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 ✅
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
# Template for adding new agent to http_api.py
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# {AGENT_NAME} Configuration
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{AGENT_NAME}_TELEGRAM_BOT_TOKEN = os.getenv("{AGENT_NAME}_TELEGRAM_BOT_TOKEN", "")
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{AGENT_NAME}_NAME = os.getenv("{AGENT_NAME}_NAME", "{agent_display_name}")
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{AGENT_NAME}_PROMPT_PATH = os.getenv("{AGENT_NAME}_PROMPT_PATH", "gateway-bot/{prompt_file}")
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def load_{agent_id}_prompt() -> str:
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try:
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with open({AGENT_NAME}_PROMPT_PATH, "r", encoding="utf-8") as f:
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return f.read()
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except Exception as e:
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logger.error(f"Failed to load {agent_display_name} prompt: {e}")
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return "{agent_display_name} system prompt."
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{AGENT_NAME}_SYSTEM_PROMPT = load_{agent_id}_prompt()
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@app.post("/{agent_id}/telegram/webhook")
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async def {agent_id}_telegram_webhook(update: TelegramUpdate):
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"""Webhook for {agent_display_name} Telegram bot"""
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chat_id = None
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try:
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if not update.message:
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raise HTTPException(status_code=400, detail="No message in update")
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chat_id = update.message.chat.id
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user_id = f"tg:{update.message.from_user.id}"
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text = update.message.text or ""
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# Fetch memory context
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memory_context = ""
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try:
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mem_resp = httpx.get(
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f"{MEMORY_SERVICE_URL}/memory/{user_id}",
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timeout=5.0
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)
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if mem_resp.status_code == 200:
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memory_data = mem_resp.json()
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memory_context = memory_data.get("context", "")
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except Exception as e:
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logger.warning(f"Memory fetch failed: {e}")
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# Prepare router request
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router_payload = {
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"mode": "chat",
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"message": text,
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"agent": "{agent_id}",
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"metadata": {
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"platform": "telegram",
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"chat_id": chat_id,
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"user_id": user_id
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},
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"payload": {
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"context": {
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"memory": memory_context,
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"system_prompt": {AGENT_NAME}_SYSTEM_PROMPT
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}
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}
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}
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# Call router
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router_resp = httpx.post(
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f"{ROUTER_URL}/route",
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json=router_payload,
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timeout=60.0
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)
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router_resp.raise_for_status()
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result = router_resp.json()
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answer = result.get("answer", "No response")
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# Save to memory
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try:
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httpx.post(
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f"{MEMORY_SERVICE_URL}/memory/{user_id}",
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json={"message": text, "response": answer},
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timeout=5.0
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)
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except Exception as e:
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logger.warning(f"Memory save failed: {e}")
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# Send response
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send_telegram_message({AGENT_NAME}_TELEGRAM_BOT_TOKEN, chat_id, answer)
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return {"status": "ok"}
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except Exception as e:
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logger.error(f"Error in {agent_id} webhook: {e}", exc_info=True)
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if chat_id:
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send_telegram_message(
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{AGENT_NAME}_TELEGRAM_BOT_TOKEN,
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chat_id,
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f"Помилка: {str(e)}"
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)
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raise HTTPException(status_code=500, detail=str(e))
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