296 lines
8.9 KiB
Text
296 lines
8.9 KiB
Text
================================================================================
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CODE-FORGE RAG SYSTEM - ANALYSIS SUMMARY
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================================================================================
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OVERVIEW
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--------
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Code-Forge is a production-grade RAG system using:
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- PostgreSQL + pgvector (vector database)
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- Ollama llama3.2:3b (3B parameter LLM)
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- nomic-embed-text (embedding model, 768-dim)
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- LlamaIndex (RAG framework)
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- Streamlit (UI)
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DIRECTORY STRUCTURE
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-------------------
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/Users/iamanmp/Projects/code-forge/
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├── app/core/ ← Core RAG implementation
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├── app/pages/ ← Streamlit UI
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├── test_docs/ ← Test documents
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├── tests/ ← Test suite
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└── data/postgres/ ← PostgreSQL data
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CRITICAL ISSUE #1: SLOW RESPONSE TIMES (30-40 seconds)
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======================================================
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Root Cause Breakdown:
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├─ 10-15 seconds: RAGEngine instantiation per query (BIGGEST BOTTLENECK)
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│ ├─ New Ollama LLM connection
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│ ├─ New OllamaEmbedding instance
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│ ├─ New PGVectorStore connection
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│ └─ New VectorStoreIndex creation
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├─ 15-25 seconds: LLM inference (llama3.2:3b on CPU)
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├─ 1-2 seconds: Vector search (efficient, IVFFlat index)
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└─ 2-5 seconds: Other overhead
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Top 3 Fixes:
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1. Implement RAGEngine singleton caching → Save 10-15 seconds
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2. Increase context_window to 2048-4096 → Save inference time
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3. Use larger LLM (7B+) or add GPU → Save 5-10 seconds
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CRITICAL ISSUE #2: HALLUCINATION ABOUT "ADVANCED APPOINTMENT SETTINGS"
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=====================================================================
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Root Causes:
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├─ TOP_K_RETRIEVAL = 5 (too restrictive, incomplete context)
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├─ SIMILARITY_THRESHOLD = 0.7 (filters out relevant chunks)
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├─ context_window = 1024 (too small for 5 chunks + instructions)
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├─ llama3.2:3b (small model, prone to hallucination)
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└─ Incomplete documentation (test doc is generic)
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Evidence:
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- AI generates: "Advanced appointment settings let you configure..."
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- Reality: No "advanced settings" section exists in docs
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- Should cite: APPOINTMENT_COMPREHENSIVE_GUIDE.md + APPOINTMENT_FLOW_DOC.md
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- Currently uses: Generic test1.md with basic workflow only
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Top 5 Fixes:
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1. Increase TOP_K_RETRIEVAL: 5 → 10-15 → HIGH impact
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2. Lower SIMILARITY_THRESHOLD: 0.7 → 0.5-0.6 → HIGH impact
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3. Create comprehensive appointment documentation → HIGH impact
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4. Increase context_window: 1024 → 2048 → MEDIUM impact
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5. Use larger LLM model (7B+) → MEDIUM impact
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DATA FLOW: DOCUMENT INGESTION
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=============================
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File Upload
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↓
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Text Extraction (PDF/TXT/MD/Code)
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↓
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Markdown Preprocessing (extract title, frontmatter, metadata)
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↓
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Chunking (1024 tokens, 200 overlap)
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↓
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Embedding Generation (nomic-embed-text → 768-dim vectors)
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↓
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PostgreSQL Storage (table: data_{kb_name})
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├─ Columns: text, metadata_, node_id, embedding
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└─ Indexes: IVFFlat (100 lists) on embedding
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DATA FLOW: QUERY EXECUTION
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==========================
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User Question (Streamlit chat)
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↓
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RAGEngine Instantiation [10-15s] ⚠️ SLOW
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├─ Create Ollama LLM (llama3.2:3b)
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├─ Create OllamaEmbedding
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├─ Connect to PostgreSQL
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├─ Create PGVectorStore
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└─ Create VectorStoreIndex
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↓
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Query Embedding Generation [0.5-1s]
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├─ Convert question to 768-dim vector
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└─ Send to Ollama endpoint
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↓
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Vector Search [1-2s]
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├─ Cosine similarity search via pgvector
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├─ Retrieve TOP_K=5 chunks
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└─ Filter by SIMILARITY_THRESHOLD=0.7
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↓
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Reranking [DISABLED]
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└─ Would add 30-40s, so disabled
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↓
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LLM Response Generation [15-25s] ⚠️ SLOW
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├─ Build prompt with top chunks
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├─ Call Ollama llama3.2:3b
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├─ Generate up to 400 tokens
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└─ Temperature=0.3 (deterministic)
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↓
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Response Formatting [<1s]
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├─ Extract answer
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└─ Format sources + similarity scores
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↓
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Display to User (Streamlit)
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KEY CONFIGURATION PARAMETERS
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=============================
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| Parameter | Current | Problem | Recommendation |
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|-----------|---------|---------|-----------------|
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| TOP_K_RETRIEVAL | 5 | Too low for docs | Increase to 10-15 |
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| SIMILARITY_THRESHOLD | 0.7 | Filters out chunks | Lower to 0.5-0.6 |
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| context_window | 1024 | Too small | Increase to 2048-4096 |
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| CHUNK_SIZE | 1024 | OK | Keep as is |
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| CHUNK_OVERLAP | 200 | OK | Keep as is |
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| OLLAMA_MODEL | llama3.2:3b | Small, slow | Use 7B+ |
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| OLLAMA_EMBED_MODEL | nomic-embed-text | Generic | OK for now |
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FILE LOCATIONS FOR KEY COMPONENTS
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==================================
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RAG Engine:
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/Users/iamanmp/Projects/code-forge/app/core/rag_engine.py
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- RAG query execution, embedding retrieval, LLM synthesis
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- Lines 44-55: LLM configuration
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- Lines 128-131: Vector retriever
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- Lines 140-156: Anti-hallucination prompt
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Configuration:
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/Users/iamanmp/Projects/code-forge/app/core/config.py
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- RAG parameters (CHUNK_SIZE, TOP_K_RETRIEVAL, SIMILARITY_THRESHOLD)
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- Ollama endpoints and model names
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- KB type-specific settings
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Chat Interface:
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/Users/iamanmp/Projects/code-forge/app/pages/1_Main.py
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- Streamlit chat UI
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- Line 807: RAGEngine instantiation (creates new instance per query)
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- Line 808: Query execution with settings
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Ingestion:
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/Users/iamanmp/Projects/code-forge/app/core/ingestion.py
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- Document text extraction
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- Chunking with SentenceSplitter
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- Embedding generation and PostgreSQL storage
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Database Manager:
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/Users/iamanmp/Projects/code-forge/app/core/pg_manager.py
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- PostgreSQL connection pooling
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- Vector store operations
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- Collection management
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Database Schema:
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/Users/iamanmp/Projects/code-forge/app/core/schema_pgvector.sql
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- knowledge_bases table (metadata)
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- data_{kb_name} tables (vectors per KB)
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- IVFFlat index configuration
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Markdown Preprocessor:
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/Users/iamanmp/Projects/code-forge/app/core/markdown_preprocessor.py
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- Frontmatter extraction
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- Header normalization
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- Metadata enrichment
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Test Docs:
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/Users/iamanmp/Projects/code-forge/test_docs/test1.md
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- Basic appointment workflow test document
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- Missing: Advanced settings, comprehensive guide
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QUICK WIN IMPLEMENTATION ORDER
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===============================
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1. Configuration Changes (5 minutes)
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- Increase TOP_K_RETRIEVAL: 5 → 10
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- Lower SIMILARITY_THRESHOLD: 0.7 → 0.6
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- Test with appointment queries
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2. Create Documentation (15 minutes)
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- APPOINTMENT_COMPREHENSIVE_GUIDE.md
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- APPOINTMENT_FLOW_DOC.md
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- Upload and ingest into KB
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3. RAGEngine Singleton (30 minutes)
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- Implement caching in Streamlit session state
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- Cache by KB name
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- Save 10-15 seconds per query
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4. Context Window Increase (5 minutes)
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- Change context_window: 1024 → 2048
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- Monitor quality improvement
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ARCHITECTURE STRENGTHS
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======================
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✅ ACID compliance (PostgreSQL)
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✅ Scalable multi-KB design
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✅ Efficient vector indexing (IVFFlat)
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✅ Connection pooling
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✅ Document preprocessing (markdown)
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✅ Type-safe metadata (JSONB)
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✅ Comprehensive test suite
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ARCHITECTURE WEAKNESSES
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======================
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❌ No RAGEngine caching (expensive per-query init)
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❌ Small LLM prone to hallucination
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❌ Context window too small (1024 tokens)
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❌ TOP_K too restrictive (5 chunks)
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❌ Similarity threshold too high (0.7)
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❌ CPU-based inference (slow)
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❌ Generic embeddings (not domain-specific)
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❌ Reranking disabled (adds 30-40s overhead)
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PERFORMANCE TARGETS
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==================
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Current State:
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Query time: 30-40 seconds
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├─ Init: 10-15s
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├─ Search: 1-2s
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├─ LLM: 15-25s
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└─ Other: 2-5s
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After Quick Fixes:
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Query time: 5-10 seconds
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├─ Init: <1s (cached)
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├─ Search: 1-2s
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├─ LLM: 3-7s
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└─ Other: <1s
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With Full Optimization:
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Query time: 2-5 seconds
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├─ Init: <1s (cached)
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├─ Search: 0.5-1s
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├─ LLM: 1-3s (larger model/GPU)
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└─ Other: <1s
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GENERATED REPORTS
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=================
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1. RAG_ANALYSIS.md (760 lines)
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- Complete architecture analysis
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- Data flow diagrams
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- Configuration details
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- Code issues and anti-patterns
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- Strengths and weaknesses
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2. QUICK_FIXES.md
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- Quick fix guide
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- Priority implementation matrix
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- Testing checklist
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- Performance monitoring
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3. This summary file
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- Quick reference
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- Key metrics
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- File locations
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================================================================================
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READY FOR IMPLEMENTATION
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================================================================================
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Next Steps:
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1. Review RAG_ANALYSIS.md for detailed understanding
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2. Follow QUICK_FIXES.md for implementation
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3. Start with config changes (5 min impact)
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4. Create appointment documentation (15 min impact)
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5. Implement RAGEngine singleton (30 min, 10-15s savings)
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Estimated Total Time to Fix: 1-2 hours
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Expected Result: 30-40s queries → 5-10s queries
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Hallucination Severity: High → Low
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================================================================================
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