Integrate MongoDB Atlas Vector Search into your AI workflows

MongoDB Atlas Vector Search combines document storage with vector similarity search, enabling storage and retrieval of high-dimensional embeddings for AI and machine learning applications.

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Triggers & Actions

Delete Documents

Delete documents from the vector store by metadata

Action

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Load Documents

Loads documents into the vector store using LLM embeddings.

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Search Documents

Query documents from the vector store using LLM embeddings.

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Update Documents

Updates documents in the vector store by deleting existing ones matching the metadata filter and loading new ones using LLM embeddings.

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Templates using MongoDB Atlas Vector Search

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