OpenCRE/.env.example at main · OWASP/OpenCRE · GitHub

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# ------------------------------------------------------------------------------
# OpenCRE Environment Configuration
# ------------------------------------------------------------------------------
# Copy this file to `.env` and update the values for your environment.
# Never commit your real `.env` file to version control.
# ------------------------------------------------------------------------------
# Database

DEV_DATABASE_URL=postgresql://user:password@localhost:5432/opencre

# Neo4j

NEO4J_URL=neo4j://neo4j:password@localhost:7687

# Redis

REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_URL=redis://localhost:6379
REDIS_NO_SSL=false

# Flask

FLASK_CONFIG=development
INSECURE_REQUESTS=false

# Feature Flags
# Enable the deploy/uptime health probe at GET /rest/v1/health.
# Set to one of 1, true, yes (case-insensitive) to enable; any other value
# (including unset or false) leaves it off and the endpoint returns 404.

CRE_ENABLE_HEALTH=false

# Expose MyOpenCRE in the UI (GET /api/capabilities → myopencre).
# Off by default so production can roll out independently of import auth.

CRE_ENABLE_MYOPENCRE=false

# Expose Login/Logout in the UI (GET /api/capabilities → login).
# Off by default until auth routes + User model land; enable on staging first.

CRE_ENABLE_LOGIN=false

# Embeddings

NO_GEN_EMBEDDINGS=false

# Google Auth

GOOGLE_CLIENT_ID=your-client-id
GOOGLE_CLIENT_SECRET=your-client-secret
GOOGLE_SECRET_JSON=path/to/secret.json
LOGIN_ALLOWED_DOMAINS=example.com

# GCP

GCP_NATIVE=false
GEMINI_API_KEY=your-gemini-api-key
VERTEX_CHAT_MODEL=gemini-2.5-flash
VERTEX_EMBED_CONTENT_MODEL=gemini-embedding-001

# Noise / Relevance Filter (Module B)

CRE_NOISE_FILTER_LLM_MODEL=gemini/gemini-2.5-flash-lite
CRE_NOISE_FILTER_BATCH_SIZE=10
CRE_NOISE_FILTER_MAX_CHARS=1500
CRE_NOISE_FILTER_CONFIDENCE_THRESHOLD=0.8

# Spreadsheet Auth

OpenCRE_gspread_Auth=path/to/credentials.json

# Module C — The Librarian (defaults match the OIE design doc; see
# application/utils/librarian/config_loader.py for validation)

# Retrieval backend: in_memory (sklearn cosine; SQLite dev/CI/harness) or
# pgvector (Postgres ``embedding_vec`` after Alembic c7d8e9f0a1b2 / #977).
CRE_LIBRARIAN_RETRIEVER_BACKEND=in_memory
# Candidate shortlist size produced by C.1 and reranked by C.2 (W4).
CRE_LIBRARIAN_TOP_K_RETRIEVAL=20
CRE_LIBRARIAN_TOP_K_RERANK=5
# Cross-encoder reranker model (W4), pinned.
CRE_LIBRARIAN_CROSSENCODER_MODEL=cross-encoder/ms-marco-MiniLM-L-6-v2
# Auto-link confidence threshold; below this -> human review (W5).
CRE_LIBRARIAN_LINK_THRESHOLD=0.8
CRE_LIBRARIAN_BATCH_SIZE=32
CRE_LIBRARIAN_ECE_TARGET=0.10
CRE_LIBRARIAN_CONFORMAL_ALPHA=0.10