# Async Client

> Browse 13 items tagged with Async Client.

## Items

- [hnswlib-node](https://vectordb.ever.works/items/hnswlib-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.
- [pgvector-node](https://vectordb.ever.works/items/pgvector-node) — 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.
- [pgvector-crystal](https://vectordb.ever.works/items/pgvector-crystal) — 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.
- [pgvector-dotnet](https://vectordb.ever.works/items/pgvector-dotnet) — 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.
- [Apache Lucene](https://vectordb.ever.works/items/apache-lucene) — 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.
- [Chroma Explorer](https://vectordb.ever.works/items/chroma-explorer) — 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.
- [Chroma-go](https://vectordb.ever.works/items/chroma-go) — 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.
- [Chroma-hnswlib](https://vectordb.ever.works/items/chroma-hnswlib) — 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.
- [chromem-go](https://vectordb.ever.works/items/chromem-go) — Go embedded SDK (chromem-go) mimicking Chroma interface for in-memory/persistent vector DB ops with async queries via channels, batch upsert. For Rust/Go app integration without deps; LangChain-like chains; zero-overhead vs external JS/Python clients or APIs.
- [Dense Passage Retrieval (DPR)](https://vectordb.ever.works/items/dense-passage-retrieval-dpr) — Python SDK from Meta for dense passage retrieval using dual BERT encoders and FAISS indexing, supporting batch embedding generation, async queries via multiprocessing. Enables Python/JS app Q&A pipelines and LangChain retrievers; 9-19% better accuracy than BM25 lexical APIs.
- [DocArray](https://vectordb.ever.works/items/docarray) — Python SDK for multi-modal Document handling with serialization, batch transport via DocList/DocVec, async processing with Pydantic validation. Suited for Python/JS/Rust app integration, LangChain document loaders; more structured than raw tensor APIs.
- [EntityDB](https://vectordb.ever.works/items/entitydb) — JavaScript SDK for browser-based vector DB using IndexedDB and Transformers.js (WASM), with batch insert/query via async promises, cosine similarity. For JS app integration, LangChain browser agents; fully client-side vs server APIs.
- [FastEmbed](https://vectordb.ever.works/items/fastembed-qdrant) — Python/Rust/Go/JS SDK for fast embedding generation via ONNX Runtime with batch embed (list inputs), async multiprocessing support. Optimized for app integration, LangChain embedding modules; 12x CPU speedup vs PyTorch libs, no GPU/API dependency.

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_Canonical page: https://vectordb.ever.works/tags/async-client_