rag-architect

To design a hybrid search pipeline for a RAG system, you combine semantic vector search using embeddings with keyword matching using BM25, then apply a reranking model to improve retrieval precision. This ensures both semantic context and exact keyword matches are captured.

Evaluating retrieval quality in a RAG application involves measuring precision and recall using dedicated evaluation frameworks like ragas. It provides built-in checkpoints and metrics to validate that your document retrieval pipeline returns accurate and relevant context before production deployment.

Chunking documents and generating embeddings for vector search involves splitting text into manageable segments and using embedding models like sentence-transformers to convert chunks into vector representations. These vectors are then stored in a configured vector store like Qdrant for scalable indexing.

Yes, langchain works seamlessly with qdrant-client to build scalable indexing for RAG systems. This integration supports idempotent ingestion, allowing you to orchestrate the entire document ingestion and retrieval lifecycle while maintaining reliable vector store configurations.

You need reranking in your semantic search pipeline when initial retrieval results from hybrid search require reordering to prioritize the most relevant context. Applying reranking with models like Cohere significantly enhances retrieval precision for knowledge-grounded AI applications.

Using only vector search for document retrieval limits exact keyword matching capabilities, which is problematic for queries requiring specific terms. A hybrid search pipeline combining vector search with BM25 keyword retrieval overcomes this constraint, ensuring robust context augmentation.