pgvector-node

Node.js bindings (JavaScript/TypeScript SDK) for HNSWLib C++ library, enabling fast ANN vector search with async operations and V8 optimization. Supports L2/cosine distances, file persistence, filtering, and LangChain integration for seamless app embedding. Ideal for web/serverless JS RAG; lighter JS alternative to Python hnswlib bindings vs. full vector DBs like Chroma.

Crystal SDK/client for pgvector PostgreSQL extension, providing idiomatic bindings for vector storage and similarity search. Supports embeddings workflows (OpenAI/Cohere), hybrid/sparse search via crystal-pg driver. For integrating Postgres vector ops into Crystal apps; official community client vs. direct SQL or other lang bindings.

Official .NET SDK/client (C#/F#) for pgvector, supporting async vector insert/query with HNSW/IVF indexes over Postgres. Integrates with Npgsql, Dapper, Entity Framework for type-safe operations. Suited for enterprise .NET app integration in RAG/semantic search; official bindings vs. community or direct SQL.

Lightweight Node.js library for low-latency on-device vector similarity search using HNSW and BM25 hybrid, with CRUD, metadata filtering, and persistence for edge RAG pipelines. Enables real-time semantic search without servers; more lightweight than cloud Qdrant.

High-performance Java library providing SDK functions for vector search with HNSW-based ANN, supporting async indexing via IndexWriter futures, batch document ingestion, and configurable dimensions up to 1024+. Ideal for Java app integration, LangChain via wrappers, offering finer control and lower latency vs native REST APIs of cloud vector DBs.

macOS desktop client library/app for ChromaDB with GUI for managing collections and embeddings. Features batch operations, real-time queries, and direct integration without heavy API reliance. Suited for app development workflows and LangChain debugging; simpler than raw Python/JS SDKs for visual exploration.