# ── LLM ─────────────────────────────────────────────────────────────────────────
# OpenAI API-compatible LLM
LLM_ENABLED=true
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-...
LLM_MODEL_NAME=gpt-4o-mini
LLM_BASE_URL=
# Set true for reasoning models (DeepSeek R1, deepseek-v4-flash, o1, etc.)
LLM_ENABLE_REASONING=false

# ── Neo4j ───────────────────────────────────────────────────────────────────────
NEO4J_AUTH=neo4j/your_password

# ── Langfuse (self-hosted tracing) ──────────────────────────────────────────────
# Set to true to enable per-node token tracking and evaluation scores
LANGFUSE_ENABLED=false

# After setting up Langfuse (docker compose up langfuse-server),
# go to http://localhost:3000, create a project, and copy keys here:
LANGFUSE_HOST=http://localhost:3000
LANGFUSE_PUBLIC_KEY=pk-lf-...
LANGFUSE_SECRET_KEY=sk-lf-...

# Secrets for Langfuse server (only needed for docker compose)
# Generate with: openssl rand -hex 32
NEXTAUTH_SECRET=change_me
LANGFUSE_ENCRYPTION_KEY=change_me
LANGFUSE_SALT=langfuse
LANGFUSE_DB_PASSWORD=langfuse_pass

# ── Grading backend ──────────────────────────────────────────────────────────────
# "reranker" — Jina Reranker v3 (fast, no tokens consumed, recommended)
# "llm"      — batched LLM call (fallback)
GRADE_BACKEND=reranker

# ── Jina Reranker v3 (used when GRADE_BACKEND=reranker) ──────────────────────────
# backend: "local" (free, ~2GB RAM) or "api" (requires JINA_API_KEY)
RERANKER_BACKEND=local
RERANKER_MODEL=jinaai/jina-reranker-v3
# Minimum relevance score to count a doc as relevant (0.0–1.0)
RERANKER_THRESHOLD=0.0
# Only needed for RERANKER_BACKEND=api
# JINA_API_KEY=jina_...

# ── Mem0 (self-hosted long-term memory) ─────────────────────────────────────────
MEM0_ENABLED=false

# ── Web search (Tavily, optional) ───────────────────────────────────────────────
WEB_SEARCH_ENABLED=false