c1b935a0b9
* Initial plan * Initialize reverse documentation directory and create analysis plan Co-authored-by: raphaelmansuy <1003084+raphaelmansuy@users.noreply.github.com> * Add executive summary and comprehensive architecture documentation Co-authored-by: raphaelmansuy <1003084+raphaelmansuy@users.noreply.github.com> * Add comprehensive data models and dependency migration documentation Co-authored-by: raphaelmansuy <1003084+raphaelmansuy@users.noreply.github.com> * Complete comprehensive TypeScript migration documentation suite Co-authored-by: raphaelmansuy <1003084+raphaelmansuy@users.noreply.github.com> --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: raphaelmansuy <1003084+raphaelmansuy@users.noreply.github.com>
84 lines
3.2 KiB
Markdown
84 lines
3.2 KiB
Markdown
# LightRAG Analysis Scratchpad
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## Initial Observations (Phase 1 - Repository Structure)
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### Core Files Analysis
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- **lightrag.py** (141KB): Main class implementation - massive file with ~2200 lines
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- **operate.py** (164KB): Core operations for entity extraction, chunking, querying - ~1782 lines
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- **utils.py** (106KB): Utility functions - extensive helper library
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- **base.py** (30KB): Base classes and interfaces for storage abstractions
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- **prompt.py** (28KB): LLM prompt templates
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- **utils_graph.py** (43KB): Graph utility functions
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### Architecture Components Identified
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#### 1. Storage Layer (lightrag/kg/)
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Storage implementations (15+ files):
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- **JSON-based**: json_kv_impl.py, json_doc_status_impl.py
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- **Vector DBs**: nano_vector_db_impl.py, faiss_impl.py, milvus_impl.py, qdrant_impl.py
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- **Graph DBs**: networkx_impl.py, neo4j_impl.py (79KB), memgraph_impl.py (49KB)
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- **SQL**: postgres_impl.py (200KB - largest storage impl!)
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- **NoSQL**: mongo_impl.py (95KB), redis_impl.py (46KB)
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- **Shared**: shared_storage.py (48KB) - centralized storage management
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Key insight: PostgreSQL implementation is massive - suggests it's a comprehensive reference implementation
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#### 2. LLM Integration Layer (lightrag/llm/)
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LLM providers (14 files):
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- openai.py (24KB - most comprehensive)
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- binding_options.py (27KB) - configuration management
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- ollama.py, azure_openai.py, anthropic.py, bedrock.py
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- hf.py, llama_index_impl.py, lmdeploy.py
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- jina.py (embedding), zhipu.py, nvidia_openai.py, siliconcloud.py, lollms.py
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#### 3. API Layer (lightrag/api/)
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REST API implementation:
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- lightrag_server.py - FastAPI server
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- routers/ - modular route handlers
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- query_routes.py - query endpoints
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- document_routes.py - document management
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- graph_routes.py - graph visualization
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- ollama_api.py - Ollama compatibility layer
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#### 4. WebUI (lightrag_webui/)
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TypeScript/React frontend - already exists! This provides reference for:
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- TypeScript type definitions (lightrag.ts)
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- API client patterns
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- Data models used by frontend
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## Priority Order for Documentation
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1. Executive Summary + Architecture Overview
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2. Core Data Models (base.py, types.py)
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3. Storage Layer Architecture
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4. LLM Integration Patterns
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5. Query Pipeline
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6. Indexing Pipeline
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7. Dependency Migration Guide
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8. TypeScript Implementation Roadmap
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## Documentation Progress - Update
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### Completed Documents (4/8):
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1. ✅ Executive Summary (16KB) - Complete system overview
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2. ✅ Architecture Documentation (33KB) - 6 comprehensive Mermaid diagrams
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3. ✅ Data Models and Schemas (27KB) - Complete type system
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4. ✅ Dependency Migration Guide (27KB) - Full npm mapping with complexity assessment
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### Next Priority Documents:
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5. Storage Layer Implementation Guide - Deep dive into each storage backend
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6. TypeScript Project Structure and Migration Roadmap
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7. LLM Integration Patterns
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8. API Reference with TypeScript Types
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### Key Insights for Remaining Docs:
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- Focus on practical implementation examples
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- Include performance considerations
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- Document error handling patterns
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- Provide testing strategies
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- Add deployment configurations
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### Total Documentation So Far:
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- ~103KB of technical documentation
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- 6 Mermaid architecture diagrams
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- 50+ code comparison examples
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- Complete dependency mapping for 40+ packages
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