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. 

• See vectorStorePGVector

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.

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

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