#rag

Professional diagram following Retrieval Augmented Generation architecture. Components: 1. Document Loader -> Splitting -> Embeddings. 2. Vector DB Storage. 3. Query Rewrite -> Retrieval -> Re-ranking. 4. Contextual Prompt -> LLM Generation. Use blue/violet gradients and high-quality technical icons.

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.

Build RAG systems with LlamaIndex. Workflow: 1. Load documents (PDF, DOCX, web). 2. Node parser for chunking. 3. Create embeddings with LLM. 4. Build index (Vector, Tree, Keyword). 5. Query engine for retrieval. 6. Response synthesizer. 7. Sub-question query engine. 8. Chat engine for conversations. Use ServiceContext for configuration and implement hybrid retrieval.