Store and query embeddings with metadata using chromadb and sentence-transformers.
npx skills add https://github.com/Tommy-yw/RunbookHermes --skill chroma-tommy-yw
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/Tommy-yw/RunbookHermes/tree/main/optional-skills/mlops/chroma Command: npx skills add https://github.com/Tommy-yw/RunbookHermes --skill chroma-tommy-yw
Chroma solves the problem of efficiently storing, querying, and managing vector embeddings for AI applications, making it easier to implement semantic search, retrieval-augmented generation, and document retrieval.
Use the Chroma skill to add new documents to the 'my_collection' collection with metadata and perform a query to find similar documents.
High-intent search queries and answers about installing and using this skill.
You can build a semantic search engine by storing document embeddings and metadata in a vector database, then querying the collection to retrieve the most similar documents. This Skill uses chromadb to manage the vector storage.
To build RAG applications, you store text embeddings alongside metadata in a chromadb collection, then query that collection to retrieve relevant context. This process relies on sentence-transformers to generate the embeddings for retrieval.
Yes, sentence-transformers is required to generate the vector embeddings from your documents before storing and querying them in the chromadb vector database for local development.
The best way to retrieve relevant documents is to perform a vector-based search within a chromadb collection. This allows you to find similar documents quickly by matching both semantic content and attached metadata.
Yes, you can run this entirely locally for development. It utilizes the open-source chromadb and sentence-transformers projects to manage embeddings and storage without requiring external API calls.
markus41Manage vector embeddings for semantic search across Pinecone, Weaviate, Chroma, and pgvector.
anukkrit149Build Retrieval-Augmented Generation systems grounding LLM answers in vector databases.
JonathanMitchell1234Implement production AI systems with LLM integration and RAG.
zli5460Create FAISS indexes and perform nearest neighbor searches on large vector datasets.
