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Connect AI assistants to ChromaDB for vector storage and semantic retrieval.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "py.chroma.mcp" yet — see the docs or source repo.
Use py.chroma.mcp to connect to my ChromaDB collection, store these product documents as paragraph-level vectors, and keep metadata like title, source URL, and updated time.
A summary of the ingestion result, including chunk count, collection details, and metadata fields.
Use py.chroma.mcp to search ChromaDB for the 5 most relevant items about 'best practices for enterprise knowledge base access control,' and include similarity scores and source details.
A ranked list of relevant matches with text snippets, similarity scores, and source references.
Using py.chroma.mcp, first retrieve context from ChromaDB related to 'how to configure a vector database backup strategy,' then organize it into reference material for answering a user question.
A well-structured set of supporting context materials ready for a downstream QA model.
Create a shared multilingual team memory with semantic search for MCP clients.
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Expose modular retrieval and reasoning tools to AI assistants through MCP.
Give AI assistants persistent memory, semantic search, and layered context management.