feat(fabric): decommission Swapper from critical path, NCS = source of truth
- Node Worker: replace swapper_vision with ollama_vision (direct Ollama API) - Node Worker: add NATS subjects for stt/tts/image (stubs ready) - Node Worker: remove SWAPPER_URL dependency from config - Router: vision calls go directly to Ollama /api/generate with images - Router: local LLM calls go directly to Ollama /api/generate - Router: add OLLAMA_URL and PREFER_NODE_WORKER=true feature flag - Router: /v1/models now uses NCS global capabilities pool - NCS: SWAPPER_URL="" -> skip Swapper probing (status=disabled) - Swapper configs: remove all hardcoded model lists, keep only runtime URLs, timeouts, limits - docker-compose.node1.yml: add OLLAMA_URL, PREFER_NODE_WORKER for router; SWAPPER_URL= for NCS; remove swapper-service from node-worker depends_on - docker-compose.node2-sofiia.yml: same changes for NODA2 Swapper service still runs but is NOT in the critical inference path. Source of truth for models is now NCS -> Ollama /api/tags. Made-with: Cursor
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Apple
@@ -1,186 +1,37 @@
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# Swapper Configuration for Node #1 (Production Server)
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# Optimized Multimodal Stack: LLM + Vision + OCR + Document + Audio
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# Hetzner GEX44 - NVIDIA RTX 4000 SFF Ada (20GB VRAM)
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#
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# ВАЖЛИВО: Ембедінги через зовнішні API:
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# - Text: Cohere API (embed-multilingual-v3.0, 1024 dim)
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# - Image: Vision Encoder (OpenCLIP ViT-L/14, 768 dim)
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# НЕ використовуємо локальні embedding моделі!
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# NOTE: Swapper is now a runtime gateway / executor only.
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# Source of truth for models is NCS (Node Capabilities Service).
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# No hardcoded model lists — Swapper queries NCS or Ollama /api/tags at startup.
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swapper:
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mode: multi-active
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max_concurrent_models: 4 # LLM + OCR + STT + TTS (до 15GB)
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node_id: noda1
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runtimes:
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ollama:
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url: http://172.18.0.1:11434
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timeout: 300
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# comfyui:
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# url: http://127.0.0.1:8188
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limits:
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llm_concurrency: 2
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vision_concurrency: 1
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max_concurrent_models: 4
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model_swap_timeout: 300
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gpu_enabled: true
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timeouts:
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llm_ms: 120000
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vision_ms: 180000
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stt_ms: 60000
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tts_ms: 60000
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image_gen_ms: 300000
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gpu:
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enabled: true
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metal_acceleration: false
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default_model: qwen3-8b
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lazy_load_ocr: true
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lazy_load_audio: true
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# Автоматичне вивантаження при нестачі VRAM
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auto_unload_on_oom: true
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vram_threshold_gb: 18 # Починати вивантажувати при 18GB
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models:
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# ============================================
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# LLM MODELS (Ollama) - тільки qwen3
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# ============================================
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# Primary LLM - Qwen3 8B (includes math, coding, reasoning)
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qwen3-8b:
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path: ollama:qwen3:8b
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type: llm
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size_gb: 5.2
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priority: high
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description: "Qwen3 8B - primary LLM with math, coding, reasoning capabilities"
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capabilities:
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- chat
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- math
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- coding
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- reasoning
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- multilingual
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# ============================================
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# VISION MODELS (Ollama)
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# ============================================
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# Vision Model - Qwen3-VL 8B
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qwen3-vl-8b:
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path: ollama:qwen3-vl:8b
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type: vision
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size_gb: 6.1
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priority: high
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description: "Qwen3-VL 8B for image understanding and visual reasoning"
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capabilities:
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- image_understanding
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- visual_qa
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- diagram_analysis
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- ocr_basic
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# ============================================
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# OCR/DOCUMENT MODELS (HuggingFace)
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# ============================================
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# GOT-OCR2.0 - Best for documents, tables, formulas
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got-ocr2:
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path: huggingface:stepfun-ai/GOT-OCR2_0
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type: ocr
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size_gb: 7.0
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priority: high
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description: "Best OCR for documents, tables, formulas, handwriting"
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capabilities:
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- documents
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- tables
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- formulas
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- handwriting
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- multilingual
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# Donut - Document Understanding (no external OCR, 91% CORD)
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donut-base:
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path: huggingface:naver-clova-ix/donut-base
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type: ocr
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size_gb: 3.0
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priority: high
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description: "Document parsing without OCR engine (91% CORD accuracy)"
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capabilities:
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- document_parsing
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- receipts
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- forms
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- invoices
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# Donut fine-tuned for receipts/invoices (CORD dataset)
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donut-cord:
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path: huggingface:naver-clova-ix/donut-base-finetuned-cord-v2
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type: ocr
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size_gb: 3.0
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priority: medium
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description: "Donut fine-tuned for receipts extraction"
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capabilities:
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- receipts
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- invoices
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- structured_extraction
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# IBM Granite Docling - Document conversion with structure preservation
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granite-docling:
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path: huggingface:ds4sd/docling-ibm-granite-vision-1b
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type: document
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size_gb: 2.5
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priority: high
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description: "IBM Granite Docling for PDF/document structure extraction"
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capabilities:
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- pdf_conversion
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- table_extraction
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- formula_extraction
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- layout_preservation
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- doctags_format
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# ============================================
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# AUDIO MODELS - STT (Speech-to-Text)
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# ============================================
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# Faster Whisper Large-v3 - Best STT quality
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faster-whisper-large:
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path: huggingface:Systran/faster-whisper-large-v3
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type: stt
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size_gb: 3.0
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priority: high
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description: "Faster Whisper Large-v3 - best quality, 99 languages"
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capabilities:
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- speech_recognition
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- transcription
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- multilingual
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- timestamps
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- ukrainian
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# Whisper Small - Fast/lightweight for quick transcription
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whisper-small:
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path: huggingface:openai/whisper-small
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type: stt
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size_gb: 0.5
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priority: medium
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description: "Whisper Small for fast transcription"
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capabilities:
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- speech_recognition
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- transcription
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# ============================================
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# AUDIO MODELS - TTS (Text-to-Speech)
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# ============================================
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# Coqui XTTS-v2 - Best multilingual TTS with Ukrainian support
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xtts-v2:
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path: huggingface:coqui/XTTS-v2
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type: tts
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size_gb: 2.0
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priority: high
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description: "XTTS-v2 multilingual TTS with voice cloning, Ukrainian support"
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capabilities:
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- text_to_speech
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- voice_cloning
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- multilingual
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- ukrainian
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- 17_languages
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# ============================================
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# IMAGE GENERATION MODELS (HuggingFace/Diffusers)
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# ============================================
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# FLUX.2 Klein 4B - High quality image generation with lazy loading
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flux-klein-4b:
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path: huggingface:black-forest-labs/FLUX.2-klein-base-4B
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type: image_generation
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size_gb: 15.4
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priority: medium
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description: "FLUX.2 Klein 4B - high quality image generation, lazy loaded on demand"
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capabilities:
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- text_to_image
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- high_quality
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- 1024x1024
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- artistic
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default_params:
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num_inference_steps: 50
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guidance_scale: 4.0
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width: 1024
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height: 1024
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vram_threshold_gb: 18
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storage:
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models_dir: /app/models
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@@ -188,33 +39,8 @@ storage:
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swap_dir: /app/swap
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huggingface_cache: /root/.cache/huggingface
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ollama:
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url: http://172.18.0.1:11434
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timeout: 300
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huggingface:
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device: cuda
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torch_dtype: float16
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trust_remote_code: true
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low_cpu_mem_usage: true
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# ============================================
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# EMBEDDING SERVICES (External APIs)
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# НЕ через Swapper - окремі сервіси!
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# ============================================
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#
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# Text Embeddings:
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# Service: Memory Service → Cohere API
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# Model: embed-multilingual-v3.0
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# Dimension: 1024
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# Endpoint: Memory Service handles internally
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#
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# Image/Multimodal Embeddings:
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# Service: Vision Encoder (port 8001)
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# Model: OpenCLIP ViT-L/14
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# Dimension: 768
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# Endpoint: http://vision-encoder:8001/embed
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#
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# Vector Storage:
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# Qdrant (port 6333) - separate collections for text vs image embeddings
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# ВАЖЛИВО: НЕ змішувати embedding spaces в одній колекції!
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