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.
111 lines
5.2 KiB
YAML
111 lines
5.2 KiB
YAML
# 💎 QUALITY CONFIG - Best Possible Results
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# When you want the highest quality search and AI responses
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# Perfect for: learning new codebases, research, complex analysis
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#═══════════════════════════════════════════════════════════════════════
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# 🎯 QUALITY-OPTIMIZED SETTINGS - Everything tuned for best results!
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#═══════════════════════════════════════════════════════════════════════
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# 📝 Chunking for maximum context and quality
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chunking:
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max_size: 3000 # Larger chunks = more context per result
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min_size: 200 # Ensure substantial content per chunk
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strategy: semantic # Smart splitting that respects code structure
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# 🌊 Conservative streaming (favor quality over speed)
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streaming:
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enabled: true
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threshold_bytes: 2097152 # 2MB - less aggressive chunking
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# 📁 Comprehensive file inclusion
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files:
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min_file_size: 20 # Include even small files (might contain important info)
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# 🎯 Minimal exclusions (include more content)
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exclude_patterns:
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- "node_modules/**" # Still skip these (too much noise)
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- ".git/**" # Git history not useful for code search
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- "__pycache__/**" # Python bytecode
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- "*.pyc"
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- ".venv/**"
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- "build/**" # Compiled artifacts
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- "dist/**"
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# Note: We keep logs, docs, configs that might have useful context
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include_patterns:
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- "**/*" # Include everything not explicitly excluded
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# 🧠 Best embedding quality
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embedding:
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preferred_method: ollama # Highest quality embeddings (needs Ollama)
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ollama_model: nomic-embed-text # Excellent code understanding
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ml_model: sentence-transformers/all-MiniLM-L6-v2 # Good fallback
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batch_size: 16 # Smaller batches for stability
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# 🔍 Search optimized for comprehensive results
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search:
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default_top_k: 15 # More results to choose from
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enable_bm25: true # Use both semantic and keyword matching
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similarity_threshold: 0.05 # Very permissive (show more possibilities)
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expand_queries: true # Automatic query expansion for better recall
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# 🤖 High-quality AI analysis
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llm:
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synthesis_model: auto # Use best available model
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enable_synthesis: true # AI explanations by default
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synthesis_temperature: 0.4 # Good balance of accuracy and insight
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cpu_optimized: false # Use powerful models if available
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enable_thinking: true # Show detailed reasoning process
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max_expansion_terms: 10 # Comprehensive query expansion
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#═══════════════════════════════════════════════════════════════════════
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# 💎 WHAT THIS CONFIG MAXIMIZES:
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#
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# 🎯 Search comprehensiveness - find everything relevant
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# 🎯 Result context - larger chunks with more information
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# 🎯 AI explanation quality - detailed, thoughtful analysis
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# 🎯 Query understanding - automatic expansion and enhancement
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# 🎯 Semantic accuracy - best embedding models available
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#
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# ⚖️ TRADE-OFFS:
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# ⏳ Slower indexing (larger chunks, better embeddings)
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# ⏳ Slower searching (query expansion, more results)
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# 💾 More storage space (larger index, more files included)
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# 🧠 More memory usage (larger batches, bigger models)
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# ⚡ Higher CPU/GPU usage (better models)
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#
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# 🎯 PERFECT FOR:
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# • Learning new, complex codebases
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# • Research and analysis tasks
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# • When you need to understand WHY code works a certain way
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# • Finding subtle connections and patterns
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# • Code review and security analysis
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# • Academic or professional research
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#
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# 💻 REQUIREMENTS:
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# • Ollama installed and running (ollama serve)
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# • At least one language model (ollama pull qwen3:1.7b)
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# • Decent computer specs (4GB+ RAM recommended)
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# • Patience for thorough analysis 😊
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#
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# 🚀 TO USE THIS CONFIG:
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# 1. Install Ollama: curl -fsSL https://ollama.ai/install.sh | sh
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# 2. Start Ollama: ollama serve
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# 3. Install a model: ollama pull qwen3:1.7b
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# 4. Copy config: cp examples/config-quality.yaml .mini-rag/config.yaml
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# 5. Index project: ./rag-mini index /path/to/project
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# 6. Enjoy comprehensive analysis: ./rag-mini explore /path/to/project
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#═══════════════════════════════════════════════════════════════════════
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# 🧪 ADVANCED QUALITY TUNING (optional):
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#
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# For even better results, try these model combinations:
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# • ollama pull nomic-embed-text:latest (best embeddings)
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# • ollama pull qwen3:1.7b (good general model)
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# • ollama pull llama3.2 (excellent for analysis)
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#
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# Or adjust these settings for your specific needs:
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# • similarity_threshold: 0.3 (more selective results)
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# • max_size: 4000 (even more context per result)
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# • enable_thinking: false (hide reasoning, show just answers)
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# • synthesis_temperature: 0.2 (more conservative AI responses) |