Vector Databases in RAG Applications

What is RAG

RAG (Retrieval-Augmented Generation) combines the advantages of retrieval and generation, enhancing large model generation capabilities by retrieving relevant knowledge.

Workflow

User Question → Vectorization → Retrieve Similar Documents → Concatenate Context → LLM Generates Answer

Vector Database Comparison

Pinecone

- Fully managed service
- Low-latency queries
- Suitable for production environments

Milvus

- Open-source solution
- Supports multiple indexes
- Can be self-deployed

Chroma

- Lightweight
- Suitable for development and testing
- Easy to integrate

Practical Recommendations

1. Document Chunking: Reasonable chunk size (500-1000 tokens)
2. Metadata: Add source, time and other metadata
3. Hybrid Search: Combine keyword and vector search
4. Re-ranking: Secondary sorting of retrieval results

Summary

RAG is an effective solution for building knowledge base Q&A systems, and vector databases are the core infrastructure.