chroma

To perform semantic search, you store embeddings and metadata in a vector database, then query them based on vector similarity. This skill provides an API to add documents and retrieve them using semantic understanding rather than exact keyword matches.

Building a local RAG application requires an open-source vector database to store and retrieve document embeddings. This skill integrates with popular embedding libraries to provide seamless data manipulation and advanced retrieval capabilities for your generation tasks.

Yes, you can filter vector database search results using metadata properties. The skill supports advanced search capabilities that allow you to narrow down retrieved documents by specifying metadata filters alongside your semantic queries.

Chroma integrates with sentence-transformers to generate and manage embeddings for document retrieval. This combination allows you to automatically transform text into vectors, store them in the database, and perform semantic searches without manual embedding pipelines.

Yes, you need a separate embedding library like sentence-transformers to generate embeddings before storing them. The skill acts as the vector database for storage and retrieval, relying on these dependencies to process raw text into searchable vectors.

An open-source vector database is designed primarily for local development and open-source projects, meaning it may lack enterprise-scale distributed features. It efficiently handles metadata filtering and semantic search but might require additional infrastructure for large-scale production deployments.