# Copy this file to .env and fill in your values.
# Run 'make check-env' to validate your configuration before first run.
# NEVER commit .env — it is gitignored.

# ── Required ────────────────────────────────────────────────────────────────

# Base URL of your OpenAI-compatible LLM API
# OpenAI:    https://api.openai.com/v1
# Azure:     https://YOUR-RESOURCE.openai.azure.com/openai/deployments/YOUR-DEPLOYMENT
# HuaTuo:    https://api.huatuogpt.cn/v1
LLM_BASE_URL=https://api.openai.com/v1

# Your API key for the LLM provider above
LLM_API_KEY=your_api_key_here

# Model name — must match your provider's model identifier
# OpenAI examples:  gpt-4  gpt-4o  gpt-3.5-turbo
# Claude examples:  claude-opus-4-6  claude-sonnet-4-6
LLM_MODEL=gpt-4

# Your email address — required by NCBI/PubMed API (https://www.ncbi.nlm.nih.gov/home/develop/api/)
# NCBI will use this to contact you if your scripts cause problems.
PUBMED_EMAIL=your_email@example.com

# ── Optional ─────────────────────────────────────────────────────────────────

# Use a faster/cheaper model for Judge and Scheduling agents (~30–40% faster overall)
# If unset, LLM_MODEL is used for all agents.
# FAST_LLM_MODEL=gpt-3.5-turbo

# ── Hypertension RAG Service ─────────────────────────────────────────────────
# These settings configure the evidence retrieval service.
# In Docker Compose, HYPERTENSION_API_URL and QDRANT_HOST are set automatically.
# You only need to configure the embedding and reranker API keys below.

HYPERTENSION_API_URL=http://localhost:8000
HYPERTENSION_API_TIMEOUT=60

# Number of chunks to request from /search (client aggregates into max_papers papers)
RAG_SEARCH_TOP_K=15

# Max papers to surface to downstream agents (after aggregation by evidence_id)
RAG_MAX_PAPERS=6

# Max supporting passages to keep per paper
RAG_MAX_PASSAGES_PER_PAPER=3

# ── Embedding & Reranker (for Hypertension RAG search) ───────────────────────
# The evidence database uses zhipu embedding (dim=2048).
# You need a Zhipu API key for search queries to match the pre-built vectors.

EMBEDDER=zhipu
EMBED_DIM=2048
ZHIPU_API_KEY=your_zhipu_api_key_here

# Reranker uses an OpenAI-compatible API (defaults to LLM_BASE_URL/LLM_API_KEY)
RERANKER=api
RERANKER_MODEL=BAAI/bge-reranker-v2-m3