Store and query embeddings with metadata filtering for semantic search.
npx skills add https://github.com/afel6/scal-ai-pipeline --skill chroma-afel6
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/afel6/scal-ai-pipeline/tree/main/hermes_skills_library/optional-skills/mlops/chroma Command: npx skills add https://github.com/afel6/scal-ai-pipeline --skill chroma-afel6
Manage and query high-volume embeddings and their metadata for AI apps with a simple, self-hosted vector store.
Index embeddings into a collection and run a sample semantic search.
High-intent search queries and answers about installing and using this skill.
To store and query embeddings for semantic search, you use a vector database to create collections, add documents, and retrieve results with metadata filtering. This approach supports RAG pipelines and document retrieval.
A vector database is used in AI memory and RAG pipelines to manage and query high-volume embeddings alongside their metadata. It enables efficient semantic search and document retrieval for AI applications.
Yes, you can filter document retrieval results by metadata in an embedding database. This allows for precise retrieval by narrowing down the queried embeddings based on specific metadata attributes.
Yes, this open-source vector database scales from notebooks to production clusters. It provides a simple four-function API to create collections, add documents, query, and retrieve results efficiently across deployments.
Yes, you can perform both vector and full-text search using a self-hosted embedding store. It allows you to store embeddings and metadata efficiently while executing comprehensive search queries for AI applications.
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.
markus41Manage vector embeddings for semantic search across Pinecone, Weaviate, Chroma, and pgvector.
devMoezUpsert vectors and query a managed Pinecone index for semantic retrieval.
