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Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. Chroma gives you the tools to:\n\n-   store embeddings and their metadata\n-   embed documents and queries\n-   search embeddings\n\nChroma prioritizes:\n\n-   simplicity and developer productivity\n-   analysis on top of search\n-   it also happens to be very quick\n\n\n### Qdrant \n\n[Qdrant](https://qdrant.tech/documentation/) is powering the next generation of AI applications with advanced and high-performant vector similarity search technology. \n\nQdrant is a vector database \u0026 vector similarity search engine. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!\n\n - Easy to Use API. Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilize ready-made client for Python or other programming languages with additional functionality.\n - Fast and Accurate. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results.\n - Filtrable. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.\n\n ## Use-cases \n  - [Chroma DB for taxonomy embedding](Chroma_taxonomy.md)\n  - [Chroma DB for Matching KG](Chroma_kg.md)\n  - [Qdrant DB for Matching KG](Qdrant_kg.md)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenja1972%2Fvectordb","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbenja1972%2Fvectordb","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenja1972%2Fvectordb/lists"}