Store embeddings and metadata in a local vector database with filtering.
npx skills add https://github.com/founderphantom/zola-agent --skill chroma-founderphantom
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/founderphantom/zola-agent/tree/main/optional-skills/mlops/chroma Command: npx skills add https://github.com/founderphantom/zola-agent --skill chroma-founderphantom
Chroma provides a local, open-source embedding database to store embeddings and metadata, enabling fast semantic search and memory-powered AI apps without relying on hosted services.
Install chromadb, create a collection, and start using it to store embeddings locally.
High-intent search queries and answers about installing and using this skill.
Store embeddings locally for RAG using an open-source vector database like Chroma. It provides persistent storage and metadata filtering, enabling self-hosted semantic search for AI applications without relying on external hosted services.
Add a memory layer to AI agents using a local vector database. It stores document embeddings and metadata, allowing agents to perform fast semantic search and retrieve relevant context quickly during interactive tasks.
Yes, you can use Chroma with LangChain and LlamaIndex. It offers framework integrations specifically designed for building on-device or self-hosted RAG workflows and memory-powered AI applications.
Yes, this open-source vector database supports metadata-based filtering. You can store embeddings alongside metadata and use it to filter results, enabling precise semantic search for RAG workflows in production environments.
Set up a local vector store for semantic search in notebooks by installing the chromadb package, creating a collection, and storing embeddings. It provides a simple 4-function API for persistent storage across environments.
coco-researchStore and search multimodal media with ChromaDB hybrid semantic and metadata retrieval.
AxelooooImplement RAG pipelines with vector retrieval and context-grounded generation.
nimeshgurungDesign and optimize LLM prompts with RAG and agent workflows.
DaddyElonMusk69Store and retrieve embeddings for semantic search and document retrieval.
