Turso

Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.

LanceDB documents a multimodal lakehouse for AI at docs.lancedb.com, built on the open-source Lance columnar format for storing vectors, metadata, raw bytes, and embeddings in unified tables. LanceDB OSS is an embedded library with Python, TypeScript, and Rust SDKs for local development; LanceDB Enterprise is a distributed managed lakehouse for search, curation, feature engineering, and training workflows per docs.lancedb.com. Features include vector/semantic search, BM25 full-text search, hybrid search with SQL filters, versioning, and cloud object-store integration (S3, GCS, Azure).

Voyage AI documents state-of-the-art embedding models and rerankers at docs.voyageai.com for retrieval-augmented generation and semantic search. The embeddings API at POST https://api.voyageai.com/v1/embeddings converts text into dense vectors; the Python `voyageai` package exposes `voyageai.Client.embed()` with models such as voyage-4-large, voyage-4, voyage-4-lite, voyage-code-3, and domain-specific variants per docs.voyageai.com/docs/embeddings. Docs describe `input_type` query vs document prompts for retrieval tasks, optional `output_dimension` (256–2048), and `output_dtype` quantization (float, int8, binary). Rerankers score query–document relevance to refine initial retrieval results. Voyage AI is part of MongoDB's AI stack (MongoDB docs reference Voyage embeddings) and integrates with vector stores and LLMs as modular RAG components.