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c201b3badd
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.gitignore
vendored
4
.gitignore
vendored
@ -41,14 +41,10 @@ Thumbs.db
|
||||
|
||||
# RAG system specific
|
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.claude-rag/
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.mini-rag/
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*.lance/
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*.db
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manifest.json
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||||
|
||||
# Claude Code specific
|
||||
.claude/
|
||||
|
||||
# Logs and temporary files
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||||
*.log
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||||
*.tmp
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||||
|
||||
109
PR_BODY.md
109
PR_BODY.md
@ -1,109 +0,0 @@
|
||||
## 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
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||||
- 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
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||||
- Support for advanced use cases (15+ results, 4000+ character chunks)
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||||
- Better coding assistance and analysis
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||||
- Professional-grade RAG experience
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||||
|
||||
## 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
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||||
|
||||
### 3. Consistent Implementation
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||||
- Updated all Ollama API calls to use consistent context settings
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||||
- 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
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||||
|
||||
Based on comprehensive research findings:
|
||||
|
||||
### Model Context Capabilities
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||||
- **qwen3:0.6b/1.7b**: 32K token maximum
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||||
- **qwen3:4b**: 131K token maximum (YaRN extended)
|
||||
|
||||
### Recommended Context Sizes
|
||||
```yaml
|
||||
# Conservative (fast, low memory)
|
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num_ctx: 8192 # ~6MB memory, excellent for exploration
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||||
|
||||
# Balanced (recommended for most users)
|
||||
num_ctx: 16384 # ~12MB memory, handles complex analysis
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||||
|
||||
# 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
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||||
- `mini_rag/explorer.py`: Consistent context application
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||||
- `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
135
PR_DRAFT.md
@ -1,135 +0,0 @@
|
||||
# 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.**
|
||||
86
README.md
86
README.md
@ -12,40 +12,19 @@
|
||||
## How It Works
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
Start([🚀 Start FSS-Mini-RAG]) --> Interface{Choose Interface}
|
||||
graph LR
|
||||
Files[📁 Your Code/Documents] --> Index[🔍 Index]
|
||||
Index --> Chunks[✂️ Smart Chunks]
|
||||
Chunks --> Embeddings[🧠 Semantic Vectors]
|
||||
Embeddings --> Database[(💾 Vector DB)]
|
||||
|
||||
Interface -->|Beginners| TUI[🖥️ Interactive TUI<br/>./rag-tui]
|
||||
Interface -->|Power Users| CLI[⚡ Advanced CLI<br/>./rag-mini <command>]
|
||||
Query[❓ user auth] --> Search[🎯 Hybrid Search]
|
||||
Database --> Search
|
||||
Search --> Results[📋 Ranked Results]
|
||||
|
||||
TUI --> SelectFolder[📁 Select Folder to Index]
|
||||
CLI --> SelectFolder
|
||||
|
||||
SelectFolder --> Index[🔍 Index Documents<br/>Creates searchable database]
|
||||
|
||||
Index --> Ready{📚 Ready to Search}
|
||||
|
||||
Ready -->|Quick Answers| Search[🔍 Search Mode<br/>Fast semantic search]
|
||||
Ready -->|Deep Analysis| Explore[🧠 Explore Mode<br/>AI-powered analysis]
|
||||
|
||||
Search --> SearchResults[📋 Instant Results<br/>Ranked by relevance]
|
||||
Explore --> ExploreResults[💬 AI Conversation<br/>Context + reasoning]
|
||||
|
||||
SearchResults --> More{Want More?}
|
||||
ExploreResults --> More
|
||||
|
||||
More -->|Different Query| Ready
|
||||
More -->|Advanced Features| CLI
|
||||
More -->|Done| End([✅ Success!])
|
||||
|
||||
CLI -.->|Full Power| AdvancedFeatures[⚡ Advanced Features:<br/>• Batch processing<br/>• Custom parameters<br/>• Automation scripts<br/>• Background server]
|
||||
|
||||
style Start fill:#e8f5e8,stroke:#4caf50,stroke-width:2px
|
||||
style CLI fill:#fff9c4,stroke:#f57c00,stroke-width:3px
|
||||
style AdvancedFeatures fill:#fff9c4,stroke:#f57c00,stroke-width:2px
|
||||
style Search fill:#e3f2fd,stroke:#2196f3,stroke-width:2px
|
||||
style Explore fill:#f3e5f5,stroke:#9c27b0,stroke-width:2px
|
||||
style End fill:#e8f5e8,stroke:#4caf50,stroke-width:2px
|
||||
style Files fill:#e3f2fd
|
||||
style Results fill:#e8f5e8
|
||||
style Database fill:#fff3e0
|
||||
```
|
||||
|
||||
## What This Is
|
||||
@ -79,7 +58,6 @@ FSS-Mini-RAG offers **two distinct experiences** optimized for different use cas
|
||||
|
||||
## Quick Start (2 Minutes)
|
||||
|
||||
**Linux/macOS:**
|
||||
```bash
|
||||
# 1. Install everything
|
||||
./install_mini_rag.sh
|
||||
@ -92,19 +70,6 @@ FSS-Mini-RAG offers **two distinct experiences** optimized for different use cas
|
||||
./rag-mini explore ~/my-project # Interactive exploration
|
||||
```
|
||||
|
||||
**Windows:**
|
||||
```cmd
|
||||
# 1. Install everything
|
||||
install_windows.bat
|
||||
|
||||
# 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.
|
||||
|
||||
## What Makes This Different
|
||||
@ -154,22 +119,12 @@ That's it. No external dependencies, no configuration required, no PhD in comput
|
||||
## Installation Options
|
||||
|
||||
### Recommended: Full Installation
|
||||
|
||||
**Linux/macOS:**
|
||||
```bash
|
||||
./install_mini_rag.sh
|
||||
# Handles Python setup, dependencies, optional AI models
|
||||
```
|
||||
|
||||
**Windows:**
|
||||
```cmd
|
||||
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
|
||||
@ -177,30 +132,13 @@ install_windows.bat
|
||||
# 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:**
|
||||
```bash
|
||||
python3 -m venv .venv
|
||||
source .venv/bin/activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
**Windows:**
|
||||
```cmd
|
||||
python -m venv .venv
|
||||
.venv\Scripts\activate.bat
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
**Note**: The experimental copy & run feature is provided for convenience but may fail on some systems. If you encounter issues, use the full installer for reliable setup.
|
||||
|
||||
## System Requirements
|
||||
@ -228,7 +166,7 @@ This implementation prioritizes:
|
||||
|
||||
## Next Steps
|
||||
|
||||
- **New users**: Run `./rag-mini` (Linux/macOS) or `rag.bat` (Windows) for guided experience
|
||||
- **New users**: Run `./rag-mini` for guided experience
|
||||
- **Developers**: Read [`TECHNICAL_GUIDE.md`](docs/TECHNICAL_GUIDE.md) for implementation details
|
||||
- **Contributors**: See [`CONTRIBUTING.md`](CONTRIBUTING.md) for development setup
|
||||
|
||||
|
||||
@ -1,36 +0,0 @@
|
||||
feat: Add comprehensive Windows compatibility and enhanced LLM model setup
|
||||
|
||||
🚀 Major cross-platform enhancement making FSS-Mini-RAG fully Windows and Linux compatible
|
||||
|
||||
## Windows Compatibility
|
||||
- **New Windows installer**: `install_windows.bat` - rock-solid, no-hang installation
|
||||
- **Simple Windows launcher**: `rag.bat` - unified entry point matching Linux experience
|
||||
- **PowerShell alternative**: `install_mini_rag.ps1` for advanced Windows users
|
||||
- **Cross-platform README**: Side-by-side Linux/Windows commands and examples
|
||||
|
||||
## Enhanced LLM Model Setup (Both Platforms)
|
||||
- **Intelligent model detection**: Automatically detects existing Qwen3 models
|
||||
- **Interactive model selection**: Choose from qwen3:0.6b, 1.7b, or 4b with clear guidance
|
||||
- **Ollama progress streaming**: Real-time download progress for model installation
|
||||
- **Smart configuration**: Auto-saves selected model as default in config.yaml
|
||||
- **Graceful fallbacks**: Clear guidance when Ollama unavailable
|
||||
|
||||
## Installation Experience Improvements
|
||||
- **Fixed script continuation**: TUI launch no longer terminates installation process
|
||||
- **Comprehensive model guidance**: Users get proper LLM setup instead of silent failures
|
||||
- **Complete indexing**: Full codebase indexing (not just code files)
|
||||
- **Educational flow**: Better explanation of AI features and model choices
|
||||
|
||||
## Technical Enhancements
|
||||
- **Robust error handling**: Installation scripts handle edge cases gracefully
|
||||
- **Path handling**: Existing cross-platform path utilities work seamlessly on Windows
|
||||
- **Dependency management**: Clean virtual environment setup on both platforms
|
||||
- **Configuration persistence**: Model preferences saved for consistent experience
|
||||
|
||||
## User Impact
|
||||
- **Zero-friction Windows adoption**: Windows users get same smooth experience as Linux
|
||||
- **Complete AI feature setup**: No more "LLM not working" confusion for new users
|
||||
- **Educational value preserved**: Maintains beginner-friendly approach across platforms
|
||||
- **Production-ready**: Both platforms now fully functional out-of-the-box
|
||||
|
||||
This makes FSS-Mini-RAG truly accessible to the entire developer community! 🎉
|
||||
@ -117,7 +117,7 @@ def login_user(email, password):
|
||||
|
||||
**Models you might see:**
|
||||
- **qwen3:0.6b** - Ultra-fast, good for most questions
|
||||
- **qwen3:4b** - Slower but more detailed
|
||||
- **llama3.2** - Slower but more detailed
|
||||
- **auto** - Picks the best available model
|
||||
|
||||
---
|
||||
|
||||
@ -49,7 +49,7 @@ ollama run qwen3:0.6b "Hello, can you expand this query: authentication"
|
||||
|-------|------|-----------|---------|
|
||||
| qwen3:0.6b | 522MB | Fast ⚡ | Excellent ✅ |
|
||||
| qwen3:1.7b | 1.4GB | Medium | Excellent ✅ |
|
||||
| qwen3:4b | 2.5GB | Slow | Excellent ✅ |
|
||||
| qwen3:3b | 2.0GB | Slow | Excellent ✅ |
|
||||
|
||||
## CPU-Optimized Configuration
|
||||
|
||||
|
||||
@ -22,8 +22,8 @@ This guide shows how to configure FSS-Mini-RAG with different LLM providers for
|
||||
llm:
|
||||
provider: ollama
|
||||
ollama_host: localhost:11434
|
||||
synthesis_model: qwen3:1.7b
|
||||
expansion_model: qwen3:1.7b
|
||||
synthesis_model: llama3.2
|
||||
expansion_model: llama3.2
|
||||
enable_synthesis: false
|
||||
synthesis_temperature: 0.3
|
||||
cpu_optimized: true
|
||||
@ -33,13 +33,13 @@ llm:
|
||||
**Setup:**
|
||||
1. Install Ollama: `curl -fsSL https://ollama.ai/install.sh | sh`
|
||||
2. Start service: `ollama serve`
|
||||
3. Download model: `ollama pull qwen3:1.7b`
|
||||
3. Download model: `ollama pull llama3.2`
|
||||
4. Test: `./rag-mini search /path/to/project "test" --synthesize`
|
||||
|
||||
**Recommended Models:**
|
||||
- `qwen3:0.6b` - Ultra-fast, good for CPU-only systems
|
||||
- `qwen3:1.7b` - Balanced quality and speed (recommended)
|
||||
- `qwen3:4b` - Higher quality, excellent for most use cases
|
||||
- `llama3.2` - Balanced quality and speed
|
||||
- `llama3.1:8b` - Higher quality, needs more RAM
|
||||
|
||||
### LM Studio
|
||||
|
||||
|
||||
@ -34,24 +34,7 @@ graph LR
|
||||
|
||||
## Configuration
|
||||
|
||||
### Easy Configuration (TUI)
|
||||
|
||||
Use the interactive Configuration Manager in the TUI:
|
||||
|
||||
1. **Start TUI**: `./rag-tui` or `rag.bat` (Windows)
|
||||
2. **Select Option 6**: Configuration Manager
|
||||
3. **Choose Option 2**: Toggle query expansion
|
||||
4. **Follow prompts**: Get explanation and easy on/off toggle
|
||||
|
||||
The TUI will:
|
||||
- Explain benefits and requirements clearly
|
||||
- Check if Ollama is available
|
||||
- Show current status (enabled/disabled)
|
||||
- Save changes automatically
|
||||
|
||||
### Manual Configuration (Advanced)
|
||||
|
||||
Edit `config.yaml` directly:
|
||||
Edit `config.yaml`:
|
||||
|
||||
```yaml
|
||||
# Search behavior settings
|
||||
|
||||
@ -143,8 +143,8 @@ python3 -c "import mini_rag; print('✅ Installation successful')"
|
||||
|
||||
2. **Install a model:**
|
||||
```bash
|
||||
ollama pull qwen2.5:3b # Good balance of speed and quality
|
||||
# Or: ollama pull qwen3:4b # Larger but better quality
|
||||
ollama pull qwen3:0.6b # Fast, small model
|
||||
# Or: ollama pull llama3.2 # Larger but better
|
||||
```
|
||||
|
||||
3. **Test connection:**
|
||||
|
||||
@ -23,9 +23,8 @@ That's it! The TUI will guide you through everything.
|
||||
### User Flow
|
||||
1. **Select Project** → Choose directory to search
|
||||
2. **Index Project** → Process files for search
|
||||
3. **Search Content** → Find what you need quickly
|
||||
4. **Explore Project** → Interactive AI-powered discovery (NEW!)
|
||||
5. **Configure System** → Customize search behavior
|
||||
3. **Search Content** → Find what you need
|
||||
4. **Explore Results** → See full context and files
|
||||
|
||||
## Main Menu Options
|
||||
|
||||
@ -111,63 +110,7 @@ That's it! The TUI will guide you through everything.
|
||||
./rag-mini-enhanced context /path/to/project "login()"
|
||||
```
|
||||
|
||||
### 4. Explore Project (NEW!)
|
||||
|
||||
**Purpose**: Interactive AI-powered discovery with conversation memory
|
||||
|
||||
**What Makes Explore Different**:
|
||||
- **Conversational**: Ask follow-up questions that build on previous answers
|
||||
- **AI Reasoning**: Uses thinking mode for deeper analysis and explanations
|
||||
- **Educational**: Perfect for understanding unfamiliar codebases
|
||||
- **Context Aware**: Remembers what you've already discussed
|
||||
|
||||
**Interactive Process**:
|
||||
1. **First Question Guidance**: Clear prompts with example questions
|
||||
2. **Starter Suggestions**: Random helpful questions to get you going
|
||||
3. **Natural Follow-ups**: Ask "why?", "how?", "show me more" naturally
|
||||
4. **Session Memory**: AI remembers your conversation context
|
||||
|
||||
**Explore Mode Features**:
|
||||
|
||||
**Quick Start Options**:
|
||||
- **Option 1 - Help**: Show example questions and explore mode capabilities
|
||||
- **Option 2 - Status**: Project information and current exploration session
|
||||
- **Option 3 - Suggest**: Get a random starter question picked from 7 curated examples
|
||||
|
||||
**Starter Questions** (randomly suggested):
|
||||
- "What are the main components of this project?"
|
||||
- "How is error handling implemented?"
|
||||
- "Show me the authentication and security logic"
|
||||
- "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"
|
||||
|
||||
**Advanced Usage**:
|
||||
- **Deep Questions**: "Why is this function slow?" "How does the security work?"
|
||||
- **Code Analysis**: "Explain this algorithm" "What could go wrong here?"
|
||||
- **Architecture**: "How do these components interact?" "What's the design pattern?"
|
||||
- **Best Practices**: "Is this code following best practices?" "How would you improve this?"
|
||||
|
||||
**What You Learn**:
|
||||
- **Conversational AI**: How to have productive technical conversations with AI
|
||||
- **Code Understanding**: Deep analysis capabilities beyond simple search
|
||||
- **Context Building**: How conversation memory improves over time
|
||||
- **Question Techniques**: Effective ways to explore unfamiliar code
|
||||
|
||||
**CLI Commands Shown**:
|
||||
```bash
|
||||
./rag-mini explore /path/to/project # Start interactive exploration
|
||||
```
|
||||
|
||||
**Perfect For**:
|
||||
- Understanding new codebases
|
||||
- Code review and analysis
|
||||
- Learning from existing projects
|
||||
- Documenting complex systems
|
||||
- Onboarding new team members
|
||||
|
||||
### 5. View Status
|
||||
### 4. View Status
|
||||
|
||||
**Purpose**: Check system health and project information
|
||||
|
||||
@ -196,61 +139,32 @@ That's it! The TUI will guide you through everything.
|
||||
./rag-mini status /path/to/project
|
||||
```
|
||||
|
||||
### 6. Configuration Manager (ENHANCED!)
|
||||
### 5. Configuration
|
||||
|
||||
**Purpose**: Interactive configuration with user-friendly options
|
||||
**Purpose**: View and understand system settings
|
||||
|
||||
**New Interactive Features**:
|
||||
- **Live Configuration Dashboard** - See current settings with clear status
|
||||
- **Quick Configuration Options** - Change common settings without YAML editing
|
||||
- **Guided Setup** - Explanations and presets for each option
|
||||
- **Validation** - Input checking and helpful error messages
|
||||
**Configuration Display**:
|
||||
- **Current settings** - Chunk size, strategy, file patterns
|
||||
- **File location** - Where config is stored
|
||||
- **Setting explanations** - What each option does
|
||||
- **Quick actions** - View or edit config directly
|
||||
|
||||
**Main Configuration Options**:
|
||||
**Key Settings Explained**:
|
||||
- **chunking.max_size** - How large each searchable piece is
|
||||
- **chunking.strategy** - Smart (semantic) vs simple (fixed size)
|
||||
- **files.exclude_patterns** - Skip certain files/directories
|
||||
- **embedding.preferred_method** - AI model preference
|
||||
- **search.default_top_k** - How many results to show
|
||||
|
||||
**1. Adjust Chunk Size**:
|
||||
- **Presets**: Small (1000), Medium (2000), Large (3000), or custom
|
||||
- **Guidance**: Performance vs accuracy explanations
|
||||
- **Smart Validation**: Range checking and recommendations
|
||||
|
||||
**2. Toggle Query Expansion**:
|
||||
- **Educational Info**: Clear explanation of benefits and requirements
|
||||
- **Easy Toggle**: Simple on/off with confirmation
|
||||
- **System Check**: Verifies Ollama availability for AI features
|
||||
|
||||
**3. Configure Search Behavior**:
|
||||
- **Result Count**: Adjust default number of search results (1-100)
|
||||
- **BM25 Toggle**: Enable/disable keyword matching boost
|
||||
- **Similarity Threshold**: Fine-tune match sensitivity (0.0-1.0)
|
||||
|
||||
**4. View/Edit Configuration File**:
|
||||
- **Full File Viewer**: Display complete config with syntax highlighting
|
||||
- **Editor Instructions**: Commands for nano, vim, VS Code
|
||||
- **YAML Help**: Format explanation and editing tips
|
||||
|
||||
**5. Reset to Defaults**:
|
||||
- **Safe Reset**: Confirmation before resetting all settings
|
||||
- **Clear Explanations**: Shows what defaults will be restored
|
||||
- **Backup Reminder**: Suggests saving current config first
|
||||
|
||||
**6. Advanced Settings**:
|
||||
- **File Filtering**: Min file size, exclude patterns (view only)
|
||||
- **Performance Settings**: Batch sizes, streaming thresholds
|
||||
- **LLM Preferences**: Model rankings and selection priorities
|
||||
|
||||
**Key Settings Dashboard**:
|
||||
- 📁 **Chunk size**: 2000 characters (with emoji indicators)
|
||||
- 🧠 **Chunking strategy**: semantic
|
||||
- 🔍 **Search results**: 10 results
|
||||
- 📊 **Embedding method**: ollama
|
||||
- 🚀 **Query expansion**: enabled/disabled
|
||||
- ⚡ **LLM synthesis**: enabled/disabled
|
||||
**Interactive Options**:
|
||||
- **[V]iew config** - See full configuration file
|
||||
- **[E]dit path** - Get command to edit configuration
|
||||
|
||||
**What You Learn**:
|
||||
- **Configuration Impact**: How settings affect search quality and speed
|
||||
- **Interactive YAML**: Easier than manual editing for beginners
|
||||
- **Best Practices**: Recommended settings for different project types
|
||||
- **System Understanding**: How all components work together
|
||||
- How configuration affects search quality
|
||||
- YAML configuration format
|
||||
- Which settings to adjust for different projects
|
||||
- Where to find advanced options
|
||||
|
||||
**CLI Commands Shown**:
|
||||
```bash
|
||||
@ -258,13 +172,7 @@ cat /path/to/project/.mini-rag/config.yaml # View config
|
||||
nano /path/to/project/.mini-rag/config.yaml # Edit config
|
||||
```
|
||||
|
||||
**Perfect For**:
|
||||
- Beginners who find YAML intimidating
|
||||
- Quick adjustments without memorizing syntax
|
||||
- Understanding what each setting actually does
|
||||
- Safe experimentation with guided validation
|
||||
|
||||
### 7. CLI Command Reference
|
||||
### 6. CLI Command Reference
|
||||
|
||||
**Purpose**: Complete command reference for transitioning to CLI
|
||||
|
||||
|
||||
@ -68,9 +68,9 @@ search:
|
||||
llm:
|
||||
provider: ollama # Use local Ollama
|
||||
ollama_host: localhost:11434 # Default Ollama location
|
||||
synthesis_model: qwen3:1.7b # Good all-around model
|
||||
# alternatives: qwen3:0.6b (faster), qwen2.5:3b (balanced), qwen3:4b (quality)
|
||||
expansion_model: qwen3:1.7b
|
||||
synthesis_model: llama3.2 # Good all-around model
|
||||
# alternatives: qwen3:0.6b (faster), llama3.2:3b (balanced), llama3.1:8b (quality)
|
||||
expansion_model: llama3.2
|
||||
enable_synthesis: false
|
||||
synthesis_temperature: 0.3
|
||||
cpu_optimized: true
|
||||
|
||||
@ -102,7 +102,7 @@ llm:
|
||||
# For even better results, try these model combinations:
|
||||
# • ollama pull nomic-embed-text:latest (best embeddings)
|
||||
# • ollama pull qwen3:1.7b (good general model)
|
||||
# • ollama pull qwen3:4b (excellent for analysis)
|
||||
# • ollama pull llama3.2 (excellent for analysis)
|
||||
#
|
||||
# Or adjust these settings for your specific needs:
|
||||
# • similarity_threshold: 0.3 (more selective results)
|
||||
|
||||
@ -112,7 +112,7 @@ llm:
|
||||
synthesis_model: auto # Which AI model to use for explanations
|
||||
# 'auto': Picks best available model - RECOMMENDED
|
||||
# 'qwen3:0.6b': Ultra-fast, good for CPU-only computers
|
||||
# 'qwen3:4b': Slower but more detailed explanations
|
||||
# 'llama3.2': Slower but more detailed explanations
|
||||
|
||||
expansion_model: auto # Model for query expansion (usually same as synthesis)
|
||||
|
||||
|
||||
@ -1,458 +0,0 @@
|
||||
# FSS-Mini-RAG PowerShell Installation Script
|
||||
# Interactive installer that sets up Python environment and dependencies
|
||||
|
||||
# Enable advanced features
|
||||
$ErrorActionPreference = "Stop"
|
||||
|
||||
# Color functions for better output
|
||||
function Write-ColorOutput($message, $color = "White") {
|
||||
Write-Host $message -ForegroundColor $color
|
||||
}
|
||||
|
||||
function Write-Header($message) {
|
||||
Write-Host "`n" -NoNewline
|
||||
Write-ColorOutput "=== $message ===" "Cyan"
|
||||
}
|
||||
|
||||
function Write-Success($message) {
|
||||
Write-ColorOutput "✅ $message" "Green"
|
||||
}
|
||||
|
||||
function Write-Warning($message) {
|
||||
Write-ColorOutput "⚠️ $message" "Yellow"
|
||||
}
|
||||
|
||||
function Write-Error($message) {
|
||||
Write-ColorOutput "❌ $message" "Red"
|
||||
}
|
||||
|
||||
function Write-Info($message) {
|
||||
Write-ColorOutput "ℹ️ $message" "Blue"
|
||||
}
|
||||
|
||||
# Get script directory
|
||||
$ScriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path
|
||||
|
||||
# Main installation function
|
||||
function Main {
|
||||
Write-Host ""
|
||||
Write-ColorOutput "╔══════════════════════════════════════╗" "Cyan"
|
||||
Write-ColorOutput "║ FSS-Mini-RAG Installer ║" "Cyan"
|
||||
Write-ColorOutput "║ Fast Semantic Search for Code ║" "Cyan"
|
||||
Write-ColorOutput "╚══════════════════════════════════════╝" "Cyan"
|
||||
Write-Host ""
|
||||
|
||||
Write-Info "PowerShell installation process:"
|
||||
Write-Host " • Python environment setup"
|
||||
Write-Host " • Smart configuration based on your system"
|
||||
Write-Host " • Optional AI model downloads (with consent)"
|
||||
Write-Host " • Testing and verification"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Note: You'll be asked before downloading any models" "Cyan"
|
||||
Write-Host ""
|
||||
|
||||
$continue = Read-Host "Begin installation? [Y/n]"
|
||||
if ($continue -eq "n" -or $continue -eq "N") {
|
||||
Write-Host "Installation cancelled."
|
||||
exit 0
|
||||
}
|
||||
|
||||
# Run installation steps
|
||||
Check-Python
|
||||
Create-VirtualEnvironment
|
||||
|
||||
# Check Ollama availability
|
||||
$ollamaAvailable = Check-Ollama
|
||||
|
||||
# Get installation preferences
|
||||
Get-InstallationPreferences $ollamaAvailable
|
||||
|
||||
# Install dependencies
|
||||
Install-Dependencies
|
||||
|
||||
# Setup models if available
|
||||
if ($ollamaAvailable) {
|
||||
Setup-OllamaModel
|
||||
}
|
||||
|
||||
# Test installation
|
||||
if (Test-Installation) {
|
||||
Show-Completion
|
||||
} else {
|
||||
Write-Error "Installation test failed"
|
||||
Write-Host "Please check error messages and try again."
|
||||
exit 1
|
||||
}
|
||||
}
|
||||
|
||||
function Check-Python {
|
||||
Write-Header "Checking Python Installation"
|
||||
|
||||
# Try different Python commands
|
||||
$pythonCmd = $null
|
||||
$pythonVersion = $null
|
||||
|
||||
foreach ($cmd in @("python", "python3", "py")) {
|
||||
try {
|
||||
$version = & $cmd --version 2>&1
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
$pythonCmd = $cmd
|
||||
$pythonVersion = ($version -split " ")[1]
|
||||
break
|
||||
}
|
||||
} catch {
|
||||
continue
|
||||
}
|
||||
}
|
||||
|
||||
if (-not $pythonCmd) {
|
||||
Write-Error "Python not found!"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Please install Python 3.8+ from:" "Yellow"
|
||||
Write-Host " • https://python.org/downloads"
|
||||
Write-Host " • Make sure to check 'Add Python to PATH' during installation"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "After installing Python, run this script again." "Cyan"
|
||||
exit 1
|
||||
}
|
||||
|
||||
# Check version
|
||||
$versionParts = $pythonVersion -split "\."
|
||||
$major = [int]$versionParts[0]
|
||||
$minor = [int]$versionParts[1]
|
||||
|
||||
if ($major -lt 3 -or ($major -eq 3 -and $minor -lt 8)) {
|
||||
Write-Error "Python $pythonVersion found, but 3.8+ required"
|
||||
Write-Host "Please upgrade Python to 3.8 or higher."
|
||||
exit 1
|
||||
}
|
||||
|
||||
Write-Success "Found Python $pythonVersion ($pythonCmd)"
|
||||
$script:PythonCmd = $pythonCmd
|
||||
}
|
||||
|
||||
function Create-VirtualEnvironment {
|
||||
Write-Header "Creating Python Virtual Environment"
|
||||
|
||||
$venvPath = Join-Path $ScriptDir ".venv"
|
||||
|
||||
if (Test-Path $venvPath) {
|
||||
Write-Info "Virtual environment already exists at $venvPath"
|
||||
$recreate = Read-Host "Recreate it? (y/N)"
|
||||
if ($recreate -eq "y" -or $recreate -eq "Y") {
|
||||
Write-Info "Removing existing virtual environment..."
|
||||
Remove-Item -Recurse -Force $venvPath
|
||||
} else {
|
||||
Write-Success "Using existing virtual environment"
|
||||
return
|
||||
}
|
||||
}
|
||||
|
||||
Write-Info "Creating virtual environment at $venvPath"
|
||||
try {
|
||||
& $script:PythonCmd -m venv $venvPath
|
||||
if ($LASTEXITCODE -ne 0) {
|
||||
throw "Virtual environment creation failed"
|
||||
}
|
||||
Write-Success "Virtual environment created"
|
||||
} catch {
|
||||
Write-Error "Failed to create virtual environment"
|
||||
Write-Host "This might be because python venv module is not available."
|
||||
Write-Host "Try installing Python from python.org with full installation."
|
||||
exit 1
|
||||
}
|
||||
|
||||
# Activate virtual environment and upgrade pip
|
||||
$activateScript = Join-Path $venvPath "Scripts\Activate.ps1"
|
||||
if (Test-Path $activateScript) {
|
||||
& $activateScript
|
||||
Write-Success "Virtual environment activated"
|
||||
|
||||
Write-Info "Upgrading pip..."
|
||||
try {
|
||||
& python -m pip install --upgrade pip --quiet
|
||||
} catch {
|
||||
Write-Warning "Could not upgrade pip, continuing anyway..."
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function Check-Ollama {
|
||||
Write-Header "Checking Ollama (AI Model Server)"
|
||||
|
||||
try {
|
||||
$response = Invoke-WebRequest -Uri "http://localhost:11434/api/version" -TimeoutSec 5 -ErrorAction SilentlyContinue
|
||||
if ($response.StatusCode -eq 200) {
|
||||
Write-Success "Ollama server is running"
|
||||
return $true
|
||||
}
|
||||
} catch {
|
||||
# Ollama not running, check if installed
|
||||
}
|
||||
|
||||
try {
|
||||
& ollama version 2>$null
|
||||
if ($LASTEXITCODE -eq 0) {
|
||||
Write-Warning "Ollama is installed but not running"
|
||||
$startOllama = Read-Host "Start Ollama now? (Y/n)"
|
||||
if ($startOllama -ne "n" -and $startOllama -ne "N") {
|
||||
Write-Info "Starting Ollama server..."
|
||||
Start-Process -FilePath "ollama" -ArgumentList "serve" -WindowStyle Hidden
|
||||
Start-Sleep -Seconds 3
|
||||
|
||||
try {
|
||||
$response = Invoke-WebRequest -Uri "http://localhost:11434/api/version" -TimeoutSec 5 -ErrorAction SilentlyContinue
|
||||
if ($response.StatusCode -eq 200) {
|
||||
Write-Success "Ollama server started"
|
||||
return $true
|
||||
}
|
||||
} catch {
|
||||
Write-Warning "Failed to start Ollama automatically"
|
||||
Write-Host "Please start Ollama manually: ollama serve"
|
||||
return $false
|
||||
}
|
||||
}
|
||||
return $false
|
||||
}
|
||||
} catch {
|
||||
# Ollama not installed
|
||||
}
|
||||
|
||||
Write-Warning "Ollama not found"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Ollama provides the best embedding quality and performance." "Cyan"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Options:" "White"
|
||||
Write-ColorOutput "1) Install Ollama automatically" "Green" -NoNewline
|
||||
Write-Host " (recommended)"
|
||||
Write-ColorOutput "2) Manual installation" "Yellow" -NoNewline
|
||||
Write-Host " - Visit https://ollama.com/download"
|
||||
Write-ColorOutput "3) Continue without Ollama" "Blue" -NoNewline
|
||||
Write-Host " (uses ML fallback)"
|
||||
Write-Host ""
|
||||
|
||||
$choice = Read-Host "Choose [1/2/3]"
|
||||
|
||||
switch ($choice) {
|
||||
"1" {
|
||||
Write-Info "Opening Ollama download page..."
|
||||
Start-Process "https://ollama.com/download"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Please:" "Yellow"
|
||||
Write-Host " 1. Download and install Ollama from the opened page"
|
||||
Write-Host " 2. Run 'ollama serve' in a new terminal"
|
||||
Write-Host " 3. Re-run this installer"
|
||||
Write-Host ""
|
||||
Read-Host "Press Enter to exit"
|
||||
exit 0
|
||||
}
|
||||
"2" {
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Manual Ollama installation:" "Yellow"
|
||||
Write-Host " 1. Visit: https://ollama.com/download"
|
||||
Write-Host " 2. Download and install for Windows"
|
||||
Write-Host " 3. Run: ollama serve"
|
||||
Write-Host " 4. Re-run this installer"
|
||||
Read-Host "Press Enter to exit"
|
||||
exit 0
|
||||
}
|
||||
"3" {
|
||||
Write-Info "Continuing without Ollama (will use ML fallback)"
|
||||
return $false
|
||||
}
|
||||
default {
|
||||
Write-Warning "Invalid choice, continuing without Ollama"
|
||||
return $false
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function Get-InstallationPreferences($ollamaAvailable) {
|
||||
Write-Header "Installation Configuration"
|
||||
|
||||
Write-ColorOutput "FSS-Mini-RAG can run with different embedding backends:" "Cyan"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "• Ollama" "Green" -NoNewline
|
||||
Write-Host " (recommended) - Best quality, local AI server"
|
||||
Write-ColorOutput "• ML Fallback" "Yellow" -NoNewline
|
||||
Write-Host " - Offline transformers, larger but always works"
|
||||
Write-ColorOutput "• Hash-based" "Blue" -NoNewline
|
||||
Write-Host " - Lightweight fallback, basic similarity"
|
||||
Write-Host ""
|
||||
|
||||
if ($ollamaAvailable) {
|
||||
$recommended = "light (Ollama detected)"
|
||||
Write-ColorOutput "✓ Ollama detected - light installation recommended" "Green"
|
||||
} else {
|
||||
$recommended = "full (no Ollama)"
|
||||
Write-ColorOutput "⚠ No Ollama - full installation recommended for better quality" "Yellow"
|
||||
}
|
||||
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Installation options:" "White"
|
||||
Write-ColorOutput "L) Light" "Green" -NoNewline
|
||||
Write-Host " - Ollama + basic deps (~50MB) " -NoNewline
|
||||
Write-ColorOutput "← Best performance + AI chat" "Cyan"
|
||||
Write-ColorOutput "F) Full" "Yellow" -NoNewline
|
||||
Write-Host " - Light + ML fallback (~2-3GB) " -NoNewline
|
||||
Write-ColorOutput "← Works without Ollama" "Cyan"
|
||||
Write-Host ""
|
||||
|
||||
$choice = Read-Host "Choose [L/F] or Enter for recommended ($recommended)"
|
||||
|
||||
if ($choice -eq "") {
|
||||
if ($ollamaAvailable) {
|
||||
$choice = "L"
|
||||
} else {
|
||||
$choice = "F"
|
||||
}
|
||||
}
|
||||
|
||||
switch ($choice.ToUpper()) {
|
||||
"L" {
|
||||
$script:InstallType = "light"
|
||||
Write-ColorOutput "Selected: Light installation" "Green"
|
||||
}
|
||||
"F" {
|
||||
$script:InstallType = "full"
|
||||
Write-ColorOutput "Selected: Full installation" "Yellow"
|
||||
}
|
||||
default {
|
||||
Write-Warning "Invalid choice, using light installation"
|
||||
$script:InstallType = "light"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
function Install-Dependencies {
|
||||
Write-Header "Installing Python Dependencies"
|
||||
|
||||
if ($script:InstallType -eq "light") {
|
||||
Write-Info "Installing core dependencies (~50MB)..."
|
||||
Write-ColorOutput " Installing: lancedb, pandas, numpy, PyYAML, etc." "Blue"
|
||||
|
||||
try {
|
||||
& pip install -r (Join-Path $ScriptDir "requirements.txt") --quiet
|
||||
if ($LASTEXITCODE -ne 0) {
|
||||
throw "Dependency installation failed"
|
||||
}
|
||||
Write-Success "Dependencies installed"
|
||||
} catch {
|
||||
Write-Error "Failed to install dependencies"
|
||||
Write-Host "Try: pip install -r requirements.txt"
|
||||
exit 1
|
||||
}
|
||||
} else {
|
||||
Write-Info "Installing full dependencies (~2-3GB)..."
|
||||
Write-ColorOutput "This includes PyTorch and transformers - will take several minutes" "Yellow"
|
||||
|
||||
try {
|
||||
& pip install -r (Join-Path $ScriptDir "requirements-full.txt")
|
||||
if ($LASTEXITCODE -ne 0) {
|
||||
throw "Dependency installation failed"
|
||||
}
|
||||
Write-Success "All dependencies installed"
|
||||
} catch {
|
||||
Write-Error "Failed to install dependencies"
|
||||
Write-Host "Try: pip install -r requirements-full.txt"
|
||||
exit 1
|
||||
}
|
||||
}
|
||||
|
||||
Write-Info "Verifying installation..."
|
||||
try {
|
||||
& python -c "import lancedb, pandas, numpy" 2>$null
|
||||
if ($LASTEXITCODE -ne 0) {
|
||||
throw "Package verification failed"
|
||||
}
|
||||
Write-Success "Core packages verified"
|
||||
} catch {
|
||||
Write-Error "Package verification failed"
|
||||
exit 1
|
||||
}
|
||||
}
|
||||
|
||||
function Setup-OllamaModel {
|
||||
# Implementation similar to bash version but adapted for PowerShell
|
||||
Write-Header "Ollama Model Setup"
|
||||
# For brevity, implementing basic version
|
||||
Write-Info "Ollama model setup available - see bash version for full implementation"
|
||||
}
|
||||
|
||||
function Test-Installation {
|
||||
Write-Header "Testing Installation"
|
||||
|
||||
Write-Info "Testing basic functionality..."
|
||||
|
||||
try {
|
||||
& python -c "from mini_rag import CodeEmbedder, ProjectIndexer, CodeSearcher; print('✅ Import successful')" 2>$null
|
||||
if ($LASTEXITCODE -ne 0) {
|
||||
throw "Import test failed"
|
||||
}
|
||||
Write-Success "Python imports working"
|
||||
return $true
|
||||
} catch {
|
||||
Write-Error "Import test failed"
|
||||
return $false
|
||||
}
|
||||
}
|
||||
|
||||
function Show-Completion {
|
||||
Write-Header "Installation Complete!"
|
||||
|
||||
Write-ColorOutput "FSS-Mini-RAG is now installed!" "Green"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Quick Start Options:" "Cyan"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "🎯 TUI (Beginner-Friendly):" "Green"
|
||||
Write-Host " rag-tui.bat"
|
||||
Write-Host " # Interactive interface with guided setup"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "💻 CLI (Advanced):" "Blue"
|
||||
Write-Host " rag-mini.bat index C:\path\to\project"
|
||||
Write-Host " rag-mini.bat search C:\path\to\project `"query`""
|
||||
Write-Host " rag-mini.bat status C:\path\to\project"
|
||||
Write-Host ""
|
||||
Write-ColorOutput "Documentation:" "Cyan"
|
||||
Write-Host " • README.md - Complete technical documentation"
|
||||
Write-Host " • docs\GETTING_STARTED.md - Step-by-step guide"
|
||||
Write-Host " • examples\ - Usage examples and sample configs"
|
||||
Write-Host ""
|
||||
|
||||
$runTest = Read-Host "Run quick test now? [Y/n]"
|
||||
if ($runTest -ne "n" -and $runTest -ne "N") {
|
||||
Run-QuickTest
|
||||
}
|
||||
|
||||
Write-Host ""
|
||||
Write-ColorOutput "🎉 Setup complete! FSS-Mini-RAG is ready to use." "Green"
|
||||
}
|
||||
|
||||
function Run-QuickTest {
|
||||
Write-Header "Quick Test"
|
||||
|
||||
Write-Info "Testing with FSS-Mini-RAG codebase..."
|
||||
|
||||
$ragDir = Join-Path $ScriptDir ".mini-rag"
|
||||
if (Test-Path $ragDir) {
|
||||
Write-Success "Project already indexed, running search..."
|
||||
} else {
|
||||
Write-Info "Indexing FSS-Mini-RAG system for demo..."
|
||||
& python (Join-Path $ScriptDir "rag-mini.py") index $ScriptDir
|
||||
if ($LASTEXITCODE -ne 0) {
|
||||
Write-Error "Test indexing failed"
|
||||
return
|
||||
}
|
||||
}
|
||||
|
||||
Write-Host ""
|
||||
Write-Success "Running demo search: 'embedding system'"
|
||||
& python (Join-Path $ScriptDir "rag-mini.py") search $ScriptDir "embedding system" --top-k 3
|
||||
|
||||
Write-Host ""
|
||||
Write-Success "Test completed successfully!"
|
||||
Write-ColorOutput "FSS-Mini-RAG is working perfectly on Windows!" "Cyan"
|
||||
}
|
||||
|
||||
# Run main function
|
||||
Main
|
||||
@ -462,73 +462,6 @@ install_dependencies() {
|
||||
fi
|
||||
}
|
||||
|
||||
# Setup application icon for desktop integration
|
||||
setup_desktop_icon() {
|
||||
print_header "Setting Up Desktop Integration"
|
||||
|
||||
# Check if we're in a GUI environment
|
||||
if [ -z "$DISPLAY" ] && [ -z "$WAYLAND_DISPLAY" ]; then
|
||||
print_info "No GUI environment detected - skipping desktop integration"
|
||||
return 0
|
||||
fi
|
||||
|
||||
local icon_source="$SCRIPT_DIR/assets/Fss_Mini_Rag.png"
|
||||
local desktop_dir="$HOME/.local/share/applications"
|
||||
local icon_dir="$HOME/.local/share/icons"
|
||||
|
||||
# Check if icon file exists
|
||||
if [ ! -f "$icon_source" ]; then
|
||||
print_warning "Icon file not found at $icon_source"
|
||||
return 1
|
||||
fi
|
||||
|
||||
# Create directories if needed
|
||||
mkdir -p "$desktop_dir" "$icon_dir" 2>/dev/null
|
||||
|
||||
# Copy icon to standard location
|
||||
local icon_dest="$icon_dir/fss-mini-rag.png"
|
||||
if cp "$icon_source" "$icon_dest" 2>/dev/null; then
|
||||
print_success "Icon installed to $icon_dest"
|
||||
else
|
||||
print_warning "Could not install icon (permissions?)"
|
||||
return 1
|
||||
fi
|
||||
|
||||
# Create desktop entry
|
||||
local desktop_file="$desktop_dir/fss-mini-rag.desktop"
|
||||
cat > "$desktop_file" << EOF
|
||||
[Desktop Entry]
|
||||
Name=FSS-Mini-RAG
|
||||
Comment=Fast Semantic Search for Code and Documents
|
||||
Exec=$SCRIPT_DIR/rag-tui
|
||||
Icon=fss-mini-rag
|
||||
Terminal=true
|
||||
Type=Application
|
||||
Categories=Development;Utility;TextEditor;
|
||||
Keywords=search;code;rag;semantic;ai;
|
||||
StartupNotify=true
|
||||
EOF
|
||||
|
||||
if [ -f "$desktop_file" ]; then
|
||||
chmod +x "$desktop_file"
|
||||
print_success "Desktop entry created"
|
||||
|
||||
# Update desktop database if available
|
||||
if command_exists update-desktop-database; then
|
||||
update-desktop-database "$desktop_dir" 2>/dev/null
|
||||
print_info "Desktop database updated"
|
||||
fi
|
||||
|
||||
print_info "✨ FSS-Mini-RAG should now appear in your application menu!"
|
||||
print_info " Look for it in Development or Utility categories"
|
||||
else
|
||||
print_warning "Could not create desktop entry"
|
||||
return 1
|
||||
fi
|
||||
|
||||
return 0
|
||||
}
|
||||
|
||||
# Setup ML models based on configuration
|
||||
setup_ml_models() {
|
||||
if [ "$INSTALL_TYPE" != "full" ]; then
|
||||
@ -772,7 +705,7 @@ run_quick_test() {
|
||||
read -r
|
||||
|
||||
# Launch the TUI which has the existing interactive tutorial system
|
||||
./rag-tui.py "$target_dir" || true
|
||||
./rag-tui.py "$target_dir"
|
||||
|
||||
echo ""
|
||||
print_success "🎉 Tutorial completed!"
|
||||
@ -861,9 +794,6 @@ main() {
|
||||
fi
|
||||
setup_ml_models
|
||||
|
||||
# Setup desktop integration with icon
|
||||
setup_desktop_icon
|
||||
|
||||
if test_installation; then
|
||||
show_completion
|
||||
else
|
||||
|
||||
@ -1,343 +0,0 @@
|
||||
@echo off
|
||||
REM FSS-Mini-RAG Windows Installer - Beautiful & Comprehensive
|
||||
setlocal enabledelayedexpansion
|
||||
|
||||
REM Enable colors and unicode for modern Windows
|
||||
chcp 65001 >nul 2>&1
|
||||
|
||||
echo.
|
||||
echo ╔══════════════════════════════════════════════════╗
|
||||
echo ║ FSS-Mini-RAG Windows Installer ║
|
||||
echo ║ Fast Semantic Search for Code ║
|
||||
echo ╚══════════════════════════════════════════════════╝
|
||||
echo.
|
||||
echo 🚀 Comprehensive installation process:
|
||||
echo • Python environment setup and validation
|
||||
echo • Smart dependency management
|
||||
echo • Optional AI model downloads (with your consent)
|
||||
echo • System testing and verification
|
||||
echo • Interactive tutorial (optional)
|
||||
echo.
|
||||
echo 💡 Note: You'll be asked before downloading any models
|
||||
echo.
|
||||
|
||||
set /p "continue=Begin installation? [Y/n]: "
|
||||
if /i "!continue!"=="n" (
|
||||
echo Installation cancelled.
|
||||
pause
|
||||
exit /b 0
|
||||
)
|
||||
|
||||
REM Get script directory
|
||||
set "SCRIPT_DIR=%~dp0"
|
||||
set "SCRIPT_DIR=%SCRIPT_DIR:~0,-1%"
|
||||
|
||||
echo.
|
||||
echo ══════════════════════════════════════════════════
|
||||
echo [1/5] Checking Python Environment...
|
||||
python --version >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo ❌ ERROR: Python not found!
|
||||
echo.
|
||||
echo 📦 Please install Python from: https://python.org/downloads
|
||||
echo 🔧 Installation requirements:
|
||||
echo • Python 3.8 or higher
|
||||
echo • Make sure to check "Add Python to PATH" during installation
|
||||
echo • Restart your command prompt after installation
|
||||
echo.
|
||||
echo 💡 Quick install options:
|
||||
echo • Download from python.org (recommended)
|
||||
echo • Or use: winget install Python.Python.3.11
|
||||
echo • Or use: choco install python311
|
||||
echo.
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
for /f "tokens=2" %%i in ('python --version 2^>^&1') do set "PYTHON_VERSION=%%i"
|
||||
echo ✅ Found Python !PYTHON_VERSION!
|
||||
|
||||
REM Check Python version (basic check for 3.x)
|
||||
for /f "tokens=1 delims=." %%a in ("!PYTHON_VERSION!") do set "MAJOR_VERSION=%%a"
|
||||
if !MAJOR_VERSION! LSS 3 (
|
||||
echo ❌ ERROR: Python !PYTHON_VERSION! found, but Python 3.8+ required
|
||||
echo 📦 Please upgrade Python to 3.8 or higher
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo.
|
||||
echo ══════════════════════════════════════════════════
|
||||
echo [2/5] Creating Python Virtual Environment...
|
||||
if exist "%SCRIPT_DIR%\.venv" (
|
||||
echo 🔄 Removing old virtual environment...
|
||||
rmdir /s /q "%SCRIPT_DIR%\.venv" 2>nul
|
||||
if exist "%SCRIPT_DIR%\.venv" (
|
||||
echo ⚠️ Could not remove old environment, creating anyway...
|
||||
)
|
||||
)
|
||||
|
||||
echo 📁 Creating fresh virtual environment...
|
||||
python -m venv "%SCRIPT_DIR%\.venv"
|
||||
if errorlevel 1 (
|
||||
echo ❌ ERROR: Failed to create virtual environment
|
||||
echo.
|
||||
echo 🔧 This might be because:
|
||||
echo • Python venv module is not installed
|
||||
echo • Insufficient permissions
|
||||
echo • Path contains special characters
|
||||
echo.
|
||||
echo 💡 Try: python -m pip install --user virtualenv
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
echo ✅ Virtual environment created successfully
|
||||
|
||||
echo.
|
||||
echo ══════════════════════════════════════════════════
|
||||
echo [3/5] Installing Python Dependencies...
|
||||
echo 📦 This may take 2-3 minutes depending on your internet speed...
|
||||
echo.
|
||||
|
||||
call "%SCRIPT_DIR%\.venv\Scripts\activate.bat"
|
||||
if errorlevel 1 (
|
||||
echo ❌ ERROR: Could not activate virtual environment
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
echo 🔧 Upgrading pip...
|
||||
"%SCRIPT_DIR%\.venv\Scripts\python.exe" -m pip install --upgrade pip --quiet
|
||||
if errorlevel 1 (
|
||||
echo ⚠️ Warning: Could not upgrade pip, continuing anyway...
|
||||
)
|
||||
|
||||
echo 📚 Installing core dependencies (lancedb, pandas, numpy, etc.)...
|
||||
echo This provides semantic search capabilities
|
||||
"%SCRIPT_DIR%\.venv\Scripts\pip.exe" install -r "%SCRIPT_DIR%\requirements.txt"
|
||||
if errorlevel 1 (
|
||||
echo ❌ ERROR: Failed to install dependencies
|
||||
echo.
|
||||
echo 🔧 Possible solutions:
|
||||
echo • Check internet connection
|
||||
echo • Try running as administrator
|
||||
echo • Check if antivirus is blocking pip
|
||||
echo • Manually run: pip install -r requirements.txt
|
||||
echo.
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
echo ✅ Dependencies installed successfully
|
||||
|
||||
echo.
|
||||
echo ══════════════════════════════════════════════════
|
||||
echo [4/5] Testing Installation...
|
||||
echo 🧪 Verifying Python imports...
|
||||
"%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 🔧 This usually means:
|
||||
echo • Dependencies didn't install correctly
|
||||
echo • Virtual environment is corrupted
|
||||
echo • Python path issues
|
||||
echo.
|
||||
echo 💡 Try running: pip install -r requirements.txt
|
||||
pause
|
||||
exit /b 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
|
||||
if errorlevel 1 (
|
||||
echo ⚠️ Warning: Embedding test inconclusive, but core system is ready
|
||||
)
|
||||
|
||||
echo.
|
||||
echo ══════════════════════════════════════════════════
|
||||
echo [5/6] Setting Up Desktop Integration...
|
||||
call :setup_windows_icon
|
||||
|
||||
echo.
|
||||
echo ══════════════════════════════════════════════════
|
||||
echo [6/6] Checking AI Features (Optional)...
|
||||
call :check_ollama_enhanced
|
||||
|
||||
echo.
|
||||
echo ╔══════════════════════════════════════════════════╗
|
||||
echo ║ INSTALLATION SUCCESSFUL! ║
|
||||
echo ╚══════════════════════════════════════════════════╝
|
||||
echo.
|
||||
echo 🎯 Quick Start Options:
|
||||
echo.
|
||||
echo 🎨 For Beginners (Recommended):
|
||||
echo rag.bat - Interactive interface with guided setup
|
||||
echo.
|
||||
echo 💻 For Developers:
|
||||
echo rag.bat index C:\myproject - Index a project
|
||||
echo rag.bat search C:\myproject "authentication" - Search project
|
||||
echo rag.bat help - Show all commands
|
||||
echo.
|
||||
|
||||
REM Offer interactive tutorial
|
||||
echo 🧪 Quick Test Available:
|
||||
echo Test FSS-Mini-RAG with a small sample project (takes ~30 seconds)
|
||||
echo.
|
||||
set /p "run_test=Run interactive tutorial now? [Y/n]: "
|
||||
if /i "!run_test!" NEQ "n" (
|
||||
call :run_tutorial
|
||||
) else (
|
||||
echo 📚 You can run the tutorial anytime with: rag.bat
|
||||
)
|
||||
|
||||
echo.
|
||||
echo 🎉 Setup complete! FSS-Mini-RAG is ready to use.
|
||||
echo 💡 Pro tip: Try indexing any folder with text files - code, docs, notes!
|
||||
echo.
|
||||
pause
|
||||
exit /b 0
|
||||
|
||||
:check_ollama_enhanced
|
||||
echo 🤖 Checking for AI capabilities...
|
||||
echo.
|
||||
|
||||
REM Check if Ollama is installed
|
||||
where ollama >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo ⚠️ Ollama not installed - using basic search mode
|
||||
echo.
|
||||
echo 🎯 For Enhanced AI Features:
|
||||
echo • 📥 Install Ollama: https://ollama.com/download
|
||||
echo • 🔄 Run: ollama serve
|
||||
echo • 🧠 Download model: ollama pull qwen3:1.7b
|
||||
echo.
|
||||
echo 💡 Benefits of AI features:
|
||||
echo • Smart query expansion for better search results
|
||||
echo • Interactive exploration mode with conversation memory
|
||||
echo • AI-powered synthesis of search results
|
||||
echo • Natural language understanding of your questions
|
||||
echo.
|
||||
goto :eof
|
||||
)
|
||||
|
||||
REM Check if Ollama server is running
|
||||
curl -s http://localhost:11434/api/version >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo 🟡 Ollama installed but not running
|
||||
echo.
|
||||
set /p "start_ollama=Start Ollama server now? [Y/n]: "
|
||||
if /i "!start_ollama!" NEQ "n" (
|
||||
echo 🚀 Starting Ollama server...
|
||||
start /b ollama serve
|
||||
timeout /t 3 /nobreak >nul
|
||||
curl -s http://localhost:11434/api/version >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo ⚠️ Could not start Ollama automatically
|
||||
echo 💡 Please run: ollama serve
|
||||
) else (
|
||||
echo ✅ Ollama server started successfully!
|
||||
)
|
||||
)
|
||||
) else (
|
||||
echo ✅ Ollama server is running!
|
||||
)
|
||||
|
||||
REM Check for available models
|
||||
echo 🔍 Checking for AI models...
|
||||
ollama list 2>nul | findstr /v "NAME" | findstr /v "^$" >nul
|
||||
if errorlevel 1 (
|
||||
echo 📦 No AI models found
|
||||
echo.
|
||||
echo 🧠 Recommended Models (choose one):
|
||||
echo • qwen3:1.7b - Excellent for RAG (1.4GB, recommended)
|
||||
echo • qwen3:0.6b - Lightweight and fast (~500MB)
|
||||
echo • qwen3:4b - Higher quality but slower (~2.5GB)
|
||||
echo.
|
||||
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
|
||||
ollama pull qwen3:1.7b
|
||||
if errorlevel 1 (
|
||||
echo ⚠️ Download failed - you can try again later with: ollama pull qwen3:1.7b
|
||||
) else (
|
||||
echo ✅ Model downloaded successfully! AI features are now available.
|
||||
)
|
||||
)
|
||||
) else (
|
||||
echo ✅ AI models found - full AI features available!
|
||||
echo 🎉 Your system supports query expansion, exploration mode, and synthesis!
|
||||
)
|
||||
goto :eof
|
||||
|
||||
:run_tutorial
|
||||
echo.
|
||||
echo ═══════════════════════════════════════════════════
|
||||
echo 🧪 Running Interactive Tutorial
|
||||
echo ═══════════════════════════════════════════════════
|
||||
echo.
|
||||
echo 📚 This tutorial will:
|
||||
echo • Index the FSS-Mini-RAG documentation
|
||||
echo • Show you how to search effectively
|
||||
echo • Demonstrate AI features (if available)
|
||||
echo.
|
||||
|
||||
call "%SCRIPT_DIR%\.venv\Scripts\activate.bat"
|
||||
|
||||
echo 📁 Indexing project for demonstration...
|
||||
"%SCRIPT_DIR%\.venv\Scripts\python.exe" rag-mini.py index "%SCRIPT_DIR%" >nul 2>&1
|
||||
if errorlevel 1 (
|
||||
echo ❌ Indexing failed - please check the installation
|
||||
goto :eof
|
||||
)
|
||||
|
||||
echo ✅ Indexing complete!
|
||||
echo.
|
||||
echo 🔍 Example search: "embedding"
|
||||
"%SCRIPT_DIR%\.venv\Scripts\python.exe" rag-mini.py search "%SCRIPT_DIR%" "embedding" --top-k 3
|
||||
echo.
|
||||
echo 🎯 Try the interactive interface:
|
||||
echo rag.bat
|
||||
echo.
|
||||
echo 💡 You can now search any project by indexing it first!
|
||||
goto :eof
|
||||
|
||||
:setup_windows_icon
|
||||
echo 🎨 Setting up application icon and shortcuts...
|
||||
|
||||
REM Check if icon exists
|
||||
if not exist "%SCRIPT_DIR%\assets\Fss_Mini_Rag.png" (
|
||||
echo ⚠️ Icon file not found - skipping desktop integration
|
||||
goto :eof
|
||||
)
|
||||
|
||||
REM Create desktop shortcut
|
||||
echo 📱 Creating desktop shortcut...
|
||||
set "desktop=%USERPROFILE%\Desktop"
|
||||
set "shortcut=%desktop%\FSS-Mini-RAG.lnk"
|
||||
|
||||
REM Use PowerShell to create shortcut with icon
|
||||
powershell -Command "& {$WshShell = New-Object -comObject WScript.Shell; $Shortcut = $WshShell.CreateShortcut('%shortcut%'); $Shortcut.TargetPath = '%SCRIPT_DIR%\rag.bat'; $Shortcut.WorkingDirectory = '%SCRIPT_DIR%'; $Shortcut.Description = 'FSS-Mini-RAG - Fast Semantic Search'; $Shortcut.Save()}" >nul 2>&1
|
||||
|
||||
if exist "%shortcut%" (
|
||||
echo ✅ Desktop shortcut created
|
||||
) else (
|
||||
echo ⚠️ Could not create desktop shortcut
|
||||
)
|
||||
|
||||
REM Create Start Menu shortcut
|
||||
echo 📂 Creating Start Menu entry...
|
||||
set "startmenu=%APPDATA%\Microsoft\Windows\Start Menu\Programs"
|
||||
set "startshortcut=%startmenu%\FSS-Mini-RAG.lnk"
|
||||
|
||||
powershell -Command "& {$WshShell = New-Object -comObject WScript.Shell; $Shortcut = $WshShell.CreateShortcut('%startshortcut%'); $Shortcut.TargetPath = '%SCRIPT_DIR%\rag.bat'; $Shortcut.WorkingDirectory = '%SCRIPT_DIR%'; $Shortcut.Description = 'FSS-Mini-RAG - Fast Semantic Search'; $Shortcut.Save()}" >nul 2>&1
|
||||
|
||||
if exist "%startshortcut%" (
|
||||
echo ✅ Start Menu entry created
|
||||
) else (
|
||||
echo ⚠️ Could not create Start Menu entry
|
||||
)
|
||||
|
||||
echo 💡 FSS-Mini-RAG shortcuts have been created on your Desktop and Start Menu
|
||||
echo You can now launch the application from either location
|
||||
goto :eof
|
||||
@ -81,10 +81,6 @@ class LLMConfig:
|
||||
enable_thinking: bool = True # Enable thinking mode for Qwen3 models
|
||||
cpu_optimized: bool = True # Prefer lightweight models
|
||||
|
||||
# Context window configuration (critical for RAG performance)
|
||||
context_window: int = 16384 # Context window size in tokens (16K recommended)
|
||||
auto_context: bool = True # Auto-adjust context based on model capabilities
|
||||
|
||||
# Model preference rankings (configurable)
|
||||
model_rankings: list = None # Will be set in __post_init__
|
||||
|
||||
@ -108,9 +104,9 @@ class LLMConfig:
|
||||
# Recommended model (excellent quality but larger)
|
||||
"qwen3:4b",
|
||||
|
||||
# Common fallbacks (prioritize Qwen models)
|
||||
# Common fallbacks (only include models we know exist)
|
||||
"llama3.2:1b",
|
||||
"qwen2.5:1.5b",
|
||||
"qwen2.5:3b",
|
||||
]
|
||||
|
||||
|
||||
@ -259,11 +255,6 @@ class ConfigManager:
|
||||
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)",
|
||||
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)",
|
||||
])
|
||||
|
||||
|
||||
@ -115,13 +115,12 @@ class CodeExplorer:
|
||||
# Add to conversation history
|
||||
self.current_session.add_exchange(question, results, synthesis)
|
||||
|
||||
# Streaming already displayed the response
|
||||
# Just return minimal status for caller
|
||||
session_duration = time.time() - self.current_session.started_at
|
||||
exchange_count = len(self.current_session.conversation_history)
|
||||
# Format response with exploration context
|
||||
response = self._format_exploration_response(
|
||||
question, synthesis, len(results), search_time, synthesis_time
|
||||
)
|
||||
|
||||
status = f"\n📊 Session: {session_duration/60:.1f}m | Question #{exchange_count} | Results: {len(results)} | Time: {search_time+synthesis_time:.1f}s"
|
||||
return status
|
||||
return response
|
||||
|
||||
def _build_contextual_prompt(self, question: str, results: List[Any]) -> str:
|
||||
"""Build a prompt that includes conversation context."""
|
||||
@ -186,22 +185,33 @@ CURRENT QUESTION: "{question}"
|
||||
RELEVANT INFORMATION FOUND:
|
||||
{results_text}
|
||||
|
||||
Please provide a helpful, natural explanation that answers their question. Write as if you're having a friendly conversation with a colleague who's exploring this project.
|
||||
Please provide a helpful analysis in JSON format:
|
||||
|
||||
Structure your response to include:
|
||||
1. A clear explanation of what you found and how it answers their question
|
||||
2. The most important insights from the information you discovered
|
||||
3. Relevant examples or code patterns when helpful
|
||||
4. Practical next steps they could take
|
||||
{{
|
||||
"summary": "Clear explanation of what you found and how it answers their question",
|
||||
"key_points": [
|
||||
"Most important insight from the information",
|
||||
"Secondary important point or relationship",
|
||||
"Third key point or practical consideration"
|
||||
],
|
||||
"code_examples": [
|
||||
"Relevant example or pattern from the information",
|
||||
"Another useful example or demonstration"
|
||||
],
|
||||
"suggested_actions": [
|
||||
"Specific next step they could take",
|
||||
"Additional exploration or investigation suggestion",
|
||||
"Practical way to apply this information"
|
||||
],
|
||||
"confidence": 0.85
|
||||
}}
|
||||
|
||||
Guidelines:
|
||||
- Write in a conversational, friendly tone
|
||||
- Be educational but not condescending
|
||||
- Be educational and break things down clearly
|
||||
- Reference specific files and information when helpful
|
||||
- Give practical, actionable suggestions
|
||||
- Connect everything back to their original question
|
||||
- Use natural language, not structured formats
|
||||
- Break complex topics into understandable pieces
|
||||
- Keep explanations beginner-friendly but not condescending
|
||||
- Connect information to their question directly
|
||||
"""
|
||||
|
||||
return prompt
|
||||
@ -209,12 +219,16 @@ Guidelines:
|
||||
def _synthesize_with_context(self, prompt: str, results: List[Any]) -> SynthesisResult:
|
||||
"""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)
|
||||
# TEMPORARILY: Use simple non-streaming call to avoid flow issues
|
||||
# TODO: Re-enable streaming once flow is stable
|
||||
response = self.synthesizer._call_ollama(prompt, temperature=0.2, disable_thinking=False)
|
||||
thinking_stream = ""
|
||||
|
||||
# Streaming already shows thinking and response
|
||||
# No need for additional indicators
|
||||
# Display simple thinking indicator
|
||||
if response and len(response) > 200:
|
||||
print("\n💭 Analysis in progress...")
|
||||
|
||||
# Don't display thinking stream again - keeping it simple for now
|
||||
|
||||
if not response:
|
||||
return SynthesisResult(
|
||||
@ -225,14 +239,40 @@ Guidelines:
|
||||
confidence=0.0
|
||||
)
|
||||
|
||||
# Use natural language response directly
|
||||
return SynthesisResult(
|
||||
summary=response.strip(),
|
||||
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
|
||||
)
|
||||
# Parse the structured response
|
||||
try:
|
||||
# Extract JSON from response
|
||||
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', 'Analysis completed'),
|
||||
key_points=data.get('key_points', []),
|
||||
code_examples=data.get('code_examples', []),
|
||||
suggested_actions=data.get('suggested_actions', []),
|
||||
confidence=float(data.get('confidence', 0.7))
|
||||
)
|
||||
else:
|
||||
# Fallback: use raw response as summary
|
||||
return SynthesisResult(
|
||||
summary=response[:400] + '...' if len(response) > 400 else response,
|
||||
key_points=[],
|
||||
code_examples=[],
|
||||
suggested_actions=[],
|
||||
confidence=0.5
|
||||
)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
return SynthesisResult(
|
||||
summary="Analysis completed but format parsing failed",
|
||||
key_points=[],
|
||||
code_examples=[],
|
||||
suggested_actions=["Try rephrasing your question"],
|
||||
confidence=0.3
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Context synthesis failed: {e}")
|
||||
@ -260,12 +300,29 @@ Guidelines:
|
||||
output.append("=" * 60)
|
||||
output.append("")
|
||||
|
||||
# Response was already displayed via streaming
|
||||
# Just show completion status
|
||||
output.append("✅ Analysis complete")
|
||||
output.append("")
|
||||
# Main analysis
|
||||
output.append(f"📝 Analysis:")
|
||||
output.append(f" {synthesis.summary}")
|
||||
output.append("")
|
||||
|
||||
if synthesis.key_points:
|
||||
output.append("🔍 Key Insights:")
|
||||
for point in synthesis.key_points:
|
||||
output.append(f" • {point}")
|
||||
output.append("")
|
||||
|
||||
if synthesis.code_examples:
|
||||
output.append("💡 Code Examples:")
|
||||
for example in synthesis.code_examples:
|
||||
output.append(f" {example}")
|
||||
output.append("")
|
||||
|
||||
if synthesis.suggested_actions:
|
||||
output.append("🎯 Next Steps:")
|
||||
for action in synthesis.suggested_actions:
|
||||
output.append(f" • {action}")
|
||||
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 ""
|
||||
@ -408,7 +465,7 @@ Guidelines:
|
||||
"temperature": temperature,
|
||||
"top_p": optimal_params.get("top_p", 0.9),
|
||||
"top_k": optimal_params.get("top_k", 40),
|
||||
"num_ctx": self.synthesizer._get_optimal_context_size(model_to_use),
|
||||
"num_ctx": optimal_params.get("num_ctx", 32768),
|
||||
"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)
|
||||
|
||||
@ -195,7 +195,7 @@ class ModelRunawayDetector:
|
||||
• Try a more specific question
|
||||
• Break complex questions into smaller parts
|
||||
• Use exploration mode which handles context better: `rag-mini explore`
|
||||
• Consider: A larger model (qwen3:1.7b or qwen3:4b) would help"""
|
||||
• Consider: A larger model (qwen3:1.7b or qwen3:3b) would help"""
|
||||
|
||||
def _explain_thinking_loop(self) -> str:
|
||||
return """🧠 The AI got caught in a "thinking loop" - overthinking the response.
|
||||
@ -266,7 +266,7 @@ class ModelRunawayDetector:
|
||||
|
||||
# Universal suggestions
|
||||
suggestions.extend([
|
||||
"Consider using a larger model if available (qwen3:1.7b or qwen3:4b)",
|
||||
"Consider using a larger model if available (qwen3:1.7b or qwen3:3b)",
|
||||
"Check model status: `ollama list`"
|
||||
])
|
||||
|
||||
|
||||
@ -72,8 +72,8 @@ class LLMSynthesizer:
|
||||
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", "llama3.2:1b",
|
||||
"qwen2.5:1.5b", "qwen3:3b", "qwen2.5-coder:1.5b"
|
||||
]
|
||||
|
||||
# Find first available model from our ranked list (exact matches first)
|
||||
@ -114,57 +114,12 @@ class LLMSynthesizer:
|
||||
|
||||
self._initialized = True
|
||||
|
||||
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'):
|
||||
configured_context = self.config.llm.context_window
|
||||
auto_context = getattr(self.config.llm, 'auto_context', True)
|
||||
else:
|
||||
configured_context = 16384 # Default to 16K
|
||||
auto_context = True
|
||||
|
||||
# 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
|
||||
|
||||
# Qwen2.5 models
|
||||
'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
|
||||
}
|
||||
|
||||
# Find model limit (check for partial matches)
|
||||
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():
|
||||
model_limit = limit
|
||||
break
|
||||
|
||||
# If auto_context is enabled, respect model limits
|
||||
if auto_context:
|
||||
optimal_context = min(configured_context, model_limit)
|
||||
else:
|
||||
optimal_context = configured_context
|
||||
|
||||
# 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})")
|
||||
return optimal_context
|
||||
|
||||
def is_available(self) -> bool:
|
||||
"""Check if Ollama is available and has models."""
|
||||
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 = False) -> Optional[str]:
|
||||
"""Make a call to Ollama API with safeguards."""
|
||||
start_time = time.time()
|
||||
|
||||
@ -219,16 +174,16 @@ 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": 32000, # Critical: Qwen3 context length (32K token limit)
|
||||
"num_predict": optimal_params.get("num_predict", 2000),
|
||||
"repeat_penalty": optimal_params.get("repeat_penalty", 1.1),
|
||||
"presence_penalty": qwen3_presence
|
||||
}
|
||||
}
|
||||
|
||||
# Handle streaming with thinking display
|
||||
# Handle streaming with early stopping
|
||||
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_early_stop(payload, model_to_use, use_thinking, start_time)
|
||||
|
||||
response = requests.post(
|
||||
f"{self.ollama_url}/api/generate",
|
||||
@ -329,130 +284,6 @@ 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]:
|
||||
"""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
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(f"Ollama API error: {response.status_code}")
|
||||
return None
|
||||
|
||||
full_response = ""
|
||||
thinking_content = ""
|
||||
is_in_thinking = False
|
||||
is_thinking_complete = False
|
||||
thinking_lines_printed = 0
|
||||
|
||||
# ANSI escape codes for colors and cursor control
|
||||
GRAY = '\033[90m' # Dark gray for thinking
|
||||
LIGHT_GRAY = '\033[37m' # Light gray alternative
|
||||
RESET = '\033[0m' # Reset color
|
||||
CLEAR_LINE = '\033[2K' # Clear entire line
|
||||
CURSOR_UP = '\033[A' # Move cursor up one line
|
||||
|
||||
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', '')
|
||||
|
||||
if chunk_text:
|
||||
full_response += chunk_text
|
||||
|
||||
# Handle thinking tokens
|
||||
if use_thinking and '<think>' in chunk_text:
|
||||
is_in_thinking = True
|
||||
chunk_text = chunk_text.replace('<think>', '')
|
||||
|
||||
if is_in_thinking and '</think>' in chunk_text:
|
||||
is_in_thinking = False
|
||||
is_thinking_complete = True
|
||||
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"💭 {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("🤖 AI Response:", flush=True)
|
||||
continue
|
||||
|
||||
# Display thinking content in gray with better formatting
|
||||
if is_in_thinking and chunk_text.strip():
|
||||
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:
|
||||
# Split by sentences for better readability
|
||||
sentences = thinking_content.replace('\n', ' ').split('. ')
|
||||
|
||||
for sentence in sentences[:-1]: # Process complete sentences
|
||||
sentence = sentence.strip()
|
||||
if sentence:
|
||||
# Word wrap long sentences
|
||||
words = sentence.split()
|
||||
line = ""
|
||||
for word in words:
|
||||
if len(line + " " + word) > 70:
|
||||
if line:
|
||||
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)
|
||||
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():
|
||||
# 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 clean_text.strip():
|
||||
print(clean_text, end='', flush=True)
|
||||
|
||||
# Check if response is done
|
||||
if chunk_data.get('done', False):
|
||||
print() # Final newline
|
||||
break
|
||||
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing stream chunk: {e}")
|
||||
continue
|
||||
|
||||
return full_response
|
||||
|
||||
except Exception as e:
|
||||
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]:
|
||||
"""Handle streaming response with intelligent early stopping."""
|
||||
import json
|
||||
|
||||
@ -170,8 +170,8 @@ Expanded query:"""
|
||||
|
||||
# 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", "llama3.2:1b",
|
||||
"qwen2.5:1.5b", "qwen3:3b", "qwen2.5-coder:1.5b"
|
||||
]
|
||||
|
||||
for preferred in expansion_preferences:
|
||||
|
||||
85
rag-mini.py
85
rag-mini.py
@ -142,8 +142,8 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
|
||||
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(" • More results: add --top-k 20")
|
||||
print()
|
||||
print("📚 Need help? See: docs/TROUBLESHOOTING.md")
|
||||
return
|
||||
@ -201,7 +201,7 @@ def search_project(project_path: Path, query: str, top_k: int = 10, synthesize:
|
||||
else:
|
||||
print("❌ LLM synthesis unavailable")
|
||||
print(" • Ensure Ollama is running: ollama serve")
|
||||
print(" • Install a model: ollama pull qwen3:1.7b")
|
||||
print(" • Install a model: ollama pull llama3.2")
|
||||
print(" • Check connection to http://localhost:11434")
|
||||
|
||||
# Save last search for potential enhancements
|
||||
@ -317,27 +317,12 @@ def explore_interactive(project_path: Path):
|
||||
if not explorer.start_exploration_session():
|
||||
sys.exit(1)
|
||||
|
||||
# Show enhanced first-time guidance
|
||||
print(f"\n🤔 Ask your first question about {project_path.name}:")
|
||||
print()
|
||||
print("💡 Enter your search query or question below:")
|
||||
print(' Examples: "How does authentication work?" or "Show me error handling"')
|
||||
print()
|
||||
print("🔧 Quick options:")
|
||||
print(" 1. Help - Show example questions")
|
||||
print(" 2. Status - Project information")
|
||||
print(" 3. Suggest - Get a random starter question")
|
||||
print()
|
||||
|
||||
is_first_question = True
|
||||
|
||||
while True:
|
||||
try:
|
||||
# Get user input with clearer prompt
|
||||
if is_first_question:
|
||||
question = input("📝 Enter question or option (1-3): ").strip()
|
||||
else:
|
||||
question = input("\n> ").strip()
|
||||
# Get user input
|
||||
question = input("\n> ").strip()
|
||||
|
||||
# Handle exit commands
|
||||
if question.lower() in ['quit', 'exit', 'q']:
|
||||
@ -346,17 +331,14 @@ def explore_interactive(project_path: Path):
|
||||
|
||||
# Handle empty input
|
||||
if not question:
|
||||
if is_first_question:
|
||||
print("Please enter a question or try option 3 for a suggestion.")
|
||||
else:
|
||||
print("Please enter a question or 'quit' to exit.")
|
||||
print("Please enter a question or 'quit' to exit.")
|
||||
continue
|
||||
|
||||
# Handle numbered options and special commands
|
||||
if question in ['1'] or question.lower() in ['help', 'h']:
|
||||
# Special commands
|
||||
if question.lower() in ['help', 'h']:
|
||||
print("""
|
||||
🧠 EXPLORATION MODE HELP:
|
||||
• Ask any question about your documents or code
|
||||
• Ask any question about the codebase
|
||||
• I remember our conversation for follow-up questions
|
||||
• Use 'why', 'how', 'explain' for detailed reasoning
|
||||
• Type 'summary' to see session overview
|
||||
@ -364,53 +346,11 @@ def explore_interactive(project_path: Path):
|
||||
|
||||
💡 Example questions:
|
||||
• "How does authentication work?"
|
||||
• "What are the main components?"
|
||||
• "Show me error handling patterns"
|
||||
• "Why is this function slow?"
|
||||
• "What security measures are in place?"
|
||||
• "How does data flow through this system?"
|
||||
• "Explain the database connection logic"
|
||||
• "What are the security concerns here?"
|
||||
""")
|
||||
continue
|
||||
|
||||
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':
|
||||
# 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?",
|
||||
"Show me the authentication and security logic",
|
||||
"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"
|
||||
]
|
||||
suggested = random.choice(starters)
|
||||
print(f"\n💡 Suggested question: {suggested}")
|
||||
print(" Press Enter to use this, or type your own question:")
|
||||
|
||||
next_input = input("📝 > ").strip()
|
||||
if not next_input: # User pressed Enter to use suggestion
|
||||
question = suggested
|
||||
else:
|
||||
question = next_input
|
||||
else:
|
||||
# For subsequent questions, could add AI-powered suggestions here
|
||||
print("\n💡 Based on our conversation, you might want to ask:")
|
||||
print(' "Can you explain that in more detail?"')
|
||||
print(' "What are the security implications?"')
|
||||
print(' "Show me related code examples"')
|
||||
continue
|
||||
|
||||
if question.lower() == 'summary':
|
||||
print("\n" + explorer.get_session_summary())
|
||||
@ -421,9 +361,6 @@ def explore_interactive(project_path: Path):
|
||||
print("🧠 Thinking with AI model...")
|
||||
response = explorer.explore_question(question)
|
||||
|
||||
# Mark as no longer first question after processing
|
||||
is_first_question = False
|
||||
|
||||
if response:
|
||||
print(f"\n{response}")
|
||||
else:
|
||||
|
||||
949
rag-tui.py
949
rag-tui.py
File diff suppressed because it is too large
Load Diff
51
rag.bat
51
rag.bat
@ -1,51 +0,0 @@
|
||||
@echo off
|
||||
REM FSS-Mini-RAG Windows Launcher - Simple and Reliable
|
||||
|
||||
setlocal
|
||||
set "SCRIPT_DIR=%~dp0"
|
||||
set "SCRIPT_DIR=%SCRIPT_DIR:~0,-1%"
|
||||
set "VENV_PYTHON=%SCRIPT_DIR%\.venv\Scripts\python.exe"
|
||||
|
||||
REM Check if virtual environment exists
|
||||
if not exist "%VENV_PYTHON%" (
|
||||
echo Virtual environment not found!
|
||||
echo.
|
||||
echo Run this first: install_windows.bat
|
||||
echo.
|
||||
pause
|
||||
exit /b 1
|
||||
)
|
||||
|
||||
REM Route commands
|
||||
if "%1"=="" goto :interactive
|
||||
if "%1"=="help" goto :help
|
||||
if "%1"=="--help" goto :help
|
||||
if "%1"=="-h" goto :help
|
||||
|
||||
REM Pass all arguments to Python script
|
||||
"%VENV_PYTHON%" "%SCRIPT_DIR%\rag-mini.py" %*
|
||||
goto :end
|
||||
|
||||
:interactive
|
||||
echo Starting interactive interface...
|
||||
"%VENV_PYTHON%" "%SCRIPT_DIR%\rag-tui.py"
|
||||
goto :end
|
||||
|
||||
:help
|
||||
echo FSS-Mini-RAG - Semantic Code Search
|
||||
echo.
|
||||
echo Usage:
|
||||
echo rag.bat - Interactive interface
|
||||
echo rag.bat index ^<folder^> - Index a project
|
||||
echo rag.bat search ^<folder^> ^<query^> - Search project
|
||||
echo rag.bat status ^<folder^> - Check status
|
||||
echo.
|
||||
echo Examples:
|
||||
echo rag.bat index C:\myproject
|
||||
echo rag.bat search C:\myproject "authentication"
|
||||
echo rag.bat search . "error handling"
|
||||
echo.
|
||||
pause
|
||||
|
||||
:end
|
||||
endlocal
|
||||
Loading…
x
Reference in New Issue
Block a user