devrel-agents-tutorial/.env.example at main · launchdarkly-labs/devrel-agents-tutorial · GitHub

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# LaunchDarkly Configuration
# Get your SDK key from: https://app.launchdarkly.com/settings/projects
LD_SDK_KEY=your_launchdarkly_sdk_key_here
LD_API_KEY="your key here"

# ============================================
# Authentication Method Selection
# ============================================

# Choose authentication method: "sso" or "api-key"
# AUTH_METHOD=sso  # Use Bedrock via AWS SSO
AUTH_METHOD=api-key  # Default: backward compatible with direct API keys

# ============================================
# SSO Configuration (when AUTH_METHOD=sso)
# ============================================

AWS_REGION=us-east-1
AWS_PROFILE=your-sso-profile-name     # Your AWS SSO profile name

# Bedrock Inference Profile Region (optional)
# Controls the region prefix for auto-corrected model IDs
# If not set, automatically derived from AWS_REGION (e.g., us-east-1 → us)
# Options: us, eu, ap, ca, sa, af, me
# BEDROCK_INFERENCE_REGION=us  # Default: auto-detected from AWS_REGION

# ============================================
# API Key Configuration (when AUTH_METHOD=api-key)
# ============================================

# Get your Anthropic API key from: https://console.anthropic.com/
ANTHROPIC_API_KEY=your_anthropic_api_key_here

# Get your OpenAI API key from: https://platform.openai.com/api-keys
OPENAI_API_KEY=your_openai_api_key_here

# Get your Mistral API key from: https://console.mistral.ai/
MISTRAL_API_KEY=your_mistral_api_key_here

# ============================================
# Provider-Specific Configuration
# ============================================

# Bedrock Embedding Configuration (when using Bedrock embeddings)
BEDROCK_EMBEDDING_DIMENSIONS=1024  # Options: 256, 512, 1024
BEDROCK_EMBEDDING_MODEL=amazon.titan-embed-text-v2:0

# Optional: MCP Tool Configuration
# These are used for advanced research capabilities (leave blank if not using MCP tools)
ARXIV_MCP_SERVER_PATH=/Users/your_username/.local/bin/arxiv-mcp-server
SEMANTIC_SCHOLAR_SERVER_PATH=/tmp/semantic_scholar_server.py

# Optional: Database Configuration (if using database MCP tools)
# DATABASE_URL=postgresql://user:password@localhost:5432/your_database

# Optional: GitHub Integration (if using GitHub MCP server)
# GITHUB_TOKEN=your_github_personal_access_token

# Optional: Slack Integration (if using Slack MCP server)
# SLACK_BOT_TOKEN=xoxb-your-slack-bot-token
# SLACK_SIGNING_SECRET=your_slack_signing_secret

# Application Configuration
# API Server Configuration
API_HOST=localhost
API_PORT=8000

# Streamlit UI Configuration
UI_HOST=localhost
UI_PORT=8501

# Vector Store Configuration
VECTOR_STORE_PATH=data/vector_store/
EMBEDDING_MODEL=amazon.titan-embed-text-v2:0  # Bedrock Titan V2
# EMBEDDING_MODEL=text-embedding-3-small  # Default: OpenAI (backward compatible)
CHUNK_SIZE=1000
CHUNK_OVERLAP=200

# Optional: Override embedding provider auto-detection
# EMBEDDING_PROVIDER=bedrock  # Force Bedrock embeddings
# EMBEDDING_PROVIDER=openai   # Force OpenAI embeddings (requires OPENAI_API_KEY)

# Development Mode
DEBUG=true
LOG_LEVEL=INFO