rag-implementation

To build a RAG pipeline, orchestrate vector stores, embeddings, and retrieval patterns like LangGraph flows. This connects LLM responses to factual external knowledge sources, preventing hallucination-prone reasoning in document question answering.

Comparing vector databases involves evaluating Pinecone, Weaviate, Milvus, Chroma, Qdrant, and pgvector. You compare their capabilities to store document embeddings and tune similarity searches for your specific retrieval-augmented generation requirements.

Reranking improves retrieval-augmented generation by ordering retrieved document chunks more accurately. Combined with HyDE, contextual compression, and hybrid searches, reranking ensures the LLM receives the most relevant context for factual answers.

Yes, you can use LangGraph to orchestrate dense, sparse, and hybrid search patterns. This workflow manages document retrieval, reranking, and contextual compression to feed precise external knowledge into LLM responses with citations.

Yes, document chunking and prompt engineering with citations are required for a knowledge-base chatbot. These operational readiness steps ensure the retrieval pipeline supplies properly segmented context and structured outputs for factual responses.

An LLM hallucinates without proper retrieval pipelines connecting it to vector databases and embeddings. Implementing contextual compression, parent document strategies, and evaluation metrics keeps the generated answers grounded in factual knowledge.