Store and search embeddings with a four-function API for a local vector database.
npx skills add https://github.com/OpenLAIR/dr-claw --skill chroma-openlair
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/rag/chroma Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill chroma-openlair
Chroma provides a scalable, open-source embedding database to store vectors and associated metadata, enabling fast similarity search and retrieval for AI-powered applications.
Install chromadb, create a collection, and begin storing embeddings for similarity search.
High-intent search queries and answers about installing and using this skill.
Store embeddings using Chroma's simple four-function API to add vectors and associated metadata, enabling fast similarity search and retrieval for AI-powered applications.
Yes, metadata filtering is supported during document retrieval for RAG workflows by storing associated metadata alongside vectors, enabling targeted similarity search based on specific document attributes.
Yes, sentence-transformers is required as a dependency to provide a compatible embedding model that generates vectors for the database, alongside a running Chroma instance for storage and retrieval.
Yes, the self-hosted vector database API works across notebooks and production deployments by providing a consistent four-function interface for storing and searching embeddings in both Python and JavaScript client environments.
Integrate semantic search into document retrieval applications using an open-source embedding database with a simple four-function API to store vectors and execute fast similarity searches across stored documents.
Quill-AgentStore and retrieve embeddings and metadata in a Chroma vector database.
MarbleSodasStore and query embedding vectors with metadata for semantic retrieval.
zhuangbiaoweiManage an open-source embedding database for semantic search and retrieval.
BermudaLocalsStore embeddings and metadata locally for fast similarity search.
