.env.example

# TriBridRAG Environment Configuration
# Copy this file to .env and fill in the values

# ===== API Keys =====
VOYAGE_API_KEY=your-voyage-key
COHERE_API_KEY=your-cohere-key
JINA_API_KEY=your-jina-key
LANGTRACE_API_KEY=your-langtrace-key  # Optional (UI checks presence only)

# Generation and OpenAI embeddings call the local LiteLLM gateway. Their upstream
# provider secrets, including OPENAI_API_KEY, belong only in infra/litellm.env.
LITELLM_BASE_URL=http://127.0.0.1:54000/v1
LITELLM_API_KEY=sk-ragweld-local

# ===== Database =====
# PostgreSQL (pgvector + FTS)
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
POSTGRES_DB=tribrid_rag
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres

# Docker bind-mount location for DB data (recommended).
# Default in docker-compose.yml: ../tribrid-rag-db (sibling to this repo)
# TRIBRID_DB_DIR=/Users/davidmontgomery/tribrid-rag-db

# Neo4j (Knowledge Graph)
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=password

# Neo4j container memory (for large corpora / graph indexing).
# These are applied by docker-compose.yml.
# NEO4J_HEAP_INIT=2G
# NEO4J_HEAP_MAX=4G
# NEO4J_PAGECACHE=2G

# ===== Model Selection (validated runtime config) =====
# Embedding + generation models/providers are configured via Pydantic config:
# - Global template: tribrid_config.json
# - Per-corpus overrides: stored in Postgres corpus_configs
#
# Use the UI (e.g. Retrieval → Generation Models) instead of adding model
# settings here.

# ===== Server =====
SERVER_HOST=0.0.0.0
SERVER_PORT=58012
DEBUG=false

# ===== Observability =====
GRAFANA_URL=http://localhost:3000
PROMETHEUS_URL=http://localhost:59090
METRICS_ENABLED=true
TRACING_ENABLED=true

# ===== Paths =====
MODELS_DIR=models
DATA_DIR=data