MongoDB Atlas Vector Store

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title: MongoDB Atlas Vector Store description: The MongoDB Atlas Vector Store node is a component used for upserting embedded data and performing similarity or MMR (Maximal Marginal Relevance) search queries using MongoDB Atlas, a managed cloud MongoDB database. This node allows for efficient storage and retrieval of vector embeddings, making it suitable for various AI and machine learning applications.

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Node Details

• Name: MongoDBAtlas_VectorStores
• Type: MongoDB Atlas
• Category: Vector Stores
• Version: 1.0

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Base Classes

• MongoDB Atlas
• VectorStoreRetriever
• BaseRetriever

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Parameters

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Credential

• Label: Connect Credential
• Name: credential
• Type: credential
• Credential Names: mongoDBUrlApi

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Inputs

1. Document
   • Type: Document
   • List: true
   • Optional: true
2. Embeddings
   • Type: Embeddings
3. Database
   • Type: string
   • Placeholder: DB_NAME
4. Collection Name
   • Type: string
   • Placeholder: <COLLECTION_NAME>
5. Index Name
   • Type: string
   • Placeholder: <VECTOR_INDEX_NAME>
6. Content Field
   • Type: string
   • Default: “text”
   • Description: Name of the field (column) that contains the actual content
   • Optional: true
7. Embedded Field
   • Type: string
   • Default: “embedding”
   • Description: Name of the field (column) that contains the Embedding
   • Optional: true
8. Top K
   • Type: number
   • Default: 4
   • Description: Number of top results to fetch
   • Optional: true
9. MMR-related inputs (added via addMMRInputParams function)

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Outputs

1. MongoDB Retriever
   • Base Classes: [MongoDB Atlas, VectorStoreRetriever, BaseRetriever]
2. MongoDB Vector Store
   • Base Classes: [MongoDB Atlas, MongoDBAtlasVectorSearch]

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Functionality

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Upsert Method

• Adds documents and their embeddings to the MongoDB Atlas database
• Returns the number of added documents and the added documents themselves

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Init Method

• Initializes the MongoDB Atlas vector store or retriever
• Sets up the connection to the MongoDB Atlas database
• Creates a MongoDBAtlasVectorSearch instance for vector operations

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Usage

1. Storing document embeddings in a scalable, cloud-based MongoDB solution
2. Performing similarity searches on stored embeddings
3. Integrating vector search capabilities into larger AI and machine learning workflows

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Note