mcp_config.yaml

# MCP Configuration for Agentic RAG System

# Server Configuration
server:
  host: "0.0.0.0"
  port: 8000
  reload: false
  workers: 1
  log_level: "info"

# Agent Configuration
agent:
  name: "rag-agent"
  description: "Agentic RAG system with LLM reasoning"
  max_tokens: 1024
  temperature: 0.7

# LLM Configuration (Ollama)
llm:
  provider: "ollama"
  url: "http://localhost:11434"
  model: "mistral"  # Options: mistral, llama3, neural-chat, etc.
  timeout: 120
  retry_attempts: 3

# Vector Store Configuration (ChromaDB)
vector_store:
  type: "chromadb"
  persist_dir: "./vector_store"
  collection_name: "documents"
  embedding_model: "all-MiniLM-L6-v2"
  similarity_metric: "cosine"

# RAG Configuration
rag:
  retrieval_method: "semantic_search"
  n_results_default: 3
  n_results_max: 10
  context_injection: true
  include_metadata: true

# Tools Configuration
tools:
  document_search:
    enabled: true
    description: "Search over documents in vector store"
  
  web_search:
    enabled: false
    description: "Web search tool for fallback queries"
    api_key: "${WEB_SEARCH_API_KEY}"

# Document Processing
document_processing:
  supported_formats:
    - ".txt"
    - ".pdf"
    - ".docx"
    - ".md"
  chunk_size: 512
  chunk_overlap: 50

# Logging
logging:
  level: "INFO"
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
  file: "logs/rag_agent.log"

# Security
security:
  enable_auth: false
  max_requests_per_minute: 60
  allowed_origins:
    - "http://localhost:3000"
    - "http://localhost:8501"

# Frontend (Streamlit)
streamlit:
  enabled: true
  port: 8501
  theme: "light"