.env.example

# =============================================================================
# CogGen Environment Configuration
# Copy this file to .env and fill in your API keys
# =============================================================================

# --- LLM Provider (via LiteLLM) ---
# Option 1: OpenAI
# OPENAI_API_KEY=sk-xxxxx

# Option 2: Azure OpenAI
# AZURE_API_KEY=your-azure-api-key
# AZURE_API_BASE=https://your-resource.openai.azure.com/
# AZURE_API_VERSION=2025-01-01-preview

# --- Web Search ---
TAVILY_API_KEY=tvly-xxxxx

# --- Search Mode ---
# "snippet" = use search result snippets only (faster, lower API cost)
# "full"    = extract full page content via Tavily Extract (slower, more thorough)
# COGGEN_SEARCH_MODE=snippet

# --- Prompt Language ---
# "en" for English (with image/chart generation)
# "en_no_image" for English (text-only, no chart generation)
PROMPT_LANG=en

# --- Model Configuration ---
# Each role maps to a specific capability need; override via env vars.
# All default to gpt-4.1 unless noted.
#
# Core reasoning: planning, revision, relevance judgment, cognitive detection
# COGGEN_REASONING_MODEL=gpt-4.1
#
# Long-form writing: section content generation
# COGGEN_WRITING_MODEL=gpt-4.1
#
# Long-context tasks: ADK default LLM, query generation from gaps
# COGGEN_HIGH_CONTEXT_MODEL=gpt-4.1
#
# Lightweight tasks: fact extraction, information aggregation
# COGGEN_MINI_MODEL=gpt-4.1
#
# Chart code generation: ECharts / visualization code
# COGGEN_CODE_MODEL=gpt-4.1

# --- Chart Rendering ---
# ECHARTS_VALIDATION=simple    # simple | enhanced | full
# ECHARTS_DATA_VERIFY=false

# --- Semantic Chunking ---
# Embedding model for RAG chunking (auto-downloaded from HuggingFace on first run)
# COGGEN_CHUNK_MODEL=minishlab/potion-base-8M
# CHUNK_SIZE=1024