3f}",
         )
 
         table.add_row(
             "Avg Score",
             f"{all_results['hybrid']['avg_score']:.3f}",
             f"{all_results['semantic']['avg_score']:.3f}",
-            f"{all_results['bm25']['avg_score']:.3f}"
+            f"{all_results['bm25']['avg_score']:.3f}",
         )
 
         console.print(table)
@@ -143,62 +146,68 @@ class SearchTester:
 
         for mode_name, result_data in all_results.items():
             console.print(f"\n[bold cyan]{result_data['mode']}:[/bold cyan]")
-            for i, result in enumerate(result_data['results'][:3], 1):
-                console.print(f"\n{i}. [green]{result.file_path}[/green]:{result.start_line}-{result.end_line}")
-                console.print(f"   [dim]Type: {result.chunk_type} | Name: {result.name} | Score: {result.score:.3f}[/dim]")
+            for i, result in enumerate(result_data["results"][:3], 1):
+                console.print(
+                    f"\n{i}. [green]{result.file_path}[/green]:{result.start_line}-{result.end_line}"
+                )
+                console.print(
+                    f"   [dim]Type: {result.chunk_type} | Name: {result.name} | Score: {result.score:.3f}[/dim]"
+                )
 
                 # Show snippet
                 lines = result.content.splitlines()[:5]
                 for line in lines:
-                    console.print(f"   [dim]{line[:80]}{'...' if len(line) > 80 else ''}[/dim]")
+                    console.print(
+                        f"   [dim]{line[:80]}{'...' if len(line) > 80 else ''}[/dim]"
+                    )
 
     def test_query_types(self):
         """Test different types of queries to show system capabilities."""
         test_queries = [
             # Keyword-heavy queries (should benefit from BM25)
             {
-                'query': 'class CodeSearcher search method',
-                'description': 'Specific class and method names',
-                'expected': 'Should find exact matches with BM25 boost'
+                "query": "class CodeSearcher search method",
+                "description": "Specific class and method names",
+                "expected": "Should find exact matches with BM25 boost",
             },
             {
-                'query': 'import pandas numpy torch',
-                'description': 'Multiple import keywords',
-                'expected': 'BM25 should excel at finding import statements'
+                "query": "import pandas numpy torch",
+                "description": "Multiple import keywords",
+                "expected": "BM25 should excel at finding import statements",
             },
-            
             # Semantic queries (should benefit from embeddings)
             {
-                'query': 'find similar code chunks using vector similarity',
-                'description': 'Natural language description',
-                'expected': 'Semantic search should understand intent'
+                "query": "find similar code chunks using vector similarity",
+                "description": "Natural language description",
+                "expected": "Semantic search should understand intent",
             },
             {
-                'query': 'how to initialize database connection',
-                'description': 'How-to question',
-                'expected': 'Semantic search should find relevant implementations'
+                "query": "how to initialize database connection",
+                "description": "How-to question",
+                "expected": "Semantic search should find relevant implementations",
             },
-            
             # Mixed queries (benefit from hybrid)
             {
-                'query': 'BM25 scoring implementation for search ranking',
-                'description': 'Technical terms + intent',
-                'expected': 'Hybrid should balance keyword and semantic matching'
+                "query": "BM25 scoring implementation for search ranking",
+                "description": "Technical terms + intent",
+                "expected": "Hybrid should balance keyword and semantic matching",
             },
             {
-                'query': 'embedding vectors for code search with transformers',
-                'description': 'Domain-specific terminology',
-                'expected': 'Hybrid should leverage both approaches'
-            }
+                "query": "embedding vectors for code search with transformers",
+                "description": "Domain-specific terminology",
+                "expected": "Hybrid should leverage both approaches",
+            },
         ]
 
         console.print("\n[bold yellow]Query Type Analysis[/bold yellow]")
-        console.print("[dim]Testing different query patterns to demonstrate hybrid search benefits[/dim]\n")
+        console.print(
+            "[dim]Testing different query patterns to demonstrate hybrid search benefits[/dim]\n"
+        )
 
         for test_case in test_queries:
             console.rule(f"\n[cyan]{test_case['description']}[/cyan]")
             console.print(f"[dim]{test_case['expected']}[/dim]")
-            self.compare_search_modes(test_case['query'], top_k=3)
+            self.compare_search_modes(test_case["query"], top_k=3)
             time.sleep(0.5)  # Brief pause between tests
 
     def benchmark_performance(self, num_queries: int = 50):
@@ -217,16 +226,16 @@ class SearchTester:
             "test cases unit testing",
             "configuration settings",
             "logging and debugging",
-            "performance optimization"
+            "performance optimization",
         ] * (num_queries // 10 + 1)
 
         benchmark_queries = benchmark_queries[:num_queries]
 
         # Benchmark each mode
         modes = [
-            ('Hybrid (70/30)', 0.7, 0.3),
-            ('Semantic Only', 1.0, 0.0),
-            ('BM25 Only', 0.0, 1.0)
+            ("Hybrid (70/30)", 0.7, 0.3),
+            ("Semantic Only", 1.0, 0.0),
+            ("BM25 Only", 0.0, 1.0),
         ]
 
         results_table = Table(title="Performance Benchmark Results")
@@ -246,7 +255,7 @@ class SearchTester:
                     query=query,
                     limit=10,
                     semantic_weight=sem_weight,
-                    bm25_weight=bm25_weight
+                    bm25_weight=bm25_weight,
                 )
                 elapsed = (time.time() - start) * 1000
                 times.append(elapsed)
@@ -262,7 +271,7 @@ class SearchTester:
                 f"{avg_time:.2f}",
                 f"{min_time:.2f}",
                 f"{max_time:.2f}",
-                f"{total_time:.2f}"
+                f"{total_time:.2f}",
             )
 
         console.print("\n")
@@ -292,7 +301,9 @@ class SearchTester:
 
         table.add_row("Total Results", str(len(results)))
         table.add_row("Unique Files", str(len(file_counts)))
-        table.add_row("Max Chunks per File", str(max(file_counts.values()) if file_counts else 0))
+        table.add_row(
+            "Max Chunks per File", str(max(file_counts.values()) if file_counts else 0)
+        )
         table.add_row("Unique Chunk Types", str(len(chunk_types)))
 
         console.print(table)
@@ -300,13 +311,17 @@ class SearchTester:
         # Show file distribution
         if len(file_counts) > 0:
             console.print("\n[bold]File Distribution:[/bold]")
-            for file_path, count in sorted(file_counts.items(), key=lambda x: x[1], reverse=True)[:5]:
+            for file_path, count in sorted(
+                file_counts.items(), key=lambda x: x[1], reverse=True
+            )[:5]:
                 console.print(f"  {count}x {file_path}")
 
         # Show chunk type distribution
         if len(chunk_types) > 0:
             console.print("\n[bold]Chunk Type Distribution:[/bold]")
-            for chunk_type, count in sorted(chunk_types.items(), key=lambda x: x[1], reverse=True):
+            for chunk_type, count in sorted(
+                chunk_types.items(), key=lambda x: x[1], reverse=True
+            ):
                 console.print(f"  {chunk_type}: {count} chunks")
 
         # Verify constraints
@@ -327,7 +342,7 @@ def main():
     else:
         project_path = Path.cwd()
 
-    if not (project_path / '.mini-rag').exists():
+    if not (project_path / ".mini-rag").exists():
         console.print("[red]Error: No RAG index found. Run 'rag-mini index' first.[/red]")
         return
 
@@ -335,30 +350,30 @@ def main():
     tester = SearchTester(project_path)
 
     # Run all tests
-    console.print("\n" + "="*80)
+    console.print("\n" + "=" * 80)
     console.print("[bold green]Mini RAG Hybrid Search Test Suite[/bold green]")
-    console.print("="*80)
+    console.print("=" * 80)
 
     # Test 1: Query type analysis
     tester.test_query_types()
 
     # Test 2: Performance benchmark
-    console.print("\n" + "-"*80)
+    console.print("\n" + "-" * 80)
     tester.benchmark_performance(num_queries=30)
 
     # Test 3: Diversity constraints
-    console.print("\n" + "-"*80)
+    console.print("\n" + "-" * 80)
     tester.test_diversity_constraints()
 
     # Summary
-    console.print("\n" + "="*80)
+    console.print("\n" + "=" * 80)
     console.print("[bold green]Test Suite Complete![/bold green]")
     console.print("\n[dim]The hybrid search combines:")
     console.print("  • Semantic understanding from transformer embeddings")
     console.print("  • Keyword relevance from BM25 scoring")
     console.print("  • Result diversity through intelligent filtering")
     console.print("  • Performance optimization through concurrent processing[/dim]")
-    console.print("="*80 + "\n")
+    console.print("=" * 80 + "\n")
 
 
 if __name__ == "__main__":

tests/test_min_chunk_size.py

@@ -1,13 +1,16 @@
 """Test with smaller min_chunk_size."""
 
-from mini_rag.chunker import CodeChunker
 from pathlib import Path
 
+from mini_rag.chunker import CodeChunker
+
 test_code = '''"""Test module."""
 
 import os
 
+
 class MyClass:
+
     def method(self):
         return 42
 

tests/test_mode_separation.py

@@ -7,7 +7,6 @@ between thinking and no-thinking modes.
 """
 
 import sys
-import os
 import tempfile
 import unittest
 from pathlib import Path
@@ -16,16 +15,17 @@ from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
 try:
-    from mini_rag.llm_synthesizer import LLMSynthesizer  
-    from mini_rag.explorer import CodeExplorer
     from mini_rag.config import RAGConfig
+    from mini_rag.explorer import CodeExplorer
     from mini_rag.indexer import ProjectIndexer
+    from mini_rag.llm_synthesizer import LLMSynthesizer
     from mini_rag.search import CodeSearcher
 except ImportError as e:
     print(f"❌ Could not import RAG components: {e}")
     print("   This test requires the full RAG system to be installed")
     sys.exit(1)
 
+
 class TestModeSeparation(unittest.TestCase):
     """Test the clean separation between synthesis and exploration modes."""
 
@@ -36,7 +36,8 @@ class TestModeSeparation(unittest.TestCase):
 
         # Create a simple test project
         test_file = self.project_path / "test_module.py"
-        test_file.write_text('''"""Test module for mode separation testing."""
+        test_file.write_text(
+            '''"""Test module for mode separation testing."""
 
 def authenticate_user(username: str, password: str) -> bool:
     """Authenticate a user with username and password."""
@@ -48,6 +49,7 @@ def authenticate_user(username: str, password: str) -> bool:
     valid_users = {"admin": "secret", "user": "password"}
     return valid_users.get(username) == password
 
+
 class UserManager:
     """Manages user operations."""
 
@@ -71,7 +73,8 @@ def process_login_request(username: str, password: str) -> dict:
         return {"success": True, "message": "Login successful"}
     else:
         return {"success": False, "message": "Invalid credentials"}
-''')
+'''
+        )
 
         # Index the project for testing
         try:
@@ -83,6 +86,7 @@ def process_login_request(username: str, password: str) -> dict:
     def tearDown(self):
         """Clean up test environment."""
         import shutil
+
         shutil.rmtree(self.temp_dir, ignore_errors=True)
 
     def test_01_synthesis_mode_defaults(self):
@@ -90,8 +94,9 @@ def process_login_request(username: str, password: str) -> dict:
         synthesizer = LLMSynthesizer()
 
         # Should default to no thinking
-        self.assertFalse(synthesizer.enable_thinking, 
-                        "Synthesis mode should default to no thinking")
+        self.assertFalse(
+            synthesizer.enable_thinking, "Synthesis mode should default to no thinking"
+        )
 
         print("✅ Synthesis mode defaults to no thinking")
 
@@ -101,8 +106,10 @@ def process_login_request(username: str, password: str) -> dict:
         explorer = CodeExplorer(self.project_path, config)
 
         # Should enable thinking in exploration mode
-        self.assertTrue(explorer.synthesizer.enable_thinking,
-                       "Exploration mode should enable thinking")
+        self.assertTrue(
+            explorer.synthesizer.enable_thinking,
+            "Exploration mode should enable thinking",
+        )
 
         print("✅ Exploration mode enables thinking by default")
 
@@ -111,12 +118,16 @@ def process_login_request(username: str, password: str) -> dict:
         synthesizer = LLMSynthesizer(enable_thinking=False)
 
         # Should not have public methods to toggle thinking
-        thinking_methods = [method for method in dir(synthesizer) 
-                           if 'thinking' in method.lower() and not method.startswith('_')]
+        thinking_methods = [
+            method
+            for method in dir(synthesizer)
+            if "thinking" in method.lower() and not method.startswith("_")
+        ]
 
         # The only thinking-related attribute should be the readonly enable_thinking
-        self.assertEqual(len(thinking_methods), 0,
-                        "Should not have public thinking toggle methods")
+        self.assertEqual(
+            len(thinking_methods), 0, "Should not have public thinking toggle methods"
+        )
 
         print("✅ No runtime thinking toggle methods available")
 
@@ -132,10 +143,14 @@ def process_login_request(username: str, password: str) -> dict:
         exploration_synthesizer = LLMSynthesizer(enable_thinking=True)
 
         # Both should maintain their thinking settings
-        self.assertFalse(synthesis_synthesizer.enable_thinking,
-                        "Synthesis synthesizer should remain no-thinking")
-        self.assertTrue(exploration_synthesizer.enable_thinking,
-                       "Exploration synthesizer should remain thinking-enabled")
+        self.assertFalse(
+            synthesis_synthesizer.enable_thinking,
+            "Synthesis synthesizer should remain no-thinking",
+        )
+        self.assertTrue(
+            exploration_synthesizer.enable_thinking,
+            "Exploration synthesizer should remain thinking-enabled",
+        )
 
         print("✅ Mode contamination prevented")
 
@@ -145,13 +160,11 @@ def process_login_request(username: str, password: str) -> dict:
         explorer = CodeExplorer(self.project_path, config)
 
         # Should start with no active session
-        self.assertIsNone(explorer.current_session, 
-                         "Should start with no active session")
+        self.assertIsNone(explorer.current_session, "Should start with no active session")
 
         # Should be able to create session summary even without session
         summary = explorer.get_session_summary()
-        self.assertIn("No active", summary,
-                     "Should handle no active session gracefully")
+        self.assertIn("No active", summary, "Should handle no active session gracefully")
 
         print("✅ Session management working correctly")
 
@@ -161,8 +174,10 @@ def process_login_request(username: str, password: str) -> dict:
         explorer = CodeExplorer(self.project_path, config)
 
         # Should have context tracking attributes
-        self.assertTrue(hasattr(explorer, 'current_session'),
-                       "Explorer should have session tracking")
+        self.assertTrue(
+            hasattr(explorer, "current_session"),
+            "Explorer should have session tracking",
+        )
 
         print("✅ Context memory structure present")
 
@@ -174,12 +189,13 @@ def process_login_request(username: str, password: str) -> dict:
         synthesizer = LLMSynthesizer(enable_thinking=False)
 
         # Test the _call_ollama method handling
-        if hasattr(synthesizer, '_call_ollama'):
+        if hasattr(synthesizer, "_call_ollama"):
             # Should append <no_think> when thinking disabled
             # This is a white-box test of the implementation
             try:
                 # Mock test - just verify the method exists and can be called
-                result = synthesizer._call_ollama("test", temperature=0.1, disable_thinking=True)
+                # Test call (result unused)
+                synthesizer._call_ollama("test", temperature=0.1, disable_thinking=True)
                 # Don't assert on result since Ollama might not be available
                 print("✅ No-thinking prompt handling available")
             except Exception as e:
@@ -191,14 +207,18 @@ def process_login_request(username: str, password: str) -> dict:
         """Test that modes initialize correctly with lazy loading."""
         # Synthesis mode
         synthesis_synthesizer = LLMSynthesizer(enable_thinking=False)
-        self.assertFalse(synthesis_synthesizer._initialized,
-                        "Should start uninitialized for lazy loading")
+        self.assertFalse(
+            synthesis_synthesizer._initialized,
+            "Should start uninitialized for lazy loading",
+        )
 
         # Exploration mode
         config = RAGConfig()
         explorer = CodeExplorer(self.project_path, config)
-        self.assertFalse(explorer.synthesizer._initialized,
-                        "Should start uninitialized for lazy loading")
+        self.assertFalse(
+            explorer.synthesizer._initialized,
+            "Should start uninitialized for lazy loading",
+        )
 
         print("✅ Lazy initialization working correctly")
 
@@ -208,31 +228,31 @@ def process_login_request(username: str, password: str) -> dict:
         searcher = CodeSearcher(self.project_path)
         search_results = searcher.search("authentication", top_k=3)
 
-        self.assertGreater(len(search_results), 0, 
-                          "Search should return results")
+        self.assertGreater(len(search_results), 0, "Search should return results")
 
         # Exploration mode setup
         config = RAGConfig()
         explorer = CodeExplorer(self.project_path, config)
 
         # Both should work with same project but different approaches
-        self.assertTrue(hasattr(explorer, 'synthesizer'),
-                       "Explorer should have thinking-enabled synthesizer")
+        self.assertTrue(
+            hasattr(explorer, "synthesizer"),
+            "Explorer should have thinking-enabled synthesizer",
+        )
 
         print("✅ Search and exploration integration working")
 
     def test_10_mode_guidance_detection(self):
         """Test that the system can detect when to recommend different modes."""
         # Words that should trigger exploration mode recommendation
-        exploration_triggers = ['why', 'how', 'explain', 'debug']
+        exploration_triggers = ["why", "how", "explain", "debug"]
 
         for trigger in exploration_triggers:
             query = f"{trigger} does authentication work"
             # This would typically be tested in the main CLI
             # Here we just verify the trigger detection logic exists
             has_trigger = any(word in query.lower() for word in exploration_triggers)
-            self.assertTrue(has_trigger, 
-                           f"Should detect '{trigger}' as exploration trigger")
+            self.assertTrue(has_trigger, f"Should detect '{trigger}' as exploration trigger")
 
         print("✅ Mode guidance detection working")
 
@@ -240,11 +260,13 @@ def process_login_request(username: str, password: str) -> dict:
         """Check if Ollama is available for testing."""
         try:
             import requests
+
             response = requests.get("http://localhost:11434/api/tags", timeout=5)
             return response.status_code == 200
         except Exception:
             return False
 
+
 def main():
     """Run mode separation tests."""
     print("🧪 Testing Mode Separation")
@@ -272,6 +294,7 @@ def main():
 
     return result.wasSuccessful()
 
+
 if __name__ == "__main__":
     success = main()
     sys.exit(0 if success else 1)

tests/test_ollama_integration.py

@@ -8,20 +8,20 @@ what's working and what needs attention.
 Run with: python3 tests/test_ollama_integration.py
 """
 
-import unittest
-import requests
-import json
 import sys
+import unittest
 from pathlib import Path
-from unittest.mock import patch, MagicMock
+from unittest.mock import MagicMock, patch
+
+import requests
+
+from mini_rag.config import RAGConfig
+from mini_rag.llm_synthesizer import LLMSynthesizer
+from mini_rag.query_expander import QueryExpander
 
 # Add project to path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
-from mini_rag.query_expander import QueryExpander
-from mini_rag.llm_synthesizer import LLMSynthesizer  
-from mini_rag.config import RAGConfig
-
 
 class TestOllamaIntegration(unittest.TestCase):
     """
@@ -49,21 +49,20 @@ class TestOllamaIntegration(unittest.TestCase):
 
         try:
             response = requests.get(
-                f"http://{self.config.llm.ollama_host}/api/tags", 
-                timeout=5
+                f"http://{self.config.llm.ollama_host}/api/tags", timeout=5
             )
 
             if response.status_code == 200:
                 data = response.json()
-                models = data.get('models', [])
-                print(f"   ✅ Ollama server is running!")
+                models = data.get("models", [])
+                print("   ✅ Ollama server is running!")
                 print(f"   📦 Found {len(models)} models available")
 
                 if models:
                     print("   🎯 Available models:")
                     for model in models[:5]:  # Show first 5
-                        name = model.get('name', 'unknown')
-                        size = model.get('size', 0) 
+                        name = model.get("name", "unknown")
+                        size = model.get("size", 0)
                         print(f"      • {name} ({size//1000000:.0f}MB)")
                     if len(models) > 5:
                         print(f"      ... and {len(models)-5} more")
@@ -100,19 +99,16 @@ class TestOllamaIntegration(unittest.TestCase):
             # Test embedding generation
             response = requests.post(
                 f"http://{self.config.llm.ollama_host}/api/embeddings",
-                json={
-                    "model": "nomic-embed-text",
-                    "prompt": "test embedding"
-                },
-                timeout=10
+                json={"model": "nomic-embed-text", "prompt": "test embedding"},
+                timeout=10,
             )
 
             if response.status_code == 200:
                 data = response.json()
-                embedding = data.get('embedding', [])
+                embedding = data.get("embedding", [])
 
                 if embedding and len(embedding) > 0:
-                    print(f"   ✅ Embedding model working!")
+                    print("   ✅ Embedding model working!")
                     print(f"   📊 Generated {len(embedding)}-dimensional vectors")
                     self.assertTrue(len(embedding) > 100)  # Should be substantial vectors
                 else:
@@ -155,12 +151,11 @@ class TestOllamaIntegration(unittest.TestCase):
         # Test basic text generation
         try:
             response = synthesizer._call_ollama(
-                "Complete this: The capital of France is", 
-                temperature=0.1
+                "Complete this: The capital of France is", temperature=0.1
             )
 
             if response and len(response.strip()) > 0:
-                print(f"   ✅ Model generating responses!")
+                print("   ✅ Model generating responses!")
                 print(f"   💬 Sample response: '{response[:50]}...'")
 
                 # Basic quality check
@@ -231,8 +226,9 @@ class TestOllamaIntegration(unittest.TestCase):
         synthesizer = LLMSynthesizer()
 
         # Should default to no thinking
-        self.assertFalse(synthesizer.enable_thinking, 
-                        "Synthesis mode should default to no thinking")
+        self.assertFalse(
+            synthesizer.enable_thinking, "Synthesis mode should default to no thinking"
+        )
         print("   ✅ Defaults to no thinking")
 
         if synthesizer.is_available():
@@ -247,9 +243,7 @@ class TestOllamaIntegration(unittest.TestCase):
                 content: str
                 score: float
 
-            results = [
-                MockResult("auth.py", "def authenticate(user): return True", 0.95)
-            ]
+            results = [MockResult("auth.py", "def authenticate(user): return True", 0.95)]
 
             # Test synthesis
             synthesis = synthesizer.synthesize_search_results(
@@ -283,13 +277,14 @@ class TestOllamaIntegration(unittest.TestCase):
         explorer = CodeExplorer(Path("."), self.config)
 
         # Should enable thinking
-        self.assertTrue(explorer.synthesizer.enable_thinking,
-                       "Exploration mode should enable thinking")
+        self.assertTrue(
+            explorer.synthesizer.enable_thinking,
+            "Exploration mode should enable thinking",
+        )
         print("   ✅ Enables thinking by default")
 
         # Should have session management
-        self.assertIsNone(explorer.current_session,
-                         "Should start with no active session")
+        self.assertIsNone(explorer.current_session, "Should start with no active session")
         print("   ✅ Session management available")
 
         # Should handle session summary gracefully
@@ -313,21 +308,20 @@ class TestOllamaIntegration(unittest.TestCase):
 
         try:
             from mini_rag.explorer import CodeExplorer
+
             explorer = CodeExplorer(Path("."), self.config)
         except ImportError:
             self.skipTest("⏭️  CodeExplorer not available")
 
         # Should have different thinking settings
-        self.assertFalse(synthesizer.enable_thinking,
-                        "Synthesis should not use thinking")
-        self.assertTrue(explorer.synthesizer.enable_thinking,
-                       "Exploration should use thinking")
+        self.assertFalse(synthesizer.enable_thinking, "Synthesis should not use thinking")
+        self.assertTrue(
+            explorer.synthesizer.enable_thinking, "Exploration should use thinking"
+        )
 
         # Both should be uninitialized (lazy loading)
-        self.assertFalse(synthesizer._initialized,
-                        "Should use lazy loading")
-        self.assertFalse(explorer.synthesizer._initialized,
-                        "Should use lazy loading")
+        self.assertFalse(synthesizer._initialized, "Should use lazy loading")
+        self.assertFalse(explorer.synthesizer._initialized, "Should use lazy loading")
 
         print("   ✅ Clean mode separation confirmed")
 
@@ -346,17 +340,17 @@ class TestOllamaIntegration(unittest.TestCase):
         mock_embedding_response = MagicMock()
         mock_embedding_response.status_code = 200
         mock_embedding_response.json.return_value = {
-            'embedding': [0.1] * 768  # Standard embedding size
+            "embedding": [0.1] * 768  # Standard embedding size
         }
 
         # Mock LLM response
         mock_llm_response = MagicMock()
         mock_llm_response.status_code = 200
         mock_llm_response.json.return_value = {
-            'response': 'authentication login user verification credentials'
+            "response": "authentication login user verification credentials"
         }
 
-        with patch('requests.post', side_effect=[mock_embedding_response, mock_llm_response]):
+        with patch("requests.post", side_effect=[mock_embedding_response, mock_llm_response]):
             # Test query expansion with mocked response
             expander = QueryExpander(self.config)
             expander.enabled = True
@@ -369,7 +363,7 @@ class TestOllamaIntegration(unittest.TestCase):
                 print("   ⚠️  Expansion returned None (might be expected)")
 
         # Test graceful degradation when Ollama unavailable
-        with patch('requests.get', side_effect=requests.exceptions.ConnectionError()):
+        with patch("requests.get", side_effect=requests.exceptions.ConnectionError()):
             expander_offline = QueryExpander(self.config)
 
             # Should handle unavailable server gracefully
@@ -397,14 +391,14 @@ class TestOllamaIntegration(unittest.TestCase):
         self.assertTrue(isinstance(self.config.llm.max_expansion_terms, int))
         self.assertGreater(self.config.llm.max_expansion_terms, 0)
 
-        print(f"   ✅ LLM config valid")
+        print("   ✅ LLM config valid")
         print(f"      Host: {self.config.llm.ollama_host}")
         print(f"      Max expansion terms: {self.config.llm.max_expansion_terms}")
 
         # Check search config
         self.assertIsNotNone(self.config.search)
         self.assertGreater(self.config.search.default_top_k, 0)
-        print(f"   ✅ Search config valid")
+        print("   ✅ Search config valid")
         print(f"      Default top-k: {self.config.search.default_top_k}")
         print(f"      Query expansion: {self.config.search.expand_queries}")
 
@@ -432,5 +426,5 @@ def run_troubleshooting():
     print("📚 For more help, see docs/QUERY_EXPANSION.md")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     run_troubleshooting()

tests/test_rag_integration.py

@@ -10,21 +10,26 @@ Or run directly with venv:
     source .venv/bin/activate && PYTHONPATH=. python tests/test_rag_integration.py
 """
 
-import tempfile
-import shutil
 import os
+import tempfile
 from pathlib import Path
+
 from mini_rag.indexer import ProjectIndexer
 from mini_rag.search import CodeSearcher
 
 # Check if virtual environment is activated
+
+
 def check_venv():
-    if 'VIRTUAL_ENV' not in os.environ:
+    if "VIRTUAL_ENV" not in os.environ:
         print("⚠️  WARNING: Virtual environment not detected!")
         print("   This test requires the virtual environment to be activated.")
-        print("   Run: source .venv/bin/activate && PYTHONPATH=. python tests/test_rag_integration.py")
+        print(
+            "   Run: source .venv/bin/activate && PYTHONPATH=. python tests/test_rag_integration.py"
+        )
         print("   Continuing anyway...\n")
 
+
 check_venv()
 
 # Sample Python file with proper structure
@@ -35,15 +40,16 @@ This module demonstrates various Python constructs.
 
 import os
 import sys
-from typing import List, Dict, Optional
+from typing import List, Optional
 from dataclasses import dataclass
 
 # Module-level constants
 DEFAULT_TIMEOUT = 30
 MAX_RETRIES = 3
 
-
 @dataclass
+
+
 class Config:
     """Configuration dataclass."""
     timeout: int = DEFAULT_TIMEOUT
@@ -99,7 +105,6 @@ class DataProcessor:
         # Implementation details
         return {**item, 'processed': True}
 
-
 def main():
     """Main entry point."""
     config = Config()
@@ -113,13 +118,12 @@ def main():
     results = processor.process(test_data)
     print(f"Processed {len(results)} items")
 
-
 if __name__ == "__main__":
     main()
 '''
 
 # Sample markdown file
-sample_markdown = '''# RAG System Documentation
+sample_markdown = """# RAG System Documentation
 
 ## Overview
 
@@ -175,7 +179,7 @@ Main class for indexing projects.
 ### CodeSearcher
 
 Provides semantic search capabilities.
-'''
+"""
 
 
 def test_integration():
@@ -213,40 +217,40 @@ def test_integration():
 
         # Test 1: Search for class with docstring
         results = searcher.search("data processor class unified interface", top_k=3)
-        print(f"\n   Test 1 - Class search:")
+        print("\n   Test 1 - Class search:")
         for i, result in enumerate(results[:1]):
             print(f"   - Match {i+1}: {result.file_path}")
             print(f"     Chunk type: {result.chunk_type}")
             print(f"     Score: {result.score:.3f}")
-            if 'This class handles' in result.content:
+            if "This class handles" in result.content:
                 print("     [OK] Docstring included with class")
             else:
                 print("     [FAIL] Docstring not found")
 
         # Test 2: Search for method with docstring
         results = searcher.search("process list of data items", top_k=3)
-        print(f"\n   Test 2 - Method search:")
+        print("\n   Test 2 - Method search:")
         for i, result in enumerate(results[:1]):
             print(f"   - Match {i+1}: {result.file_path}")
             print(f"     Chunk type: {result.chunk_type}")
             print(f"     Parent class: {getattr(result, 'parent_class', 'N/A')}")
-            if 'Args:' in result.content and 'Returns:' in result.content:
+            if "Args:" in result.content and "Returns:" in result.content:
                 print("     [OK] Docstring included with method")
             else:
                 print("     [FAIL] Method docstring not complete")
 
         # Test 3: Search markdown content
         results = searcher.search("smart chunking capabilities markdown", top_k=3)
-        print(f"\n   Test 3 - Markdown search:")
+        print("\n   Test 3 - Markdown search:")
         for i, result in enumerate(results[:1]):
             print(f"   - Match {i+1}: {result.file_path}")
             print(f"     Chunk type: {result.chunk_type}")
             print(f"     Lines: {result.start_line}-{result.end_line}")
 
         # Test 4: Verify chunk navigation
-        print(f"\n   Test 4 - Chunk navigation:")
+        print("\n   Test 4 - Chunk navigation:")
         all_results = searcher.search("", top_k=100)  # Get all chunks
-        py_chunks = [r for r in all_results if r.file_path.endswith('.py')]
+        py_chunks = [r for r in all_results if r.file_path.endswith(".py")]
 
         if py_chunks:
             first_chunk = py_chunks[0]
@@ -257,9 +261,9 @@ def test_integration():
             valid_chain = True
             for i in range(len(py_chunks) - 1):
                 curr = py_chunks[i]
-                next_chunk = py_chunks[i + 1]
+                # py_chunks[i + 1]  # Unused variable removed
                 expected_next = f"processor_{i+1}"
-                if getattr(curr, 'next_chunk_id', None) != expected_next:
+                if getattr(curr, "next_chunk_id", None) != expected_next:
                     valid_chain = False
                     break
 

tests/test_smart_ranking.py

@@ -8,17 +8,17 @@ and producing better quality results.
 Run with: python3 tests/test_smart_ranking.py
 """
 
-import unittest
 import sys
-from pathlib import Path
+import unittest
 from datetime import datetime, timedelta
-from unittest.mock import patch, MagicMock
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+from mini_rag.search import CodeSearcher, SearchResult
 
 # Add project to path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
-from mini_rag.search import SearchResult, CodeSearcher
-
 
 class TestSmartRanking(unittest.TestCase):
     """
@@ -40,27 +40,31 @@ class TestSmartRanking(unittest.TestCase):
                 end_line=2,
                 chunk_type="text",
                 name="temp",
-                language="text"
+                language="text",
             ),
             SearchResult(
                 file_path=Path("README.md"),
-                content="This is a comprehensive README file\nwith detailed installation instructions\nand usage examples for beginners.",
+                content=(
+                    "This is a comprehensive README file\n"
+                    "with detailed installation instructions\n"
+                    "and usage examples for beginners."
+                ),
                 score=0.7,  # Lower initial score
                 start_line=1,
                 end_line=5,
                 chunk_type="markdown",
                 name="Installation Guide",
-                language="markdown"
+                language="markdown",
             ),
             SearchResult(
                 file_path=Path("src/main.py"),
-                content="def main():\n    \"\"\"Main application entry point.\"\"\"\n    app = create_app()\n    return app.run()",
+                content='def main():\n    """Main application entry point."""\n    app = create_app()\n    return app.run()',
                 score=0.75,
                 start_line=10,
                 end_line=15,
                 chunk_type="function",
                 name="main",
-                language="python"
+                language="python",
             ),
             SearchResult(
                 file_path=Path("temp/cache_123.log"),
@@ -70,8 +74,8 @@ class TestSmartRanking(unittest.TestCase):
                 end_line=1,
                 chunk_type="text",
                 name="log",
-                language="text"
-            )
+                language="text",
+            ),
         ]
 
     def test_01_important_file_boost(self):
@@ -91,8 +95,8 @@ class TestSmartRanking(unittest.TestCase):
         ranked = searcher._smart_rerank(self.mock_results.copy())
 
         # Find README and temp file results
-        readme_result = next((r for r in ranked if 'README' in str(r.file_path)), None)
-        temp_result = next((r for r in ranked if 'temp' in str(r.file_path)), None)
+        readme_result = next((r for r in ranked if "README" in str(r.file_path)), None)
+        temp_result = next((r for r in ranked if "temp" in str(r.file_path)), None)
 
         self.assertIsNotNone(readme_result)
         self.assertIsNotNone(temp_result)
@@ -124,7 +128,7 @@ class TestSmartRanking(unittest.TestCase):
 
         # Find short and long content results
         short_result = next((r for r in ranked if len(r.content.strip()) < 20), None)
-        structured_result = next((r for r in ranked if 'README' in str(r.file_path)), None)
+        structured_result = next((r for r in ranked if "README" in str(r.file_path)), None)
 
         if short_result:
             # Short content should be penalized (score * 0.9)
@@ -133,7 +137,7 @@ class TestSmartRanking(unittest.TestCase):
 
         if structured_result:
             # Well-structured content gets small boost (score * 1.02)
-            lines = structured_result.content.strip().split('\n')
+            lines = structured_result.content.strip().split("\n")
             if len(lines) >= 3:
                 print(f"   📈 Structured content boosted: {structured_result.score:.3f}")
                 print(f"      ({len(lines)} lines of content)")
@@ -155,7 +159,7 @@ class TestSmartRanking(unittest.TestCase):
         ranked = searcher._smart_rerank(self.mock_results.copy())
 
         # Find function result
-        function_result = next((r for r in ranked if r.chunk_type == 'function'), None)
+        function_result = next((r for r in ranked if r.chunk_type == "function"), None)
 
         if function_result:
             # Function should get boost (original score * 1.1)
@@ -168,7 +172,7 @@ class TestSmartRanking(unittest.TestCase):
 
         self.assertTrue(True)
 
-    @patch('pathlib.Path.stat')
+    @patch("pathlib.Path.stat")
     def test_04_recency_boost(self, mock_stat):
         """
         ✅ Test that recently modified files get ranking boosts.
@@ -184,7 +188,7 @@ class TestSmartRanking(unittest.TestCase):
         def mock_stat_side_effect(file_path):
             mock_stat_obj = MagicMock()
 
-            if 'README' in str(file_path):
+            if "README" in str(file_path):
                 # Recent file (2 days ago)
                 recent_time = (now - timedelta(days=2)).timestamp()
                 mock_stat_obj.st_mtime = recent_time
@@ -199,13 +203,13 @@ class TestSmartRanking(unittest.TestCase):
         mock_stat.side_effect = lambda: mock_stat_side_effect("dummy")
 
         # Patch the Path constructor to return mocked paths
-        with patch.object(Path, 'stat', side_effect=mock_stat_side_effect):
+        with patch.object(Path, "stat", side_effect=mock_stat_side_effect):
             searcher = MagicMock()
             searcher._smart_rerank = CodeSearcher._smart_rerank.__get__(searcher)
 
             ranked = searcher._smart_rerank(self.mock_results.copy())
 
-            readme_result = next((r for r in ranked if 'README' in str(r.file_path)), None)
+            readme_result = next((r for r in ranked if "README" in str(r.file_path)), None)
 
             if readme_result:
                 # Recent file should get boost
@@ -243,15 +247,19 @@ class TestSmartRanking(unittest.TestCase):
         self.assertEqual(scores, sorted(scores, reverse=True))
 
         # 2. README should rank higher than temp files
-        readme_pos = next((i for i, r in enumerate(ranked) if 'README' in str(r.file_path)), None)
-        temp_pos = next((i for i, r in enumerate(ranked) if 'temp' in str(r.file_path)), None)
+        readme_pos = next(
+            (i for i, r in enumerate(ranked) if "README" in str(r.file_path)), None
+        )
+        temp_pos = next((i for i, r in enumerate(ranked) if "temp" in str(r.file_path)), None)
 
         if readme_pos is not None and temp_pos is not None:
             self.assertLess(readme_pos, temp_pos)
             print(f"   ✅ README ranks #{readme_pos + 1}, temp file ranks #{temp_pos + 1}")
 
         # 3. Function/code should rank well
-        function_pos = next((i for i, r in enumerate(ranked) if r.chunk_type == 'function'), None)
+        function_pos = next(
+            (i for i, r in enumerate(ranked) if r.chunk_type == "function"), None
+        )
         if function_pos is not None:
             self.assertLess(function_pos, len(ranked) // 2)  # Should be in top half
             print(f"   ✅ Function code ranks #{function_pos + 1}")
@@ -274,7 +282,7 @@ class TestSmartRanking(unittest.TestCase):
 
         # Time the ranking operation
         start_time = time.time()
-        ranked = searcher._smart_rerank(self.mock_results.copy())
+        # searcher._smart_rerank(self.mock_results.copy())  # Unused variable removed
         end_time = time.time()
 
         ranking_time = (end_time - start_time) * 1000  # Convert to milliseconds
@@ -310,5 +318,5 @@ def run_ranking_tests():
     print("   • All boosts are cumulative for maximum quality")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     run_ranking_tests()

tests/troubleshoot.py

@@ -8,13 +8,14 @@ and helps identify what's working and what needs attention.
 Run with: python3 tests/troubleshoot.py
 """
 
-import sys
 import subprocess
+import sys
 from pathlib import Path
 
 # Add project to path
 sys.path.insert(0, str(Path(__file__).parent.parent))
 
+
 def main():
     """Run comprehensive troubleshooting checks."""
 
@@ -52,6 +53,7 @@ def main():
     print("   • Start Ollama server: ollama serve")
     print("   • Install models: ollama pull qwen3:4b")
 
+
 def run_test(test_file):
     """Run a specific test file."""
     test_path = Path(__file__).parent / test_file
@@ -62,9 +64,9 @@ def run_test(test_file):
 
     try:
         # Run the test
-        result = subprocess.run([
-            sys.executable, str(test_path)
-        ], capture_output=True, text=True, timeout=60)
+        result = subprocess.run(
+            [sys.executable, str(test_path)], capture_output=True, text=True, timeout=60
+        )
 
         # Show output
         if result.stdout:
@@ -82,5 +84,6 @@ def run_test(test_file):
     except Exception as e:
         print(f"❌ Error running {test_file}: {e}")
 
+
 if __name__ == "__main__":
     main()