Core System Flow

Your RAG system follows this pipeline: Document Processing → Embedding Generation → Vector Storage → Query Processing → Retrieval → Context Management → Response Generation

Key Components Breakdown

1. Document Processing Pipeline

• Purpose: Transform documents into searchable chunks
• Key Challenge: Balancing chunk size (context vs relevance)
• Common Mistake: Poor chunking strategy loses semantic meaning

2. Embedding Generation

• Purpose: Convert text to vector representations
• Options: External APIs (OpenAI) vs self-hosted models
• Key Mistake: Not batching API calls efficiently

3. Vector Storage

• Purpose: Fast similarity search over embeddings
• Index Types: Start with flat, upgrade to HNSW for scale
• Common Pitfall: Using wrong similarity metric (cosine vs euclidean)

4. Query Processing

• Purpose: Optimize user queries for search
• Features: Text preprocessing, intent detection, filter extraction
• Mistake: Over-processing and losing important query terms