mirror of
https://github.com/run-llama/multi-agents-workflow.git
synced 2026-07-01 21:24:00 -04:00
Marcus Schiesser
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
import asyncio
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import logging
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from fastapi import APIRouter, HTTPException, Request, status
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from llama_index.core.workflow import Workflow
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from app.examples.factory import create_agent
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from app.api.routers.models import (
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ChatData,
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)
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from app.api.routers.vercel_response import VercelStreamResponse
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chat_router = r = APIRouter()
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logger = logging.getLogger("uvicorn")
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# streaming endpoint - delete if not needed
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@r.post("")
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async def chat(
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request: Request,
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data: ChatData,
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):
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try:
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last_message_content = data.get_last_message_content()
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# TODO: use message history
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# messages = data.get_history_messages()
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# TODO: generate filters based on doc_ids
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# for now just use all documents
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# doc_ids = data.get_chat_document_ids()
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# TODO: use params
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# params = data.data or {}
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agent: Workflow = create_agent()
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task = asyncio.create_task(agent.run(input=last_message_content))
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return VercelStreamResponse(request, task, agent.stream_events, data)
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except Exception as e:
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logger.exception("Error in agent", exc_info=True)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Error in agent: {e}",
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) from e
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