Use Cases
End-to-end recipes for hybrid BM25 + dense search, ColBERT reranking, and multimodal OCR-free RAG.
| Title | Description | Link | Code |
|---|---|---|---|
| Hybrid search with Postgres Native BM25 and VectorChord | This article explores how to implement Hybrid Search in Postgres using VectorChord-bm25 for keyword-based retrieval and VectorChord for semantic search. | Hybrid Search | Github Repo |
| Supercharge vector search with ColBERT rerank in PostgreSQL | This blog will demonstrate using the PostgreSQL extension VectorChord and pgvector with ColBERT rerank. | ColBERT Rerank | Github Repo |
| Beyond Text: Unlock OCR-Free RAG in PostgreSQL with Modal & VectorChord | This post is your guide to building exactly that: an OCR-free RAG system directly within your familiar PostgreSQL database. We’ll leverage the power of the ColQwen2 Vision Language Model, the efficiency of VectorChord for multi-vector search in Postgres, and the scalability of Modal for GPU-powered embedding generation. | OCR-Free RAG | Github Repo |
In the era of RAG (Retrieval-Augmented Generation), efficiently retrieving relevant data from vast datasets is crucial for businesses and developers. Traditiona
Traditional vector search methods typically employ sentence embeddings to locate similar content. However, generating sentence embeddings through pooling token
Building effective Retrieval-Augmented Generation (RAG) systems for documents often feels like wrestling with messy, complex pipelines. Especially when dealing