Implement MongoDB Vector Search Node in n8n.io

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    • Type: Epic
    • Resolution: Unresolved
    • Priority: Major - P3
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    • Affects Version/s: None
    • Component/s: None
    • Vector Search in n8n.io
    • Node Drivers
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      1. What would you like to communicate to the user about this feature?
      2. Would you like the user to see examples of the syntax and/or executable code and its output?
      3. Which versions of the driver/connector does this apply to?
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      1. What would you like to communicate to the user about this feature? 2. Would you like the user to see examples of the syntax and/or executable code and its output? 3. Which versions of the driver/connector does this apply to?
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      Engineer(s): Bailey Pearson, Durran Jordan
      2025-06-20: Target date unchanged

      Known risks or blockers:

      • Potential calendar delays due to external review needed (n8n): they have not reviewed any of the PRs we have put up, and attempts to get their attention via Prakul have not been successful

      Completed over the last 2 weeks:

      • Three pull requests up for best practices audit fixups, adding a new node for search index management, and support for pre/post search queries

      Focus over the next 2 weeks:

      • We are pausing work until the existing PRs get feedback from the n8n team

      Engineer(s): Bailey Pearson, Durran Jordan
      2025-06-06: Target date set to 2025-07-11

      Known risks or blockers:

      • Potential calendar delays due to external review needed (n8n)

      Completed over the last 2 weeks:

      • Project started and getting set up. One PR for the audit of the vector search node needs review.

      Focus over the next 2 weeks:

      • Search index management and updates to the CRUD node.
      Show
      Engineer(s): Bailey Pearson, Durran Jordan 2025-06-20: Target date unchanged Known risks or blockers: Potential calendar delays due to external review needed (n8n): they have not reviewed any of the PRs we have put up, and attempts to get their attention via Prakul have not been successful Completed over the last 2 weeks: Three pull requests up for best practices audit fixups, adding a new node for search index management, and support for pre/post search queries Focus over the next 2 weeks: We are pausing work until the existing PRs get feedback from the n8n team Engineer(s): Bailey Pearson, Durran Jordan 2025-06-06: Target date set to 2025-07-11 Known risks or blockers: Potential calendar delays due to external review needed (n8n) Completed over the last 2 weeks: Project started and getting set up. One PR for the audit of the vector search node needs review. Focus over the next 2 weeks: Search index management and updates to the CRUD node.
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      Develop a new node (connector) within the n8n framework (a third-party open-source app dev framework) to enable integration with MongoDB for vector search functionality. This feature will allow n8n users to leverage MongoDB as a vector store, facilitating advanced similarity searches for vectorized data. 

      Use Case

      As a developer using n8n
      I want to be able to use MongoDB Vector Search
      So that I can build AI applications like natural language chatbots, semantic search engines, etc.

      Customer notes which talk about some N8n requests - https://docs.google.com/document/d/14vrxlRdkuDTjbtXKCqudxHkZeQOvTFy4OXJQ6ounDz0/edit?pli=1&tab=t.50y9ug4ihhhn#heading=h.tpqd0cdo1vlu

      Dependencies

      None

      Risks/Unknowns

      None

      Acceptance Criteria

      Implementation Requirements

      Use MongoDB vector store LangChain integration as a reference.

      Connection Setup:

      • Allow users to configure MongoDB connection settings, including hostname, port, database, username, and password.

      Index Management:

      • Create, manage, and delete vector indexes. Allow specifying vector dimensions and distance metrics (e.g., cosine similarity, Euclidean).

      CRUD Operations:

      • Insert vectorized data into MongoDB collections with metadata. Update and delete entries.  

      Query Support:

      • Perform similarity searches with user-defined query vectors. Support filters based on metadata (e.g., filtering by category or tags).

      Testing Requirements

      • Add standard unit tests and add them to MongoDB Evergreen testing suite
      • Integrate with the MongoDB AI/ML pipeline

      Documentation Requirements

      • Docs ticket dependency for adding to "AI Integration" section  in vector search doc

       

       

      Additional links:

      https://docs.n8n.io/integrations/creating-nodes/overview/

      https://docs.n8n.io/help-community/contributing/

      https://github.com/n8n-io/n8n/tree/master/packages/%40n8n/nodes-langchain/nodes/vector_store/VectorStorePinecone

            Assignee:
            Bailey Pearson
            Reporter:
            Prakul Agarwal
            Daria Pardue Daria Pardue
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              Created:
              Updated:
              5 weeks, 2 days
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