rag-architect

Design a RAG pipeline by configuring modular sub-agents for chunking, embedding selection, vector store setup, hybrid search, and answer synthesis to ensure grounded answers with built-in hallucination checks and precise source citations.

Configure a vector store for retrieval-augmented generation by selecting Chroma, Pinecone, or pgvector, then applying appropriate indexing and hybrid retrieval strategies to optimize semantic search performance across your data sources.

Yes, the RAG architecture supports hybrid search and re-ranking through its modular HybridSearchEngine component, enabling precise document Q&A retrieval before synthesizing grounded answers.

You need chunking strategies and embedding model selection when building knowledge bases requiring semantic search, ensuring data is properly partitioned and vectorized for accurate retrieval and compliance-ready content generation.

Yes, you can build document Q&A systems with citations using pgvector as the vector store, leveraging the AnswerSynthesizer to generate grounded responses with precise source tracking and hallucination detection.

Your retrieval-augmented generation pipeline may produce ungrounded answers if lacking proper re-ranking, hybrid search configuration, or hallucination detection during the answer synthesis phase. Implement the built-in HallucinationDetector to enforce source grounding.