Store and query embeddings locally with metadata filtering for semantic search.
npx skills add https://github.com/AissenLiu/EasyHermes --skill chroma-aissenliu
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/AissenLiu/EasyHermes/tree/main/hermes-agent/optional-skills/mlops/chroma Command: npx skills add https://github.com/AissenLiu/EasyHermes --skill chroma-aissenliu
Chroma provides a local, self-hosted vector database to store embeddings and metadata, enabling memory, fast semantic search, and retrieval-augmented workflows for AI applications.
Index your documents with embeddings into a local Chroma database and perform semantic search.
High-intent search queries and answers about installing and using this skill.
Store embeddings locally for semantic search by using a self-hosted vector database that persists embeddings with metadata, enabling fast retrieval and offline development for AI applications.
A local vector database in RAG workflows provides memory by storing document embeddings and metadata, enabling fast vector search and document retrieval to augment LLM pipelines.
Yes, Chroma works with LangChain and LlamaIndex for seamless embedding workflows, supporting integration-ready deployment across notebooks and production environments.
Yes, the local embedding store supports metadata filtering, allowing you to filter documents by metadata alongside performing vector search for precise retrieval.
The best way to deploy a vector database for offline development is using a persistent local store that saves embeddings and metadata directly on your machine.
No, you do not need an internet connection to query your local embedding store, as it supports fully offline environments and persistent local storage for AI applications.
Greenmamba29Generate and store vector embeddings for semantic search pipelines.
rukikotooQuery the econometrics RAG knowledge base to retrieve relevant document chunks.
cxnaiveStore and retrieve vector embeddings with metadata filtering for RAG applications.
jacardlStore embeddings with metadata in a local vector database for semantic search.
