pgvector-dotnet

JavaScript/TypeScript SDK/client (Node.js/Deno/Bun) for pgvector PostgreSQL extension, enabling async vector storage and similarity queries. Supports REST-like ops via pg driver, Prisma integration. For JS app integration in RAG/semantic search; official bindings vs. direct SQL or other SDKs.

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.

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.

Go SDK (chroma-go) client library for ChromaDB with async goroutine-based queries, batch collection creation/ingest via HNSW config, in-process persistence. Enables Go app integration and LangChain workflows; offers native speed and type-safety vs Python/JS HTTP clients or native REST APIs.

Python library (chroma-hnswlib) fork of hnswlib for ChromaDB indexing with async batch vector ingest, HNSW param tuning (ef_construction/search). Core for Python/JS app embedding pipelines and LangChain; faster in-process ANN vs remote API calls to vector DBs.