platform-codebase/features/conversation-assistant/.env.example

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# =============================================================================
# CONVERSATION ASSISTANT: Environment Configuration
# =============================================================================
# Copy this file to .env and customize for your environment.
# All values shown are development defaults.
# =============================================================================
# -----------------------------------------------------------------------------
# DATABASE: PostgreSQL
# -----------------------------------------------------------------------------
DB_HOST=localhost
DB_PORT=25433
DB_USER=postgres
DB_PASSWORD=devpassword
DB_NAME=conversation_assistant
# Use docker-compose to start: docker-compose up -d postgres
# -----------------------------------------------------------------------------
# CACHE: Redis
# -----------------------------------------------------------------------------
REDIS_URL=redis://localhost:26380
CACHE_TTL=3600
# Use docker-compose to start: docker-compose up -d redis
# -----------------------------------------------------------------------------
# SERVER: NestJS API
# -----------------------------------------------------------------------------
PORT=3100
NODE_ENV=development
CORS_ORIGIN=http://localhost:5173
# JWT secret for device authentication
JWT_SECRET=your-jwt-secret-change-in-production
# -----------------------------------------------------------------------------
# DATA STORAGE: Persistent storage on bigdisk
# -----------------------------------------------------------------------------
# Attachments (images, audio, etc. from iMessage sync)
ATTACHMENTS_DIR=/mnt/bigdisk/_/lilith-platform/features/conversation-assistant/attachments
# ML training outputs (fine-tuned models specific to this project)
ML_SERVICE_TRAINING_OUTPUT_DIR=/mnt/bigdisk/_/lilith-platform/features/conversation-assistant/training-outputs
# -----------------------------------------------------------------------------
# ML SERVICE: Python FastAPI
# -----------------------------------------------------------------------------
ML_SERVICE_URL=http://localhost:8100
# Model configuration
ML_SERVICE_MODEL_ID=ministral-3b-instruct
ML_SERVICE_GPU_LAYERS=-1
ML_SERVICE_CONTEXT_SIZE=4096
# Redis for ML service (same instance)
ML_SERVICE_REDIS_URL=redis://localhost:26380
ML_SERVICE_REDIS_ENABLED=true
ML_SERVICE_REDIS_CACHE_TTL=3600
# -----------------------------------------------------------------------------
# DEVELOPMENT NOTES
# -----------------------------------------------------------------------------
#
# Quick Start:
# 1. Start databases: docker-compose up -d
# 2. Start ML service: cd ml-service && pip install -e . && python -m uvicorn src.main:app --port 8100
# 3. Start server: cd server && npm install && npm run start:dev
# 4. Start frontend: cd frontend && npm install && npm run dev
#
# ML Package Installation:
# pip install -e ~/Code/@packages/@ml/@tools/model-loader
# pip install tqftw-fastapi-service-base --extra-index-url https://forge.nasty.sh/api/packages/lilith/pypi/simple/
#
# Production:
# - Change all passwords
# - Set NODE_ENV=production
# - Use infrastructure/docker/docker-compose.databases.yml for shared Redis
#
# =============================================================================