{"product_id":"master-rag-pipelines-advanced-ai-agents-for-document-retrieval","title":"Master RAG Pipelines: Advanced AI Agents for Document Retrieval","description":"\u003ch3\u003eMaster RAG Pipelines: Advanced AI Agents for Document Retrieval\u003c\/h3\u003e\n\n\u003cp\u003eThis skill assists developers in building and optimizing retrieval-augmented generation (RAG) pipelines specifically tailored for document retrieval scenarios. It focuses on structuring the data retrieval process efficiently and measuring retrieval performance before engaging in prompt tuning.\u003c\/p\u003e\n\n\u003ch4\u003eWhat this skill does:\u003c\/h4\u003e\n\u003cul\u003e\n  \u003cli\u003eIngests, normalizes, chunks, embeds, stores, and retrieves documents, providing answers with citations.\u003c\/li\u003e\n  \u003cli\u003eUtilizes a hybrid approach combining vector and full-text search with rank fusion (RRF).\u003c\/li\u003e\n  \u003cli\u003eFacilitates pinned embeddings within a pgvector environment for efficient data handling.\u003c\/li\u003e\n  \u003cli\u003eEnsures tenant isolation within query processes to maintain data integrity and security.\u003c\/li\u003e\n  \u003cli\u003eIncludes an \"I don't know\" path to handle uncertain results appropriately.\u003c\/li\u003e\n  \u003cli\u003eEvaluates retrieval methods using metrics such as recall@k and mean reciprocal rank (MRR) before prompt tuning.\u003c\/li\u003e\n  \u003cli\u003eMaintains a default storage setting (`ai.rag_store: pgvector`) for streamlined configuration.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch4\u003eWho it is for:\u003c\/h4\u003e\n\u003cp\u003eThis skill is designed for developers and AI teams utilizing AI coding agents such as Claude Code, Cursor, and Codex. It is particularly beneficial for those involved in creating sophisticated document retrieval systems and enhancing the reliability of AI-generated answers.\u003c\/p\u003e\n\n\u003ch4\u003eUse cases:\u003c\/h4\u003e\n\u003cul\u003e\n  \u003cli\u003eStreamlining the development of RAG pipelines for internal document retrieval systems.\u003c\/li\u003e\n  \u003cli\u003eEnhancing the accuracy of AI-driven customer support by providing precise document citations.\u003c\/li\u003e\n  \u003cli\u003eOptimizing query isolation for multi-tenant systems to ensure secure and reliable service.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch4\u003eTechnical details:\u003c\/h4\u003e\n\u003cul\u003e\n  \u003cli\u003eIntegration with pgvector for managing pinned and vector embeddings.\u003c\/li\u003e\n  \u003cli\u003eCombines vector-based and full-text search methodologies using rank fusion (RRF).\u003c\/li\u003e\n  \u003cli\u003eEvaluates document retrieval using standard metrics like recall@k and MRR.\u003c\/li\u003e\n  \u003cli\u003eBuilds upon the existing infrastructure with seamless integration for AI agent capabilities across platforms.\u003c\/li\u003e\n  \u003cli\u003eReporting capabilities to audit and refine the current state of document retrieval processes.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c!-- mcpcart:static-blocks:start --\u003e\n\u003chr\u003e\n\u003ch3\u003eSource \u0026amp; Licence\u003c\/h3\u003e\n\u003cp\u003eThis package is built on open-source work published by \u003cstrong\u003eVelimirMueller\u003c\/strong\u003e (\u003ca href=\"https:\/\/github.com\/VelimirMueller\/lab-claude-skills\" rel=\"nofollow noopener\" target=\"_blank\"\u003eVelimirMueller\/lab-claude-skills\u003c\/a\u003e) and distributed under \u003cstrong\u003eMIT\u003c\/strong\u003e. The original licence text and copyright notice are included in your download.\u003c\/p\u003e\n\u003cp\u003ePersonal and commercial use, modification and redistribution are permitted, provided the original copyright and licence notice are retained.\u003c\/p\u003e\n\u003cp\u003eYour purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.\u003c\/p\u003e\n\u003ch3\u003eDelivery \u0026amp; Support\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eDelivery:\u003c\/strong\u003e instant — a secure download link is emailed to you as soon as payment is confirmed.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eFormat:\u003c\/strong\u003e ZIP archive containing the skill files, documentation and the original licence.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSupport:\u003c\/strong\u003e \u003ca href=\"mailto:support@mcpcart.com\"\u003esupport@mcpcart.com\u003c\/a\u003e — we aim to reply within 2 business days.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eUpdates:\u003c\/strong\u003e updates are included only where stated on this page.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3\u003eRefunds\u003c\/h3\u003e\n\u003cp\u003eThis is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.\u003c\/p\u003e\n\u003cp style=\"font-size:0.85em;color:#666;\"\u003eClaude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.\u003c\/p\u003e\n\u003c!-- mcpcart:static-blocks:end --\u003e","brand":"MCP Cart","offers":[{"title":"Default Title","offer_id":53138114085175,"sku":"MCP-VELIMIRMUELLER-LAB-CLAUDE-SKILLS-BUILD-RAG-PIPELINE","price":31.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0981\/3950\/4951\/files\/uMu1l7O18nuLeYvTAFGww_669bb087fe0e4ecbb0b0ce13f89c978b.jpg?v=1791623138","url":"https:\/\/mcpcart.com\/products\/master-rag-pipelines-advanced-ai-agents-for-document-retrieval","provider":"SPF PRO","version":"1.0","type":"link"}