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base: BobAi:919f7284a97fe0d74c407534d8bc081f342b440e
Branches Tags
BobAi:main
BobAi:agent-user-testing
BobAi:fix/proper-python-packaging
BobAi:context-window-configuration
BobAi:feature/context-window-configuration
BobAi:improve-installer-experience
BobAi:v1.0-simple-search
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compare: BobAi:03d177c8e041f96c6b7f27954d2c5e8e462d8b7e
Branches Tags
BobAi:agent-user-testing
BobAi:main
BobAi:fix/proper-python-packaging
BobAi:context-window-configuration
BobAi:feature/context-window-configuration
BobAi:improve-installer-experience
BobAi:v1.0-simple-search

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19
.flake8
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@ -1,19 +0,0 @@
[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

196
.github/workflows/ci.yml vendored
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@ -1,196 +0,0 @@
name: CI/CD Pipeline
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main ]
jobs:
test:
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, windows-latest]
python-version: ["3.10", "3.11", "3.12"]
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Cache dependencies
uses: actions/cache@v4
with:
path: |
~/.cache/pip
~/.local/share/virtualenvs
key: ${{ runner.os }}-python-${{ matrix.python-version }}-${{ hashFiles('**/requirements.txt') }}
restore-keys: |
${{ runner.os }}-python-${{ matrix.python-version }}-
- name: Create virtual environment
run: |
python -m venv .venv
shell: bash
- name: Install dependencies
run: |
# Activate virtual environment and install dependencies
if [[ "$RUNNER_OS" == "Windows" ]]; then
source .venv/Scripts/activate
else
source .venv/bin/activate
fi
python -m pip install --upgrade pip
pip install -r requirements.txt
shell: bash
- name: Run comprehensive tests
run: |
# Set OS-appropriate emojis and activate venv
if [[ "$RUNNER_OS" == "Windows" ]]; then
source .venv/Scripts/activate
OK="[OK]"
SKIP="[SKIP]"
else
source .venv/bin/activate
OK="✅"
SKIP="⚠️"
fi
echo "$OK Virtual environment activated"
# Run basic import tests
python -c "from mini_rag import CodeEmbedder, ProjectIndexer, CodeSearcher; print('$OK Core imports successful')"
# Run the actual test suite
if [ -f "tests/test_fixes.py" ]; then
echo "$OK Running comprehensive test suite..."
python tests/test_fixes.py || echo "$SKIP Test suite completed with warnings"
else
echo "$SKIP test_fixes.py not found, running basic tests only"
fi
# Test config system with proper venv
python -c "
import os
ok_emoji = '$OK' if os.name != 'nt' else '[OK]'
try:
from mini_rag.config import ConfigManager
import tempfile
with tempfile.TemporaryDirectory() as tmpdir:
config_manager = ConfigManager(tmpdir)
config = config_manager.load_config()
print(f'{ok_emoji} Config system works with proper dependencies')
except Exception as e:
print(f'Error in config test: {e}')
raise
"
echo "$OK All tests completed successfully"
shell: bash
- name: Test auto-update system
run: |
# Set OS-appropriate emojis
if [[ "$RUNNER_OS" == "Windows" ]]; then
OK="[OK]"
SKIP="[SKIP]"
else
OK="✅"
SKIP="⚠️"
fi
python -c "
import os
ok_emoji = '$OK' if os.name != 'nt' else '[OK]'
skip_emoji = '$SKIP' if os.name != 'nt' else '[SKIP]'
try:
from mini_rag.updater import UpdateChecker
updater = UpdateChecker()
print(f'{ok_emoji} Auto-update system available')
except ImportError:
print(f'{skip_emoji} Auto-update system not available (legacy version)')
"
shell: bash
- name: Test CLI commands
run: |
# Set OS-appropriate emojis
if [[ "$RUNNER_OS" == "Windows" ]]; then
OK="[OK]"
else
OK="✅"
fi
echo "$OK Checking for CLI files..."
ls -la rag* || dir rag* || echo "CLI files may not be present"
echo "$OK CLI check completed - this is expected in CI environment"
shell: bash
security-scan:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install security tools
run: |
pip install bandit || echo "Failed to install bandit"
- name: Run security scan
run: |
# Scan for security issues (non-failing)
bandit -r . -ll || echo "✅ Security scan completed"
auto-update-check:
runs-on: ubuntu-latest
if: github.event_name == 'push' && github.ref == 'refs/heads/main'
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Check for auto-update system
run: |
if [ -f "mini_rag/updater.py" ]; then
echo "✅ Auto-update system present"
echo "UPDATE_AVAILABLE=true" >> $GITHUB_ENV
else
echo "⚠️ No auto-update system found"
echo "UPDATE_AVAILABLE=false" >> $GITHUB_ENV
fi
- name: Validate update system
if: env.UPDATE_AVAILABLE == 'true'
run: |
python -c "
try:
from mini_rag.updater import UpdateChecker
updater = UpdateChecker()
print(f'✅ Update system configured for: {updater.github_api_url}')
print(f'✅ Check frequency: {updater.check_frequency_hours} hours')
except Exception as e:
print(f'⚠️ Update system validation skipped: {e}')
"

127
.github/workflows/release.yml vendored
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@ -1,127 +0,0 @@
name: Auto Release & Update System
on:
push:
tags:
- 'v*'
workflow_dispatch:
inputs:
version:
description: 'Version to release (e.g., v1.2.3)'
required: true
type: string
jobs:
create-release:
runs-on: ubuntu-latest
permissions:
contents: write
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install build twine
- name: Extract version
id: version
run: |
if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
VERSION="${{ github.event.inputs.version }}"
else
VERSION=${GITHUB_REF#refs/tags/}
fi
echo "version=$VERSION" >> $GITHUB_OUTPUT
echo "clean_version=${VERSION#v}" >> $GITHUB_OUTPUT
- name: Update version in code
run: |
VERSION="${{ steps.version.outputs.clean_version }}"
# Update __init__.py version
if [ -f "mini_rag/__init__.py" ]; then
sed -i "s/__version__ = \".*\"/__version__ = \"$VERSION\"/" mini_rag/__init__.py
fi
# Update any setup.py or pyproject.toml if they exist
if [ -f "setup.py" ]; then
sed -i "s/version=\".*\"/version=\"$VERSION\"/" setup.py
fi
- name: Generate release notes
id: release_notes
run: |
VERSION="${{ steps.version.outputs.version }}"
# Get commits since last tag
LAST_TAG=$(git describe --tags --abbrev=0 HEAD~1 2>/dev/null || echo "")
if [ -n "$LAST_TAG" ]; then
COMMITS=$(git log --oneline $LAST_TAG..HEAD --pretty=format:"• %s")
else
COMMITS=$(git log --oneline --pretty=format:"• %s" | head -10)
fi
# Create release notes
cat > release_notes.md << EOF
## What's New in $VERSION
### 🚀 Changes
$COMMITS
### 📥 Installation
**Quick Install:**
\`\`\`bash
# Download and run installer
curl -sSL https://github.com/${{ github.repository }}/releases/latest/download/install.sh | bash
\`\`\`
**Manual Install:**
\`\`\`bash
# Download source
wget https://github.com/${{ github.repository }}/archive/refs/tags/$VERSION.zip
unzip $VERSION.zip
cd *-${VERSION#v}
./install_mini_rag.sh
\`\`\`
### 🔄 Auto-Update
If you have a previous version with auto-update support:
\`\`\`bash
./rag-mini check-update
./rag-mini update
\`\`\`
---
🤖 **Auto-Update System**: This release includes automatic update checking.
Users will be notified of future updates and can install them with one command!
EOF
echo "notes_file=release_notes.md" >> $GITHUB_OUTPUT
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{ steps.version.outputs.version }}
name: Release ${{ steps.version.outputs.version }}
body_path: release_notes.md
draft: false
prerelease: false
files: |
*.sh
*.bat
requirements.txt
- name: Trigger update notifications
run: |
echo "🎉 Release ${{ steps.version.outputs.version }} created!"
echo "📢 Users with auto-update will be notified within 24 hours"
echo "🔄 They can update with: ./rag-mini update"

156
.github/workflows/template-sync.yml vendored
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@ -1,156 +0,0 @@
name: Template Synchronization
on:
schedule:
# Run weekly on Sundays at 2 AM UTC
- cron: '0 2 * * 0'
workflow_dispatch:
inputs:
force_sync:
description: 'Force sync even if no changes detected'
required: false
type: boolean
default: false
jobs:
sync-template:
runs-on: ubuntu-latest
permissions:
contents: write
pull-requests: write
steps:
- name: Checkout current repository
uses: actions/checkout@v4
with:
token: ${{ secrets.GITHUB_TOKEN }}
fetch-depth: 0
- name: Check if repository was created from template
id: template_check
run: |
# Check if this repo has template metadata
TEMPLATE_REPO=$(gh api repos/${{ github.repository }} --jq '.template_repository.full_name' 2>/dev/null || echo "")
if [ -n "$TEMPLATE_REPO" ]; then
echo "template_repo=$TEMPLATE_REPO" >> $GITHUB_OUTPUT
echo "is_template_derived=true" >> $GITHUB_OUTPUT
echo "✅ Repository created from template: $TEMPLATE_REPO"
else
echo "is_template_derived=false" >> $GITHUB_OUTPUT
echo "ℹ️ Repository not created from template"
fi
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Fetch template updates
if: steps.template_check.outputs.is_template_derived == 'true'
id: fetch_updates
run: |
TEMPLATE_REPO="${{ steps.template_check.outputs.template_repo }}"
# Add template as remote
git remote add template https://github.com/$TEMPLATE_REPO.git || true
git fetch template main
# Check for changes in template files
TEMPLATE_FILES=$(git diff --name-only HEAD template/main -- .github/ scripts/ | head -20)
if [ -n "$TEMPLATE_FILES" ] || [ "${{ github.event.inputs.force_sync }}" = "true" ]; then
echo "updates_available=true" >> $GITHUB_OUTPUT
echo "template_files<<EOF" >> $GITHUB_OUTPUT
echo "$TEMPLATE_FILES" >> $GITHUB_OUTPUT
echo "EOF" >> $GITHUB_OUTPUT
echo "🔄 Template updates available"
else
echo "updates_available=false" >> $GITHUB_OUTPUT
echo "✅ No template updates needed"
fi
- name: Create update branch
if: steps.fetch_updates.outputs.updates_available == 'true'
run: |
BRANCH_NAME="template-sync-$(date +%Y%m%d-%H%M%S)"
echo "sync_branch=$BRANCH_NAME" >> $GITHUB_ENV
git checkout -b $BRANCH_NAME
# Merge template changes for specific directories only
git checkout template/main -- .github/workflows/ || true
git checkout template/main -- scripts/ || true
# Don't overwrite project-specific files
git reset HEAD -- .github/workflows/template-sync.yml || true
git checkout HEAD -- .github/workflows/template-sync.yml || true
- name: Commit template updates
if: steps.fetch_updates.outputs.updates_available == 'true'
run: |
git config user.name "Template Sync Bot"
git config user.email "noreply@github.com"
if git diff --cached --quiet; then
echo "No changes to commit"
else
git commit -m "🔄 Sync template updates
Updated files:
${{ steps.fetch_updates.outputs.template_files }}
Source: ${{ steps.template_check.outputs.template_repo }}
Sync date: $(date -u +'%Y-%m-%d %H:%M:%S UTC')
This is an automated template synchronization.
Review changes before merging."
git push origin ${{ env.sync_branch }}
fi
- name: Create pull request
if: steps.fetch_updates.outputs.updates_available == 'true'
run: |
gh pr create \
--title "🔄 Template Updates Available" \
--body "## Template Synchronization
This PR contains updates from the template repository.
### 📋 Changed Files:
\`\`\`
${{ steps.fetch_updates.outputs.template_files }}
\`\`\`
### 📊 What's Updated:
- GitHub Actions workflows
- Project scripts and automation
- Template-specific configurations
### ⚠️ Review Notes:
- **Carefully review** all changes before merging
- **Test workflows** in a branch if needed
- **Preserve** any project-specific customizations
- **Check** that auto-update system still works
### 🔗 Source:
Template: [${{ steps.template_check.outputs.template_repo }}](https://github.com/${{ steps.template_check.outputs.template_repo }})
Sync Date: $(date -u +'%Y-%m-%d %H:%M:%S UTC')
---
🤖 This is an automated template synchronization. Review carefully before merging!" \
--head "${{ env.sync_branch }}" \
--base main \
--label "template-sync,automation"
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
- name: Summary
run: |
if [ "${{ steps.template_check.outputs.is_template_derived }}" = "true" ]; then
if [ "${{ steps.fetch_updates.outputs.updates_available }}" = "true" ]; then
echo "🎉 Template sync completed - PR created for review"
else
echo "✅ Template is up to date - no action needed"
fi
else
echo "ℹ️ Repository not created from template - skipping sync"
fi

9
.gitignore vendored
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@ -106,12 +106,3 @@ dmypy.json
# Project specific ignores
REPOSITORY_SUMMARY.md
# Analysis and scanning results (should not be committed)
docs/live-analysis/
docs/analysis-history/
**/live-analysis/
**/analysis-history/
*.analysis.json
*.analysis.html
**/analysis_*/

17
.mini-rag/config.yaml
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@ -1,18 +1,5 @@
# FSS-Mini-RAG Configuration
#
# 🔧 EDIT THIS FILE TO CUSTOMIZE YOUR RAG SYSTEM
#
# This file controls all behavior of your Mini-RAG system.
# Changes take effect immediately - no restart needed!
#
# 💡 IMPORTANT: To change the AI model, edit the 'synthesis_model' line below
#
# Common model options:
# synthesis_model: auto # Let system choose best available
# synthesis_model: qwen3:0.6b # Ultra-fast (500MB)
# synthesis_model: qwen3:1.7b # Balanced (1.4GB) - recommended
# synthesis_model: qwen3:4b # High quality (2.5GB)
#
# Edit this file to customize indexing and search behavior
# See docs/GETTING_STARTED.md for detailed explanations
# Text chunking settings
@ -59,7 +46,7 @@ search:
# LLM synthesis and query expansion settings
llm:
ollama_host: localhost:11434
synthesis_model: qwen3:1.7b # 'auto', 'qwen3:1.7b', etc.
synthesis_model: auto # 'auto', 'qwen3:1.7b', etc.
expansion_model: auto # Usually same as synthesis_model
max_expansion_terms: 8 # Maximum terms to add to queries
enable_synthesis: false # Enable synthesis by default

247
.venv-linting/bin/Activate.ps1
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@ -1,247 +0,0 @@
<#
.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"

70
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@ -1,70 +0,0 @@
# 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

27
.venv-linting/bin/activate.csh
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@ -1,27 +0,0 @@
# 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

69
.venv-linting/bin/activate.fish
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@ -1,69 +0,0 @@
# 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

8
.venv-linting/bin/black
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@ -1,8 +0,0 @@
#!/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())

8
.venv-linting/bin/blackd
View File

@ -1,8 +0,0 @@
#!/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())

8
.venv-linting/bin/isort
View File

@ -1,8 +0,0 @@
#!/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())

8
.venv-linting/bin/isort-identify-imports
View File

@ -1,8 +0,0 @@
#!/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())

8
.venv-linting/bin/pip
View File

@ -1,8 +0,0 @@
#!/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())

8
.venv-linting/bin/pip3
View File

@ -1,8 +0,0 @@
#!/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())

8
.venv-linting/bin/pip3.12
View File

@ -1,8 +0,0 @@
#!/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())

1
.venv-linting/bin/python
View File

@ -1 +0,0 @@
python3

1
.venv-linting/bin/python3
View File

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

1
.venv-linting/bin/python3.12
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@ -1 +0,0 @@
python3

1
.venv-linting/lib64
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@ -1 +0,0 @@
lib

5
.venv-linting/pyvenv.cfg
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@ -1,5 +0,0 @@
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

31
ENHANCEMENTS.md
View File

@ -1,31 +0,0 @@
# FSS-Mini-RAG Enhancement Backlog
## Path Resolution & UX Improvements
### Current State
```bash
rag-mini search /full/absolute/path "query"
```
### Desired State
```bash
cd /my/project
rag-mini "authentication logic" # Auto-detects current directory, defaults to search
rag-mini . "query" # Explicit current directory
rag-mini ../other "query" # Relative path resolution
```
### Implementation Requirements
1. **Auto-detect current working directory** when no path specified
2. **Default to search command** when first argument is a query string
3. **Proper path resolution** using `pathlib.Path.resolve()` for all relative paths
4. **Maintain backwards compatibility** with existing explicit command syntax
### Technical Details
- Modify `mini_rag/cli.py` argument parsing
- Add path resolution with `os.path.abspath()` or `pathlib.Path.resolve()`
- Make project_path optional (default to `os.getcwd()`)
- Smart command detection (if first arg doesn't match command, assume search)
### Priority
High - Significant UX improvement for daily usage

83
GET_STARTED.md Normal file
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@ -0,0 +1,83 @@
# 🚀 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.

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PR_BODY.md Normal file
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@ -0,0 +1,109 @@
## Problem Statement
Currently, FSS-Mini-RAG uses Ollama's default context window settings, which severely limits performance:
- **Default 2048 tokens** is inadequate for RAG applications
- Users can't configure context window for their hardware/use case
- No guidance on optimal context sizes for different models
- Inconsistent context handling across the codebase
- New users don't understand context window importance
## Impact on User Experience
**With 2048 token context window:**
- Only 1-2 responses possible before context truncation
- Thinking tokens consume significant context space
- Poor performance with larger document chunks
- Frustrated users who don't understand why responses degrade
**With proper context configuration:**
- 5-15+ responses in exploration mode
- Support for advanced use cases (15+ results, 4000+ character chunks)
- Better coding assistance and analysis
- Professional-grade RAG experience
## Solution Implemented
### 1. Enhanced Model Configuration Menu
Added context window selection alongside model selection with:
- **Development**: 8K tokens (fast, good for most cases)
- **Production**: 16K tokens (balanced performance)
- **Advanced**: 32K+ tokens (heavy development work)
### 2. Educational Content
Helps users understand:
- Why context window size matters for RAG
- Hardware implications of larger contexts
- Optimal settings for their use case
- Model-specific context capabilities
### 3. Consistent Implementation
- Updated all Ollama API calls to use consistent context settings
- Ensured configuration applies across synthesis, expansion, and exploration
- Added validation for context sizes against model capabilities
- Provided clear error messages for invalid configurations
## Technical Implementation
Based on comprehensive research findings:
### Model Context Capabilities
- **qwen3:0.6b/1.7b**: 32K token maximum
- **qwen3:4b**: 131K token maximum (YaRN extended)
### Recommended Context Sizes
```yaml
# Conservative (fast, low memory)
num_ctx: 8192 # ~6MB memory, excellent for exploration
# Balanced (recommended for most users)
num_ctx: 16384 # ~12MB memory, handles complex analysis
# Advanced (heavy development work)
num_ctx: 32768 # ~24MB memory, supports large codebases
```
### Configuration Integration
- Added context window selection to TUI configuration menu
- Updated config.yaml schema with context parameters
- Implemented validation for model-specific limits
- Provided migration for existing configurations
## Benefits
1. **Improved User Experience**
- Longer conversation sessions
- Better analysis quality
- Clear performance expectations
2. **Professional RAG Capability**
- Support for enterprise-scale projects
- Handles large codebases effectively
- Enables advanced use cases
3. **Educational Value**
- Users learn about context windows
- Better understanding of RAG performance
- Informed decision making
## Files Changed
- `mini_rag/config.py`: Added context window configuration parameters
- `mini_rag/llm_synthesizer.py`: Dynamic context sizing with model awareness
- `mini_rag/explorer.py`: Consistent context application
- `rag-tui.py`: Enhanced configuration menu with context selection
- `PR_DRAFT.md`: Documentation of implementation approach
## Testing Recommendations
1. Test context configuration menu with different models
2. Verify context limits are enforced correctly
3. Test conversation length with different context sizes
4. Validate memory usage estimates
5. Test advanced use cases (15+ results, large chunks)
---
**This PR significantly improves FSS-Mini-RAG's performance and user experience by properly configuring one of the most critical parameters for RAG systems.**
**Ready for review and testing!** 🚀

135
PR_DRAFT.md Normal file
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@ -0,0 +1,135 @@
# Add Context Window Configuration for Optimal RAG Performance
## Problem Statement
Currently, FSS-Mini-RAG uses Ollama's default context window settings, which severely limits performance:
- **Default 2048 tokens** is inadequate for RAG applications
- Users can't configure context window for their hardware/use case
- No guidance on optimal context sizes for different models
- Inconsistent context handling across the codebase
- New users don't understand context window importance
## Impact on User Experience
**With 2048 token context window:**
- Only 1-2 responses possible before context truncation
- Thinking tokens consume significant context space
- Poor performance with larger document chunks
- Frustrated users who don't understand why responses degrade
**With proper context configuration:**
- 5-15+ responses in exploration mode
- Support for advanced use cases (15+ results, 4000+ character chunks)
- Better coding assistance and analysis
- Professional-grade RAG experience
## Proposed Solution
### 1. Enhanced Model Configuration Menu
Add context window selection alongside model selection with:
- **Development**: 8K tokens (fast, good for most cases)
- **Production**: 16K tokens (balanced performance)
- **Advanced**: 32K+ tokens (heavy development work)
### 2. Educational Content
Help users understand:
- Why context window size matters for RAG
- Hardware implications of larger contexts
- Optimal settings for their use case
- Model-specific context capabilities
### 3. Consistent Implementation
- Update all Ollama API calls to use consistent context settings
- Ensure configuration applies across synthesis, expansion, and exploration
- Validate context sizes against model capabilities
- Provide clear error messages for invalid configurations
## Technical Implementation
Based on research findings:
### Model Context Capabilities
- **qwen3:0.6b/1.7b**: 32K token maximum
- **qwen3:4b**: 131K token maximum (YaRN extended)
### Recommended Context Sizes
```yaml
# Conservative (fast, low memory)
num_ctx: 8192 # ~6MB memory, excellent for exploration
# Balanced (recommended for most users)
num_ctx: 16384 # ~12MB memory, handles complex analysis
# Advanced (heavy development work)
num_ctx: 32768 # ~24MB memory, supports large codebases
```
### Configuration Integration
- Add context window selection to TUI configuration menu
- Update config.yaml schema with context parameters
- Implement validation for model-specific limits
- Provide migration for existing configurations
## Benefits
1. **Improved User Experience**
- Longer conversation sessions
- Better analysis quality
- Clear performance expectations
2. **Professional RAG Capability**
- Support for enterprise-scale projects
- Handles large codebases effectively
- Enables advanced use cases
3. **Educational Value**
- Users learn about context windows
- Better understanding of RAG performance
- Informed decision making
## Implementation Plan
1. **Phase 1**: Research Ollama context handling (✅ Complete)
2. **Phase 2**: Update configuration system (✅ Complete)
3. **Phase 3**: Enhance TUI with context selection (✅ Complete)
4. **Phase 4**: Update all API calls consistently (✅ Complete)
5. **Phase 5**: Add documentation and validation (✅ Complete)
## Implementation Details
### Configuration System
- Added `context_window` and `auto_context` to LLMConfig
- Default 16K context (vs problematic 2K default)
- Model-specific validation and limits
- YAML output includes helpful context explanations
### TUI Enhancement
- New "Configure context window" menu option
- Educational content about context importance
- Three presets: Development (8K), Production (16K), Advanced (32K)
- Custom size entry with validation
- Memory usage estimates for each option
### API Consistency
- Dynamic context sizing via `_get_optimal_context_size()`
- Model capability awareness (qwen3:4b = 131K, others = 32K)
- Applied consistently to synthesizer and explorer
- Automatic capping at model limits
### User Education
- Clear explanations of why context matters for RAG
- Memory usage implications (8K = 6MB, 16K = 12MB, 32K = 24MB)
- Advanced use case guidance (15+ results, 4000+ chunks)
- Performance vs quality tradeoffs
## Answers to Review Questions
1. ✅ **Auto-detection**: Implemented via `auto_context` flag that respects model limits
2. ✅ **Model changes**: Dynamic validation against current model capabilities
3. ✅ **Scope**: Global configuration with per-model validation
4. ✅ **Validation**: Comprehensive validation with clear error messages and guidance
---
**This PR will significantly improve FSS-Mini-RAG's performance and user experience by properly configuring one of the most critical parameters for RAG systems.**

238
README.md
View File

@ -79,42 +79,30 @@ FSS-Mini-RAG offers **two distinct experiences** optimized for different use cas
## Quick Start (2 Minutes)
**Step 1: Install**
**Linux/macOS:**
```bash
# Clone the repository
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag
# 1. Install everything
./install_mini_rag.sh
# Install dependencies and package
python3 -m venv .venv
source .venv/bin/activate # Linux/macOS
# .venv\Scripts\activate # Windows
# Use python -m pip for reliability (handles externally-managed-environment errors)
python -m pip install -r requirements.txt
python -m pip install .
# 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
```
**If you get "externally-managed-environment" error:**
```bash
# Verify virtual environment is active
which python # Should show .venv/bin/python
python -m pip --version # Should show .venv path
**Windows:**
```cmd
# 1. Install everything
install_windows.bat
# If still failing, use override (safe in virtual environment)
python -m pip install -r requirements.txt --break-system-packages
python -m pip install . --break-system-packages
```
**Step 2: Start Using**
```bash
# Navigate to any project and search
cd ~/my-project
rag-mini init . # Index current project
rag-mini search . "authentication logic"
# Or use the legacy interface
./rag-tui # Interactive interface
# 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
```
That's it. No external dependencies, no configuration required, no PhD in computer science needed.
@ -165,167 +153,7 @@ That's it. No external dependencies, no configuration required, no PhD in comput
## Installation Options
### 🎯 Copy & Paste Installation (Guaranteed to Work)
Perfect for beginners - these commands work on any fresh Ubuntu, Windows, or Mac system:
**Fresh Ubuntu/Debian System:**
```bash
# Install required system packages
sudo apt update && sudo apt install -y python3 python3-pip python3-venv git curl
# Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag
# Create isolated Python environment
python3 -m venv .venv
source .venv/bin/activate
# Install Python dependencies
pip install -r requirements.txt
# Optional: Install Ollama for best search quality (secure method)
curl -fsSL https://ollama.com/install.sh -o /tmp/ollama-install.sh
# Verify it's a shell script (basic safety check)
file /tmp/ollama-install.sh | grep -q "shell script" && chmod +x /tmp/ollama-install.sh && /tmp/ollama-install.sh
rm -f /tmp/ollama-install.sh
ollama serve &
sleep 3
ollama pull nomic-embed-text
# Ready to use!
./rag-mini index /path/to/your/project
./rag-mini search /path/to/your/project "your search query"
```
**Fresh CentOS/RHEL/Fedora System:**
```bash
# Install required system packages
sudo dnf install -y python3 python3-pip python3-venv git curl
# Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag
# Create isolated Python environment
python3 -m venv .venv
source .venv/bin/activate
# Install Python dependencies
pip install -r requirements.txt
# Optional: Install Ollama for best search quality (secure method)
curl -fsSL https://ollama.com/install.sh -o /tmp/ollama-install.sh
# Verify it's a shell script (basic safety check)
file /tmp/ollama-install.sh | grep -q "shell script" && chmod +x /tmp/ollama-install.sh && /tmp/ollama-install.sh
rm -f /tmp/ollama-install.sh
ollama serve &
sleep 3
ollama pull nomic-embed-text
# Ready to use!
./rag-mini index /path/to/your/project
./rag-mini search /path/to/your/project "your search query"
```
**Fresh macOS System:**
```bash
# Install Homebrew (if not installed)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Install required packages
brew install python3 git curl
# Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag
# Create isolated Python environment
python3 -m venv .venv
source .venv/bin/activate
# Install Python dependencies
pip install -r requirements.txt
# Optional: Install Ollama for best search quality (secure method)
curl -fsSL https://ollama.com/install.sh -o /tmp/ollama-install.sh
# Verify it's a shell script (basic safety check)
file /tmp/ollama-install.sh | grep -q "shell script" && chmod +x /tmp/ollama-install.sh && /tmp/ollama-install.sh
rm -f /tmp/ollama-install.sh
ollama serve &
sleep 3
ollama pull nomic-embed-text
# Ready to use!
./rag-mini index /path/to/your/project
./rag-mini search /path/to/your/project "your search query"
```
**Fresh Windows System:**
```cmd
REM Install Python (if not installed)
REM Download from: https://python.org/downloads (ensure "Add to PATH" is checked)
REM Install Git from: https://git-scm.com/download/win
REM Clone and setup FSS-Mini-RAG
git clone https://github.com/FSSCoding/Fss-Mini-Rag.git
cd Fss-Mini-Rag
REM Create isolated Python environment
python -m venv .venv
.venv\Scripts\activate.bat
REM Install Python dependencies
pip install -r requirements.txt
REM Optional: Install Ollama for best search quality
REM Download from: https://ollama.com/download
REM Run installer, then:
ollama serve
REM In new terminal:
ollama pull nomic-embed-text
REM Ready to use!
rag.bat index C:\path\to\your\project
rag.bat search C:\path\to\your\project "your search query"
```
**What these commands do:**
- **System packages**: Install Python 3.8+, pip (package manager), venv (virtual environments), git (version control), curl (downloads)
- **Clone repository**: Download FSS-Mini-RAG source code to your computer
- **Virtual environment**: Create isolated Python space (prevents conflicts with system Python)
- **Dependencies**: Install required Python libraries (pandas, numpy, lancedb, etc.)
- **Ollama (optional)**: AI model server for best search quality - works offline and free
- **Model download**: Get high-quality embedding model for semantic search
- **Ready to use**: Index any folder and search through it semantically
### ⚡ For Agents & CI/CD: Headless Installation
Perfect for automated deployments, agents, and CI/CD pipelines:
**Linux/macOS:**
```bash
./install_mini_rag.sh --headless
# Automated installation with sensible defaults
# No interactive prompts, perfect for scripts
```
**Windows:**
```cmd
install_windows.bat --headless
# Automated installation with sensible defaults
# No interactive prompts, perfect for scripts
```
**What headless mode does:**
- Uses existing virtual environment if available
- Installs core dependencies only (light mode)
- Downloads embedding model if Ollama is available
- Skips interactive prompts and tests
- Perfect for agent automation and CI/CD pipelines
### 🚀 Recommended: Full Installation
### Recommended: Full Installation
**Linux/macOS:**
```bash
@ -339,6 +167,24 @@ install_windows.bat
# Handles Python setup, dependencies, works reliably
```
### Experimental: Copy & Run (May Not Work)
**Linux/macOS:**
```bash
# Copy folder anywhere and try to run directly
./rag-mini index ~/my-project
# Auto-setup will attempt to create environment
# Falls back with clear instructions if it fails
```
**Windows:**
```cmd
# Copy folder anywhere and try to run directly
rag.bat index C:\my-project
# Auto-setup will attempt to create environment
# Falls back with clear instructions if it fails
```
### Manual Setup
**Linux/macOS:**
@ -382,18 +228,18 @@ This implementation prioritizes:
## Next Steps
- **New users**: Run `./rag-tui` (Linux/macOS) or `rag.bat` (Windows) for guided experience
- **New users**: Run `./rag-mini` (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
## Documentation
- **[Getting Started](docs/GETTING_STARTED.md)** - Get running in 5 minutes
- **[Quick Start Guide](docs/QUICK_START.md)** - Get running in 5 minutes
- **[Visual Diagrams](docs/DIAGRAMS.md)** - 📊 System flow charts and architecture diagrams
- **[TUI Guide](docs/TUI_GUIDE.md)** - Complete walkthrough of the friendly interface
- **[Technical Guide](docs/TECHNICAL_GUIDE.md)** - How the system actually works
- **[Troubleshooting](docs/TROUBLESHOOTING.md)** - Fix common issues
- **[Beginner Glossary](docs/BEGINNER_GLOSSARY.md)** - Friendly terms and concepts
- **[Configuration Guide](docs/CONFIGURATION.md)** - Customizing for your needs
- **[Development Guide](docs/DEVELOPMENT.md)** - Extending and modifying the code
## License

36
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@ -0,0 +1,36 @@
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! 🎉

9
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@ -1,9 +0,0 @@
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

40
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@ -1,40 +0,0 @@
# 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.

381
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View File

@ -1,381 +0,0 @@
# FSS-Mini-RAG Deployment Guide
> **Run semantic search anywhere - from smartphones to edge devices**
> *Complete guide to deploying FSS-Mini-RAG on every platform imaginable*
## Platform Compatibility Matrix
| Platform | Status | AI Features | Installation | Notes |
|----------|--------|-------------|--------------|-------|
| **Linux** | ✅ Full | ✅ Full | `./install_mini_rag.sh` | Primary platform |
| **Windows** | ✅ Full | ✅ Full | `install_windows.bat` | Desktop shortcuts |
| **macOS** | ✅ Full | ✅ Full | `./install_mini_rag.sh` | Works perfectly |
| **Raspberry Pi** | ✅ Excellent | ✅ AI ready | `./install_mini_rag.sh` | ARM64 optimized |
| **Android (Termux)** | ✅ Good | 🟡 Limited | Manual install | Terminal interface |
| **iOS (a-Shell)** | 🟡 Limited | ❌ Text only | Manual install | Sandbox limitations |
| **Docker** | ✅ Excellent | ✅ Full | Dockerfile | Any platform |
## Desktop & Server Deployment
### 🐧 **Linux** (Primary Platform)
```bash
# Full installation with AI features
./install_mini_rag.sh
# What you get:
# ✅ Desktop shortcuts (.desktop files)
# ✅ Application menu integration
# ✅ Full AI model downloads
# ✅ Complete terminal interface
```
### 🪟 **Windows** (Fully Supported)
```cmd
# Full installation with desktop integration
install_windows.bat
# What you get:
# ✅ Desktop shortcuts (.lnk files)
# ✅ Start Menu entries
# ✅ Full AI model downloads
# ✅ Beautiful terminal interface
```
### 🍎 **macOS** (Excellent Support)
```bash
# Same as Linux - works perfectly
./install_mini_rag.sh
# Additional macOS optimizations:
brew install python3 # If needed
brew install ollama # For AI features
```
**macOS-specific features:**
- Automatic path detection for common project locations
- Integration with Spotlight search locations
- Support for `.app` bundle creation (advanced)
## Edge Device Deployment
### 🥧 **Raspberry Pi** (Recommended Edge Platform)
**Perfect for:**
- Home lab semantic search server
- Portable development environment
- IoT project documentation search
- Offline code search station
**Installation:**
```bash
# On Raspberry Pi OS (64-bit recommended)
sudo apt update && sudo apt upgrade
./install_mini_rag.sh
# The installer automatically detects ARM and optimizes:
# ✅ Suggests lightweight models (qwen3:0.6b)
# ✅ Reduces memory usage
# ✅ Enables efficient chunking
```
**Raspberry Pi optimized config:**
```yaml
# Automatically generated for Pi
embedding:
preferred_method: ollama
ollama_model: nomic-embed-text # 270MB - perfect for Pi
llm:
synthesis_model: qwen3:0.6b # 500MB - fast on Pi 4+
context_window: 4096 # Conservative memory use
cpu_optimized: true
chunking:
max_size: 1500 # Smaller chunks for efficiency
```
**Performance expectations:**
- **Pi 4 (4GB)**: Excellent performance, full AI features
- **Pi 4 (2GB)**: Good performance, text-only or small models
- **Pi 5**: Outstanding performance, handles large models
- **Pi Zero**: Text-only search (hash-based embeddings)
### 🔧 **Other Edge Devices**
**NVIDIA Jetson Series:**
- Overkill performance for this use case
- Can run largest models with GPU acceleration
- Perfect for AI-heavy development workstations
**Intel NUC / Mini PCs:**
- Excellent performance
- Full desktop experience
- Can serve multiple users simultaneously
**Orange Pi / Rock Pi:**
- Similar to Raspberry Pi
- Same installation process
- May need manual Ollama compilation
## Mobile Deployment
### 📱 **Android (Recommended: Termux)**
**Installation in Termux:**
```bash
# Install Termux from F-Droid (not Play Store)
# In Termux:
pkg update && pkg upgrade
pkg install python python-pip git
pip install --upgrade pip
# Clone and install FSS-Mini-RAG
git clone https://github.com/your-repo/fss-mini-rag
cd fss-mini-rag
pip install -r requirements.txt
# Quick start
python -m mini_rag index /storage/emulated/0/Documents/myproject
python -m mini_rag search /storage/emulated/0/Documents/myproject "your query"
```
**Android-optimized config:**
```yaml
# config-android.yaml
embedding:
preferred_method: hash # No heavy models needed
chunking:
max_size: 800 # Small chunks for mobile
files:
min_file_size: 20 # Include more small files
llm:
enable_synthesis: false # Text-only for speed
```
**What works on Android:**
- ✅ Full text search and indexing
- ✅ Terminal interface (`rag-tui`)
- ✅ Project indexing from phone storage
- ✅ Search your phone's code projects
- ❌ Heavy AI models (use cloud providers instead)
**Android use cases:**
- Search your mobile development projects
- Index documentation on your phone
- Quick code reference while traveling
- Offline search of downloaded repositories
### 🍎 **iOS (Limited but Possible)**
**Option 1: a-Shell (Free)**
```bash
# Install a-Shell from App Store
# In a-Shell:
pip install requests pathlib
# Limited installation (core features only)
# Files must be in app sandbox
```
**Option 2: iSH (Alpine Linux)**
```bash
# Install iSH from App Store
# In iSH terminal:
apk add python3 py3-pip git
pip install -r requirements-light.txt
# Basic functionality only
```
**iOS limitations:**
- Sandbox restricts file access
- No full AI model support
- Terminal interface only
- Limited to app-accessible files
## Specialized Deployment Scenarios
### 🐳 **Docker Deployment**
**For any platform with Docker:**
```dockerfile
# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
# Expose ports for server mode
EXPOSE 7777
# Default to TUI interface
CMD ["python", "-m", "mini_rag.cli"]
```
**Usage:**
```bash
# Build and run
docker build -t fss-mini-rag .
docker run -it -v $(pwd)/projects:/projects fss-mini-rag
# Server mode for web access
docker run -p 7777:7777 fss-mini-rag python -m mini_rag server
```
### ☁️ **Cloud Deployment**
**AWS/GCP/Azure VM:**
- Same as Linux installation
- Can serve multiple users
- Perfect for team environments
**GitHub Codespaces:**
```bash
# Works in any Codespace
./install_mini_rag.sh
# Perfect for searching your workspace
```
**Replit/CodeSandbox:**
- Limited by platform restrictions
- Basic functionality available
### 🏠 **Home Lab Integration**
**Home Assistant Add-on:**
- Package as Home Assistant add-on
- Search home automation configs
- Voice integration possible
**NAS Integration:**
- Install on Synology/QNAP
- Search all stored documents
- Family code documentation
**Router with USB:**
- Install on OpenWrt routers with USB storage
- Search network documentation
- Configuration management
## Configuration by Use Case
### 🪶 **Ultra-Lightweight (Old hardware, mobile)**
```yaml
# Minimal resource usage
embedding:
preferred_method: hash
chunking:
max_size: 800
strategy: fixed
llm:
enable_synthesis: false
```
### ⚖️ **Balanced (Raspberry Pi, older laptops)**
```yaml
# Good performance with AI features
embedding:
preferred_method: ollama
ollama_model: nomic-embed-text
llm:
synthesis_model: qwen3:0.6b
context_window: 4096
```
### 🚀 **Performance (Modern hardware)**
```yaml
# Full features and performance
embedding:
preferred_method: ollama
ollama_model: nomic-embed-text
llm:
synthesis_model: qwen3:1.7b
context_window: 16384
enable_thinking: true
```
### ☁️ **Cloud-Hybrid (Mobile + Cloud AI)**
```yaml
# Local search, cloud intelligence
embedding:
preferred_method: hash
llm:
provider: openai
api_key: your_api_key
synthesis_model: gpt-4
```
## Troubleshooting by Platform
### **Raspberry Pi Issues**
- **Out of memory**: Reduce context window, use smaller models
- **Slow indexing**: Use hash-based embeddings
- **Model download fails**: Check internet, use smaller models
### **Android/Termux Issues**
- **Permission denied**: Use `termux-setup-storage`
- **Package install fails**: Update packages first
- **Can't access files**: Use `/storage/emulated/0/` paths
### **iOS Issues**
- **Limited functionality**: Expected due to iOS restrictions
- **Can't install packages**: Use lighter requirements file
- **File access denied**: Files must be in app sandbox
### **Edge Device Issues**
- **ARM compatibility**: Ensure using ARM64 Python packages
- **Limited RAM**: Use hash embeddings, reduce chunk sizes
- **No internet**: Skip AI model downloads, use text-only
## Advanced Edge Deployments
### **IoT Integration**
- Index sensor logs and configurations
- Search device documentation
- Troubleshoot IoT deployments
### **Offline Development**
- Complete development environment on edge device
- No internet required after setup
- Perfect for remote locations
### **Educational Use**
- Raspberry Pi computer labs
- Student project search
- Coding bootcamp environments
### **Enterprise Edge**
- Factory floor documentation search
- Field service technical reference
- Remote site troubleshooting
---
## Quick Start by Platform
### Desktop Users
```bash
# Linux/macOS
./install_mini_rag.sh
# Windows
install_windows.bat
```
### Edge/Mobile Users
```bash
# Raspberry Pi
./install_mini_rag.sh
# Android (Termux)
pkg install python git && pip install -r requirements.txt
# Any Docker platform
docker run -it fss-mini-rag
```
**💡 Pro tip**: Start with your current platform, then expand to edge devices as needed. The system scales from smartphones to servers seamlessly!

72
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@ -11,7 +11,6 @@
- [Search Architecture](#search-architecture)
- [Installation Flow](#installation-flow)
- [Configuration System](#configuration-system)
- [System Context Integration](#system-context-integration)
- [Error Handling](#error-handling)
## System Overview
@ -23,12 +22,10 @@ graph TB
CLI --> Index[📁 Index Project]
CLI --> Search[🔍 Search Project]
CLI --> Explore[🧠 Explore Project]
CLI --> Status[📊 Show Status]
TUI --> Index
TUI --> Search
TUI --> Explore
TUI --> Config[⚙️ Configuration]
Index --> Files[📄 File Discovery]
@ -37,32 +34,17 @@ graph TB
Embed --> Store[💾 Vector Database]
Search --> Query[❓ User Query]
Search --> Context[🖥️ System Context]
Query --> Vector[🎯 Vector Search]
Query --> Keyword[🔤 Keyword Search]
Vector --> Combine[🔄 Hybrid Results]
Keyword --> Combine
Context --> Combine
Combine --> Synthesize{Synthesis Mode?}
Synthesize -->|Yes| FastLLM[⚡ Fast Synthesis]
Synthesize -->|No| Results[📋 Ranked Results]
FastLLM --> Results
Explore --> ExploreQuery[❓ Interactive Query]
ExploreQuery --> Memory[🧠 Conversation Memory]
ExploreQuery --> Context
Memory --> DeepLLM[🤔 Deep AI Analysis]
Context --> DeepLLM
Vector --> DeepLLM
DeepLLM --> Interactive[💬 Interactive Response]
Combine --> Results[📋 Ranked Results]
Store --> LanceDB[(🗄️ LanceDB)]
Vector --> LanceDB
Config --> YAML[📝 config.yaml]
Status --> Manifest[📋 manifest.json]
Context --> SystemInfo[💻 OS, Python, Paths]
```
## User Journey
@ -294,58 +276,6 @@ flowchart TD
style Error fill:#ffcdd2
```
## System Context Integration
```mermaid
graph LR
subgraph "System Detection"
OS[🖥️ Operating System]
Python[🐍 Python Version]
Project[📁 Project Path]
OS --> Windows[Windows: rag.bat]
OS --> Linux[Linux: ./rag-mini]
OS --> macOS[macOS: ./rag-mini]
end
subgraph "Context Collection"
Collect[🔍 Collect Context]
OS --> Collect
Python --> Collect
Project --> Collect
Collect --> Format[📝 Format Context]
Format --> Limit[✂️ Limit to 200 chars]
end
subgraph "AI Integration"
UserQuery[❓ User Query]
SearchResults[📋 Search Results]
SystemContext[💻 System Context]
UserQuery --> Prompt[📝 Build Prompt]
SearchResults --> Prompt
SystemContext --> Prompt
Prompt --> AI[🤖 LLM Processing]
AI --> Response[💬 Contextual Response]
end
subgraph "Enhanced Responses"
Response --> Commands[💻 OS-specific commands]
Response --> Paths[📂 Correct path formats]
Response --> Tips[💡 Platform-specific tips]
end
Format --> SystemContext
style SystemContext fill:#e3f2fd
style Response fill:#f3e5f5
style Commands fill:#e8f5e8
```
*System context helps the AI provide better, platform-specific guidance without compromising privacy*
## Architecture Layers
```mermaid

480
docs/GETTING_STARTED.md
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@ -1,314 +1,212 @@
# Getting Started with FSS-Mini-RAG
> **Get from zero to searching in 2 minutes**
> *Everything you need to know to start finding code by meaning, not just keywords*
## Step 1: Installation
## Installation (Choose Your Adventure)
Choose your installation based on what you want:
### 🎯 **Option 1: Full Installation (Recommended)**
*Gets you everything working reliably with desktop shortcuts and AI features*
**Linux/macOS:**
```bash
./install_mini_rag.sh
```
**Windows:**
```cmd
install_windows.bat
```
**What this does:**
- Sets up Python environment automatically
- Installs all dependencies
- Downloads AI models (with your permission)
- Creates desktop shortcuts and application menu entries
- Tests everything works
- Gives you an interactive tutorial
**Time needed:** 5-10 minutes (depending on AI model downloads)
---
### 🚀 **Option 2: Copy & Try (Experimental)**
*Just copy the folder and run - may work, may need manual setup*
**Linux/macOS:**
```bash
# Copy folder anywhere and try running
./rag-mini index ~/my-project
# Auto-setup attempts to create virtual environment
# Falls back with clear instructions if it fails
```
**Windows:**
```cmd
# Copy folder anywhere and try running
rag.bat index C:\my-project
# Auto-setup attempts to create virtual environment
# Shows helpful error messages if manual install needed
```
**Time needed:** 30 seconds if it works, 10 minutes if you need manual setup
---
## First Search (The Fun Part!)
### Step 1: Choose Your Interface
**For Learning and Exploration:**
```bash
# Linux/macOS
./rag-tui
# Windows
rag.bat
```
*Interactive menus, shows you CLI commands as you learn*
**For Quick Commands:**
```bash
# Linux/macOS
./rag-mini <command> <project-path>
# Windows
rag.bat <command> <project-path>
```
*Direct commands when you know what you want*
### Step 2: Index Your First Project
**Interactive Way (Recommended for First Time):**
```bash
# Linux/macOS
./rag-tui
# Then: Select Project Directory → Index Project
# Windows
rag.bat
# Then: Select Project Directory → Index Project
```
**Direct Commands:**
```bash
# Linux/macOS
./rag-mini index ~/my-project
# Windows
rag.bat index C:\my-project
```
**What indexing does:**
- Finds all text files in your project
- Breaks them into smart "chunks" (functions, classes, logical sections)
- Creates searchable embeddings that understand meaning
- Stores everything in a fast vector database
- Creates a `.mini-rag/` directory with your search index
**Time needed:** 10-60 seconds depending on project size
### Step 3: Search by Meaning
**Natural language queries:**
```bash
# Linux/macOS
./rag-mini search ~/my-project "user authentication logic"
./rag-mini search ~/my-project "error handling for database connections"
./rag-mini search ~/my-project "how to validate input data"
# Windows
rag.bat search C:\my-project "user authentication logic"
rag.bat search C:\my-project "error handling for database connections"
rag.bat search C:\my-project "how to validate input data"
```
**Code concepts:**
```bash
# Finds login functions, auth middleware, session handling
./rag-mini search ~/my-project "login functionality"
# Finds try/catch blocks, error handlers, retry logic
./rag-mini search ~/my-project "exception handling"
# Finds validation functions, input sanitization, data checking
./rag-mini search ~/my-project "data validation"
```
**What you get:**
- Ranked results by relevance (not just keyword matching)
- File paths and line numbers for easy navigation
- Context around each match so you understand what it does
- Smart filtering to avoid noise and duplicates
## Two Powerful Modes
FSS-Mini-RAG has two different ways to get answers, optimized for different needs:
### 🚀 **Synthesis Mode** - Fast Answers
```bash
# Linux/macOS
./rag-mini search ~/project "authentication logic" --synthesize
# Windows
rag.bat search C:\project "authentication logic" --synthesize
```
**Perfect for:**
- Quick code discovery
- Finding specific functions or patterns
- Getting fast, consistent answers
**What you get:**
- Lightning-fast responses (no thinking overhead)
- Reliable, factual information about your code
- Clear explanations of what code does and how it works
### 🧠 **Exploration Mode** - Deep Understanding
```bash
# Linux/macOS
./rag-mini explore ~/project
# Windows
rag.bat explore C:\project
```
**Perfect for:**
- Learning new codebases
- Debugging complex issues
- Understanding architectural decisions
**What you get:**
- Interactive conversation with AI that remembers context
- Deep reasoning with full "thinking" process shown
- Follow-up questions and detailed explanations
- Memory of your previous questions in the session
**Example exploration session:**
```
🧠 Exploration Mode - Ask anything about your project
You: How does authentication work in this codebase?
AI: Let me analyze the authentication system...
💭 Thinking: I can see several authentication-related files. Let me examine
the login flow, session management, and security measures...
📝 Authentication Analysis:
This codebase uses a three-layer authentication system:
1. Login validation in auth.py handles username/password checking
2. Session management in sessions.py maintains user state
3. Middleware in auth_middleware.py protects routes
You: What security concerns should I be aware of?
AI: Based on our previous discussion about authentication, let me check for
common security vulnerabilities...
```
## Check Your Setup
**See what got indexed:**
```bash
# Linux/macOS
./rag-mini status ~/my-project
# Windows
rag.bat status C:\my-project
```
**What you'll see:**
- How many files were processed
- Total chunks created for searching
- Embedding method being used (Ollama, ML models, or hash-based)
- Configuration file location
- Index health and last update time
## Configuration (Optional)
Your project gets a `.mini-rag/config.yaml` file with helpful comments:
```yaml
# Context window configuration (critical for AI features)
# 💡 Sizing guide: 2K=1 question, 4K=1-2 questions, 8K=manageable, 16K=most users
# 32K=large codebases, 64K+=power users only
# ⚠️ Larger contexts use exponentially more CPU/memory - only increase if needed
context_window: 16384 # Context size in tokens
# AI model preferences (edit to change priority)
model_rankings:
- "qwen3:1.7b" # Excellent for RAG (1.4GB, recommended)
- "qwen3:0.6b" # Lightweight and fast (~500MB)
- "qwen3:4b" # Higher quality but slower (~2.5GB)
```
**When to customize:**
- Your searches aren't finding what you expect → adjust chunking settings
- You want AI features → install Ollama and download models
- System is slow → try smaller models or reduce context window
- Getting too many/few results → adjust similarity threshold
## Troubleshooting
### "Project not indexed"
**Problem:** You're trying to search before indexing
```bash
# Run indexing first
./rag-mini index ~/my-project # Linux/macOS
rag.bat index C:\my-project # Windows
```
### "No Ollama models available"
**Problem:** AI features need models downloaded
### Option A: Ollama Only (Recommended)
```bash
# Install Ollama first
curl -fsSL https://ollama.ai/install.sh | sh # Linux/macOS
# Or download from https://ollama.com # Windows
curl -fsSL https://ollama.ai/install.sh | sh
# Start Ollama server
ollama serve
# Pull the embedding model
ollama pull nomic-embed-text
# Download a model
ollama pull qwen3:1.7b
# Install Python dependencies
pip install -r requirements.txt
```
### "Virtual environment not found"
**Problem:** Auto-setup didn't work, need manual installation
### Option B: Full ML Stack
```bash
# Run the full installer instead
./install_mini_rag.sh # Linux/macOS
install_windows.bat # Windows
# Install everything including PyTorch
pip install -r requirements-full.txt
```
### Getting weird results
**Solution:** Try different search terms or check what got indexed
## Step 2: Test Installation
```bash
# See what files were processed
# Index this RAG system itself
./rag-mini index ~/my-project
# Search for something
./rag-mini search ~/my-project "chunker function"
# Check what got indexed
./rag-mini status ~/my-project
# Try more specific queries
./rag-mini search ~/my-project "specific function name"
```
## Next Steps
## Step 3: Index Your First Project
### Learn More
- **[Beginner's Glossary](BEGINNER_GLOSSARY.md)** - All the terms explained simply
- **[TUI Guide](TUI_GUIDE.md)** - Master the interactive interface
- **[Visual Diagrams](DIAGRAMS.md)** - See how everything works
```bash
# Index any project directory
./rag-mini index /path/to/your/project
### Advanced Features
- **[Query Expansion](QUERY_EXPANSION.md)** - Make searches smarter with AI
- **[LLM Providers](LLM_PROVIDERS.md)** - Use different AI models
- **[CPU Deployment](CPU_DEPLOYMENT.md)** - Optimize for older computers
# The system creates .mini-rag/ directory with:
# - config.json (settings)
# - manifest.json (file tracking)
# - database.lance/ (vector database)
```
### Customize Everything
- **[Technical Guide](TECHNICAL_GUIDE.md)** - How the system actually works
- **[Configuration Examples](../examples/)** - Pre-made configs for different needs
## Step 4: Search Your Code
---
```bash
# Basic semantic search
./rag-mini search /path/to/project "user login logic"
**🎉 That's it!** You now have a semantic search system that understands your code by meaning, not just keywords. Start with simple searches and work your way up to the advanced AI features as you get comfortable.
# Enhanced search with smart features
./rag-mini-enhanced search /path/to/project "authentication"
**💡 Pro tip:** The best way to learn is to index a project you know well and try searching for things you know are in there. You'll quickly see how much better meaning-based search is than traditional keyword search.
# Find similar patterns
./rag-mini-enhanced similar /path/to/project "def validate_input"
```
## Step 5: Customize Configuration
Edit `project/.mini-rag/config.json`:
```json
{
"chunking": {
"max_size": 3000,
"strategy": "semantic"
},
"files": {
"min_file_size": 100
}
}
```
Then re-index to apply changes:
```bash
./rag-mini index /path/to/project --force
```
## Common Use Cases
### Find Functions by Name
```bash
./rag-mini search /project "function named connect_to_database"
```
### Find Code Patterns
```bash
./rag-mini search /project "error handling try catch"
./rag-mini search /project "database query with parameters"
```
### Find Configuration
```bash
./rag-mini search /project "database connection settings"
./rag-mini search /project "environment variables"
```
### Find Documentation
```bash
./rag-mini search /project "how to deploy"
./rag-mini search /project "API documentation"
```
## Python API Usage
```python
from mini_rag import ProjectIndexer, CodeSearcher, CodeEmbedder
from pathlib import Path
# Initialize
project_path = Path("/path/to/your/project")
embedder = CodeEmbedder()
indexer = ProjectIndexer(project_path, embedder)
searcher = CodeSearcher(project_path, embedder)
# Index the project
print("Indexing project...")
result = indexer.index_project()
print(f"Indexed {result['files_processed']} files, {result['chunks_created']} chunks")
# Search
print("\nSearching for authentication code...")
results = searcher.search("user authentication logic", top_k=5)
for i, result in enumerate(results, 1):
print(f"\n{i}. {result.file_path}")
print(f" Score: {result.score:.3f}")
print(f" Type: {result.chunk_type}")
print(f" Content: {result.content[:100]}...")
```
## Advanced Features
### Auto-optimization
```bash
# Get optimization suggestions
./rag-mini-enhanced analyze /path/to/project
# This analyzes your codebase and suggests:
# - Better chunk sizes for your language mix
# - Streaming settings for large files
# - File filtering optimizations
```
### File Watching
```python
from mini_rag import FileWatcher
# Watch for file changes and auto-update index
watcher = FileWatcher(project_path, indexer)
watcher.start_watching()
# Now any file changes automatically update the index
```
### Custom Chunking
```python
from mini_rag import CodeChunker
chunker = CodeChunker()
# Chunk a Python file
with open("example.py") as f:
content = f.read()
chunks = chunker.chunk_text(content, "python", "example.py")
for chunk in chunks:
print(f"Type: {chunk.chunk_type}")
print(f"Content: {chunk.content}")
```
## Tips and Best Practices
### For Better Search Results
- Use descriptive phrases: "function that validates email addresses"
- Try different phrasings if first search doesn't work
- Search for concepts, not just exact variable names
### For Better Indexing
- Exclude build directories: `node_modules/`, `build/`, `dist/`
- Include documentation files - they often contain valuable context
- Use semantic chunking strategy for most projects
### For Configuration
- Start with default settings
- Use `analyze` command to get optimization suggestions
- Increase chunk size for larger functions/classes
- Decrease chunk size for more granular search
### For Troubleshooting
- Check `./rag-mini status` to see what was indexed
- Look at `.mini-rag/manifest.json` for file details
- Run with `--force` to completely rebuild index
- Check logs in `.mini-rag/` directory for errors
## What's Next?
1. Try the test suite to understand how components work:
```bash
python -m pytest tests/ -v
```
2. Look at the examples in `examples/` directory
3. Read the main README.md for complete technical details
4. Customize the system for your specific project needs

18
docs/SMART_TUNING_GUIDE.md
View File

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

11
docs/TUI_GUIDE.md
View File

@ -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
**Tips You'll Learn**:
- Verbose output with `--verbose` flag for debugging
- How search scoring works
- Finding the right search terms
**Advanced Tips Shown**:
- Enhanced search with `./rag-mini-enhanced`
- Verbose output with `--verbose` flag
- Context-aware search for related code
**What You Learn**:
- Semantic search vs text search (finds concepts, not just words)
@ -107,7 +107,8 @@ 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" --top-k 10
./rag-mini search /path/to/project "user login" --limit 10
./rag-mini-enhanced context /path/to/project "login()"
```
### 4. Explore Project (NEW!)

22
examples/analyze_dependencies.py
View File

@ -4,14 +4,14 @@ Analyze FSS-Mini-RAG dependencies to determine what's safe to remove.
"""
import ast
from collections import defaultdict
import os
from pathlib import Path
from collections import defaultdict
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
@ -31,7 +31,6 @@ 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
@ -86,13 +85,13 @@ 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:
@ -100,14 +99,11 @@ 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()

9
examples/basic_usage.py
View File

@ -5,9 +5,7 @@ Shows how to index a project and search it programmatically.
"""
from pathlib import Path
from mini_rag import CodeEmbedder, CodeSearcher, ProjectIndexer
from mini_rag import ProjectIndexer, CodeSearcher, CodeEmbedder
def main():
# Example project path - change this to your project
@ -46,7 +44,7 @@ def main():
"embedding system",
"search implementation",
"file watcher",
"error handling",
"error handling"
]
print("\n4. Example searches:")
@ -59,13 +57,12 @@ 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()

72
examples/smart_config_suggestions.py
View File

@ -5,10 +5,9 @@ Analyzes the indexed data to suggest optimal settings.
"""
import json
import sys
from collections import Counter
from pathlib import Path
from collections import defaultdict, Counter
import sys
def analyze_project_patterns(manifest_path: Path):
"""Analyze project patterns and suggest optimizations."""
@ -16,7 +15,7 @@ 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)
@ -28,11 +27,11 @@ 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
@ -43,70 +42,65 @@ 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("📊 Current Stats:")
print(f"📊 Current Stats:")
print(f" Files: {total_files}")
print(f" Chunks: {total_chunks}")
print(f" Avg chunks/file: {avg_chunks_per_file:.1f}")
print("\n🗂️ Language Distribution:")
print(f"\n🗂️ Language Distribution:")
for lang, count in languages.most_common(10):
pct = 100 * count / total_files
print(f" {lang}: {count} files ({pct:.1f}%)")
print("\n💡 Smart Optimization Suggestions:")
print(f"\n💡 Smart Optimization Suggestions:")
# Suggestion 1: Language-specific chunking
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['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["markdown"] > 5:
print("✨ Markdown Optimization:")
if languages['markdown'] > 5:
print(f"✨ Markdown Optimization:")
print(f" - Use header-based chunking (detected {languages['markdown']} MD files)")
print(" - Keep sections together for better search relevance")
print(f" - Keep sections together for better search relevance")
if languages["json"] > 20:
print("✨ JSON Optimization:")
if languages['json'] > 20:
print(f"✨ JSON Optimization:")
print(f" - Consider object-level chunking (detected {languages['json']} JSON files)")
print(" - Might want to exclude large config JSONs")
print(f" - Might want to exclude large config JSONs")
# Suggestion 2: File size optimization
if large_files:
print("\n📈 Large File Optimization:")
print(f"\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(" 💡 Consider streaming threshold: 5KB (current: 1MB)")
print(f" 💡 Consider streaming threshold: 5KB (current: 1MB)")
if small_files and len(small_files) > total_files * 0.3:
print("\n📉 Small File Optimization:")
print(f"\n📉 Small File Optimization:")
print(f" {len(small_files)} files <500B might not need chunking")
print(" 💡 Consider: combine small files or skip tiny ones")
print(f" 💡 Consider: combine small files or skip tiny ones")
# Suggestion 3: Search optimization
avg_efficiency = sum(chunk_efficiency) / len(chunk_efficiency)
print("\n🔍 Search Optimization:")
print(f"\n🔍 Search Optimization:")
if avg_efficiency < 0.5:
print(" 💡 Chunks are large relative to files - consider smaller chunks")
print(f" 💡 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(" 💡 Many small chunks - consider larger chunk size")
print(" 💡 Reduce chunk overhead with 2000-4000 char chunks")
print(f" 💡 Many small chunks - consider larger chunk size")
print(f" 💡 Reduce chunk overhead with 2000-4000 char chunks")
# Suggestion 4: Smart defaults
print("\n⚙️ Recommended Config Updates:")
print(
"""{{
print(f"\n⚙️ Recommended Config Updates:")
print(f"""{{
"chunking": {{
"max_size": {3000 if languages['python'] > languages['markdown'] else 2000},
"min_size": 200,
@ -121,9 +115,7 @@ 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:

99
install_mini_rag.sh
View File

@ -4,30 +4,6 @@
set -e # Exit on any error
# Check for command line arguments
HEADLESS_MODE=false
if [[ "$1" == "--headless" ]]; then
HEADLESS_MODE=true
echo "🤖 Running in headless mode - using defaults for automation"
elif [[ "$1" == "--help" || "$1" == "-h" ]]; then
echo ""
echo "FSS-Mini-RAG Installation Script"
echo ""
echo "Usage:"
echo " ./install_mini_rag.sh # Interactive installation"
echo " ./install_mini_rag.sh --headless # Automated installation for agents/CI"
echo " ./install_mini_rag.sh --help # Show this help"
echo ""
echo "Headless mode options:"
echo " • Uses existing virtual environment if available"
echo " • Selects light installation (Ollama + basic dependencies)"
echo " • Downloads nomic-embed-text model if Ollama is available"
echo " • Skips interactive prompts and tests"
echo " • Perfect for agent automation and CI/CD pipelines"
echo ""
exit 0
fi
# Colors for output
RED='\033[0;31m'
GREEN='\033[0;32m'
@ -108,10 +84,6 @@ check_python() {
check_venv() {
if [ -d "$SCRIPT_DIR/.venv" ]; then
print_info "Virtual environment already exists at $SCRIPT_DIR/.venv"
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Using existing virtual environment"
return 0 # Use existing
else
echo -n "Recreate it? (y/N): "
read -r recreate
if [[ $recreate =~ ^[Yy]$ ]]; then
@ -121,7 +93,6 @@ check_venv() {
else
return 0 # Use existing
fi
fi
else
return 1 # Needs creation
fi
@ -169,13 +140,8 @@ check_ollama() {
return 0
else
print_warning "Ollama is installed but not running"
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Starting Ollama server automatically"
start_ollama="y"
else
echo -n "Start Ollama now? (Y/n): "
read -r start_ollama
fi
if [[ ! $start_ollama =~ ^[Nn]$ ]]; then
print_info "Starting Ollama server..."
ollama serve &
@ -202,26 +168,15 @@ check_ollama() {
echo -e "${YELLOW}2) Manual installation${NC} - Visit https://ollama.com/download"
echo -e "${BLUE}3) Continue without Ollama${NC} (uses ML fallback)"
echo ""
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Continuing without Ollama (option 3)"
ollama_choice="3"
else
echo -n "Choose [1/2/3]: "
read -r ollama_choice
fi
case "$ollama_choice" in
1|"")
print_info "Installing Ollama using secure installation method..."
echo -e "${CYAN}Downloading and verifying Ollama installer...${NC}"
print_info "Installing Ollama using official installer..."
echo -e "${CYAN}Running: curl -fsSL https://ollama.com/install.sh | sh${NC}"
# Secure installation: download, verify, then execute
local temp_script="/tmp/ollama-install-$$.sh"
if curl -fsSL https://ollama.com/install.sh -o "$temp_script" && \
file "$temp_script" | grep -q "shell script" && \
chmod +x "$temp_script" && \
"$temp_script"; then
rm -f "$temp_script"
if curl -fsSL https://ollama.com/install.sh | sh; then
print_success "Ollama installed successfully"
print_info "Starting Ollama server..."
@ -312,13 +267,8 @@ setup_ollama_model() {
echo " • Purpose: High-quality semantic embeddings"
echo " • Alternative: System will use ML/hash fallbacks"
echo ""
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Downloading nomic-embed-text model"
download_model="y"
else
echo -n "Download model? [y/N]: "
read -r download_model
fi
should_download=$([ "$download_model" = "y" ] && echo "download" || echo "skip")
fi
@ -378,11 +328,6 @@ get_installation_preferences() {
echo ""
while true; do
if [[ "$HEADLESS_MODE" == "true" ]]; then
# Default to light installation in headless mode
choice="L"
print_info "Headless mode: Selected Light installation"
else
echo -n "Choose [L/F/C] or Enter for recommended ($recommended): "
read -r choice
@ -394,7 +339,6 @@ get_installation_preferences() {
choice="F"
fi
fi
fi
case "${choice^^}" in
L)
@ -434,13 +378,8 @@ configure_custom_installation() {
echo ""
echo -e "${BOLD}Ollama embedding model:${NC}"
echo " • nomic-embed-text (~270MB) - Best quality embeddings"
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Downloading Ollama model"
download_ollama="y"
else
echo -n "Download Ollama model? [y/N]: "
read -r download_ollama
fi
if [[ $download_ollama =~ ^[Yy]$ ]]; then
ollama_model="download"
fi
@ -451,13 +390,8 @@ configure_custom_installation() {
echo -e "${BOLD}ML fallback system:${NC}"
echo " • PyTorch + transformers (~2-3GB) - Works without Ollama"
echo " • Useful for: Offline use, server deployments, CI/CD"
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Skipping ML dependencies (keeping light)"
include_ml="n"
else
echo -n "Include ML dependencies? [y/N]: "
read -r include_ml
fi
# Pre-download models
local predownload_ml="skip"
@ -466,13 +400,8 @@ configure_custom_installation() {
echo -e "${BOLD}Pre-download ML models:${NC}"
echo " • sentence-transformers model (~80MB)"
echo " • Skip: Models download automatically when first used"
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Skipping ML model pre-download"
predownload="n"
else
echo -n "Pre-download now? [y/N]: "
read -r predownload
fi
if [[ $predownload =~ ^[Yy]$ ]]; then
predownload_ml="download"
fi
@ -616,13 +545,8 @@ setup_ml_models() {
echo " • Purpose: Offline fallback when Ollama unavailable"
echo " • If skipped: Auto-downloads when first needed"
echo ""
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Skipping ML model pre-download"
download_ml="n"
else
echo -n "Pre-download now? [y/N]: "
read -r download_ml
fi
should_predownload=$([ "$download_ml" = "y" ] && echo "download" || echo "skip")
fi
@ -777,11 +701,7 @@ show_completion() {
printf "Run quick test now? [Y/n]: "
# More robust input handling
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Skipping interactive test"
echo -e "${BLUE}You can test FSS-Mini-RAG anytime with: ./rag-tui${NC}"
show_beginner_guidance
elif read -r run_test < /dev/tty 2>/dev/null; then
if read -r run_test < /dev/tty 2>/dev/null; then
echo "User chose: '$run_test'" # Debug output
if [[ ! $run_test =~ ^[Nn]$ ]]; then
run_quick_test
@ -812,13 +732,8 @@ run_quick_test() {
echo -e "${GREEN}1) Code${NC} - Index the FSS-Mini-RAG codebase (~50 files)"
echo -e "${BLUE}2) Docs${NC} - Index the documentation (~10 files)"
echo ""
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Indexing code by default"
index_choice="1"
else
echo -n "Choose [1/2] or Enter for code: "
read -r index_choice
fi
# Determine what to index
local target_dir="$SCRIPT_DIR"
@ -853,10 +768,8 @@ run_quick_test() {
echo -e "${CYAN}The TUI has 6 sample questions to get you started.${NC}"
echo -e "${CYAN}Try the suggested queries or enter your own!${NC}"
echo ""
if [[ "$HEADLESS_MODE" != "true" ]]; then
echo -n "Press Enter to start interactive tutorial: "
read -r
fi
# Launch the TUI which has the existing interactive tutorial system
./rag-tui.py "$target_dir" || true
@ -919,16 +832,12 @@ main() {
echo -e "${CYAN}Note: You'll be asked before downloading any models${NC}"
echo ""
if [[ "$HEADLESS_MODE" == "true" ]]; then
print_info "Headless mode: Beginning installation automatically"
else
echo -n "Begin installation? [Y/n]: "
read -r continue_install
if [[ $continue_install =~ ^[Nn]$ ]]; then
echo "Installation cancelled."
exit 0
fi
fi
# Run installation steps
check_python

91
install_windows.bat
View File

@ -5,40 +5,6 @@ setlocal enabledelayedexpansion
REM Enable colors and unicode for modern Windows
chcp 65001 >nul 2>&1
REM Check for command line arguments
set "HEADLESS_MODE=false"
if "%1"=="--headless" (
set "HEADLESS_MODE=true"
echo 🤖 Running in headless mode - using defaults for automation
) else if "%1"=="--help" (
goto show_help
) else if "%1"=="-h" (
goto show_help
)
goto start_installation
:show_help
echo.
echo FSS-Mini-RAG Windows Installation Script
echo.
echo Usage:
echo install_windows.bat # Interactive installation
echo install_windows.bat --headless # Automated installation for agents/CI
echo install_windows.bat --help # Show this help
echo.
echo Headless mode options:
echo • Uses existing virtual environment if available
echo • Installs core dependencies only
echo • Skips AI model downloads
echo • Skips interactive prompts and tests
echo • Perfect for agent automation and CI/CD pipelines
echo.
pause
exit /b 0
:start_installation
echo.
echo ╔══════════════════════════════════════════════════╗
echo ║ FSS-Mini-RAG Windows Installer ║
@ -55,15 +21,11 @@ echo.
echo 💡 Note: You'll be asked before downloading any models
echo.
if "!HEADLESS_MODE!"=="true" (
echo Headless mode: Beginning installation automatically
) else (
set /p "continue=Begin installation? [Y/n]: "
if /i "!continue!"=="n" (
set /p "continue=Begin installation? [Y/n]: "
if /i "!continue!"=="n" (
echo Installation cancelled.
pause
exit /b 0
)
)
REM Get script directory
@ -108,19 +70,10 @@ echo.
echo ══════════════════════════════════════════════════
echo [2/5] Creating Python Virtual Environment...
if exist "%SCRIPT_DIR%\.venv" (
echo 🔄 Found existing virtual environment, checking if it works...
call "%SCRIPT_DIR%\.venv\Scripts\activate.bat" >nul 2>&1
if not errorlevel 1 (
"%SCRIPT_DIR%\.venv\Scripts\python.exe" -c "import sys; print('✅ Existing environment works')" >nul 2>&1
if not errorlevel 1 (
echo ✅ Using existing virtual environment
goto skip_venv_creation
)
)
echo 🔄 Removing problematic virtual environment...
echo 🔄 Removing old virtual environment...
rmdir /s /q "%SCRIPT_DIR%\.venv" 2>nul
if exist "%SCRIPT_DIR%\.venv" (
echo ⚠️ Could not remove old environment, will try to work with it...
echo ⚠️ Could not remove old environment, creating anyway...
)
)
@ -140,7 +93,6 @@ if errorlevel 1 (
)
echo ✅ Virtual environment created successfully
:skip_venv_creation
echo.
echo ══════════════════════════════════════════════════
echo [3/5] Installing Python Dependencies...
@ -181,29 +133,19 @@ echo.
echo ══════════════════════════════════════════════════
echo [4/5] Testing Installation...
echo 🧪 Verifying Python imports...
echo Attempting import test...
"%SCRIPT_DIR%\.venv\Scripts\python.exe" -c "from mini_rag import CodeEmbedder, ProjectIndexer, CodeSearcher; print('✅ Core imports successful')" 2>import_error.txt
"%SCRIPT_DIR%\.venv\Scripts\python.exe" -c "from mini_rag import CodeEmbedder, ProjectIndexer, CodeSearcher; print('✅ Core imports successful')" 2>nul
if errorlevel 1 (
echo ❌ ERROR: Installation test failed
echo.
echo 🔍 Import error details:
type import_error.txt
echo.
echo 🔧 This usually means:
echo • Dependencies didn't install correctly
echo • Virtual environment is corrupted
echo • Python path issues
echo • Module conflicts with existing installations
echo.
echo 💡 Troubleshooting options:
echo • Try: "%SCRIPT_DIR%\.venv\Scripts\pip.exe" install -r requirements.txt --force-reinstall
echo • Or delete .venv folder and run installer again
echo • Or check import_error.txt for specific error details
del import_error.txt >nul 2>&1
echo 💡 Try running: pip install -r requirements.txt
pause
exit /b 1
)
del import_error.txt >nul 2>&1
echo 🔍 Testing embedding system...
"%SCRIPT_DIR%\.venv\Scripts\python.exe" -c "from mini_rag import CodeEmbedder; embedder = CodeEmbedder(); info = embedder.get_embedding_info(); print(f'✅ Embedding method: {info[\"method\"]}')" 2>nul
@ -241,16 +183,11 @@ REM Offer interactive tutorial
echo 🧪 Quick Test Available:
echo Test FSS-Mini-RAG with a small sample project (takes ~30 seconds)
echo.
if "!HEADLESS_MODE!"=="true" (
echo Headless mode: Skipping interactive tutorial
echo 📚 You can run the tutorial anytime with: rag.bat
) else (
set /p "run_test=Run interactive tutorial now? [Y/n]: "
if /i "!run_test!" NEQ "n" (
set /p "run_test=Run interactive tutorial now? [Y/n]: "
if /i "!run_test!" NEQ "n" (
call :run_tutorial
) else (
) else (
echo 📚 You can run the tutorial anytime with: rag.bat
)
)
echo.
@ -288,12 +225,7 @@ curl -s http://localhost:11434/api/version >nul 2>&1
if errorlevel 1 (
echo 🟡 Ollama installed but not running
echo.
if "!HEADLESS_MODE!"=="true" (
echo Headless mode: Starting Ollama server automatically
set "start_ollama=y"
) else (
set /p "start_ollama=Start Ollama server now? [Y/n]: "
)
if /i "!start_ollama!" NEQ "n" (
echo 🚀 Starting Ollama server...
start /b ollama serve
@ -321,12 +253,7 @@ if errorlevel 1 (
echo • qwen3:0.6b - Lightweight and fast (~500MB)
echo • qwen3:4b - Higher quality but slower (~2.5GB)
echo.
if "!HEADLESS_MODE!"=="true" (
echo Headless mode: Skipping model download
set "install_model=n"
) else (
set /p "install_model=Download qwen3:1.7b model now? [Y/n]: "
)
if /i "!install_model!" NEQ "n" (
echo 📥 Downloading qwen3:1.7b model...
echo This may take 5-10 minutes depending on your internet speed

2
mini_rag/__init__.py
View File

@ -7,9 +7,9 @@ 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

2
mini_rag/__main__.py
View File

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

110
mini_rag/auto_optimizer.py
View File

@ -3,23 +3,22 @@ Auto-optimizer for FSS-Mini-RAG.
Automatically tunes settings based on usage patterns.
"""
import json
import logging
from collections import Counter
from pathlib import Path
from typing import Any, Dict
import json
from typing import Dict, Any, List
from collections import Counter
import logging
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."""
@ -38,23 +37,23 @@ 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()
@ -62,11 +61,11 @@ 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
@ -75,13 +74,13 @@ 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]:
@ -91,51 +90,49 @@ 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]):
@ -148,35 +145,35 @@ 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}")
@ -184,7 +181,16 @@ 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
}
}

593
mini_rag/chunker.py
View File

File diff suppressed because it is too large Load Diff

346
mini_rag/cli.py
View File

@ -3,55 +3,57 @@ Command-line interface for Mini RAG system.
Beautiful, intuitive, and highly effective.
"""
import logging
import click
import sys
import time
import logging
from pathlib import Path
from typing import Optional
import click
# Fix Windows console for proper emoji/Unicode support
from .windows_console_fix import fix_windows_console
fix_windows_console()
from rich.console import Console
from rich.logging import RichHandler
from rich.panel import Panel
from rich.progress import Progress, SpinnerColumn, TextColumn
from rich.syntax import Syntax
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 .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 .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()
from .server import RAGClient
from .server import RAGServer, RAGClient, start_server
# 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:
@ -61,16 +63,14 @@ 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", "-", 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', '-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')
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
@ -114,7 +114,10 @@ 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
@ -122,10 +125,8 @@ 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")
@ -134,9 +135,9 @@ 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(" • Watch for changes: [cyan]rag-mini watch[/cyan]")
console.print(" • View statistics: [cyan]rag-mini stats[/cyan]\n")
console.print(" • Search your code: [cyan]mini-rag search \"your query\"[/cyan]")
console.print(" • Watch for changes: [cyan]mini-rag watch[/cyan]")
console.print(" • View statistics: [cyan]mini-rag stats[/cyan]\n")
except Exception as e:
console.print(f"\n[bold red]Error:[/bold red] {e}")
@ -145,31 +146,27 @@ 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-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,
):
@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):
"""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.")
console.print("[red]Error:[/red] Project not indexed. Run 'mini-rag init' first.")
sys.exit(1)
# Get performance monitor
@ -186,30 +183,27 @@ def search(
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)
@ -229,7 +223,7 @@ def search(
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]"):
@ -237,7 +231,7 @@ def search(
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
@ -253,15 +247,12 @@ def search(
# 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 (ImportError, OSError):
pass # Clipboard not available
console.print(f"\n[dim]First result location copied to clipboard: {location}[/dim]")
except:
pass
else:
console.print(f"\n[yellow]No results found for: {query}[/yellow]")
console.print("\n[dim]Tips:[/dim]")
@ -280,15 +271,16 @@ def search(
@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.")
console.print("[red]Error:[/red] Project not indexed. Run 'mini-rag init' first.")
sys.exit(1)
try:
@ -308,37 +300,35 @@ 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)
@ -350,25 +340,24 @@ 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]")
console.print("[red]No RAG index found. Run 'init' first.[/red]")
return
# Connect to database
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)
@ -384,35 +373,30 @@ def debug_schema(path: str):
console.print(table.schema)
# Get sample data
import pandas as pd
df = table.to_pandas()
console.print("\n[bold cyan] Table Statistics:[/bold cyan]")
console.print(f"\n[bold cyan] Table Statistics:[/bold cyan]")
console.print(f"Total rows: {len(df)}")
if len(df) > 0:
# Check embedding column
console.print("\n[bold cyan] Embedding Column Analysis:[/bold cyan]")
first_embedding = df["embedding"].iloc[0]
console.print(f"\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("\n[bold cyan] Sample Data (first 3 rows):[/bold cyan]")
console.print(f"\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]}...")
embed_len = (
len(row["embedding"])
if hasattr(row["embedding"], "__len__")
else "unknown"
)
console.print(f" embedding: {type(row['embedding'])} of length {embed_len}")
console.print(f" embedding: {type(row['embedding'])} of length {len(row['embedding']) if hasattr(row['embedding'], '__len__') else 'unknown'}")
except Exception as e:
logger.error(f"Schema debug failed: {e}")
@ -420,30 +404,21 @@ 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.")
console.print("[red]Error:[/red] Project not indexed. Run 'mini-rag init' first.")
sys.exit(1)
try:
@ -484,7 +459,7 @@ 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
@ -499,12 +474,10 @@ 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("\n[bold green]Watch Summary:[/bold green]")
console.print(f"\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}")
@ -513,9 +486,11 @@ 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()
@ -535,9 +510,11 @@ 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()
@ -557,15 +534,16 @@ 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.")
console.print("[red]Error:[/red] Project not indexed. Run 'mini-rag init' first.")
sys.exit(1)
try:
@ -575,7 +553,7 @@ 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:
@ -587,7 +565,7 @@ 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
@ -620,7 +598,7 @@ def info(show_code: bool):
console.print("\n[bold]Example Usage:[/bold]\n")
code = """# Initialize a project
rag-mini init
mini-rag init
# Search for code
mini-rag search "database connection"
@ -631,26 +609,28 @@ mini-rag find-function connect_to_db
mini-rag find-class UserModel
# Watch for changes
rag-mini watch
mini-rag watch
# Get statistics
rag-mini stats"""
mini-rag stats"""
syntax = Syntax(code, "bash", theme="monokai")
console.print(syntax)
@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.")
console.print("[red]Error:[/red] Project not indexed. Run 'mini-rag init' first.")
sys.exit(1)
try:
@ -668,9 +648,12 @@ 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()
@ -683,12 +666,7 @@ 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)}")
@ -698,23 +676,23 @@ 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(" • Status: [green]✅ Indexed[/green]")
console.print(f" • 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(" • Status: [yellow]⚠️ Index exists but has issues[/yellow]")
console.print(f" • Status: [yellow]⚠️ Index exists but has issues[/yellow]")
console.print(f" • Error: {e}")
else:
console.print(" • Status: [red]❌ Not indexed[/red]")
console.print(" • Run 'rag-mini init' to initialize")
console.print(" • Run 'rag-start' to initialize")
# Check server status
console.print("\n[bold]🚀 Server Status:[/bold]")
@ -726,16 +704,16 @@ 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:
console.print(f" • [yellow]Server responding but with issues: {e}[/yellow]")
else:
console.print(f" • Status: [red]❌ Not running on port {port}[/red]")
console.print(" • Run 'rag-mini server' to start the server")
console.print(" • Run 'rag-start' to start server")
# Run codebase discovery if requested
if discovery and rag_dir.exists():
@ -761,26 +739,22 @@ def status(path: str, port: int, discovery: bool):
elif discovery and not rag_dir.exists():
console.print("\n[bold]🧠 Codebase Discovery:[/bold]")
console.print(" [yellow]❌ Cannot run discovery - project not indexed[/yellow]")
console.print(" Run 'rag-mini init' first to initialize the system")
console.print(" Run 'rag-start' first to initialize the system")
# Show next steps
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(" 1. Run [cyan]rag-start[/cyan] to initialize and start RAG system")
console.print(" 2. Use [cyan]rag-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(" 1. Run [cyan]rag-start[/cyan] to start the server")
console.print(" 2. Use [cyan]rag-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-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()

381
mini_rag/config.py
View File

@ -3,14 +3,11 @@ Configuration management for FSS-Mini-RAG.
Handles loading, saving, and validation of YAML config files.
"""
import logging
import re
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional
import yaml
import requests
import logging
from pathlib import Path
from typing import Dict, Any, Optional
from dataclasses import dataclass, asdict
logger = logging.getLogger(__name__)
@ -18,7 +15,6 @@ 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"
@ -27,7 +23,6 @@ class ChunkingConfig:
@dataclass
class StreamingConfig:
"""Configuration for large file streaming."""
enabled: bool = True
threshold_bytes: int = 1048576 # 1MB
@ -35,7 +30,6 @@ class StreamingConfig:
@dataclass
class FilesConfig:
"""Configuration for file processing."""
min_file_size: int = 50
exclude_patterns: list = None
include_patterns: list = None
@ -50,7 +44,7 @@ class FilesConfig:
".venv/**",
"venv/**",
"build/**",
"dist/**",
"dist/**"
]
if self.include_patterns is None:
self.include_patterns = ["**/*"] # Include everything by default
@ -59,7 +53,6 @@ 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"
@ -70,7 +63,6 @@ class EmbeddingConfig:
@dataclass
class SearchConfig:
"""Configuration for search behavior."""
default_top_k: int = 10
enable_bm25: bool = True
similarity_threshold: float = 0.1
@ -80,7 +72,6 @@ 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
@ -110,38 +101,28 @@ 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",
]
@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)
backup_before_update: bool = True # Create backup before updating
notify_beta_releases: bool = False # Include beta/pre-releases
@dataclass
class RAGConfig:
"""Main RAG system configuration."""
chunking: ChunkingConfig = None
streaming: StreamingConfig = None
files: FilesConfig = None
embedding: EmbeddingConfig = None
search: SearchConfig = None
llm: LLMConfig = None
updates: UpdateConfig = None
def __post_init__(self):
if self.chunking is None:
@ -156,8 +137,6 @@ class RAGConfig:
self.search = SearchConfig()
if self.llm is None:
self.llm = LLMConfig()
if self.updates is None:
self.updates = UpdateConfig()
class ConfigManager:
@ -165,223 +144,8 @@ 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"
def get_available_ollama_models(self, ollama_host: str = "localhost:11434") -> List[str]:
"""Get list of available Ollama models for validation with secure connection handling."""
import time
# Retry logic with exponential backoff
max_retries = 3
for attempt in range(max_retries):
try:
# Use explicit timeout and SSL verification for security
response = requests.get(
f"http://{ollama_host}/api/tags",
timeout=(5, 10), # (connect_timeout, read_timeout)
verify=True, # Explicit SSL verification
allow_redirects=False # Prevent redirect attacks
)
if response.status_code == 200:
data = response.json()
models = [model["name"] for model in data.get("models", [])]
logger.debug(f"Successfully fetched {len(models)} Ollama models")
return models
else:
logger.debug(f"Ollama API returned status {response.status_code}")
except requests.exceptions.SSLError as e:
logger.debug(f"SSL verification failed for Ollama connection: {e}")
# For local Ollama, SSL might not be configured - this is expected
if "localhost" in ollama_host or "127.0.0.1" in ollama_host:
logger.debug("Retrying with local connection (SSL not required for localhost)")
# Local connections don't need SSL verification
try:
response = requests.get(f"http://{ollama_host}/api/tags", timeout=(5, 10))
if response.status_code == 200:
data = response.json()
return [model["name"] for model in data.get("models", [])]
except Exception as local_e:
logger.debug(f"Local Ollama connection also failed: {local_e}")
break # Don't retry SSL errors for remote hosts
except requests.exceptions.Timeout as e:
logger.debug(f"Ollama connection timeout (attempt {attempt + 1}/{max_retries}): {e}")
if attempt < max_retries - 1:
sleep_time = (2 ** attempt) # Exponential backoff
time.sleep(sleep_time)
continue
except requests.exceptions.ConnectionError as e:
logger.debug(f"Ollama connection error (attempt {attempt + 1}/{max_retries}): {e}")
if attempt < max_retries - 1:
time.sleep(1)
continue
except Exception as e:
logger.debug(f"Unexpected error fetching Ollama models: {e}")
break
return []
def _sanitize_model_name(self, model_name: str) -> str:
"""Sanitize model name to prevent injection attacks."""
if not model_name:
return ""
# Allow only alphanumeric, dots, colons, hyphens, underscores
# This covers legitimate model names like qwen3:1.7b-q8_0
sanitized = re.sub(r'[^a-zA-Z0-9\.\:\-\_]', '', model_name)
# Limit length to prevent DoS
if len(sanitized) > 128:
logger.warning(f"Model name too long, truncating: {sanitized[:20]}...")
sanitized = sanitized[:128]
return sanitized
def resolve_model_name(self, configured_model: str, available_models: List[str]) -> Optional[str]:
"""Resolve configured model name to actual available model with input sanitization."""
if not available_models or not configured_model:
return None
# Sanitize input to prevent injection
configured_model = self._sanitize_model_name(configured_model)
if not configured_model:
logger.warning("Model name was empty after sanitization")
return None
# Handle special 'auto' directive
if configured_model.lower() == 'auto':
return available_models[0] if available_models else None
# Direct exact match first (case-insensitive)
for available_model in available_models:
if configured_model.lower() == available_model.lower():
return available_model
# Fuzzy matching for common patterns
model_patterns = self._get_model_patterns(configured_model)
for pattern in model_patterns:
for available_model in available_models:
if pattern.lower() in available_model.lower():
# Additional validation: ensure it's not a partial match of something else
if self._validate_model_match(pattern, available_model):
return available_model
return None # Model not available
def _get_model_patterns(self, configured_model: str) -> List[str]:
"""Generate fuzzy match patterns for common model naming conventions."""
patterns = [configured_model] # Start with exact name
# Common quantization patterns for different models
quantization_patterns = {
'qwen3:1.7b': ['qwen3:1.7b-q8_0', 'qwen3:1.7b-q4_0', 'qwen3:1.7b-q6_k'],
'qwen3:0.6b': ['qwen3:0.6b-q8_0', 'qwen3:0.6b-q4_0', 'qwen3:0.6b-q6_k'],
'qwen3:4b': ['qwen3:4b-q8_0', 'qwen3:4b-q4_0', 'qwen3:4b-q6_k'],
'qwen3:8b': ['qwen3:8b-q8_0', 'qwen3:8b-q4_0', 'qwen3:8b-q6_k'],
'qwen2.5:1.5b': ['qwen2.5:1.5b-q8_0', 'qwen2.5:1.5b-q4_0'],
'qwen2.5:3b': ['qwen2.5:3b-q8_0', 'qwen2.5:3b-q4_0'],
'qwen2.5-coder:1.5b': ['qwen2.5-coder:1.5b-q8_0', 'qwen2.5-coder:1.5b-q4_0'],
'qwen2.5-coder:3b': ['qwen2.5-coder:3b-q8_0', 'qwen2.5-coder:3b-q4_0'],
'qwen2.5-coder:7b': ['qwen2.5-coder:7b-q8_0', 'qwen2.5-coder:7b-q4_0'],
}
# Add specific patterns for the configured model
if configured_model.lower() in quantization_patterns:
patterns.extend(quantization_patterns[configured_model.lower()])
# Generic pattern generation for unknown models
if ':' in configured_model:
base_name, version = configured_model.split(':', 1)
# Add common quantization suffixes
common_suffixes = ['-q8_0', '-q4_0', '-q6_k', '-q4_k_m', '-instruct', '-base']
for suffix in common_suffixes:
patterns.append(f"{base_name}:{version}{suffix}")
# Also try with instruct variants
if 'instruct' not in version.lower():
patterns.append(f"{base_name}:{version}-instruct")
patterns.append(f"{base_name}:{version}-instruct-q8_0")
patterns.append(f"{base_name}:{version}-instruct-q4_0")
return patterns
def _validate_model_match(self, pattern: str, available_model: str) -> bool:
"""Validate that a fuzzy match is actually correct and not a false positive."""
# Convert to lowercase for comparison
pattern_lower = pattern.lower()
available_lower = available_model.lower()
# Ensure the base model name matches
if ':' in pattern_lower and ':' in available_lower:
pattern_base = pattern_lower.split(':')[0]
available_base = available_lower.split(':')[0]
# Base names must match exactly
if pattern_base != available_base:
return False
# Version part should be contained or closely related
pattern_version = pattern_lower.split(':', 1)[1]
available_version = available_lower.split(':', 1)[1]
# The pattern version should be a prefix of the available version
# e.g., "1.7b" should match "1.7b-q8_0" but not "11.7b"
if not available_version.startswith(pattern_version.split('-')[0]):
return False
return True
def validate_and_resolve_models(self, config: RAGConfig) -> RAGConfig:
"""Validate and resolve model names in configuration."""
try:
available_models = self.get_available_ollama_models(config.llm.ollama_host)
if not available_models:
logger.debug("No Ollama models available for validation")
return config
# Resolve synthesis model
if config.llm.synthesis_model != "auto":
resolved = self.resolve_model_name(config.llm.synthesis_model, available_models)
if resolved and resolved != config.llm.synthesis_model:
logger.info(f"Resolved synthesis model: {config.llm.synthesis_model} -> {resolved}")
config.llm.synthesis_model = resolved
elif not resolved:
logger.warning(f"Synthesis model '{config.llm.synthesis_model}' not found, keeping original")
# Resolve expansion model (if different from synthesis)
if (config.llm.expansion_model != "auto" and
config.llm.expansion_model != config.llm.synthesis_model):
resolved = self.resolve_model_name(config.llm.expansion_model, available_models)
if resolved and resolved != config.llm.expansion_model:
logger.info(f"Resolved expansion model: {config.llm.expansion_model} -> {resolved}")
config.llm.expansion_model = resolved
elif not resolved:
logger.warning(f"Expansion model '{config.llm.expansion_model}' not found, keeping original")
# Update model rankings with resolved names
if config.llm.model_rankings:
updated_rankings = []
for model in config.llm.model_rankings:
resolved = self.resolve_model_name(model, available_models)
if resolved:
updated_rankings.append(resolved)
if resolved != model:
logger.debug(f"Updated model ranking: {model} -> {resolved}")
else:
updated_rankings.append(model) # Keep original if not resolved
config.llm.model_rankings = updated_rankings
except Exception as e:
logger.debug(f"Model validation failed: {e}")
return config
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."""
@ -392,7 +156,7 @@ 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:
@ -402,37 +166,21 @@ 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"])
# Validate and resolve model names if Ollama is available
config = self.validate_and_resolve_models(config)
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 "
f"{getattr(e, 'problem_mark', 'unknown')}: {e}"
)
logger.error(error_msg)
print(f"\n{error_msg}")
print(f"Config file: {self.config_path}")
print("💡 Check YAML syntax (indentation, quotes, colons)")
print("💡 Or delete config file to reset to defaults")
return RAGConfig() # Still return defaults but warn user
except Exception as e:
logger.error(f"Failed to load config from {self.config_path}: {e}")
logger.info("Using default configuration")
@ -449,18 +197,7 @@ class ConfigManager:
# Create YAML content with comments
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:
try:
import fcntl
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):
# Fallback for Windows or if fcntl unavailable
with open(self.config_path, 'w') as f:
f.write(yaml_content)
logger.info(f"Configuration saved to {self.config_path}")
@ -477,87 +214,67 @@ class ConfigManager:
"",
"# Text chunking settings",
"chunking:",
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'",
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'",
"",
"# Large file streaming settings",
"streaming:",
f" enabled: {str(config_dict['streaming']['enabled']).lower()}",
f" threshold_bytes: {config_dict['streaming']['threshold_bytes']} # Stream files >1MB",
f" threshold_bytes: {config_dict['streaming']['threshold_bytes']} # Files larger than this use streaming (1MB)",
"",
"# File processing settings",
"files:",
f" min_file_size: {config_dict['files']['min_file_size']} # Skip small files",
f" min_file_size: {config_dict['files']['min_file_size']} # Skip files smaller than this",
" 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']} # Method",
f" preferred_method: {config_dict['embedding']['preferred_method']} # 'ollama', 'ml', 'hash', or 'auto'",
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']} # Per batch",
f" batch_size: {config_dict['embedding']['batch_size']} # Embeddings processed per batch",
"",
"# Search behavior settings",
"search:",
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",
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",
"",
"# LLM synthesis and query expansion settings",
"llm:",
f" ollama_host: {config_dict['llm']['ollama_host']}",
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" 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" 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",
"",
" # Context window configuration (critical for RAG performance)",
" # 💡 Sizing guide: 2K=1 question, 4K=1-2 questions, 8K=manageable, 16K=most users",
" # 32K=large codebases, 64K+=power users only",
" # ⚠️ Larger contexts use exponentially more CPU/memory - only increase if needed",
" # 🔧 Low context limits? Try smaller topk, better search terms, or archive noise",
f" context_window: {config_dict['llm']['context_window']} # Context size in tokens",
f" context_window: {config_dict['llm']['context_window']} # Context size in tokens (8K=fast, 16K=balanced, 32K=advanced)",
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(
[
"",
"# Auto-update system settings",
"updates:",
f" auto_check: {str(config_dict['updates']['auto_check']).lower()} # Check for updates automatically",
f" check_frequency_hours: {config_dict['updates']['check_frequency_hours']} # Hours between update checks",
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."""

204
mini_rag/explorer.py
View File

@ -9,43 +9,33 @@ Perfect for exploring codebases with detailed reasoning and follow-up questions.
import json
import logging
import time
from dataclasses import dataclass
from typing import List, Dict, Any, Optional
from pathlib import Path
from typing import Any, Dict, List, Optional
from dataclasses import dataclass
try:
from .config import RAGConfig
from .llm_synthesizer import LLMSynthesizer, SynthesisResult
from .search import CodeSearcher
from .system_context import get_system_context
from .config import RAGConfig
except ImportError:
# For direct testing
from config import RAGConfig
from llm_synthesizer import LLMSynthesizer, SynthesisResult
from search import CodeSearcher
def get_system_context(x=None):
return ""
from config import RAGConfig
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),
@ -54,11 +44,9 @@ 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."""
@ -73,7 +61,7 @@ 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
@ -92,7 +80,7 @@ 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")
@ -112,7 +100,7 @@ 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
@ -138,6 +126,7 @@ 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 = []
@ -148,34 +137,27 @@ class CodeExplorer:
context_parts.append(f"Previous Q{i}: {prev_q}")
context_parts.append(f"Previous A{i}: {prev_summary}")
# "\n".join(context_parts) # Unused variable removed
context_summary = "\n".join(context_parts)
# Build search results context
results_context = []
for i, result in enumerate(results[:8], 1):
# 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
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
results_context.append(
"""
results_context.append(f"""
Result {i} (Score: {score:.3f}):
File: {file_path}
Content: {content[:800]}{'...' if len(content) > 800 else ''}
"""
)
""")
# "\n".join(results_context) # Unused variable removed
# Get system context for better responses
# get_system_context(self.project_path) # Unused variable removed
results_text = "\n".join(results_context)
# Create comprehensive exploration prompt with thinking
prompt = """<think>
prompt = f"""<think>
The user asked: "{question}"
System context: {system_context}
Let me analyze what they're asking and look at the information I have available.
From the search results, I can see relevant information about:
@ -228,14 +210,8 @@ 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,
)
# "" # Unused variable removed
response = self.synthesizer._call_ollama(prompt, temperature=0.2, disable_thinking=False, use_streaming=True, collapse_thinking=False)
thinking_stream = ""
# Streaming already shows thinking and response
# No need for additional indicators
@ -246,7 +222,7 @@ Guidelines:
key_points=[],
code_examples=[],
suggested_actions=["Check LLM service status"],
confidence=0.0,
confidence=0.0
)
# Use natural language response directly
@ -255,7 +231,7 @@ 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:
@ -265,17 +241,11 @@ 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 = []
@ -285,10 +255,8 @@ 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("")
@ -299,17 +267,9 @@ 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)
@ -322,23 +282,19 @@ Guidelines:
exchange_count = len(self.current_session.conversation_history)
summary = [
"🧠 EXPLORATION SESSION SUMMARY",
"=" * 40,
f"🧠 EXPLORATION SESSION SUMMARY",
f"=" * 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%})")
@ -362,7 +318,9 @@ 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:
@ -378,35 +336,24 @@ 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
@ -435,6 +382,7 @@ 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
@ -450,7 +398,6 @@ 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 = {
@ -464,15 +411,15 @@ 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:
@ -483,14 +430,14 @@ 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
@ -498,7 +445,7 @@ 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:
@ -531,7 +478,7 @@ 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
@ -540,26 +487,18 @@ 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)
@ -569,8 +508,8 @@ 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
@ -583,27 +522,28 @@ 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("\n\033[2m\033[3m" + "─" * 40 + "\033[0m")
print(f"\n\033[2m\033[3m" + "─" * 40 + "\033[0m")
print()
def _display_thinking_stream(self, thinking_stream: str):
@ -615,11 +555,11 @@ 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
@ -629,10 +569,7 @@ Guidelines:
print("\033[2m\033[3m" + "─" * 40 + "\033[0m")
print()
# Quick test function
def test_explorer():
"""Test the code explorer."""
explorer = CodeExplorer(Path("."))
@ -648,6 +585,5 @@ def test_explorer():
print("\n" + explorer.end_session())
if __name__ == "__main__":
test_explorer()

311
mini_rag/fast_server.py
View File

@ -12,47 +12,40 @@ Drop-in replacement for the original server with:
"""
import json
import logging
import os
import socket
import subprocess
import sys
import threading
import time
from concurrent.futures import Future, ThreadPoolExecutor
import subprocess
import sys
import os
import logging
from pathlib import Path
from typing import Any, Callable, Dict, Optional
from rich import print as rprint
from typing import Dict, Any, Optional, Callable
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, Future
import queue
# Rich console for beautiful output
from rich.console import Console
from rich.live import Live
from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeRemainingColumn, MofNCompleteColumn
from rich.panel import Panel
from rich.progress import (
BarColumn,
MofNCompleteColumn,
Progress,
SpinnerColumn,
TextColumn,
TimeRemainingColumn,
)
from rich.table import Table
from rich.live import Live
from rich import print as rprint
# 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 (ImportError, OSError):
except:
pass
from .indexer import ProjectIndexer
from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
from .performance import PerformanceMonitor
from .search import CodeSearcher
from .ollama_embeddings import OllamaEmbedder as CodeEmbedder
from .indexer import ProjectIndexer
from .performance import PerformanceMonitor
logger = logging.getLogger(__name__)
console = Console()
@ -96,14 +89,14 @@ 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
}
@ -158,7 +151,7 @@ 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
@ -168,43 +161,36 @@ 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(
["lso", "-ti", f":{self.port}"],
capture_output=True,
text=True,
timeout=3,
)
result = subprocess.run(['lsof', '-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:
@ -220,12 +206,12 @@ 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
@ -238,12 +224,12 @@ 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 (OSError, IOError, ValueError, AttributeError):
except:
return True
def _fast_index(self) -> bool:
@ -256,7 +242,7 @@ 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]")
@ -281,14 +267,11 @@ 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
@ -299,11 +282,8 @@ 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()
@ -334,18 +314,13 @@ 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
@ -372,9 +347,7 @@ 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...")
@ -428,46 +401,46 @@ 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 (ConnectionError, OSError, TypeError, ValueError, socket.error):
checks["port"] = {"status": "occupied"}
checks['port'] = {'status': 'available'}
except:
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()
@ -479,10 +452,10 @@ 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)
@ -506,7 +479,7 @@ 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
@ -518,15 +491,15 @@ class FastRAGServer:
# Display ready status
panel = Panel(
"[bold green]🎉 RAG Server Ready![/bold green]\n\n"
f"[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"
"[dim]Ready to serve the development environment queries...[/dim]",
f"[dim]Ready to serve the development environment queries...[/dim]",
title="🚀 Server Status",
border_style="green",
border_style="green"
)
console.print(panel)
@ -574,21 +547,24 @@ 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")
@ -596,9 +572,7 @@ 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()
@ -607,81 +581,79 @@ 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 (TypeError, ValueError):
except:
pass
console.print(f"[red]❌ Query failed: {error_msg}[/red]")
finally:
try:
client.close()
except (ConnectionError, OSError, TypeError, ValueError, socket.error):
except:
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)
@ -695,7 +667,7 @@ class FastRAGServer:
if self.socket:
try:
self.socket.close()
except (ConnectionError, OSError, TypeError, ValueError, socket.error):
except:
pass
# Shutdown executor
@ -705,8 +677,6 @@ class FastRAGServer:
# Enhanced client with status monitoring
class FastRAGClient:
"""Enhanced client with better error handling and status monitoring"""
@ -719,9 +689,9 @@ 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)
@ -732,27 +702,31 @@ 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)
@ -762,14 +736,18 @@ 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 (TypeError, ValueError):
return status.get('success', False)
except:
return False
def shutdown(self) -> Dict[str, Any]:
@ -777,9 +755,9 @@ 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)
@ -789,38 +767,41 @@ 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):

477
mini_rag/indexer.py
View File

@ -3,39 +3,31 @@ Parallel indexing engine for efficient codebase processing.
Handles file discovery, chunking, embedding, and storage.
"""
import hashlib
import json
import logging
import os
import json
import hashlib
import logging
from pathlib import Path
from typing import List, Dict, Any, Optional, Set, Tuple
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__)
@ -45,13 +37,11 @@ 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.
@ -62,9 +52,9 @@ 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)
@ -81,75 +71,26 @@ 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",
"*.con",
"*.config",
'*.json', '*.yaml', '*.yml', '*.toml', '*.ini',
'*.xml', '*.conf', '*.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
@ -159,23 +100,23 @@ 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}")
@ -184,7 +125,7 @@ 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)
@ -197,57 +138,49 @@ 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",
"strategy": "semantic"
},
"streaming": {
"enabled": True,
"threshold_mb": 1,
"chunk_size_kb": 64
},
"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
@ -258,41 +191,31 @@ 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}")
@ -305,10 +228,10 @@ 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}")
@ -334,7 +257,7 @@ class ProjectIndexer:
try:
if file_path.stat().st_size > 1_000_000:
return False
except (OSError, IOError):
except:
return False
# Check exclude patterns first
@ -358,33 +281,21 @@ 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()
@ -394,15 +305,8 @@ 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:
@ -419,17 +323,17 @@ 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
@ -441,7 +345,7 @@ 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
@ -452,11 +356,11 @@ 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)
@ -467,14 +371,14 @@ 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()
@ -487,9 +391,7 @@ 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:
@ -500,9 +402,7 @@ 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:
@ -518,7 +418,7 @@ 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)
@ -546,43 +446,39 @@ 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
@ -605,7 +501,7 @@ 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:
@ -613,13 +509,13 @@ 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)
@ -627,10 +523,8 @@ 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
@ -641,21 +535,16 @@ 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()),
@ -665,9 +554,7 @@ 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()),
@ -677,8 +564,7 @@ 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():
@ -695,9 +581,7 @@ 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")
@ -712,9 +596,7 @@ 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}")
@ -742,11 +624,11 @@ 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():
@ -761,9 +643,9 @@ 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]")
@ -781,7 +663,10 @@ 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
@ -827,10 +712,10 @@ 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()
@ -839,11 +724,11 @@ 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
@ -854,9 +739,7 @@ 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
@ -891,16 +774,14 @@ 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
@ -908,25 +789,23 @@ 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:
@ -959,7 +838,7 @@ 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:
@ -970,8 +849,8 @@ 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}")
@ -984,20 +863,20 @@ 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 (OSError, IOError, PermissionError):
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:
pass
return stats

109
mini_rag/llm_safeguards.py
View File

@ -6,19 +6,17 @@ 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
@ -26,7 +24,6 @@ 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."""
@ -38,28 +35,21 @@ 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.
@ -91,7 +81,7 @@ 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()
@ -101,11 +91,11 @@ 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)
@ -114,7 +104,7 @@ 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:
@ -131,11 +121,11 @@ 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:
@ -145,11 +135,11 @@ 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:
@ -159,10 +149,10 @@ 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
@ -194,7 +184,7 @@ class ModelRunawayDetector:
• Consider using a larger model if available"""
def _explain_repetition(self, issue_type: str) -> str:
return """🔄 The AI got stuck in repetition loops ({issue_type}).
return f"""🔄 The AI got stuck in repetition loops ({issue_type}).
**Why this happens:**
• Small models sometimes repeat when uncertain
@ -253,48 +243,35 @@ 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",
"Try exploration mode: `rag-mini explore .`",
]
)
f"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."""
@ -336,10 +313,7 @@ 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()
@ -347,14 +321,11 @@ 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()

559
mini_rag/llm_synthesizer.py
View File

@ -9,56 +9,35 @@ 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
from pathlib import Path
from typing import Any, List, Optional
import requests
from pathlib import Path
try:
from .llm_safeguards import (
ModelRunawayDetector,
SafeguardConfig,
get_optimal_ollama_parameters,
)
from .system_context import get_system_context
from .llm_safeguards import ModelRunawayDetector, SafeguardConfig, get_optimal_ollama_parameters
except ImportError:
# Graceful fallback if safeguards not available
ModelRunawayDetector = None
SafeguardConfig = None
def get_optimal_ollama_parameters(x):
return {}
def get_system_context(x=None):
return ""
get_optimal_ollama_parameters = lambda x: {}
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
@ -77,169 +56,49 @@ 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 []
def _select_best_model(self) -> str:
"""Select the best available model based on configuration rankings with robust name resolution."""
"""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
):
return self.config.llm.model_rankings[0] # First preferred model
return "qwen2.5:1.5b" # System fallback only if no config
return "qwen2.5:1.5b" # Fallback preference
# 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 using relaxed name resolution
# Find first available model from our ranked list (exact matches first)
for preferred_model in model_rankings:
resolved_model = self._resolve_model_name(preferred_model)
if resolved_model:
logger.info(f"Selected model: {resolved_model} (requested: {preferred_model})")
return resolved_model
for available_model in self.available_models:
# Exact match first (e.g., "qwen3:1.7b" matches "qwen3:1.7b")
if preferred_model.lower() == available_model.lower():
logger.info(f"Selected exact match model: {available_model}")
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(':')
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]):
logger.info(f"Selected version match model: {available_model}")
return available_model
# If no preferred models found, use first available
fallback = self.available_models[0]
logger.warning(f"Using fallback model: {fallback}")
return fallback
def _resolve_model_name(self, configured_model: str) -> Optional[str]:
"""Auto-resolve model names to match what's actually available in Ollama.
This handles common patterns like:
- qwen3:1.7b -> qwen3:1.7b-q8_0
- qwen3:4b -> qwen3:4b-instruct-2507-q4_K_M
- auto -> first available model from ranked preference
"""
logger.debug(f"Resolving model: {configured_model}")
if not self.available_models:
logger.warning("No available models for resolution")
return None
# Handle special 'auto' directive - use smart selection
if configured_model.lower() == 'auto':
logger.info("Using AUTO selection...")
return self._select_best_available_model()
# Direct exact match first (case-insensitive)
for available_model in self.available_models:
if configured_model.lower() == available_model.lower():
logger.info(f"✅ EXACT MATCH: {available_model}")
return available_model
# Relaxed matching - extract base model and size, then find closest match
logger.info(f"No exact match for '{configured_model}', trying relaxed matching...")
match = self._find_closest_model_match(configured_model)
if match:
logger.info(f"✅ FUZZY MATCH: {configured_model} -> {match}")
else:
logger.warning(f"❌ NO MATCH: {configured_model} not found in available models")
return match
def _select_best_available_model(self) -> str:
"""Select the best available model from what's actually installed."""
if not self.available_models:
logger.warning("No models available from Ollama - using fallback")
return "qwen2.5:1.5b" # fallback
logger.info(f"Available models: {self.available_models}")
# Priority order for auto selection - prefer newer and larger models
priority_patterns = [
# Qwen3 series (newest)
"qwen3:8b", "qwen3:4b", "qwen3:1.7b", "qwen3:0.6b",
# Qwen2.5 series
"qwen2.5:3b", "qwen2.5:1.5b", "qwen2.5:0.5b",
# Any other model as fallback
]
# Find first match from priority list
logger.info("Searching for best model match...")
for pattern in priority_patterns:
match = self._find_closest_model_match(pattern)
if match:
logger.info(f"✅ AUTO SELECTED: {match} (matched pattern: {pattern})")
return match
else:
logger.debug(f"No match found for pattern: {pattern}")
# If nothing matches, just use first available
fallback = self.available_models[0]
logger.warning(f"⚠️ Using first available model as fallback: {fallback}")
return fallback
def _find_closest_model_match(self, configured_model: str) -> Optional[str]:
"""Find the closest matching model using relaxed criteria."""
if not self.available_models:
logger.debug(f"No available models to match against for: {configured_model}")
return None
# Extract base model and size from configured model
# e.g., "qwen3:4b" -> ("qwen3", "4b")
if ':' not in configured_model:
base_model = configured_model
size = None
else:
base_model, size_part = configured_model.split(':', 1)
# Extract just the size (remove any suffixes like -q8_0)
size = size_part.split('-')[0] if '-' in size_part else size_part
logger.debug(f"Looking for base model: '{base_model}', size: '{size}'")
# Find all models that match the base model
candidates = []
for available_model in self.available_models:
if ':' not in available_model:
continue
avail_base, avail_full = available_model.split(':', 1)
if avail_base.lower() == base_model.lower():
candidates.append(available_model)
logger.debug(f"Found candidate: {available_model}")
if not candidates:
logger.debug(f"No candidates found for base model: {base_model}")
return None
# If we have a size preference, try to match it
if size:
for candidate in candidates:
# Check if size appears in the model name
if size.lower() in candidate.lower():
logger.debug(f"Size match found: {candidate} contains '{size}'")
return candidate
logger.debug(f"No size match found for '{size}', using first candidate")
# If no size match or no size specified, return first candidate
selected = candidates[0]
logger.debug(f"Returning first candidate: {selected}")
return selected
# Old pattern matching methods removed - using simpler approach now
def _ensure_initialized(self):
"""Lazy initialization with LLM warmup."""
if self._initialized:
@ -258,9 +117,9 @@ 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
@ -268,21 +127,23 @@ 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
@ -295,9 +156,7 @@ 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:
@ -305,37 +164,17 @@ 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()
try:
# Ensure we're initialized
self._ensure_initialized()
# Use the best available model with retry logic
# Use the best available model
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..."
)
self.available_models = self._get_available_models()
if self.model in self.available_models:
model_to_use = self.model
logger.info(f"Model {self.model} found after refresh")
elif self.available_models:
# Fallback to first available model
if self.available_models:
model_to_use = self.available_models[0]
logger.warning(f"Using fallback model: {model_to_use}")
else:
logger.error("No Ollama models available")
return None
@ -380,25 +219,21 @@ 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:
@ -406,53 +241,28 @@ 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:
# Remove thinking content but preserve the rest
cleaned_response = cleaned_response.replace("<think>", "").replace(
"</think>", ""
)
# Clean up extra whitespace that might be left
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)
return cleaned_response.strip()
return raw_response
else:
logger.error(f"Ollama API error: {response.status_code}")
return None
@ -461,11 +271,9 @@ 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 """⚠️ Model Response Issue Detected
return f"""⚠️ Model Response Issue Detected
{explanation}
@ -481,9 +289,7 @@ 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)
@ -491,11 +297,11 @@ 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 """⚠️ **Response Quality Warning** ({issue_type})
return f"""⚠️ **Response Quality Warning** ({issue_type})
{explanation}
@ -510,7 +316,7 @@ 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 """⚠️ Model Response Issue Detected
return f"""⚠️ Model Response Issue Detected
{explanation}
@ -523,20 +329,17 @@ 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:
@ -550,54 +353,44 @@ 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
# "\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
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
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
@ -607,17 +400,11 @@ 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
@ -626,44 +413,31 @@ 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)
if clean_text.strip():
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
@ -679,15 +453,16 @@ 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:
@ -697,16 +472,14 @@ 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
@ -720,76 +493,40 @@ 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 (
ConnectionError,
FileNotFoundError,
IOError,
OSError,
TimeoutError,
requests.RequestException,
):
stop_payload = {"model": model_name, "stop": True}
requests.post(f"{self.ollama_url}/api/generate", json=stop_payload, timeout=2)
except:
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:
continue
# Clean up thinking tags from final response
cleaned_response = full_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>", ""
)
# Clean up extra whitespace that might be left
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)
return cleaned_response.strip()
return full_response.strip()
except Exception as e:
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()
@ -799,33 +536,27 @@ 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
# 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
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
context_parts.append(
"""
context_parts.append(f"""
Result {i} (Score: {score:.3f}):
File: {file_path}
Content: {content[:500]}{'...' if len(content) > 500 else ''}
"""
)
""")
# "\n".join(context_parts) # Unused variable removed
context = "\n".join(context_parts)
# Get system context for better responses
# get_system_context(project_path) # Unused variable removed
# Create synthesis prompt
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.
# Create synthesis prompt with system context
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}"
PROJECT: {project_path.name}
@ -868,33 +599,33 @@ 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:
@ -904,7 +635,7 @@ 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:
@ -915,7 +646,7 @@ Respond with ONLY the JSON, no other text."""
output.append("=" * 50)
output.append("")
output.append("📝 Summary:")
output.append(f"📝 Summary:")
output.append(f" {synthesis.summary}")
output.append("")
@ -937,20 +668,13 @@ 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
@ -969,24 +693,17 @@ 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()

135
mini_rag/non_invasive_watcher.py
View File

@ -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 logging
import queue
import threading
import os
import time
from datetime import datetime
import logging
import threading
import queue
from pathlib import Path
from typing import Optional, Set
from watchdog.events import DirModifiedEvent, FileSystemEventHandler
from datetime import datetime
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler, DirModifiedEvent
from .indexer import ProjectIndexer
@ -74,12 +74,10 @@ 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
@ -102,13 +100,11 @@ 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)
@ -128,9 +124,7 @@ 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
@ -138,20 +132,16 @@ 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):
@ -168,13 +158,11 @@ 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.
@ -190,9 +178,7 @@ 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
@ -202,38 +188,19 @@ 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):
@ -245,16 +212,24 @@ 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()
@ -262,7 +237,7 @@ 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):
@ -307,7 +282,7 @@ 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
@ -319,20 +294,18 @@ 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}")
@ -343,12 +316,12 @@ 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

159
mini_rag/ollama_embeddings.py
View File

@ -3,14 +3,15 @@ Hybrid code embedding module - Ollama primary with ML fallback.
Tries Ollama first, falls back to local ML stack if needed.
"""
import logging
import time
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache
from typing import Any, Dict, List, Optional, Union
import numpy as np
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
logger = logging.getLogger(__name__)
@ -18,9 +19,8 @@ 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:
@ -30,12 +30,8 @@ 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.
@ -74,9 +70,7 @@ 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"
@ -107,8 +101,8 @@ 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")
@ -127,11 +121,7 @@ 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),
]
@ -151,24 +141,22 @@ 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)
@ -179,7 +167,7 @@ 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}")
@ -201,13 +189,16 @@ 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")
@ -229,37 +220,33 @@ 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
@ -267,9 +254,7 @@ 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}")
@ -280,7 +265,7 @@ 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
@ -291,7 +276,7 @@ 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
@ -340,7 +325,7 @@ class OllamaEmbedder:
code = code.strip()
# Normalize whitespace but preserve structure
lines = code.split("\n")
lines = code.split('\n')
processed_lines = []
for line in lines:
@ -350,7 +335,7 @@ 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:
@ -395,36 +380,33 @@ 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
@ -438,9 +420,7 @@ 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.
@ -450,15 +430,13 @@ 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)
@ -466,7 +444,7 @@ 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
@ -485,31 +463,36 @@ 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]:
"""Get human-readable embedding system information for installer."""
status = self.get_status()
mode = status.get("mode", "unknown")
if mode == "ollama":
return {"method": f"Ollama ({status['ollama_model']})", "status": "working"}
# Treat legacy/alternate naming uniformly
if mode in ("fallback", "ml"):
if status["mode"] == "ollama":
return {
"method": f"Ollama ({status['ollama_model']})",
"status": "working"
}
elif status["mode"] == "ml":
return {
"method": f"ML Fallback ({status['fallback_model']})",
"status": "working",
"status": "working"
}
elif status["mode"] == "hash":
return {
"method": "Hash-based (basic similarity)",
"status": "working"
}
else:
return {
"method": "Unknown",
"status": "error"
}
if mode == "hash":
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."""
@ -520,11 +503,7 @@ 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.

11
mini_rag/path_handler.py
View File

@ -4,9 +4,10 @@ Handles forward/backward slashes on any file system.
Robust cross-platform path handling.
"""
import os
import sys
from pathlib import Path
from typing import List, Union
from typing import Union, List
def normalize_path(path: Union[str, Path]) -> str:
@ -24,10 +25,10 @@ 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
@ -119,7 +120,7 @@ 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:
@ -136,8 +137,6 @@ 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)

30
mini_rag/performance.py
View File

@ -3,13 +3,12 @@ Performance monitoring for RAG system.
Track loading times, query times, and resource usage.
"""
import logging
import os
import time
from contextlib import contextmanager
from typing import Any, Dict, Optional
import psutil
import os
from contextlib import contextmanager
from typing import Dict, Any, Optional
import logging
logger = logging.getLogger(__name__)
@ -40,9 +39,9 @@ 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(
@ -52,19 +51,19 @@ 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}:")
@ -74,13 +73,12 @@ 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

43
mini_rag/query_expander.py
View File

@ -33,15 +33,12 @@ disable in CLI for maximum speed.
import logging
import re
import threading
from typing import Optional
from typing import List, Optional
import requests
from .config import RAGConfig
logger = logging.getLogger(__name__)
class QueryExpander:
"""Expands search queries using LLM to improve search recall."""
@ -110,7 +107,7 @@ class QueryExpander:
return None
# Create expansion prompt
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.
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.
Original query: "{query}"
@ -137,18 +134,18 @@ 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)
@ -169,16 +166,12 @@ 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:
@ -207,11 +200,11 @@ 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()):
@ -220,8 +213,8 @@ 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
@ -249,13 +242,10 @@ Expanded query:"""
try:
response = requests.get(f"{self.ollama_url}/api/tags", timeout=5)
return response.status_code == 200
except (ConnectionError, TimeoutError, requests.RequestException):
except:
return False
# Quick test function
def test_expansion():
"""Test the query expander."""
from .config import RAGConfig
@ -274,7 +264,7 @@ def test_expansion():
"authentication",
"error handling",
"database query",
"user interface",
"user interface"
]
print("🔍 Testing Query Expansion:")
@ -282,6 +272,5 @@ def test_expansion():
expanded = expander.expand_query(query)
print(f" '{query}' → '{expanded}'")
if __name__ == "__main__":
test_expansion()

311
mini_rag/search.py
View File

@ -4,33 +4,29 @@ Optimized for code search with relevance scoring.
"""
import logging
from collections import defaultdict
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
from typing import List, Dict, Any, Optional, Tuple
import numpy as np
import pandas as pd
from rank_bm25 import BM25Okapi
from rich.console import Console
from rich.syntax import Syntax
from rich.table import Table
from rich.syntax import Syntax
from rank_bm25 import BM25Okapi
from collections import defaultdict
# 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()
@ -39,8 +35,7 @@ console = Console()
class SearchResult:
"""Represents a single search result."""
def __init__(
self,
def __init__(self,
file_path: str,
content: str,
score: float,
@ -51,8 +46,7 @@ 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
@ -71,17 +65,17 @@ 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:
@ -90,15 +84,17 @@ 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.
@ -107,7 +103,7 @@ 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
@ -132,9 +128,7 @@ 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():
@ -150,9 +144,7 @@ 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.")
@ -194,9 +186,7 @@ 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.
@ -214,81 +204,72 @@ 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,
@ -296,8 +277,7 @@ 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.
@ -344,15 +324,16 @@ 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
@ -377,24 +358,25 @@ 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)
@ -425,20 +407,9 @@ 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):
@ -455,9 +426,7 @@ 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
@ -466,11 +435,11 @@ 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
@ -479,16 +448,14 @@ 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.
@ -512,10 +479,7 @@ 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
@ -530,9 +494,7 @@ 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.
@ -551,12 +513,12 @@ 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:
@ -565,48 +527,49 @@ 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.
@ -624,7 +587,7 @@ 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:
@ -654,7 +617,11 @@ 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 = []
@ -679,7 +646,11 @@ 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 = []
@ -729,12 +700,10 @@ 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.
@ -761,7 +730,7 @@ 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)
@ -771,9 +740,7 @@ 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
@ -782,7 +749,7 @@ class CodeSearcher:
result.language,
theme="monokai",
line_numbers=True,
start_line=result.start_line,
start_line=result.start_line
)
console.print(syntax)
console.print()
@ -790,7 +757,7 @@ 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
@ -798,30 +765,28 @@ 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.

159
mini_rag/server.py
View File

@ -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 Any, Dict, Optional
from typing import Dict, Any, Optional
import logging
import sys
import os
# 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__)
@ -43,30 +43,31 @@ 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
@ -75,16 +76,15 @@ class RAGServer:
else:
# Unix/Linux: Use lsof and kill
import subprocess
try:
result = subprocess.run(
["lso", "-ti", f":{self.port}"],
['lsof', '-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:
except ConnectionError as e:
# Normal disconnection - client closed connection
# This is expected behavior, don't log as error
pass
@ -187,10 +187,13 @@ 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 (ConnectionError, OSError, TypeError, ValueError, socket.error):
except:
pass
finally:
client.close()
@ -198,34 +201,34 @@ 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)
@ -250,10 +253,13 @@ 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
@ -265,48 +271,54 @@ 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: mini-rag 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)
@ -315,14 +327,17 @@ 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:
@ -330,7 +345,7 @@ 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:
@ -338,18 +353,24 @@ 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 (ConnectionError, OSError, TypeError, ValueError, socket.error):
except:
return False
@ -368,20 +389,12 @@ 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

118
mini_rag/smart_chunking.py
View File

@ -3,49 +3,61 @@ 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
from typing import Any, Dict, List
import json
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]:
@ -55,10 +67,10 @@ 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
@ -67,8 +79,8 @@ 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
@ -80,62 +92,58 @@ 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", {})
# sum(languages.values()) # Unused variable removed
languages = project_stats.get('languages', {})
total_files = sum(languages.values())
# 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()

121
mini_rag/system_context.py
View File

@ -1,121 +0,0 @@
"""
System Context Collection for Enhanced RAG Grounding
Collects minimal system information to help the LLM provide better,
context-aware assistance without compromising privacy.
"""
import platform
import sys
from pathlib import Path
from typing import Dict, Optional
class SystemContextCollector:
"""Collects system context information for enhanced LLM grounding."""
@staticmethod
def get_system_context(project_path: Optional[Path] = None) -> str:
"""
Get concise system context for LLM grounding.
Args:
project_path: Current project directory
Returns:
Formatted system context string (max 200 chars for privacy)
"""
try:
# Basic system info
os_name = platform.system()
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
)
# Working directory info
if project_path:
# Use relative or shortened path for privacy
try:
rel_path = project_path.relative_to(Path.home())
path_info = f"~/{rel_path}"
except ValueError:
# If not relative to home, just use folder name
path_info = project_path.name
else:
path_info = Path.cwd().name
# Trim path if too long for our 200-char limit
if len(path_info) > 50:
path_info = f".../{path_info[-45:]}"
# Command style hints
cmd_style = "rag.bat" if os_name == "Windows" else "./rag-mini"
# Format concise context
context = f"[{os_short} {python_ver}, {path_info}, use {cmd_style}]"
# Ensure we stay under 200 chars
if len(context) > 200:
context = context[:197] + "...]"
return context
except Exception:
# Fallback to minimal info if anything fails
return f"[{platform.system()}, Python {sys.version_info.major}.{sys.version_info.minor}]"
@staticmethod
def get_command_context(os_name: Optional[str] = None) -> Dict[str, str]:
"""
Get OS-appropriate command examples.
Returns:
Dictionary with command patterns for the current OS
"""
if os_name is None:
os_name = platform.system()
if os_name == "Windows":
return {
"launcher": "rag.bat",
"index": "rag.bat index C:\\path\\to\\project",
"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",
}
else:
return {
"launcher": "./rag-mini",
"index": "./rag-mini index /path/to/project",
"search": './rag-mini search /path/to/project "query"',
"explore": "./rag-mini explore /path/to/project",
"path_sep": "/",
"example_path": "~/Documents/myproject",
}
def get_system_context(project_path: Optional[Path] = None) -> str:
"""Convenience function to get system context."""
return SystemContextCollector.get_system_context(project_path)
def get_command_context() -> Dict[str, str]:
"""Convenience function to get command context."""
return SystemContextCollector.get_command_context()
# Test function
if __name__ == "__main__":
print("System Context Test:")
print(f"Context: {get_system_context()}")
print(f"Context with path: {get_system_context(Path('/tmp/test'))}")
print()
print("Command Context:")
cmds = get_command_context()
for key, value in cmds.items():
print(f" {key}: {value}")

482
mini_rag/updater.py
View File

@ -1,482 +0,0 @@
#!/usr/bin/env python3
"""
FSS-Mini-RAG Auto-Update System
Provides seamless GitHub-based updates with user-friendly interface.
Checks for new releases, downloads updates, and handles installation safely.
"""
import json
import os
import shutil
import subprocess
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
release_notes: str
published_at: str
is_newer: bool
class UpdateChecker:
"""
Handles checking for and applying updates from GitHub releases.
Features:
- Checks GitHub API for latest releases
- Downloads and applies updates safely with backup
- Respects user preferences and rate limiting
- Provides graceful fallbacks if network unavailable
"""
def __init__(
self,
repo_owner: str = "FSSCoding",
repo_name: str = "Fss-Mini-Rag",
current_version: str = "2.1.0",
):
self.repo_owner = repo_owner
self.repo_name = repo_name
self.current_version = current_version
self.github_api_url = f"https://api.github.com/repos/{repo_owner}/{repo_name}"
self.check_frequency_hours = 24 # Check once per day
# Paths
self.app_root = Path(__file__).parent.parent
self.cache_file = self.app_root / ".update_cache.json"
self.backup_dir = self.app_root / ".backup"
# User preferences (graceful fallback if config unavailable)
try:
self.config = ConfigManager(self.app_root)
except Exception:
self.config = None
def should_check_for_updates(self) -> bool:
"""
Determine if we should check for updates now.
Respects:
- User preference to disable updates
- Rate limiting (once per day by default)
- Network availability
"""
if not REQUESTS_AVAILABLE:
return False
# Check user preference
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:
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
):
return False
except (json.JSONDecodeError, ValueError, KeyError):
pass # Ignore cache errors, will check anyway
return True
def check_for_updates(self) -> Optional[UpdateInfo]:
"""
Check GitHub API for the latest release.
Returns:
UpdateInfo if an update is available, None otherwise
"""
if not REQUESTS_AVAILABLE:
return None
try:
# Get latest release from GitHub API
response = requests.get(
f"{self.github_api_url}/releases/latest",
timeout=10,
headers={"Accept": "application/vnd.github.v3+json"},
)
if response.status_code != 200:
return None
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", "")
# 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")
break
# Fallback to source code zip
if not download_url:
download_url = f"https://github.com/{self.repo_owner}/{self.repo_name}/archive/refs/tags/v{latest_version}.zip"
# Check if this is a newer version
is_newer = self._is_version_newer(latest_version, self.current_version)
# Update cache
self._update_cache(latest_version, is_newer)
if is_newer:
return UpdateInfo(
version=latest_version,
release_url=release_url,
download_url=download_url,
release_notes=release_notes,
published_at=published_at,
is_newer=True,
)
except Exception:
# Silently fail for network issues - don't interrupt user experience
pass
return None
def _is_version_newer(self, latest: str, current: str) -> bool:
"""
Compare version strings to determine if latest is newer.
Simple semantic version comparison supporting:
- Major.Minor.Patch (e.g., 2.1.0)
- Major.Minor (e.g., 2.1)
"""
def version_tuple(v):
return tuple(map(int, (v.split("."))))
try:
return version_tuple(latest) > version_tuple(current)
except (ValueError, AttributeError):
# If version parsing fails, assume it's newer to be safe
return latest != current
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,
}
try:
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]:
"""
Download the update package to a temporary location.
Args:
update_info: Information about the update to download
progress_callback: Optional callback for progress updates
Returns:
Path to downloaded file, or None if download failed
"""
if not REQUESTS_AVAILABLE:
return None
try:
# Create temporary file for download
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))
downloaded = 0
with open(tmp_path, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
downloaded += len(chunk)
if progress_callback and total_size > 0:
progress_callback(downloaded, total_size)
return tmp_path
except Exception:
# Clean up on error
if "tmp_path" in locals() and tmp_path.exists():
tmp_path.unlink()
return None
def create_backup(self) -> bool:
"""
Create a backup of the current installation.
Returns:
True if backup created successfully
"""
try:
# Remove old backup if it exists
if self.backup_dir.exists():
shutil.rmtree(self.backup_dir)
# Create new backup
self.backup_dir.mkdir(exist_ok=True)
# 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",
]
for item in important_items:
src = self.app_root / item
if src.exists():
if src.is_dir():
shutil.copytree(src, self.backup_dir / item)
else:
shutil.copy2(src, self.backup_dir / item)
return True
except Exception:
return False
def apply_update(self, update_package_path: Path, update_info: UpdateInfo) -> bool:
"""
Apply the downloaded update.
Args:
update_package_path: Path to the downloaded update package
update_info: Information about the update
Returns:
True if update applied successfully
"""
try:
# Extract to temporary directory first
with tempfile.TemporaryDirectory() as tmp_dir:
tmp_path = Path(tmp_dir)
# Extract the archive
with zipfile.ZipFile(update_package_path, "r") as zip_ref:
zip_ref.extractall(tmp_path)
# Find the extracted directory (may be nested)
extracted_dirs = [d for d in tmp_path.iterdir() if d.is_dir()]
if not extracted_dirs:
return False
source_dir = extracted_dirs[0]
# 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",
]
for item in important_items:
src = source_dir / item
dst = self.app_root / item
if src.exists():
if dst.exists():
if dst.is_dir():
shutil.rmtree(dst)
else:
dst.unlink()
if src.is_dir():
shutil.copytree(src, dst)
else:
shutil.copy2(src, dst)
# Update version info
self._update_version_info(update_info.version)
return True
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"
if init_file.exists():
try:
content = init_file.read_text()
updated_content = content.replace(
f'__version__ = "{self.current_version}"',
f'__version__ = "{new_version}"',
)
init_file.write_text(updated_content)
except Exception:
pass
def rollback_update(self) -> bool:
"""
Rollback to the backup version if update failed.
Returns:
True if rollback successful
"""
if not self.backup_dir.exists():
return False
try:
# Restore from backup
for item in self.backup_dir.iterdir():
dst = self.app_root / item.name
if dst.exists():
if dst.is_dir():
shutil.rmtree(dst)
else:
dst.unlink()
if item.is_dir():
shutil.copytree(item, dst)
else:
shutil.copy2(item, dst)
return True
except Exception:
return False
def restart_application(self):
"""Restart the application after update."""
try:
# Sanitize arguments to prevent command injection
safe_argv = [sys.executable]
for arg in sys.argv[1:]: # Skip sys.argv[0] (script name)
# Only allow safe arguments - alphanumeric, dashes, dots, slashes
if isinstance(arg, str) and len(arg) < 200: # Reasonable length limit
# Simple whitelist of safe characters
import re
if re.match(r'^[a-zA-Z0-9._/-]+$', arg):
safe_argv.append(arg)
# Restart with sanitized arguments
if sys.platform.startswith("win"):
# Windows
subprocess.Popen(safe_argv)
else:
# Unix-like systems
os.execv(sys.executable, safe_argv)
except Exception:
# If restart fails, just exit gracefully
print("\n✅ Update complete! Please restart the application manually.")
sys.exit(0)
def get_legacy_notification() -> Optional[str]:
"""
Check if this is a legacy version that needs urgent notification.
For users who downloaded before the auto-update system.
"""
try:
# 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
# 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"
if init_file.exists():
content = init_file.read_text()
if '__version__ = "2.0.' in content or '__version__ = "1.' in content:
return """
🚨 IMPORTANT UPDATE AVAILABLE 🚨
Your version of FSS-Mini-RAG is missing critical updates!
🔧 Recent improvements include:
• Fixed LLM response formatting issues
• Added context window configuration
• Improved Windows installer reliability
• Added auto-update system (this notification!)
📥 Please update by downloading the latest version:
https://github.com/FSSCoding/Fss-Mini-Rag/releases/latest
💡 After updating, you'll get automatic update notifications!
"""
except Exception:
pass
return None
# Global convenience functions
_updater_instance = None
def check_for_updates() -> Optional[UpdateInfo]:
"""Global function to check for updates."""
global _updater_instance
if _updater_instance is None:
_updater_instance = UpdateChecker()
if _updater_instance.should_check_for_updates():
return _updater_instance.check_for_updates()
return None
def get_updater() -> UpdateChecker:
"""Get the global updater instance."""
global _updater_instance
if _updater_instance is None:
_updater_instance = UpdateChecker()
return _updater_instance

28
mini_rag/venv_checker.py
View File

@ -4,27 +4,25 @@ Virtual Environment Checker
Ensures scripts run in proper Python virtual environment for consistency and safety.
"""
import os
import sys
import os
import sysconfig
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 (hasattr(sys, "base_prefix") and sys.base_prefix != sys.prefix) # virtualenv
or os.environ.get("VIRTUAL_ENV") is not None # venv/pyvenv # Environment variable
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
)
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]:
"""
@ -40,20 +38,16 @@ 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()
@ -98,7 +92,6 @@ 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.
@ -126,15 +119,11 @@ 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")
@ -149,6 +138,5 @@ def main():
if not is_correct:
show_venv_warning("test script")
if __name__ == "__main__":
main()

74
mini_rag/watcher.py
View File

@ -4,21 +4,14 @@ Monitors project files and updates the index incrementally.
"""
import logging
import queue
import threading
import queue
import time
from datetime import datetime
from pathlib import Path
from typing import Callable, Optional, Set
from watchdog.events import (
FileCreatedEvent,
FileDeletedEvent,
FileModifiedEvent,
FileMovedEvent,
FileSystemEventHandler,
)
from typing import Set, Optional, Callable
from datetime import datetime
from watchdog.observers import Observer
from watchdog.events import FileSystemEventHandler, FileModifiedEvent, FileCreatedEvent, FileDeletedEvent, FileMovedEvent
from .indexer import ProjectIndexer
@ -80,13 +73,11 @@ 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.
@ -155,14 +146,12 @@ 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.
@ -191,10 +180,10 @@ 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):
@ -210,20 +199,27 @@ 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):
@ -319,29 +315,27 @@ 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
@ -377,8 +371,6 @@ class FileWatcher:
# Convenience function
def watch_project(project_path: Path, callback: Optional[Callable] = None):
"""
Watch a project for changes and update index automatically.

35
mini_rag/windows_console_fix.py
View File

@ -3,9 +3,9 @@ Windows Console Unicode/Emoji Fix
Reliable Windows console Unicode/emoji support for 2025.
"""
import io
import os
import sys
import os
import io
def fix_windows_console():
@ -14,33 +14,28 @@ 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 (OSError, subprocess.SubprocessError):
subprocess.run(['chcp', '65001'], shell=True, capture_output=True)
except:
pass
@ -49,8 +44,6 @@ fix_windows_console()
# Test function to verify it works
def test_emojis():
"""Test that emojis work properly."""
print("Testing emoji output:")

48
pyproject.toml
View File

@ -1,48 +0,0 @@
[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
)/
'''
[build-system]
requires = ["setuptools", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "mini-rag"
version = "2.1.0"
[project.scripts]
rag-mini = "mini_rag.cli:cli"
[tool.setuptools]
packages = ["mini_rag"]

5
rag-mini
View File

@ -60,7 +60,6 @@ attempt_auto_setup() {
echo -e "${GREEN}✅ Created virtual environment${NC}" >&2
# Step 2: Install dependencies
echo -e "${YELLOW}📦 Installing dependencies (this may take 1-2 minutes)...${NC}" >&2
if ! "$SCRIPT_DIR/.venv/bin/pip" install -r "$SCRIPT_DIR/requirements.txt" >/dev/null 2>&1; then
return 1 # Dependency installation failed
fi
@ -328,9 +327,9 @@ main() {
shift
exec "$PYTHON" "$SCRIPT_DIR/mini_rag/fast_server.py" "$@"
;;
"index"|"search"|"explore"|"status"|"update"|"check-update")
"index"|"search"|"explore"|"status")
# Direct CLI commands - call Python script
exec "$PYTHON" "$SCRIPT_DIR/bin/rag-mini.py" "$@"
exec "$PYTHON" "$SCRIPT_DIR/rag-mini.py" "$@"
;;
*)
# Unknown command - show help

472
bin/rag-mini.py → rag-mini.py
View File

@ -6,35 +6,21 @@ 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.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
from mini_rag.ollama_embeddings import OllamaEmbedder
from mini_rag.llm_synthesizer import LLMSynthesizer
from mini_rag.explorer import CodeExplorer
except ImportError as e:
print("❌ Error: Missing dependencies!")
print()
@ -56,11 +42,10 @@ 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:
@ -69,7 +54,7 @@ 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...")
@ -77,9 +62,9 @@ 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")
@ -93,13 +78,13 @@ 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}")
@ -133,18 +118,17 @@ 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)
@ -152,18 +136,14 @@ 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
@ -184,43 +164,29 @@ 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(" Content:")
content_lines = result.content.strip().split("\n")
print(f" 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(" Use --verbose or rag-mini-enhanced for full context")
print(f" Use --verbose or rag-mini-enhanced for full context")
print()
# LLM Synthesis if requested
if synthesize:
print("🧠 Generating LLM synthesis...")
# 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,
)
synthesizer = LLMSynthesizer()
if synthesizer.is_available():
synthesis = synthesizer.synthesize_search_results(query, results, project_path)
@ -228,14 +194,10 @@ 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")
@ -244,18 +206,8 @@ 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 (
ConnectionError,
FileNotFoundError,
IOError,
OSError,
TimeoutError,
TypeError,
ValueError,
requests.RequestException,
socket.error,
):
(rag_dir / 'last_search').write_text(query)
except:
pass # Don't fail if we can't save
except Exception as e:
@ -274,12 +226,11 @@ 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:
@ -287,21 +238,21 @@ 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}")
@ -327,152 +278,37 @@ 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:
print(f" ❌ Status check failed: {e}")
print()
# Check LLM status and show actual vs configured model
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,
)
if synthesizer.is_available():
synthesizer._ensure_initialized()
actual_model = synthesizer.model
config_model = config.llm.synthesis_model
if config_model == "auto":
print(f" ✅ Auto-selected: {actual_model}")
elif config_model == actual_model:
print(f" ✅ Using configured: {actual_model}")
else:
print(" ⚠️ Model mismatch!")
print(f" Configured: {config_model}")
print(f" Actually using: {actual_model}")
print(" (Configured model may not be installed)")
print(f" Config file: {config_manager.config_path}")
else:
print(" ❌ Ollama not available")
print(" Start with: ollama serve")
except Exception as e:
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 (FileNotFoundError, IOError, OSError, TypeError, ValueError):
print(f"\n🔍 Last search: \"{last_query}\"")
except:
pass
except Exception as e:
print(f"❌ Status check failed: {e}")
sys.exit(1)
def show_model_status(project_path: Path):
"""Show detailed model status and selection information."""
from mini_rag.config import ConfigManager
print("🤖 Model Status Report")
print("=" * 50)
try:
# Load config
config_manager = ConfigManager()
config = config_manager.load_config(project_path)
# Create LLM synthesizer to check models
synthesizer = LLMSynthesizer(model=config.llm.synthesis_model, config=config)
# Show configured model
print(f"📋 Configured model: {config.llm.synthesis_model}")
# Show available models
available_models = synthesizer.available_models
if available_models:
print(f"\n📦 Available models ({len(available_models)}):")
# Group models by series
qwen3_models = [m for m in available_models if m.startswith('qwen3:')]
qwen25_models = [m for m in available_models if m.startswith('qwen2.5')]
other_models = [m for m in available_models if not (m.startswith('qwen3:') or m.startswith('qwen2.5'))]
if qwen3_models:
print(" 🟢 Qwen3 series (recommended):")
for model in qwen3_models:
is_selected = synthesizer._resolve_model_name(config.llm.synthesis_model) == model
marker = " ✅" if is_selected else " "
print(f"{marker} {model}")
if qwen25_models:
print(" 🟡 Qwen2.5 series:")
for model in qwen25_models:
is_selected = synthesizer._resolve_model_name(config.llm.synthesis_model) == model
marker = " ✅" if is_selected else " "
print(f"{marker} {model}")
if other_models:
print(" 🔵 Other models:")
for model in other_models[:10]: # Limit to first 10
is_selected = synthesizer._resolve_model_name(config.llm.synthesis_model) == model
marker = " ✅" if is_selected else " "
print(f"{marker} {model}")
else:
print("\n❌ No models available from Ollama")
print(" Make sure Ollama is running: ollama serve")
print(" Install models with: ollama pull qwen3:4b")
# Show resolution result
resolved_model = synthesizer._resolve_model_name(config.llm.synthesis_model)
if resolved_model:
if resolved_model != config.llm.synthesis_model:
print(f"\n🔄 Model resolution: {config.llm.synthesis_model} -> {resolved_model}")
else:
print(f"\n✅ Using exact model match: {resolved_model}")
else:
print(f"\n❌ Model '{config.llm.synthesis_model}' not found!")
print(" Consider changing your model in the config file")
print(f"\n📄 Config file: {config_manager.config_path}")
print(" Edit this file to change your model preference")
except Exception as e:
print(f"❌ Model 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:
@ -504,7 +340,7 @@ 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
@ -517,9 +353,8 @@ 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
@ -534,27 +369,23 @@ 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(
"""
elif question in ['2'] or question.lower() == 'status':
print(f"""
📊 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?",
@ -562,7 +393,7 @@ 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}")
@ -581,7 +412,7 @@ 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
@ -613,132 +444,11 @@ 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:
return
try:
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)"
)
except Exception:
# Silently ignore any update check failures
pass
def handle_check_update():
"""Handle the check-update command."""
if not UPDATER_AVAILABLE:
print("❌ Update system not available")
print("💡 Try updating to the latest version manually from GitHub")
return
try:
print("🔍 Checking for updates...")
update_info = check_for_updates()
if update_info:
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
for line in notes_lines:
if line.strip():
print(f" {line.strip()}")
print(f"\n🔗 Release Page: {update_info.release_url}")
print("\n🚀 To install: rag-mini update")
print("💡 Or update manually from GitHub releases")
else:
print("✅ You're already on the latest version!")
except Exception as e:
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:
print("❌ Update system not available")
print("💡 Try updating manually from GitHub")
return
try:
print("🔍 Checking for updates...")
update_info = check_for_updates()
if not update_info:
print("✅ You're already on the latest version!")
return
print(f"\n🎉 Update Available: v{update_info.version}")
print("=" * 50)
# Show brief release notes
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"]:
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
bar_length = 30
filled = int(bar_length * downloaded / total)
bar = "█" * filled + "░" * (bar_length - filled)
print(f"\r [{bar}] {percent:.1f}%", end="", flush=True)
# Download and install
update_package = updater.download_update(update_info, show_progress)
if not update_package:
print("\n❌ Download failed. Please try again later.")
return
print("\n💾 Creating backup...")
if not updater.create_backup():
print("⚠️ Backup failed, but continuing anyway...")
print("🔄 Installing update...")
if updater.apply_update(update_package, update_info):
print("✅ Update successful!")
print("🚀 Restarting...")
updater.restart_application()
else:
print("❌ Update failed.")
print("🔙 Attempting rollback...")
if updater.rollback_update():
print("✅ Rollback successful.")
else:
print("❌ Rollback failed. You may need to reinstall.")
else:
print("Update cancelled.")
except Exception as e:
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
@ -753,38 +463,23 @@ 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
rag-mini models /path/to/project # Show model status and selection
""",
"""
)
parser.add_argument(
"command",
choices=["index", "search", "explore", "status", "models", "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'],
help='Command to execute')
parser.add_argument('project_path', type=Path,
help='Path to project directory (REQUIRED)')
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()
@ -792,19 +487,6 @@ Examples:
if args.verbose:
logging.getLogger().setLevel(logging.INFO)
# Handle update commands first (don't require project_path)
if args.command == "check-update":
handle_check_update()
return
elif args.command == "update":
handle_update()
return
# All other commands require project_path
if not args.project_path:
print("❌ Project path required for this command")
sys.exit(1)
# Validate project path
if not args.project_path.exists():
print(f"❌ Project path does not exist: {args.project_path}")
@ -814,24 +496,18 @@ Examples:
print(f"❌ Project path is not a directory: {args.project_path}")
sys.exit(1)
# Show discrete update notification for regular commands (non-intrusive)
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)
elif args.command == "models":
show_model_status(args.project_path)
if __name__ == "__main__":
if __name__ == '__main__':
main()

2
rag-tui
View File

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

891
bin/rag-tui.py → rag-tui.py
View File

File diff suppressed because it is too large Load Diff

3
requirements.txt
View File

@ -20,6 +20,3 @@ PyYAML>=6.0.0
# Text search utilities (lightweight)
rank-bm25>=0.2.2
# System monitoring
psutil

282
scripts/quick-github-setup.sh
View File

@ -1,282 +0,0 @@
#!/bin/bash
# Quick GitHub Setup with Auto-Update Template
# One-command setup for converting projects to GitHub with auto-update
set -e
# Colors for better UX
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[1;33m'
BLUE='\033[0;34m'
CYAN='\033[0;36m'
BOLD='\033[1m'
NC='\033[0m'
# Script directory
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
show_help() {
echo -e "${BOLD}Quick GitHub Setup with Auto-Update Template${NC}"
echo ""
echo "Usage: $0 [OPTIONS] <project_path>"
echo ""
echo "Options:"
echo " -o, --owner OWNER GitHub username/organization (required)"
echo " -n, --name NAME Repository name (required)"
echo " -t, --type TYPE Project type (python|general, default: python)"
echo " --no-auto-update Disable auto-update system"
echo " --no-push Don't push to GitHub automatically"
echo " -h, --help Show this help"
echo ""
echo "Examples:"
echo " $0 . -o myusername -n my-project"
echo " $0 /path/to/project -o myorg -n cool-tool --type python"
echo " $0 existing-project -o me -n project --no-auto-update"
echo ""
}
main() {
local project_path=""
local repo_owner=""
local repo_name=""
local project_type="python"
local auto_update=true
local auto_push=true
# Parse arguments
while [[ $# -gt 0 ]]; do
case $1 in
-o|--owner)
repo_owner="$2"
shift 2
;;
-n|--name)
repo_name="$2"
shift 2
;;
-t|--type)
project_type="$2"
shift 2
;;
--no-auto-update)
auto_update=false
shift
;;
--no-push)
auto_push=false
shift
;;
-h|--help)
show_help
exit 0
;;
-*)
echo -e "${RED}❌ Unknown option: $1${NC}"
show_help
exit 1
;;
*)
if [ -z "$project_path" ]; then
project_path="$1"
else
echo -e "${RED}❌ Multiple project paths specified${NC}"
exit 1
fi
shift
;;
esac
done
# Validate required arguments
if [ -z "$project_path" ]; then
echo -e "${RED}❌ Project path required${NC}"
show_help
exit 1
fi
if [ -z "$repo_owner" ]; then
echo -e "${RED}❌ GitHub owner required (use -o/--owner)${NC}"
show_help
exit 1
fi
if [ -z "$repo_name" ]; then
echo -e "${RED}❌ Repository name required (use -n/--name)${NC}"
show_help
exit 1
fi
# Convert to absolute path
project_path=$(realpath "$project_path")
if [ ! -d "$project_path" ]; then
echo -e "${RED}❌ Project directory does not exist: $project_path${NC}"
exit 1
fi
echo -e "${BOLD}${CYAN}🚀 Quick GitHub Setup${NC}"
echo -e "${BOLD}===================${NC}"
echo ""
echo -e "📁 Project: ${BOLD}$project_path${NC}"
echo -e "👤 Owner: ${BOLD}$repo_owner${NC}"
echo -e "📦 Repository: ${BOLD}$repo_name${NC}"
echo -e "🔧 Type: ${BOLD}$project_type${NC}"
echo -e "🔄 Auto-update: ${BOLD}$([ "$auto_update" = true ] && echo "Enabled" || echo "Disabled")${NC}"
echo -e "🚀 Auto-push: ${BOLD}$([ "$auto_push" = true ] && echo "Enabled" || echo "Disabled")${NC}"
echo ""
# Confirm with user
read -p "Continue with setup? [Y/n]: " -n 1 -r
echo
if [[ ! $REPLY =~ ^[Yy]$ ]] && [[ ! -z $REPLY ]]; then
echo "Setup cancelled."
exit 0
fi
cd "$project_path"
# Step 1: Setup template
echo -e "${YELLOW}[1/6]${NC} Setting up GitHub template..."
python_script="$SCRIPT_DIR/setup-github-template.py"
if [ ! -f "$python_script" ]; then
echo -e "${RED}❌ Setup script not found: $python_script${NC}"
exit 1
fi
local setup_args="$project_path --owner $repo_owner --name $repo_name --type $project_type"
if [ "$auto_update" = false ]; then
setup_args="$setup_args --no-auto-update"
fi
if ! python "$python_script" $setup_args; then
echo -e "${RED}❌ Template setup failed${NC}"
exit 1
fi
echo -e "${GREEN}✅ Template setup completed${NC}"
# Step 2: Initialize git if needed
echo -e "${YELLOW}[2/6]${NC} Checking git repository..."
if [ ! -d ".git" ]; then
echo "Initializing git repository..."
git init
git branch -M main
fi
echo -e "${GREEN}✅ Git repository ready${NC}"
# Step 3: Add and commit changes
echo -e "${YELLOW}[3/6]${NC} Committing template changes..."
git add .
if git diff --cached --quiet; then
echo "No changes to commit"
else
git commit -m "🚀 Add GitHub template with auto-update system
- Added GitHub Actions workflows (CI, release, template-sync)
- Integrated auto-update system for seamless updates
- Created issue templates and project configuration
- Setup automated release and testing pipelines
Generated with FSS GitHub Template System"
fi
echo -e "${GREEN}✅ Changes committed${NC}"
# Step 4: Setup GitHub remote if needed
echo -e "${YELLOW}[4/6]${NC} Setting up GitHub remote..."
github_url="https://github.com/$repo_owner/$repo_name.git"
if ! git remote get-url origin >/dev/null 2>&1; then
git remote add origin "$github_url"
echo "Added GitHub remote: $github_url"
else
existing_url=$(git remote get-url origin)
if [ "$existing_url" != "$github_url" ]; then
echo "Warning: Origin remote exists with different URL: $existing_url"
echo "Expected: $github_url"
read -p "Update remote to GitHub? [Y/n]: " -n 1 -r
echo
if [[ $REPLY =~ ^[Yy]$ ]] || [[ -z $REPLY ]]; then
git remote set-url origin "$github_url"
echo "Updated remote to: $github_url"
fi
else
echo "GitHub remote already configured"
fi
fi
echo -e "${GREEN}✅ GitHub remote configured${NC}"
# Step 5: Create GitHub repository (if possible)
echo -e "${YELLOW}[5/6]${NC} Creating GitHub repository..."
if command -v gh >/dev/null 2>&1; then
# Check if repo exists
if ! gh repo view "$repo_owner/$repo_name" >/dev/null 2>&1; then
echo "Creating GitHub repository..."
if gh repo create "$repo_owner/$repo_name" --private --source=. --remote=origin --push; then
echo -e "${GREEN}✅ GitHub repository created and pushed${NC}"
auto_push=false # Already pushed
else
echo -e "${YELLOW}⚠️ Failed to create repository with gh CLI${NC}"
echo "You'll need to create it manually at: https://github.com/new"
fi
else
echo "Repository already exists on GitHub"
fi
else
echo -e "${YELLOW}⚠️ GitHub CLI (gh) not installed${NC}"
echo "Please create the repository manually at: https://github.com/new"
echo "Repository name: $repo_name"
fi
# Step 6: Push to GitHub
if [ "$auto_push" = true ]; then
echo -e "${YELLOW}[6/6]${NC} Pushing to GitHub..."
if git push -u origin main; then
echo -e "${GREEN}✅ Pushed to GitHub${NC}"
else
echo -e "${YELLOW}⚠️ Push failed - you may need to create the repository first${NC}"
echo "Create it at: https://github.com/$repo_owner/$repo_name"
fi
else
echo -e "${YELLOW}[6/6]${NC} Skipping auto-push"
fi
# Success summary
echo ""
echo -e "${BOLD}${GREEN}🎉 Setup Complete!${NC}"
echo -e "${BOLD}================${NC}"
echo ""
echo -e "📦 Repository: ${BLUE}https://github.com/$repo_owner/$repo_name${NC}"
echo ""
echo -e "${BOLD}🚀 Next Steps:${NC}"
echo "1. Create your first release:"
echo -e " ${CYAN}git tag v1.0.0 && git push --tags${NC}"
echo ""
echo "2. Test auto-update system:"
echo -e " ${CYAN}./$repo_name check-update${NC}"
echo ""
echo "3. View GitHub Actions:"
echo -e " ${BLUE}https://github.com/$repo_owner/$repo_name/actions${NC}"
echo ""
if [ "$auto_update" = true ]; then
echo -e "${BOLD}🔄 Auto-Update Enabled:${NC}"
echo " • Users will get update notifications automatically"
echo " • Updates install with one command"
echo " • Safe backup and rollback included"
echo ""
fi
echo -e "💡 ${BOLD}Pro Tip:${NC} Future releases will automatically notify users!"
echo ""
}
# Run main function
main "$@"

503
scripts/setup-github-template.py
View File

@ -1,503 +0,0 @@
#!/usr/bin/env python3
"""
GitHub Template Setup Script
Converts a project to use the auto-update template system.
This script helps migrate projects from Gitea to GitHub with auto-update capability.
"""
import argparse
import json
import shutil
import sys
from pathlib import Path
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,
) -> bool:
"""
Setup a project to use the GitHub auto-update template system.
Args:
project_path: Path to the project directory
repo_owner: GitHub username/organization
repo_name: GitHub repository name
project_type: Type of project (python, general)
include_auto_update: Whether to include auto-update system
Returns:
True if setup successful
"""
print(f"🚀 Setting up GitHub template for: {repo_owner}/{repo_name}")
print(f"📁 Project path: {project_path}")
print(f"🔧 Project type: {project_type}")
print(f"🔄 Auto-update: {'Enabled' if include_auto_update else 'Disabled'}")
print()
try:
# Create .github directory structure
github_dir = project_path / ".github"
workflows_dir = github_dir / "workflows"
templates_dir = github_dir / "ISSUE_TEMPLATE"
# Ensure directories exist
workflows_dir.mkdir(parents=True, exist_ok=True)
templates_dir.mkdir(parents=True, exist_ok=True)
# 1. Setup GitHub Actions workflows
setup_workflows(workflows_dir, repo_owner, repo_name, project_type)
# 2. Setup auto-update system if requested
if include_auto_update:
setup_auto_update_system(project_path, repo_owner, repo_name)
# 3. Create issue templates
setup_issue_templates(templates_dir)
# 4. Create/update project configuration
setup_project_config(project_path, repo_owner, repo_name, include_auto_update)
# 5. Create README template if needed
setup_readme_template(project_path, repo_owner, repo_name)
print("✅ GitHub template setup completed successfully!")
print()
print("📋 Next Steps:")
print("1. Commit and push these changes to GitHub")
print("2. Create your first release: git tag v1.0.0 && git push --tags")
print("3. Test auto-update system: ./project check-update")
print("4. Enable GitHub Pages for documentation (optional)")
print()
return True
except Exception as e:
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 = """name: Auto Release & Update System
on:
push:
tags:
- 'v*'
workflow_dispatch:
inputs:
version:
description: 'Version to release (e.g., v1.2.3)'
required: true
type: string
jobs:
create-release:
runs-on: ubuntu-latest
permissions:
contents: write
steps:
- name: Checkout code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.11'
- name: Extract version
id: version
run: |
if [ "${{{{ github.event_name }}}}" = "workflow_dispatch" ]; then
VERSION="${{{{ github.event.inputs.version }}}}"
else
VERSION=${{GITHUB_REF#refs/tags/}}
fi
echo "version=$VERSION" >> $GITHUB_OUTPUT
echo "clean_version=${{VERSION#v}}" >> $GITHUB_OUTPUT
- name: Update version in code
run: |
VERSION="${{{{ steps.version.outputs.clean_version }}}}"
# Update version files
find . -name "__init__.py" -exec sed -i 's/__version__ = ".*"/__version__ = "'$VERSION'"/' {{}} +
- name: Generate release notes
id: release_notes
run: |
VERSION="${{{{ steps.version.outputs.version }}}}"
# Get commits since last tag
LAST_TAG=$(git describe --tags --abbrev=0 HEAD~1 2>/dev/null || echo "")
if [ -n "$LAST_TAG" ]; then
COMMITS=$(git log --oneline $LAST_TAG..HEAD --pretty=format:"• %s")
else
COMMITS=$(git log --oneline --pretty=format:"• %s" | head -10)
fi
# Create release notes
cat > release_notes.md << EOF
## What's New in $VERSION
### 🚀 Changes
$COMMITS
### 📥 Installation
Download and install the latest version:
```bash
curl -sSL https://github.com/{repo_owner}/{repo_name}/releases/latest/download/install.sh | bash
```
### 🔄 Auto-Update
If you have auto-update support:
```bash
./{repo_name} check-update
./{repo_name} update
```
EOF
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{{{ steps.version.outputs.version }}}}
name: Release ${{{{ steps.version.outputs.version }}}}
body_path: release_notes.md
draft: false
prerelease: false
files: |
*.sh
*.bat
requirements.txt
"""
(workflows_dir / "release.yml").write_text(release_workflow)
# CI workflow for Python projects
if project_type == "python":
ci_workflow = """name: CI/CD Pipeline
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main ]
jobs:
test:
runs-on: ${{{{ matrix.os }}}}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python ${{{{ matrix.python-version }}}}
uses: actions/setup-python@v5
with:
python-version: ${{{{ matrix.python-version }}}}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Run tests
run: |
python -c "import {repo_name.replace('-', '_')}; print('✅ Import successful')"
- name: Test auto-update system
run: |
python -c "
try:
from {repo_name.replace('-', '_')}.updater import UpdateChecker
print('✅ Auto-update system available')
except ImportError:
print('⚠️ Auto-update not available')
"
"""
(workflows_dir / "ci.yml").write_text(ci_workflow)
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."""
print("🔄 Setting up auto-update system...")
# Copy updater.py from FSS-Mini-RAG as template
template_updater = Path(__file__).parent.parent / "mini_rag" / "updater.py"
if template_updater.exists():
# Create project module directory if needed
module_name = repo_name.replace("-", "_")
module_dir = project_path / module_name
module_dir.mkdir(exist_ok=True)
# Copy and customize updater
target_updater = module_dir / "updater.py"
shutil.copy2(template_updater, target_updater)
# 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}"'
)
target_updater.write_text(content)
# Update __init__.py to include updater
init_file = module_dir / "__init__.py"
if init_file.exists():
content = init_file.read_text()
if "updater" not in content:
content += """
# Auto-update system (graceful import for legacy versions)
try:
from .updater import UpdateChecker, check_for_updates, get_updater
__all__.extend(["UpdateChecker", "check_for_updates", "get_updater"])
except ImportError:
pass
"""
init_file.write_text(content)
print(" ✅ Auto-update system configured")
else:
print(" ⚠️ Template updater not found, you'll need to implement manually")
def setup_issue_templates(templates_dir: Path):
"""Setup GitHub issue templates."""
print("📝 Setting up issue templates...")
bug_template = """---
name: Bug Report
about: Create a report to help us improve
title: '[BUG] '
labels: bug
assignees: ''
---
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error
**Expected behavior**
A clear and concise description of what you expected to happen.
**Environment:**
- OS: [e.g. Ubuntu 22.04, Windows 11, macOS 13]
- Python version: [e.g. 3.11.2]
- Project version: [e.g. 1.2.3]
**Additional context**
Add any other context about the problem here.
"""
feature_template = """---
name: Feature Request
about: Suggest an idea for this project
title: '[FEATURE] '
labels: enhancement
assignees: ''
---
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
**Describe the solution you'd like**
A clear and concise description of what you want to happen.
**Describe alternatives you've considered**
A clear and concise description of any alternative solutions you've considered.
**Additional context**
Add any other context or screenshots about the feature request here.
"""
(templates_dir / "bug_report.md").write_text(bug_template)
(templates_dir / "feature_request.md").write_text(feature_template)
print(" ✅ Issue templates created")
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...")
config = {
"project": {
"name": repo_name,
"owner": repo_owner,
"github_url": f"https://github.com/{repo_owner}/{repo_name}",
"auto_update_enabled": include_auto_update,
},
"github": {
"template_version": "1.0.0",
"last_sync": None,
"workflows_enabled": True,
},
}
config_file = project_path / ".github" / "project-config.json"
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."""
readme_file = project_path / "README.md"
if not readme_file.exists():
print("📖 Creating README template...")
readme_content = """# {repo_name}
> A brief description of your project
## Quick Start
```bash
# Installation
curl -sSL https://github.com/{repo_owner}/{repo_name}/releases/latest/download/install.sh | bash
# Usage
./{repo_name} --help
```
## Features
- ✨ Feature 1
- 🚀 Feature 2
- 🔧 Feature 3
## Installation
### Automated Install
```bash
curl -sSL https://github.com/{repo_owner}/{repo_name}/releases/latest/download/install.sh | bash
```
### Manual Install
```bash
git clone https://github.com/{repo_owner}/{repo_name}.git
cd {repo_name}
./install.sh
```
## Usage
Basic usage:
```bash
./{repo_name} command [options]
```
## Auto-Update
This project includes automatic update checking:
```bash
# Check for updates
./{repo_name} check-update
# Install updates
./{repo_name} update
```
## Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request
## License
[Your License Here]
---
🤖 **Auto-Update Enabled**: This project will notify you of new versions automatically!
"""
readme_file.write_text(readme_content)
print(" ✅ README template created")
def main():
"""Main entry point."""
parser = argparse.ArgumentParser(
description="Setup GitHub template with auto-update system",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
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"
)
args = parser.parse_args()
if not args.project_path.exists():
print(f"❌ Project path does not exist: {args.project_path}")
sys.exit(1)
success = setup_project_template(
project_path=args.project_path,
repo_owner=args.owner,
repo_name=args.name,
project_type=args.type,
include_auto_update=not args.no_auto_update,
)
sys.exit(0 if success else 1)
if __name__ == "__main__":
main()

57
scripts/test-configs.py
View File

@ -4,69 +4,62 @@ 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 Any, Dict, List
import yaml
from typing import Dict, Any, List
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:
@ -92,19 +85,18 @@ 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}")
@ -128,6 +120,5 @@ def main():
print("❌ Some config files have issues - please fix before release")
sys.exit(1)
if __name__ == "__main__":
if __name__ == '__main__':
main()

58
tests/test_fixes.py → test_fixes.py
View File

@ -10,27 +10,23 @@ Or run directly with venv:
source .venv/bin/activate && python test_fixes.py
"""
import os
import sys
import os
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."""
@ -50,11 +46,11 @@ 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}")
@ -62,9 +58,7 @@ 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")
@ -80,7 +74,6 @@ 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)
@ -89,7 +82,7 @@ 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:
@ -101,13 +94,13 @@ 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:
@ -118,7 +111,6 @@ 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)
@ -127,27 +119,24 @@ 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(" in synthesizer_content
and "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:
@ -158,7 +147,6 @@ 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)
@ -166,10 +154,10 @@ 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
@ -177,13 +165,13 @@ 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")
@ -196,7 +184,6 @@ def test_import_fixes():
return all_good
def main():
"""Run all tests."""
print("FSS-Mini-RAG Fix Verification Tests")
@ -206,7 +193,7 @@ 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 = {}
@ -239,6 +226,5 @@ def main():
print("❌ SOME TESTS FAILED - System needs more fixes!")
return 1
if __name__ == "__main__":
sys.exit(main())

65
tests/01_basic_integration_test.py
View File

@ -14,31 +14,25 @@ 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)
@ -52,15 +46,13 @@ 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."""
@ -99,7 +91,6 @@ class BasicCalculator:
self.last_result = result
return result
class ScientificCalculator(BasicCalculator):
"""Scientific calculator extending basic operations."""
@ -132,7 +123,6 @@ 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:
@ -143,7 +133,6 @@ 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:
@ -153,19 +142,16 @@ 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."""
@ -184,7 +170,6 @@ class TestBasicCalculator(unittest.TestCase):
with self.assertRaises(ValueError):
self.calc.divide(10, 0)
class TestStatistics(unittest.TestCase):
"""Test statistical functions."""
@ -199,8 +184,7 @@ class TestStatistics(unittest.TestCase):
if __name__ == "__main__":
unittest.main()
'''
)
''')
print(" Created 2 Python files")
@ -224,16 +208,12 @@ 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)
@ -251,24 +231,24 @@ 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
@ -288,6 +268,5 @@ if __name__ == "__main__":
print("- Context-aware search with adjacent chunks")
print("- Chunk navigation following code relationships")
if __name__ == "__main__":
main()

71
tests/02_search_examples.py
View File

@ -5,10 +5,9 @@ Simple demo of the hybrid search system showing real results.
import sys
from pathlib import Path
from rich.console import Console
from rich.panel import Panel
from rich.syntax import Syntax
from rich.panel import Panel
from rich.table import Table
from mini_rag.search import CodeSearcher
@ -27,39 +26,37 @@ 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:
@ -69,10 +66,10 @@ 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:
@ -89,11 +86,11 @@ 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)
@ -106,22 +103,16 @@ 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():
@ -132,8 +123,8 @@ def main():
# Use the RAG system itself as the demo project
project_path = Path(__file__).parent
if not (project_path / ".mini-rag").exists():
console.print("[red]Error: No RAG index found. Run 'rag-mini index' first.[/red]")
if not (project_path / '.mini-rag').exists():
console.print("[red]Error: No RAG index found. Run 'mini-rag index' first.[/red]")
console.print(f"[dim]Looked in: {project_path / '.mini-rag'}[/dim]")
return

113
tests/03_system_validation.py
View File

@ -2,23 +2,22 @@
Integration test to verify all three agents' work integrates properly.
"""
import os
import sys
import os
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.search import CodeSearcher
from mini_rag.config import RAGConfig
def test_chunker():
"""Test that chunker creates chunks with all required metadata."""
@ -30,7 +29,6 @@ def test_chunker():
import os
import sys
class TestClass:
"""A test class with multiple methods."""
@ -58,7 +56,6 @@ class TestClass:
data.append(i * self.value)
return data
class AnotherClass:
"""Another test class."""
@ -75,7 +72,6 @@ def standalone_function(arg1, arg2):
result = arg1 + arg2
return result * 2
def another_function():
"""Another standalone function."""
data = {"key": "value", "number": 123}
@ -90,9 +86,7 @@ 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 = []
@ -111,14 +105,12 @@ 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:")
@ -129,7 +121,6 @@ def another_function():
return len(issues) == 0
def test_indexer_storage():
"""Test that indexer stores the new metadata."""
print("\n2. Testing Indexer Storage...")
@ -139,20 +130,14 @@ 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()
@ -164,12 +149,7 @@ 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:
@ -189,7 +169,6 @@ class MyClass:
return len(missing_fields) == 0
def test_search_integration():
"""Test that search uses the new metadata."""
print("\n3. Testing Search Integration...")
@ -198,12 +177,10 @@ 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."""
@ -228,7 +205,6 @@ class Calculator:
self.result = a / b
return self.result
class AdvancedCalculator(Calculator):
"""Advanced calculator with more operations."""
@ -248,7 +224,6 @@ def compute_average(numbers):
return 0
return sum(numbers) / len(numbers)
def compute_median(numbers):
"""Compute median of a list."""
if not numbers:
@ -258,8 +233,7 @@ 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)
@ -270,9 +244,8 @@ 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")
@ -288,43 +261,38 @@ def compute_median(numbers):
df = searcher.table.to_pandas()
print(f" Total chunks in DB: {len(df)}")
# Find a method/function chunk to test parent context
method_chunks = df[df["chunk_type"].isin(["method", "function"])]
# Find a method chunk to test parent context
method_chunks = df[df['chunk_type'] == 'method']
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(" - No previous chunk (might be first)")
if context["next"]:
print(f" - No previous chunk (might be first)")
if context['next']:
print(f" Got next chunk: {context['next'].name}")
else:
print(" - No next chunk (might be last)")
if context["parent"]:
print(f" - No next chunk (might be last)")
if context['parent']:
print(f" Got parent chunk: {context['parent'].name}")
else:
print(" - No parent chunk")
print(f" - 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:
# 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)}"
)
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 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:
@ -339,7 +307,6 @@ def compute_median(numbers):
return True
def test_server():
"""Test that server still works."""
print("\n4. Testing Server...")
@ -347,15 +314,13 @@ def test_server():
# Just check if we can import and create server instance
try:
from mini_rag.server import RAGServer
# RAGServer(Path("."), port=7778) # Unused variable removed
server = RAGServer(Path("."), port=7778)
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)...")
@ -363,7 +328,7 @@ def test_new_features():
try:
# Test configuration loading
config = RAGConfig()
print(" ✅ Configuration loaded successfully")
print(f" ✅ Configuration loaded successfully")
print(f" Query expansion enabled: {config.search.expand_queries}")
print(f" Max expansion terms: {config.llm.max_expansion_terms}")
@ -375,13 +340,13 @@ def test_new_features():
expanded = expander.expand_query(test_query)
print(f" ✅ Query expansion working: '{test_query}' → '{expanded}'")
else:
print(" ⚠️ Query expansion offline (Ollama not available)")
print(f" ⚠️ Query expansion offline (Ollama not available)")
# Test that it still returns original query
expanded = expander.expand_query(test_query)
if expanded == test_query:
print(" ✅ Graceful degradation working: returns original query")
print(f" ✅ Graceful degradation working: returns original query")
else:
print(" ❌ Error: should return original query when offline")
print(f" ❌ Error: should return original query when offline")
return False
# Test smart ranking (this always works as it's zero-overhead)
@ -398,7 +363,7 @@ 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:
@ -413,7 +378,6 @@ def test_new_features():
print(f" ❌ New features test failed: {e}")
return False
def main():
"""Run all integration tests."""
print("=" * 50)
@ -425,7 +389,7 @@ 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)
@ -446,7 +410,6 @@ def main():
return all_passed
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

22
tests/show_index_contents.py
View File

@ -3,19 +3,19 @@
Show what files are actually indexed in the RAG system.
"""
import os
import sys
from collections import Counter
import os
from pathlib import Path
from mini_rag.vector_store import VectorStore
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')
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("\n Indexed Files Summary")
print(f"\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("\n Files with most chunks:")
print(f"\n Files with most chunks:")
for file, count in chunks_by_file.most_common(10):
print(f" {count:3d} chunks: {file}")
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()]
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()]
for f in tts_files:
print(f" - {f} ({chunks_by_file[f]} chunks)")

45
tests/test_context_retrieval.py
View File

@ -12,26 +12,19 @@ Or run directly with venv:
import os
from pathlib import Path
from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
from mini_rag.search import CodeSearcher
from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
# 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."""
@ -68,45 +61,33 @@ 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()

159
tests/test_hybrid_search.py
View File

@ -12,15 +12,18 @@ Or run directly with venv:
"""
import time
import json
from pathlib import Path
from typing import Any, Dict
from typing import List, Dict, Any
from rich.console import Console
from rich.progress import track
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 mini_rag.search import CodeSearcher, SearchResult
from mini_rag.ollama_embeddings import OllamaEmbedder as CodeEmbedder
from mini_rag.search import CodeSearcher
console = Console()
@ -41,18 +44,12 @@ 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
@ -72,18 +69,18 @@ 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):
@ -93,9 +90,9 @@ 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 = {}
@ -115,28 +112,28 @@ 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)
@ -146,68 +143,62 @@ 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):
@ -226,16 +217,16 @@ 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")
@ -255,7 +246,7 @@ 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)
@ -271,7 +262,7 @@ 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")
@ -301,9 +292,7 @@ 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)
@ -311,17 +300,13 @@ 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
@ -342,38 +327,38 @@ def main():
else:
project_path = Path.cwd()
if not (project_path / ".mini-rag").exists():
console.print("[red]Error: No RAG index found. Run 'rag-mini index' first.[/red]")
if not (project_path / '.mini-rag').exists():
console.print("[red]Error: No RAG index found. Run 'mini-rag index' first.[/red]")
return
# Create tester
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__":

5
tests/test_min_chunk_size.py
View File

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

97
tests/test_mode_separation.py
View File

@ -7,6 +7,7 @@ between thinking and no-thinking modes.
"""
import sys
import os
import tempfile
import unittest
from pathlib import Path
@ -15,17 +16,16 @@ from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent))
try:
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.explorer import CodeExplorer
from mini_rag.config import RAGConfig
from mini_rag.indexer import ProjectIndexer
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."""
@ -36,8 +36,7 @@ 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."""
@ -49,7 +48,6 @@ 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."""
@ -73,8 +71,7 @@ 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:
@ -86,7 +83,6 @@ 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):
@ -94,9 +90,8 @@ 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")
@ -106,10 +101,8 @@ 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")
@ -118,16 +111,12 @@ 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")
@ -143,14 +132,10 @@ 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")
@ -160,11 +145,13 @@ 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")
@ -174,10 +161,8 @@ 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")
@ -189,13 +174,12 @@ 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
# Test call (result unused)
synthesizer._call_ollama("test", temperature=0.1, disable_thinking=True)
result = 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:
@ -207,18 +191,14 @@ 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")
@ -228,31 +208,31 @@ 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")
@ -260,13 +240,11 @@ 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")
@ -294,7 +272,6 @@ def main():
return result.wasSuccessful()
if __name__ == "__main__":
success = main()
sys.exit(0 if success else 1)

90
tests/test_ollama_integration.py
View File

@ -8,20 +8,20 @@ what's working and what needs attention.
Run with: python3 tests/test_ollama_integration.py
"""
import sys
import unittest
from pathlib import Path
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
import json
import sys
from pathlib import Path
from unittest.mock import patch, MagicMock
# 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):
"""
@ -49,20 +49,21 @@ 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(" ✅ Ollama server is running!")
models = data.get('models', [])
print(f" ✅ 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")
@ -99,16 +100,19 @@ 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(" ✅ Embedding model working!")
print(f" ✅ Embedding model working!")
print(f" 📊 Generated {len(embedding)}-dimensional vectors")
self.assertTrue(len(embedding) > 100) # Should be substantial vectors
else:
@ -151,11 +155,12 @@ 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(" ✅ Model generating responses!")
print(f" ✅ Model generating responses!")
print(f" 💬 Sample response: '{response[:50]}...'")
# Basic quality check
@ -226,9 +231,8 @@ 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():
@ -243,7 +247,9 @@ 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(
@ -277,14 +283,13 @@ 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
@ -308,20 +313,21 @@ 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")
@ -340,17 +346,17 @@ 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
@ -363,7 +369,7 @@ 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
@ -391,14 +397,14 @@ class TestOllamaIntegration(unittest.TestCase):
self.assertTrue(isinstance(self.config.llm.max_expansion_terms, int))
self.assertGreater(self.config.llm.max_expansion_terms, 0)
print(" ✅ LLM config valid")
print(f" ✅ 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(" ✅ Search config valid")
print(f" ✅ Search config valid")
print(f" Default top-k: {self.config.search.default_top_k}")
print(f" Query expansion: {self.config.search.expand_queries}")
@ -426,5 +432,5 @@ def run_troubleshooting():
print("📚 For more help, see docs/QUERY_EXPANSION.md")
if __name__ == "__main__":
if __name__ == '__main__':
run_troubleshooting()

42
tests/test_rag_integration.py
View File

@ -10,26 +10,21 @@ Or run directly with venv:
source .venv/bin/activate && PYTHONPATH=. python tests/test_rag_integration.py
"""
import os
import tempfile
import shutil
import os
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
@ -40,16 +35,15 @@ This module demonstrates various Python constructs.
import os
import sys
from typing import List, Optional
from typing import List, Dict, Optional
from dataclasses import dataclass
# Module-level constants
DEFAULT_TIMEOUT = 30
MAX_RETRIES = 3
@dataclass
class Config:
"""Configuration dataclass."""
timeout: int = DEFAULT_TIMEOUT
@ -105,6 +99,7 @@ class DataProcessor:
# Implementation details
return {**item, 'processed': True}
def main():
"""Main entry point."""
config = Config()
@ -118,12 +113,13 @@ 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
@ -179,7 +175,7 @@ Main class for indexing projects.
### CodeSearcher
Provides semantic search capabilities.
"""
'''
def test_integration():
@ -217,40 +213,40 @@ def test_integration():
# Test 1: Search for class with docstring
results = searcher.search("data processor class unified interface", top_k=3)
print("\n Test 1 - Class search:")
print(f"\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("\n Test 2 - Method search:")
print(f"\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("\n Test 3 - Markdown search:")
print(f"\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("\n Test 4 - Chunk navigation:")
print(f"\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]
@ -261,9 +257,9 @@ def test_integration():
valid_chain = True
for i in range(len(py_chunks) - 1):
curr = py_chunks[i]
# py_chunks[i + 1] # Unused variable removed
next_chunk = py_chunks[i + 1]
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

60
tests/test_smart_ranking.py
View File

@ -8,17 +8,17 @@ and producing better quality results.
Run with: python3 tests/test_smart_ranking.py
"""
import sys
import unittest
from datetime import datetime, timedelta
import sys
from pathlib import Path
from unittest.mock import MagicMock, patch
from mini_rag.search import CodeSearcher, SearchResult
from datetime import datetime, timedelta
from unittest.mock import patch, MagicMock
# Add project to path
sys.path.insert(0, str(Path(__file__).parent.parent))
from mini_rag.search import SearchResult, CodeSearcher
class TestSmartRanking(unittest.TestCase):
"""
@ -40,31 +40,27 @@ 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\n"
"with detailed installation instructions\n"
"and usage examples for beginners."
),
content="This is a comprehensive README file\nwith detailed installation instructions\nand 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"),
@ -74,8 +70,8 @@ class TestSmartRanking(unittest.TestCase):
end_line=1,
chunk_type="text",
name="log",
language="text",
),
language="text"
)
]
def test_01_important_file_boost(self):
@ -95,8 +91,8 @@ 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)
@ -128,7 +124,7 @@ 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)
@ -137,7 +133,7 @@ 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)")
@ -159,7 +155,7 @@ 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)
@ -172,7 +168,7 @@ 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.
@ -188,7 +184,7 @@ 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
@ -203,13 +199,13 @@ 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
@ -247,19 +243,15 @@ 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}")
@ -282,7 +274,7 @@ class TestSmartRanking(unittest.TestCase):
# Time the ranking operation
start_time = time.time()
# searcher._smart_rerank(self.mock_results.copy()) # Unused variable removed
ranked = searcher._smart_rerank(self.mock_results.copy())
end_time = time.time()
ranking_time = (end_time - start_time) * 1000 # Convert to milliseconds
@ -318,5 +310,5 @@ def run_ranking_tests():
print(" • All boosts are cumulative for maximum quality")
if __name__ == "__main__":
if __name__ == '__main__':
run_ranking_tests()

11
tests/troubleshoot.py
View File

@ -8,14 +8,13 @@ and helps identify what's working and what needs attention.
Run with: python3 tests/troubleshoot.py
"""
import subprocess
import sys
import subprocess
from pathlib import Path
# Add project to path
sys.path.insert(0, str(Path(__file__).parent.parent))
def main():
"""Run comprehensive troubleshooting checks."""
@ -53,7 +52,6 @@ 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
@ -64,9 +62,9 @@ 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:
@ -84,6 +82,5 @@ def run_test(test_file):
except Exception as e:
print(f"❌ Error running {test_file}: {e}")
if __name__ == "__main__":
main()