Stores and retrieves document embeddings with similarity search and metadata filtering.
npx skills add https://github.com/devMoez/titan --skill chroma-devmoez
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/devMoez/titan/tree/main/optional-skills/mlops/chroma Command: npx skills add https://github.com/devMoez/titan --skill chroma-devmoez
Chroma removes the friction of storing and querying embeddings by providing a local, self-hosted database that can perform fast semantic and metadata-filtered retrieval.
Use the chroma skill to create a local collection, add your documents with optional metadata, and query it for semantically relevant matches.
High-intent search queries and answers about installing and using this skill.
To build self-hosted semantic search, store document embeddings in a local Chroma vector database to retrieve relevant text using similarity matching and metadata filtering for your RAG systems.
Metadata filtering with vector similarity search retrieves top-k relevant documents by applying where-based constraints to fields like source, category, or tags alongside semantic embedding queries.
Yes, you can use Chroma for local RAG document retrieval by storing text chunk embeddings and querying the collection to ground LLM responses with targeted contextual data.
The best way to persist embeddings locally is to save your vector database to disk, enabling fast document recall and integration with frameworks like LangChain or LlamaIndex for end-to-end pipelines.
You need a dedicated vector database like Chroma to handle fast similarity matching and apply where-based metadata constraints across large datasets for accurate contextual filtering.
zepdbIndex and search text with Zeppelin vector store and BM25 full-text indexing.
J4JoshuaGenerate grounded answers from retrieved SOP chunks using local Qwen2.5-Instruct.
nexus-labs-automationInstrument RAG memory operations with span attributes for LangChain and LlamaIndex.
Clay-HHKStore embeddings and metadata for semantic and full-text retrieval.
