Automate RAG-based regulatory document QA with citations from uploaded texts.
npx skills add https://github.com/pepeccz/msi-a --skill msia-rag
Please help me install this Agent Skill. Skill: msia-rag Source: https://github.com/pepeccz/msi-a/tree/main/skills/msia-rag Command: npx skills add https://github.com/pepeccz/msi-a --skill msia-rag
MSI-a's RAG-based workflow enables fast, accurate Q&A over regulated documents by retrieving, chunking, and citing relevant content from uploaded texts.
Upload a regulatory document and ask a question to retrieve, cite, and answer using the most relevant chunks.
High-intent search queries and answers about installing and using this skill.
Automating retrieval-augmented generation for regulatory QA requires ingesting texts, applying hierarchical chunking with heading mappings, and routing queries across hybrid LLMs to retrieve and cite relevant chunks. The workflow uses asynchronous architecture, Redis caching, and Qdrant vector store for compliance-grade responses.
Hybrid LLM routing for document-heavy workflows uses a triage-based orchestration to direct simple queries to local models and complex queries to cloud models. This asynchronous architecture optimizes processing speed and cost while maintaining accurate retrieval-augmented generation for regulatory texts.
Yes, you can combine Qdrant vector search and PostgreSQL keyword search for document retrieval. The workflow merges multi-source search results using Reciprocal Rank Fusion (RRF) to deliver high-relevance, citation-rich responses from uploaded regulatory documents.
Hierarchical chunking with heading mappings for legal texts requires parsing uploaded regulatory documents into semantically meaningful sections based on their heading hierarchies. This structure enables precise retrieval and accurate citations during the retrieval-augmented generation process.
Yes, you need asynchronous architecture and Redis caching to process regulatory documents efficiently. This setup supports triage-based LLM orchestration and hybrid routing, ensuring fast query handling and scalable retrieval-augmented generation across local and cloud models.
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