Store embeddings and metadata for fast semantic search in AI applications.
npx skills add https://github.com/AVOI-CEO/avoi-agent --skill chroma-avoi-ceo
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/AVOI-CEO/avoi-agent/tree/main/optional-skills/mlops/chroma Command: npx skills add https://github.com/AVOI-CEO/avoi-agent --skill chroma-avoi-ceo
Stores embeddings and metadata and enables fast semantic search for AI applications.
Create or load a local Chroma collection and perform semantic search over your documents.
High-intent search queries and answers about installing and using this skill.
A vector database stores embeddings and metadata to enable fast semantic search for AI applications, supporting RAG pipelines, document retrieval, and memory-enabled AI systems.
You can store embeddings and metadata for semantic search using a simple four-function API to create or load local collections, enabling fast vector and full-text retrieval.
Yes, you can self-host an open-source vector database for RAG pipelines that scales seamlessly from local notebooks to production environments without external dependencies.
The best way to perform document retrieval with metadata filtering is using an open-source vector database that natively supports storing metadata alongside vectors for filtered semantic search.
No, you do not need external dependencies to run this local vector database, as it operates independently to store embeddings and perform semantic search within your notebooks or production setup.
Greenmamba29Generate and store vector embeddings for semantic search pipelines.
rukikotooQuery the econometrics RAG knowledge base to retrieve relevant document chunks.
orezekBuild retrieval-augmented generation pipelines with LangChain document loaders, embeddings, and vector stores.
cloudlinessHost a scalable managed vector database for similarity search.
