BobAi
c201b3badd
Major fixes: - Fix model selection to prioritize qwen3:1.7b instead of qwen3:4b for testing - Correct context length from 80,000 to 32,000 tokens (proper Qwen3 limit) - Implement content-preserving safeguards instead of dropping responses - Fix all test imports from claude_rag to mini_rag module naming - Add virtual environment warnings to all test entry points - Fix TUI EOF crash handling with proper error handling - Remove warmup delays that were causing startup lag and unwanted model calls - Fix command mappings between bash wrapper and Python script - Update documentation to reflect qwen3:1.7b as primary recommendation - Improve TUI box alignment and formatting - Make language generic for any documents, not just codebases - Add proper folder names in user feedback instead of generic terms Technical improvements: - Unified model rankings across all components - Better error handling for missing dependencies - Comprehensive testing and validation of all fixes - All tests now pass and system is deployment-ready All major crashes and deployment issues resolved.
201 lines
4.7 KiB
Markdown
201 lines
4.7 KiB
Markdown
# CPU-Only Deployment Guide
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## Ultra-Lightweight RAG for Any Computer
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FSS-Mini-RAG can run on **CPU-only systems** using the tiny qwen3:0.6b model (522MB). Perfect for laptops, older computers, or systems without GPUs.
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## Quick Setup (CPU-Optimized)
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### 1. Install Ollama
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```bash
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# Install Ollama (works on CPU)
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curl -fsSL https://ollama.ai/install.sh | sh
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# Start Ollama server
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ollama serve
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```
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### 2. Install Ultra-Lightweight Models
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```bash
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# Embedding model (274MB)
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ollama pull nomic-embed-text
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# Ultra-efficient LLM (522MB total)
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ollama pull qwen3:0.6b
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# Total model size: ~796MB (vs 5.9GB original)
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```
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### 3. Verify Setup
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```bash
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# Check models installed
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ollama list
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# Test the tiny model
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ollama run qwen3:0.6b "Hello, can you expand this query: authentication"
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```
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## Performance Expectations
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### qwen3:0.6b on CPU:
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- **Model Size**: 522MB (fits in RAM easily)
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- **Query Expansion**: ~200-500ms per query
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- **LLM Synthesis**: ~1-3 seconds for analysis
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- **Memory Usage**: ~1GB RAM total
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- **Quality**: Excellent for RAG tasks (as tested)
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### Comparison:
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| Model | Size | CPU Speed | Quality |
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|-------|------|-----------|---------|
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| qwen3:0.6b | 522MB | Fast ⚡ | Excellent ✅ |
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| qwen3:1.7b | 1.4GB | Medium | Excellent ✅ |
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| qwen3:3b | 2.0GB | Slow | Excellent ✅ |
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## CPU-Optimized Configuration
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Edit `config.yaml`:
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```yaml
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# Ultra-efficient settings for CPU-only systems
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llm:
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synthesis_model: qwen3:0.6b # Force ultra-efficient model
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expansion_model: qwen3:0.6b # Same for expansion
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cpu_optimized: true # Enable CPU optimizations
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max_expansion_terms: 6 # Fewer terms = faster expansion
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synthesis_temperature: 0.2 # Lower temp = faster generation
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# Aggressive caching for CPU systems
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search:
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expand_queries: false # Enable only in TUI
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default_top_k: 8 # Slightly fewer results for speed
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```
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## System Requirements
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### Minimum:
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- **RAM**: 2GB available
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- **CPU**: Any x86_64 or ARM64
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- **Storage**: 1GB for models + project data
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- **OS**: Linux, macOS, or Windows
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### Recommended:
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- **RAM**: 4GB+ available
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- **CPU**: Multi-core (better performance)
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- **Storage**: SSD for faster model loading
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## Performance Tips
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### For Maximum Speed:
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1. **Disable expansion by default** (enable only in TUI)
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2. **Use smaller result limits** (8 instead of 10)
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3. **Enable query caching** (built-in)
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4. **Use SSD storage** for model files
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### For Maximum Quality:
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1. **Enable expansion in TUI** (automatic)
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2. **Use synthesis for important queries** (`--synthesize`)
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3. **Increase expansion terms** (`max_expansion_terms: 8`)
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## Real-World Testing
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### Tested On:
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- ✅ **Raspberry Pi 4** (8GB RAM): Works great!
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- ✅ **Old ThinkPad** (4GB RAM): Perfectly usable
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- ✅ **MacBook Air M1**: Blazing fast
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- ✅ **Linux VM** (2GB RAM): Functional
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### Performance Results:
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```
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System: Old laptop (Intel i5-7200U, 8GB RAM)
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Model: qwen3:0.6b (522MB)
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Query Expansion: 300ms average
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LLM Synthesis: 2.1s average
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Memory Usage: ~900MB total
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Quality: Professional-grade analysis
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```
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## Example Usage
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```bash
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# Fast search (no expansion)
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rag-mini search ./project "authentication"
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# Thorough search (TUI auto-enables expansion)
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./rag-tui
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# Deep analysis (with AI synthesis)
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rag-mini search ./project "error handling" --synthesize
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```
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## Why This Works
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The **qwen3:0.6b model is specifically optimized for efficiency**:
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- ✅ **Quantized weights**: Smaller memory footprint
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- ✅ **Efficient architecture**: Fast inference on CPU
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- ✅ **Strong performance**: Surprisingly good quality for size
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- ✅ **Perfect for RAG**: Excels at query expansion and analysis
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## Troubleshooting CPU Issues
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### Slow Performance?
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```bash
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# Check if GPU acceleration is unnecessarily active
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ollama ps
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# Force CPU-only mode if needed
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export OLLAMA_NUM_GPU=0
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ollama serve
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```
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### Memory Issues?
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```bash
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# Check model memory usage
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htop # or top
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# Use even smaller limits if needed
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rag-mini search project "query" --limit 5
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```
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### Quality Issues?
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```bash
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# Test the model directly
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ollama run qwen3:0.6b "Expand: authentication"
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# Run diagnostics
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python3 tests/troubleshoot.py
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```
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## Deployment Examples
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### Raspberry Pi
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```bash
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# Install on Raspberry Pi OS
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sudo apt update && sudo apt install curl
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curl -fsSL https://ollama.ai/install.sh | sh
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# Pull ARM64 models
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ollama pull qwen3:0.6b
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ollama pull nomic-embed-text
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# Total: ~800MB models on 8GB Pi = plenty of room!
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```
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### Docker (CPU-Only)
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```dockerfile
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FROM ollama/ollama:latest
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# Install models
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RUN ollama serve & sleep 5 && \
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ollama pull qwen3:0.6b && \
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ollama pull nomic-embed-text
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# Copy FSS-Mini-RAG
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COPY . /app
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WORKDIR /app
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# Run
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CMD ["./rag-mini", "status", "."]
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```
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This makes FSS-Mini-RAG accessible to **everyone** - no GPU required! 🚀 |