vakra/template_env at main · IBM/vakra · GitHub

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# =============================================================================
# enterprise-benchmark Environment Variables
# =============================================================================
# Copy this file to .env and fill in your actual values.
# .env is already listed in .gitignore — never commit real credentials.
# =============================================================================


# =============================================================================
# MCP Server - Python Tools (apis/m3/python_tools/mcp/)
# =============================================================================

# Database domain name. Builds path: db/{domain}/{domain}.sqlite
# Examples: superhero, hockey, address, movie
MCP_DOMAIN=superhero

# Directory for caching intermediate data and I/O operations
MCP_CACHE_DIR=./mcp_cache

# Comma-separated list of tables to load (leave empty to load all tables)
# MCP_TABLES=

# Unique identifier for tracking different server instances
# MCP_TOOL_UNIVERSE_ID=

# Server mode: "router" (default, routes per query), "slot_filling" (generic tools), or "selection" (domain-specific getters)
MCP_SERVER_TYPE=router

# Transport protocol: "stdio" (subprocess) or "websocket" (network)
MCP_TRANSPORT=stdio

# WebSocket server host (only used when MCP_TRANSPORT=websocket)
MCP_HOST=127.0.0.1

# WebSocket server port (only used when MCP_TRANSPORT=websocket)
MCP_PORT=8000


# =============================================================================
# REST API MCP Wrapper (apis/m3/rest/)
# =============================================================================

# Base URL of the FastAPI REST API server
FASTAPI_BASE_URL=http://localhost:8000

# Name for the MCP server instance
MCP_SERVER_NAME=fastapi-mcp-wrapper


# =============================================================================
# Container & Benchmark Runner (benchmark_runner.py)
# =============================================================================

# Path to the task 2 input directory containing benchmark JSON files
TASK_2_DIR=./task_data/

# Docker/Podman container name where the MCP server runs
MCP_CONTAINER_NAME=fastapi-mcp-server

# Container runtime to use: "docker" or "podman"
CONTAINER_RUNTIME=docker


# =============================================================================
# LLM Providers
# =============================================================================

# --- IBM RITS (agents/llm.py) ---
# API key for IBM RITS inference service (required for provider=openai via RITS)
RITS_API_KEY=your-rits-api-key-here

# --- Anthropic (agents/agent_interface.py, agents/langchain_agent_*.py) ---
# API key for Anthropic Claude models
ANTHROPIC_API_KEY=your-anthropic-api-key-here

# --- OpenAI (agents/agent_interface.py, agents/langchain_agent_*.py) ---
# API key for OpenAI models
OPENAI_API_KEY=your-openai-api-key-here

# --- Ollama (agents/langchain_agent_*.py) ---
# Set to "true" to use a local Ollama instance instead of cloud providers
# USE_OLLAMA=true

# Ollama model name (used when USE_OLLAMA=true)
OLLAMA_MODEL=llama3.1:8b

# Set to "true" to skip Claude even when ANTHROPIC_API_KEY is set
# SKIP_CLAUDE=true

# --- LiteLLM (agents/llm.py) ---
# API key passed to LiteLLM (used when the underlying provider requires one)
LITELLM_API_KEY=your-litellm-api-key-here

# Optional: base URL for a self-hosted LiteLLM proxy server
# LITELLM_BASE_URL=http://localhost:4000

# --- IBM watsonx.ai (agents/agent_interface.py, benchmark_runner.py) ---
# API key for IBM watsonx.ai
WATSONX_APIKEY=your-watsonx-api-key-here

# watsonx.ai project ID (use either PROJECT_ID or SPACE_ID, not both)
WATSONX_PROJECT_ID=your-watsonx-project-id-here

# watsonx.ai space ID (alternative to WATSONX_PROJECT_ID)
# WATSONX_SPACE_ID=your-watsonx-space-id-here

# watsonx.ai API endpoint URL
WATSONX_URL=https://us-south.ml.cloud.ibm.com

# HF token
HF_TOKEN=