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mongodb

Skill1 starsChanged 10 months agoMITView on GitHub

Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns.

11 other projects use a version of this.

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Projects with it also use redis, postgresql and 6 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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    Container-based development for isolated, reproducible environments. Use when running npm commands, installing packages, executing code, or managing project dependencies. Trigger phrases include "npm install", "run the build", "start the server", "install package", or any code execution request.

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The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 0 are distinctive, and 5 are very specific to this project.

  • 0

    Common

    Most of the 8 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 5

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Score Meta: Use {"$meta": "vectorSearchScore"} for semantic scores and {"$meta": "searchScore"} for text scores.

    line 15
  2. Very specific:

    Semantic Search: Use $vectorSearch aggregation.

    line 19
  3. Very specific:

    Text Search: Use $search (Atlas Search) in chunks collection.

    line 20
  4. Very specific:

    Joins: Always use $lookup to fetch document metadata (title, source) when returning search results.

    line 22
  5. Very specific:

    Async First: Use motor (AsyncIOMotorClient) for all database operations.

    line 26

“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

MongoDB Expert Skill

This skill provides patterns for implementing RAG logic with MongoDB Atlas.

📂 Storage Pattern

  • Two-Collection Pattern:
    • 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.
  • Score Meta: Use {"$meta": "vectorSearchScore"} for semantic scores and {"$meta": "searchScore"} for text scores.

🔍 Search Patterns

  • Semantic Search: Use $vectorSearch aggregation. Default numCandidates: 100.
  • Text Search: Use $search (Atlas Search) in chunks collection.
  • Hybrid Search: Currently implemented via manual Reciprocal Rank Fusion (RRF) in RAGService by merging results from semantic and text search.
  • Joins: Always use $lookup to fetch document metadata (title, source) when returning search results.

🛠️ Code Standards

  • Async First: Use motor (AsyncIOMotorClient) for all database operations.
  • List Embeddings: Embeddings MUST be stored and queried as Python lists of floats (e.g., 1536 dims for OpenAI).
  • ID Handling: Convert string IDs to bson.ObjectId where necessary.
  • Graceful Failure: Handle missing search indexes (code 291) with clear error messages.

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