# Embeddable

> Browse 7 items tagged with Embeddable.

## Items

- [brinicle](https://vectordb.ever.works/items/brinicle) — Brinicle is a lightweight C++ library for approximate nearest neighbor (ANN) vector search on embeddings, optimized for low-RAM environments rather than full vector databases. It features efficient graph-based indexing (HNSW-like), supports quantization for further memory reduction, and excels in languages like C++. Ideal for rapid prototyping of ML prototypes and embedded applications; lighter and more memory-efficient than Milvus, with better low-resource performance vs hnswlib.
- [LEANN](https://vectordb.ever.works/items/leann) — LEANN is a lightweight RAG-focused library for vector search on embeddings, achieving 97% storage savings via advanced compression and quantization techniques on personal devices. Implemented in Rust/Python, it supports efficient ANN indexing without full DB overhead. Ideal for embedded apps and private prototyping; far lighter than Milvus, more efficient on-device vs hnswlib.
- [sqlite-vec](https://vectordb.ever.works/items/sqlite-vec) — sqlite-vec is a Rust-based SQLite extension library for vector similarity search using diskANN indexes on embeddings, enabling lightweight ANN without separate databases. Features HNSW-like graphs, quantization support, and hybrid full-text+vector queries in embedded SQLite environments. Perfect for prototyping and on-device apps; extremely lightweight compared to Milvus, more persistent than pure hnswlib.
- [Haystack](https://vectordb.ever.works/items/haystack) — Haystack is a Python library for building vector search and embedding-based retrieval pipelines, integrating ANN indexes without requiring full databases. Key features include support for HNSW, FAISS indexes, quantization options, and multi-language embeddings. Perfect for prototyping RAG systems and embedded AI apps; more flexible than hnswlib, lighter than Milvus for development workflows.
- [LangChain4j](https://vectordb.ever.works/items/langchain4j) — LangChain4j is a Java library providing vector search and embedding capabilities for LLM applications via integrations with ANN indexes like HNSW and FAISS, without needing full vector databases. Features include support for quantization, tool calling, and seamless embedding in JVM environments like Spring Boot and Quarkus. Suited for prototyping RAG agents and embedded apps; lighter and more JVM-native than Milvus, easier integration vs hnswlib.
- [LlamaIndex](https://vectordb.ever.works/items/llamaindex) — LlamaIndex is a Python data framework library for vector search and embedding retrieval, integrating various ANN indexes like HNSW and FAISS without full database dependencies. Supports quantization, multi-modal embeddings, and advanced query engines in Python/Rust backends. Great for prototyping LLM apps and embedded RAG; more developer-friendly and lighter than Milvus, composable vs hnswlib.
- [ChromaDB](https://vectordb.ever.works/items/chromadb) — 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.

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