Stores embeddings and metadata for filtered, similarity-based retrieval.
npx skills add https://github.com/ZardLi1115/zedclaw --skill chroma-zardli1115
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/ZardLi1115/zedclaw/tree/main/optional-skills/mlops/chroma Command: npx skills add https://github.com/ZardLi1115/zedclaw --skill chroma-zardli1115
Chroma eliminates the need to manually wire up an embedding-and-retrieval layer by giving you a simple way to store vectors plus metadata and then search them semantically.
Use the chroma skill to create a persistent collection, add a few documents with metadata, and run a similarity query with a metadata filter so you can retrieve the most relevant matches immediately.
High-intent search queries and answers about installing and using this skill.
Store document embeddings with metadata by creating a persistent collection and adding vectors alongside rich fields like source and category. This enables semantic retrieval with filtering across RAG and document search workflows.
Chroma works with LangChain and LlamaIndex for building retrieval pipelines. It provides a minimal API for collection management and operations to power retrieval-augmented generation by fetching relevant chunks from your corpus.
Filter vector similarity search results using where-clause metadata constraints. Chroma supports exact-match and comparison operators to constrain retrieval results for better traceability and targeted document search.
You need sentence-transformers to build local semantic search with Chroma. It eliminates manual embedding wiring by providing a simple way to store vectors and metadata, then search them semantically using local persistent storage.
Manage collections and documents in a vector database using a minimal API for add, query, get, update, and delete operations. Chroma supports local persistent storage and server mode access for flexible deployment.
NITISH-gitbitStore, search, and retrieve embeddings and metadata with a vector database.
seb1nRank retrieved context chunks by relevance, diversity, and utility for RAG systems.
neo4j-contribIngest PDFs and text into Neo4j knowledge graphs via SimpleKGPipeline.
hhhi21gPerform high-speed k-NN search on large vector datasets with FAISS.
