Fss-Rag-Mini/examples/config-quality.yaml
BobAi a84ff94fba Improve UX with streaming tokens, fix model references, and add icon integration
This comprehensive update enhances user experience with several key improvements:

## Enhanced Streaming & Thinking Display
- Implement real-time streaming with gray thinking tokens that collapse after completion
- Fix thinking token redisplay bug with proper content filtering
- Add clear "AI Response:" headers to separate thinking from responses
- Enable streaming by default for better user engagement
- Keep thinking visible for exploration, collapse only for suggested questions

## Natural Conversation Responses
- Convert clunky JSON exploration responses to natural, conversational format
- Improve exploration prompts for friendly, colleague-style interactions
- Update summary generation with better context handling
- Eliminate double response display issues

## Model Reference Updates
- Remove all llama3.2 references in favor of qwen3 models
- Fix non-existent qwen3:3b references, replace with proper model names
- Update model rankings to prioritize working qwen models across all components
- Ensure consistent model recommendations in docs and examples

## Cross-Platform Icon Integration
- Add desktop icon setup to Linux installer with .desktop entry
- Add Windows shortcuts for desktop and Start Menu integration
- Improve installer user experience with visual branding

## Configuration & Navigation Fixes
- Fix "0" option in configuration menu to properly go back
- Improve configuration menu user-friendliness
- Update troubleshooting guides with correct model suggestions

These changes significantly improve the beginner experience while maintaining
technical accuracy and system reliability.
2025-08-15 12:20:06 +10:00

111 lines
5.2 KiB
YAML

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