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To store embeddings and run semantic search locally, you can use an open-source vector database that provides a simple API for creating collections, adding vectors, and querying similar items. This approach enables local-first deployment suitable for notebooks and production apps.

The best way to add memory to RAG workflows without a remote database is using a local, self-hosted vector database. It allows you to efficiently store embeddings with metadata filtering for accurate document retrieval during retrieval-augmented generation tasks.

Yes, you can filter vector database query results using metadata. This local embedding database provides persistent vector storage with metadata filtering, allowing you to perform accurate retrieval and narrow down similarity search results based on specific document attributes.

Yes, this local vector database works for both notebooks and production deployments. It is designed as a local-first deployment solution, allowing you to transition your semantic search and document retrieval workflows seamlessly from development to production scale.

You can create, add, and query vector collections using a simple 4-function API. This interface allows you to create collections, add embeddings, query for similar vectors, and delete collections, providing complete management for your local semantic search operations.

You need a local vector database instead of a managed cloud service when you require local persistence, self-hosted deployments, and efficient vector search without external dependencies. It is ideal for AI applications requiring memory and RAG workflows with data privacy constraints.