القائمة
Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Help the team document and maintain Architecture Decision Records (ADRs).
Expert guidance on document processing with Docling and audio transcription with Whisper.
Standard for creating technical documentation in this repository. Use this when writing new documentation in docs/ to ensure consistent hierarchy and formatting.
Expert guidance on creating accurate, visually polished Mermaid diagrams for architecture documentation.
Expert guidance on MongoDB implementation for RAG, including aggregation pipelines and search patterns.
Expert guidance on building agents and tools with Pydantic AI.
| 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.