Pinecone vector database RAG system

Implement RAG with Pinecone. Architecture: 1. Document chunking and embedding. 2. Store embeddings in Pinecone index. 3. Semantic search with similarity. 4. Metadata filtering for context. 5. Hybrid search (dense + sparse). 6. Retrieve top-k relevant chunks. 7. Augment prompt with context. 8. Generate answer with LLM. Use text-embedding-ada-002 and implement re-ranking for accuracy.