gbanyan
534fdbbcc4
- Backend: Add expert transformation router with 3-step SSE pipeline - Step 0: Generate diverse expert team (random domains) - Step 1: Each expert generates keywords for attributes - Step 2: Batch generate descriptions for expert keywords - Backend: Add simplified prompts for reliable JSON output - Frontend: Add TransformationPanel with React Flow visualization - Frontend: Add TransformationInputPanel for expert configuration - Expert count (2-8), keywords per expert (1-3) - Custom expert domains support - Frontend: Add expert keyword nodes with expert badges - Frontend: Improve description card layout (wider cards, more spacing) - Frontend: Add fallback for missing descriptions with visual indicators 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
44 lines
967 B
Python
44 lines
967 B
Python
from contextlib import asynccontextmanager
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from .routers import attributes, transformation, expert_transformation
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from .services.llm_service import ollama_provider
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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yield
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await ollama_provider.close()
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app = FastAPI(
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title="Attribute Agent API",
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description="API for analyzing objects and extracting their attributes",
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version="1.0.0",
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lifespan=lifespan,
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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app.include_router(attributes.router)
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app.include_router(transformation.router)
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app.include_router(expert_transformation.router)
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@app.get("/")
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async def root():
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return {"message": "Attribute Agent API is running"}
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@app.get("/health")
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async def health_check():
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return {"status": "healthy"}
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