Rag Agent Builder

If you're building document Q&A systems or need to ground LLM responses in your own data, this walks through the full RAG pipeline from chunking strategies to vector databases. The examples cover basic chunk-embed-retrieve patterns, hybrid search with BM25, and agentic RAG where the model decides what to retrieve. You get practical coverage of embedding management, reranking, and corrective RAG that validates retrieval quality before generating answers. The framework comparisons (LangChain, LlamaIndex) and vector DB options (Pinecone, Chroma, Weaviate) give you enough context to pick the right stack. Best for anyone moving past simple prompts into systems that need real-time knowledge or proprietary documents in the loop.