cc7e41a089
- Add conversation-assistant types to @packages/@core/types/api/ - Create docker-compose.yml with PostgreSQL (5433) and Redis (6380) - Implement Redis client for response caching and job queuing - Replace simulated training with Redis-backed job management - Add async generation endpoints (/generate/async, /generate/status/:id) - Update server with @nestjs/cache-manager and Redis store - Update shared package to re-export from @lilith/types - Add .env.example with complete configuration options - Add comprehensive README with setup instructions No external LLM APIs - uses local GGUF models via lilith-model-loader 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
142 lines
4.3 KiB
YAML
142 lines
4.3 KiB
YAML
# =============================================================================
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# CONVERSATION ASSISTANT: Development Environment
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# =============================================================================
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#
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# Complete development stack for conversation-assistant feature.
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# Uses Redis for response caching and job queuing.
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#
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# Usage:
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# docker-compose up -d # Start all services
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# docker-compose logs -f # Follow logs
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# docker-compose down # Stop all services
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#
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# Services:
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# - postgres: PostgreSQL database for persistence
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# - redis: Response cache and job queue
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# - ml-service: Python FastAPI for LLM inference (optional, can run locally)
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#
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# Network:
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# - All services on bridge network for development
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# - Production uses infrastructure/docker/docker-compose.databases.yml
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#
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# =============================================================================
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version: '3.8'
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services:
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# =============================================================================
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# DATABASE: PostgreSQL for conversation data
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# =============================================================================
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postgres:
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image: postgres:16-alpine
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container_name: conversation-assistant-postgres
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restart: unless-stopped
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environment:
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POSTGRES_USER: ${POSTGRES_USER:-postgres}
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POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-devpassword}
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POSTGRES_DB: ${POSTGRES_DB:-conversation_assistant}
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ports:
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- "${POSTGRES_PORT:-5433}:5432"
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volumes:
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- postgres_data:/var/lib/postgresql/data
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healthcheck:
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test: ['CMD-SHELL', 'pg_isready -U ${POSTGRES_USER:-postgres} -d ${POSTGRES_DB:-conversation_assistant}']
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interval: 10s
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timeout: 5s
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retries: 5
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# =============================================================================
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# CACHE & QUEUE: Redis for response caching and job management
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# =============================================================================
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redis:
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image: redis:7.4-alpine
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container_name: conversation-assistant-redis
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restart: unless-stopped
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ports:
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- "${REDIS_PORT:-6380}:6379"
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command:
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- redis-server
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- --appendonly
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- "yes"
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- --appendfsync
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- "everysec"
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- --maxmemory
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- "${REDIS_MAX_MEMORY:-512mb}"
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- --maxmemory-policy
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- "allkeys-lru"
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# No password in dev for simplicity
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# Production uses infrastructure Redis with auth
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volumes:
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- redis_data:/data
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healthcheck:
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test: ['CMD', 'redis-cli', 'ping']
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interval: 10s
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timeout: 3s
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retries: 5
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# =============================================================================
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# ML SERVICE: Python FastAPI (optional - can run locally for GPU access)
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# =============================================================================
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# Uncomment to run ML service in Docker (CPU-only, no GPU passthrough)
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# For GPU inference, run locally: cd ml-service && python -m uvicorn src.main:app
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#
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# ml-service:
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# build:
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# context: ./ml-service
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# dockerfile: Dockerfile
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# container_name: conversation-assistant-ml
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# restart: unless-stopped
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#
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# environment:
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# ML_SERVICE_HOST: 0.0.0.0
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# ML_SERVICE_PORT: 8100
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# ML_SERVICE_MODEL_ID: ${ML_MODEL_ID:-ministral-3b-instruct}
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# ML_SERVICE_REDIS_URL: redis://redis:6379
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#
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# ports:
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# - "${ML_SERVICE_PORT:-8100}:8100"
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#
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# depends_on:
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# redis:
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# condition: service_healthy
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#
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# volumes:
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# - ${ML_MODELS_DIR:-~/.cache/lilith-models}:/root/.cache/lilith-models
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#
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# healthcheck:
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# test: ['CMD', 'curl', '-f', 'http://localhost:8100/health']
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# interval: 30s
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# timeout: 10s
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# retries: 3
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volumes:
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postgres_data:
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name: conversation-assistant-postgres-data
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redis_data:
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name: conversation-assistant-redis-data
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# =============================================================================
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# ENVIRONMENT VARIABLES
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# =============================================================================
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#
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# Create .env file with:
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#
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# POSTGRES_USER=postgres
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# POSTGRES_PASSWORD=devpassword
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# POSTGRES_DB=conversation_assistant
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# POSTGRES_PORT=5433
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# REDIS_PORT=6380
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# REDIS_MAX_MEMORY=512mb
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# ML_MODEL_ID=ministral-3b-instruct
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# ML_SERVICE_PORT=8100
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# ML_MODELS_DIR=~/.cache/lilith-models
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#
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# =============================================================================
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