--- name: ms-agent-framework-rag description: Comprehensive guide for building Agentic RAG systems using Microsoft Agent Framework in C#. Use when creating RAG applications with semantic search, document indexing, and intelligent agent orchestration. Includes scaffolding scripts, reference implementations, and documentation for vector databases, embedding models, and multi-agent workflows. --- # Microsoft Agent Framework - Agentic RAG System This skill provides scaffolding and guidance for building production-ready Agentic RAG (Retrieval-Augmented Generation) systems using Microsoft Agent Framework with C#. ## Quick Start Use the scaffolding script to create a new RAG system: ```bash scripts\/create_rag_system.sh [--output-dir ] ``` Example: ```bash scripts\/create_rag_system.sh MyKnowledgeBase --output-dir .\/my-rag-project ``` ## Architecture Overview An Agentic RAG system consists of: 1. **Ingestion Layer**: Document parsing, chunking, and embedding generation 2. **Vector Store**: Semantic search index (Azure AI Search, Qdrant, or Pinecone) 3. **Agent Framework**: Multi-agent orchestration with Microsoft AutoGen 4. **LLM Integration**: Azure OpenAI or OpenAI API for generation 5. **API Layer**: RESTful endpoints for querying ## Core Components ### 1. Semantic Search - Use Azure AI Search for integrated vector + keyword search - Store embeddings with metadata (source, timestamp, tags) - Implement hybrid search (vector + BM25) for best results See `references\/semantic_search.md` for implementation details. ### 2. Multi-Agent System Build specialized agents: - **Research Agent**: Finds relevant documents - **Synthesis Agent**: Combines information from multiple sources - **Validation Agent**: Checks accuracy and citations See `references\/agent_patterns.md` for agent design patterns. ### 3. Document Processing - Supported formats: PDF, DOCX, TXT, MD, HTML - Chunking strategies: semantic, sliding window, hierarchical - Metadata extraction: title, author, date, tags See `references\/document_processing.md` for chunking strategies. ## Available Scripts ### `create_rag_system.sh` Scaffolds a complete RAG system with: - Project structure following best practices - Configuration files (appsettings.json) - Docker compose for local development - Example agents and tools Usage: ```bash scripts\/create_rag_system.sh [--output-dir ] ``` ### `ingest_documents.sh` Batch document ingestion: ```bash scripts\/ingest_documents.sh

``` ### `run_local.sh` Start the RAG system locally: ```bash scripts\/run_local.sh ``` ## Configuration Required environment variables: ```bash AZURE_OPENAI_ENDPOINT= AZURE_OPENAI_API_KEY= AZURE_SEARCH_ENDPOINT= AZURE_SEARCH_KEY= EMBEDDING_MODEL=text-embedding-ada-002 CHAT_MODEL=gpt-4 ``` ## Reference Documentation - `references\/semantic_search.md` - Vector search implementation - `references\/agent_patterns.md` - Multi-agent design patterns - `references\/document_processing.md` - Chunking and preprocessing - `references\/evaluation.md` - RAG quality metrics ## Best Practices 1. **Start Simple**: Begin with basic RAG, add agents incrementally 2. **Metadata Matters**: Rich metadata improves retrieval accuracy 3. **Hybrid Search**: Combine vector and keyword search 4. **Citation Tracking**: Always include source references 5. **Evaluation**: Use RAGAS framework for quality metrics ## Common Patterns ### Multi-Step Retrieval For complex queries, use iterative refinement: 1. Initial search with broad query 2. Research agent expands with sub-queries 3. Synthesis agent combines results 4. Validation agent checks citations ### Citation Management Always track: - Document ID - Page number - Chunk index - Relevance score See `references\/citations.md` for implementation. ## Troubleshooting ### Poor Retrieval Quality - Adjust chunk size (try 512-1024 tokens) - Use hybrid search instead of pure vector - Add more metadata for filtering - Consider re-embedding with different model ### Slow Performance - Enable caching on vector queries - Use streaming responses - Implement async document ingestion - Consider partitioning large indices ### High Costs - Use smaller models for embeddings - Cache frequently asked questions - Implement result pagination - Use batch processing for ingestion