imvectordb

Pure Rust embedding database as lightweight Axum server for low-latency on-device vector search scaling to 100M+ vectors in memory. High accuracy/speed for edge RAG; simpler than Qdrant edge.

Chroma is an open-source embedding database optimized for LLM apps, with in-memory/persistent storage and simple Python API. Features: HNSW indexing, automatic batching, metadata filtering, integrations with LangChain/LlamaIndex. Ideal for local dev, prototyping RAG; vs pgvector, easier for Python users; vs full DBs like Milvus, lighter but less scalable.

Simple in-memory vector database for Node.js that works 100% locally and in-memory by default. Uses hnswlib-node for simple vector search and Embeddings.js for simple text embeddings with support for OpenAI, Mistral and local embeddings.

Embeddable SQL OLAP engine with VSS extension for low-latency HNSW vector search on local files, ideal for edge AI prototyping and analytics. SQL-first approach for on-device vector ops vs cloud vector DBs like Qdrant.

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.

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.