BobAi
3363171820
✨ BEGINNER-FRIENDLY ENHANCEMENTS: - Add comprehensive glossary explaining RAG, embeddings, chunks in plain English - Create detailed troubleshooting guide covering installation, search issues, performance - Provide preset configs (beginner/fast/quality) with extensive helpful comments - Enhanced error messages with specific solutions and next steps 🔧 PRODUCTION RELIABILITY: - Add thread-safe caching with automatic cleanup in QueryExpander - Implement chunked processing for large batches to prevent memory issues - Enhanced concurrent embedding with intelligent batch size management - Memory leak prevention with LRU cache approximation 🏗️ ARCHITECTURE COMPLETENESS: - Maintain two-mode system (synthesis fast, exploration thinking + memory) - Preserve educational value while removing intimidation barriers - Complete testing coverage for mode separation and context memory - Full documentation reflecting clean two-mode architecture Perfect balance: genuinely beginner-friendly without compromising technical sophistication
145 lines
10 KiB
YAML
145 lines
10 KiB
YAML
# FSS-Mini-RAG Configuration - Beginner-Friendly Edition
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#
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# 🎯 QUICK START PRESETS:
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# - Keep defaults for most cases (recommended for beginners)
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# - For large projects (>10k files): increase max_size to 3000
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# - For faster search: set similarity_threshold to 0.2
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# - For better results: enable expand_queries (but slower search)
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#═════════════════════════════════════════════════════════════════════════════════
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# 📝 CHUNKING: How we break up your code files for searching
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#═════════════════════════════════════════════════════════════════════════════════
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# Think of chunks as "bite-sized pieces" of your code that the system can search through.
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# Smaller chunks = more precise results but might miss context
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# Larger chunks = more context but might be less precise
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chunking:
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max_size: 2000 # Maximum characters per chunk (2000 = ~50 lines of code)
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# 💡 ADJUST IF: Getting results that are too narrow/broad
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# Small projects: 1500 | Large projects: 3000 | Detailed analysis: 4000
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min_size: 150 # Minimum characters per chunk (150 = ~4-5 lines)
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# ⚠️ Don't go below 100 or you'll get fragments
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strategy: semantic # How to split files into chunks
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# 'semantic': Smart splitting (respects functions, classes) - RECOMMENDED
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# 'fixed': Simple splitting (just cuts at size limits) - faster but less intelligent
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#═════════════════════════════════════════════════════════════════════════════════
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# 🌊 STREAMING: How we handle really big files
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#═════════════════════════════════════════════════════════════════════════════════
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# Large files (like minified CSS or huge data files) get processed in smaller batches
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# to prevent your computer from running out of memory
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streaming:
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enabled: true # Always keep this true - prevents memory crashes
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threshold_bytes: 1048576 # Files larger than 1MB use streaming (1MB = 1048576 bytes)
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# 💡 ADJUST IF: Low memory computer = 512000 | High memory = 2097152
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#═════════════════════════════════════════════════════════════════════════════════
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# 📁 FILES: Which files to include/exclude from indexing
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#═════════════════════════════════════════════════════════════════════════════════
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files:
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min_file_size: 50 # Skip tiny files (50 bytes = ~1 line of code)
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# 💡 REASON: Tiny files usually aren't useful for searching
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# 🚫 EXCLUDE PATTERNS: Files/folders we always skip (saves time and space)
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exclude_patterns:
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- "node_modules/**" # JavaScript dependencies (huge and not your code)
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- ".git/**" # Git history (not useful for code search)
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- "__pycache__/**" # Python bytecode (generated files)
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- "*.pyc" # More Python bytecode
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- ".venv/**" # Python virtual environments
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- "venv/**" # More virtual environments
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- "build/**" # Compiled output (not source code)
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- "dist/**" # Distribution files
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# 💡 ADD YOUR OWN: Add patterns like "logs/**" or "*.tmp"
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include_patterns:
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- "**/*" # Include everything else by default
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# 💡 CUSTOMIZE: Could be ["**/*.py", "**/*.js"] for only Python/JS
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#═════════════════════════════════════════════════════════════════════════════════
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# 🧠 EMBEDDINGS: How we turn your code into searchable "vectors"
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#═════════════════════════════════════════════════════════════════════════════════
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# Embeddings are like "fingerprints" of your code that help find similar content
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# Don't worry about the technical details - the defaults work great!
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embedding:
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preferred_method: ollama # Which system to use for creating embeddings
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# 'ollama': Best quality (needs Ollama installed) - RECOMMENDED
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# 'ml': Good quality (downloads models automatically)
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# 'hash': Basic quality (works without internet)
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# 'auto': Try ollama, fall back to ml, then hash - SAFEST CHOICE
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ollama_model: nomic-embed-text # Which Ollama model to use (this one is excellent)
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ollama_host: localhost:11434 # Where to find Ollama (don't change unless you know why)
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ml_model: sentence-transformers/all-MiniLM-L6-v2 # Backup model (small and fast)
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batch_size: 32 # How many chunks to process at once
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# 💡 ADJUST IF: Slow computer = 16 | Fast computer = 64
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#═════════════════════════════════════════════════════════════════════════════════
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# 🔍 SEARCH: How the system finds and ranks results
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#═════════════════════════════════════════════════════════════════════════════════
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search:
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default_limit: 10 # How many search results to show by default
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# 💡 MORE RESULTS: 15-20 | FASTER SEARCH: 5-8
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enable_bm25: true # Also use keyword matching (like Google search)
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# 💡 EFFECT: Finds exact word matches even if semantically different
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# Keep true unless getting too many irrelevant results
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similarity_threshold: 0.1 # Minimum "similarity score" to show results (0.0-1.0)
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# 💡 HIGHER = fewer but more relevant results
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# Picky: 0.3 | Balanced: 0.1 | Show everything: 0.05
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expand_queries: false # Automatically add related search terms
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# 💡 EFFECT: "auth" becomes "auth authentication login user"
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# Better results but slower - TUI enables this automatically
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#═════════════════════════════════════════════════════════════════════════════════
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# 🤖 LLM: Settings for the AI that explains and synthesizes results
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#═════════════════════════════════════════════════════════════════════════════════
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# The LLM (Large Language Model) reads your search results and explains them in plain English
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llm:
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ollama_host: localhost:11434 # Where to find Ollama (don't change unless you know why)
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synthesis_model: auto # Which AI model to use for explanations
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# 'auto': Picks best available model - RECOMMENDED
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# 'qwen3:0.6b': Ultra-fast, good for CPU-only computers
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# 'llama3.2': Slower but more detailed explanations
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expansion_model: auto # Model for query expansion (usually same as synthesis)
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max_expansion_terms: 8 # How many extra terms to add to expanded queries
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# 💡 MORE TERMS = broader search but potentially less focused
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enable_synthesis: false # Turn on AI explanations by default
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# 💡 SET TO TRUE: If you want every search to include explanations
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# (You can always use --synthesize flag when you want it)
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synthesis_temperature: 0.3 # How "creative" the AI explanations are (0.0-1.0)
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# 💡 Lower = more factual | Higher = more creative
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# Code analysis: 0.1-0.3 | Creative writing: 0.7-0.9
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cpu_optimized: true # Prefer lightweight models for computers without graphics cards
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# 💡 DISABLE IF: You have a powerful GPU and want highest quality
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enable_thinking: true # Let AI "think out loud" for complex questions
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# 💡 EFFECT: Shows reasoning process, better for learning/debugging
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#═════════════════════════════════════════════════════════════════════════════════
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# 🎯 QUICK TROUBLESHOOTING:
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#
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# Search returns nothing? → Lower similarity_threshold to 0.05
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# Search too slow? → Set expand_queries: false and batch_size: 16
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# Results not detailed enough? → Increase max_size to 3000
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# Getting weird fragments? → Check min_size is at least 150
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# AI not working? → Make sure Ollama is running: `ollama serve`
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# Out of memory errors? → Decrease batch_size to 16 and lower threshold_bytes
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#═════════════════════════════════════════════════════════════════════════════════ |