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Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns.
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Help the team document and maintain Architecture Decision Records (ADRs).
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Standard for creating technical documentation in this repository. Use this when writing new documentation in docs/ to ensure consistent hierarchy and formatting.
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Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns.
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| name | mongodb |
| description | Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns. |
This skill provides patterns for implementing RAG logic with MongoDB Atlas.
documents: Stores source document text and global metadata.chunks: Stores text fragments, embeddings (list[float]), and a foreign key (document_id) to the source document.{"$meta": "vectorSearchScore"} for semantic scores and {"$meta": "searchScore"} for text scores.$vectorSearch aggregation. Default numCandidates: 100.$search (Atlas Search) in chunks collection.RAGService by merging results from semantic and text search.$lookup to fetch document metadata (title, source) when returning search results.motor (AsyncIOMotorClient) for all database operations.bson.ObjectId where necessary.