Store embeddings and metadata in an open-source vector database.
npx skills add https://github.com/AlvaroBiano/hermes-agent --skill chroma-alvarobiano
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/AlvaroBiano/hermes-agent/tree/main/optional-skills/mlops/chroma Command: npx skills add https://github.com/AlvaroBiano/hermes-agent --skill chroma-alvarobiano
Chroma provides an open-source embedding database to store embeddings and metadata, enabling efficient vector search and semantic retrieval for AI applications across notebooks and production environments.
Install chromadb locally and start using a vector store for semantic search.
High-intent search queries and answers about installing and using this skill.
An open-source vector database stores embeddings and metadata to enable efficient vector search and semantic retrieval for AI applications across notebooks and production environments.
You can store embeddings and metadata in a local, self-hosted vector store to perform semantic search and document retrieval for AI applications during local development.
Yes, metadata filtering and multi-language integrations are supported to power RAG workflows and enable efficient document retrieval from your local vector store.
Yes, the embedding database is designed for local development across notebooks and scales seamlessly for use in production environments with multi-language support.
Use an open-source embedding database to store document vectors and metadata, enabling your AI assistant to quickly perform semantic retrieval of relevant documents.
lewisperez999Generate text embeddings with OpenAI and search by cosine similarity in Upstash Vector or pg-vector.
ricableDetect prompt injections and jailbreak attempts in LLM inputs.
Kingly-AgencyBuild RAG pipelines that ground LLM responses with retrieved external knowledge.
CarlosAndresBF1Build RAG pipelines with vector stores and retrieval strategies for document Q&A.
