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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:

Copyscripts/create_rag_system.sh <project-name> [--output-dir <path>]

Example:

Copyscripts/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:

Copyscripts/create_rag_system.sh <project-name> [--output-dir <path>]

ingest_documents.sh

Batch document ingestion:

Copyscripts/ingest_documents.sh <source-dir> <index-name>

run_local.sh

Start the RAG system locally:

Copyscripts/run_local.sh <project-dir>

Configuration

Required environment variables:

CopyAZURE_OPENAI_ENDPOINT=<your-endpoint>
AZURE_OPENAI_API_KEY=<your-key>
AZURE_SEARCH_ENDPOINT=<your-search-endpoint>
AZURE_SEARCH_KEY=<your-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