4601c6fca8
- Vision Encoder Service (OpenCLIP ViT-L/14, GPU-accelerated)
- FastAPI app with text/image embedding endpoints (768-dim)
- Docker support with NVIDIA GPU runtime
- Port 8001, health checks, model info API
- Qdrant Vector Database integration
- Port 6333/6334 (HTTP/gRPC)
- Image embeddings storage (768-dim, Cosine distance)
- Auto collection creation
- Vision RAG implementation
- VisionEncoderClient (Python client for API)
- Image Search module (text-to-image, image-to-image)
- Vision RAG routing in DAGI Router (mode: image_search)
- VisionEncoderProvider integration
- Documentation (5000+ lines)
- SYSTEM-INVENTORY.md - Complete system inventory
- VISION-ENCODER-STATUS.md - Service status
- VISION-RAG-IMPLEMENTATION.md - Implementation details
- vision_encoder_deployment_task.md - Deployment checklist
- services/vision-encoder/README.md - Deployment guide
- Updated WARP.md, INFRASTRUCTURE.md, Jupyter Notebook
- Testing
- test-vision-encoder.sh - Smoke tests (6 tests)
- Unit tests for client, image search, routing
- Services: 17 total (added Vision Encoder + Qdrant)
- AI Models: 3 (qwen3:8b, OpenCLIP ViT-L/14, BAAI/bge-m3)
- GPU Services: 2 (Vision Encoder, Ollama)
- VRAM Usage: ~10 GB (concurrent)
Status: Production Ready ✅
102 lines
3.2 KiB
Python
102 lines
3.2 KiB
Python
#!/usr/bin/env python3
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import asyncio
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import asyncio
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import sys
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from datetime import datetime
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async def setup_stream():
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"""
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Create STREAM_RAG with required subjects in NATS JetStream.
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"""
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try:
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print("Connecting to NATS...")
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nc = await nats.connect('nats://localhost:4222')
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print(f"NATS connection successful, creating STREAM_RAG stream")
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# Get JetStream context
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js = nc.jetstream()
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# Check if STREAM_RAG already exists
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try:
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stream_info = await js.stream_info("STREAM_RAG")
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print("STREAM_RAG already exists")
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print(f"Subjects: {stream_info.config.subjects}")
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except nats.js.errors.StreamNotFound:
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print("STREAM_RAG not found, creating it...")
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# Create or update STREAM_RAG with the required subjects
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try:
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await js.add_stream(
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name="STREAM_RAG",
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subjects=[
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"parser.document.parsed",
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"rag.document.ingested",
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"rag.document.indexed",
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"message.created"
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],
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retention=nats.RetentionPolicy.WORK_QUEUE,
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storage=nats.StorageType.FILE,
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replicas=3
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)
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print("STREAM_RAG created successfully with subjects:", ",
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stream_info.config.subjects)
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except Exception as e:
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print(f"Error creating STREAM_RAG: {e}")
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return nc
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except Exception as e:
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print(f"Error connecting to NATS: {e}")
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return None
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async def test_event_parsing():
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"""Test event publishing."""
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try:
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js = (await get_nats_connection())
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print("Testing event publishing...")
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# Test publishing a parser.document.parsed message
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payload = {
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"doc_id": "test_doc_123",
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"team_id": "dao_greenfood",
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"dao_id": "dao_greenfood",
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"doc_type": "pdf",
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"pages_count": 3,
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"parsed_successful": True,
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"indexed": True,
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"visibility": "public"
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}
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await js.publish("parser.document.parsed", json.dumps(payload))
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print("Published parser.document.parsed event successfully")
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except Exception as e:
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print(f"Error publishing event: {e}")
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return False
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async def is_nats_available():
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"""Check if NATS is available."""
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return NATS_AVAILABLE
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async def publish_event(subject: str, payload: Dict[str, Any], team_id: str, trace_id: str = None, span_id: str = None) -> bool:
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"""Publish an event to NATS JetStream."""
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if not NATS_AVAILABLE:
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print("NATS is not available. Skipping NATS events...")
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return False
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try:
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nc = await get_nats_connection()
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if nc is_nats_available:
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js = nc.jetstream()
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# Publish the event
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await js.publish(subject, json.dumps(payload))
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return True
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except Exception as e:
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print(f"Error publishing event: {e}")
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return False
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except Exception as e:
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print(f"Error connecting to NATS: {e}")
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return False
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if __name__ == "__main__":
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asyncio.run(setup_stream()) |