platform-deployments/services/features/ml.yaml
Quinn Ftw e0c2edd9ef chore(features): 🔧 Update YAML configuration files in the features directory
Co-Authored-By: Lilith Autocommit <noreply@atlilith.com>
2026-02-12 21:55:43 -08:00

104 lines
3 KiB
YAML

# =============================================================================
# ML Infrastructure Services
# =============================================================================
# Machine learning infrastructure services
feature:
id: ml
name: ML Infrastructure
description: Machine learning services - image processing, generation, conversation
owner: ml-team
ports:
media-privacy: 8000
image-generation: 8002
conversation-ml: 8100
cot-reasoning: 8110
rag-retrieval: 8111
services:
- id: media-privacy
name: Media Privacy
type: ml
port: 8000
entrypoint: "~/Code/@packages/@ml/media-privacy"
description: Image and video privacy processing
gpu: true
healthCheck:
type: http
path: /health
dependencies:
- infrastructure.redis
- id: image-generation
name: Image Generation
type: ml
port: 8002
entrypoint: "~/Code/@applications/@image/image-generation/service"
startCommand: "source .venv/bin/activate && python -m uvicorn src.api.main:app --host 0.0.0.0 --port 8002"
description: AI image generation (SDXL via lilith_image_pipeline)
gpu: true
healthCheck:
type: http
path: /health
dependencies:
- infrastructure.redis
- id: conversation-ml
name: Conversation ML
type: ml
port: 8100
entrypoint: codebase/features/conversation-assistant/ml-service
startCommand: "source .venv/bin/activate && GPU_SERVICE_NAME=conversation-assistant-ml python -m uvicorn src.main:app --host 0.0.0.0 --port 8100"
description: Conversation assistant ML backend
gpu: true
healthCheck:
type: http
path: /health
dependencies:
- infrastructure.postgresql
- conversation-assistant.redis
- id: cot-reasoning
name: Chain-of-Thought Reasoning
type: ml
port: 8110
entrypoint: ~/Code/@applications/@ml/cot-reasoning
startCommand: "source .venv/bin/activate && python -m uvicorn service.src.api.main:app --host 0.0.0.0 --port ${PORT:-8110}"
description: Generic reasoning service with cultural_origin, maturity, power_dynamics stages
gpu: true
multiInstance: true
env:
COT_LLM__BACKEND: model-boss
COT_LLM__MODEL_ID: qwen2.5-1.5b-instruct
healthCheck:
type: http
path: /health
- id: rag-retrieval
name: RAG Document Retrieval
type: ml
port: 8111
entrypoint: ~/Code/@applications/@ml/rag-retrieval
startCommand: "source .venv/bin/activate && python -m uvicorn service.src.api.main:create_app --factory --host 0.0.0.0 --port ${PORT:-8111}"
description: Vector-based document retrieval for RAG augmentation
gpu: false
multiInstance: true
env:
# Requires Redis Stack with RediSearch module
RAG_REDIS__HOST: localhost
RAG_REDIS__PORT: "6384"
healthCheck:
type: http
path: /health
dependencies:
- knowledge-verification.redis
deployments:
dev:
host: apricot
autostart: false
staging:
host: black
production:
host: vps-0