Store embeddings with metadata in a local vector database for semantic search.
npx skills add https://github.com/jacardl/New-Radar --skill chroma-jacardl
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/jacardl/New-Radar/tree/main/backend/frameworks/hermes-agent/optional-skills/mlops/chroma Command: npx skills add https://github.com/jacardl/New-Radar --skill chroma-jacardl
Open-source embedding database designed to store and query text embeddings with metadata, enabling fast semantic search and retrieval for AI applications.
Set up a local chroma client, create a collection, add documents with embeddings, and run a similarity search.
High-intent search queries and answers about installing and using this skill.
To store embeddings with metadata for semantic search, you can use a local, open-source vector database. It enables fast document retrieval and RAG workflows by allowing you to store text embeddings alongside metadata and run similarity queries with a simple four-function API.
Yes, you can use a local vector database for RAG development and self-hosted production. It supports local persistence and metadata filtering, allowing you to scale your document retrieval pipelines seamlessly from notebook environments to self-hosted deployments.
The best way to build a document retrieval pipeline is using an open-source embedding database with a simple four-function API. You set up a local client, create a collection, add documents with embeddings, and execute fast similarity searches to retrieve relevant text.
Yes, this local vector store supports metadata filtering for semantic search. It allows you to store embeddings with associated metadata, enabling precise document retrieval and targeted similarity queries within your RAG workflows and AI applications.
patornBuild end-to-end RAG pipelines with LangChain document loaders, embeddings, and vector stores.
zilliztechDecompose questions, retrieve across documents, and synthesize cited answers.
ivanshamaevBuild RAG ingestion and query pipelines with hybrid retrieval and reranking.
gqf2008Store embeddings and metadata for vector and full-text search.
