86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
import json
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import logging
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from dataclasses import dataclass
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from typing import Annotated, Any
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from fastapi import APIRouter, Header
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from pydantic_ai import Agent, RunContext
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.azure import AzureProvider
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from pydantic_ai.ui.vercel_ai import VercelAIAdapter
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from starlette.requests import Request
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from starlette.responses import Response
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from app.agents import analyst, form_auditor, web_search
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from app.core.config import settings
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from app.services.extracted_data_service import get_extracted_data_service
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provider = AzureProvider(
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azure_endpoint=settings.AZURE_OPENAI_ENDPOINT,
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api_version=settings.AZURE_OPENAI_API_VERSION,
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api_key=settings.AZURE_OPENAI_API_KEY,
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)
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model = OpenAIChatModel(model_name="gpt-4o", provider=provider)
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@dataclass
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class Deps:
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extracted_data: list[dict[str, Any]]
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agent = Agent(model=model, deps_type=Deps)
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router = APIRouter(prefix="/api/v1/agent", tags=["Agent"])
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logger = logging.getLogger(__name__)
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@agent.tool
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async def build_audit_report(ctx: RunContext[Deps]):
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"""Calls the audit subagent to get a full audit report of the organization"""
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data = ctx.deps.extracted_data[0]
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result = await form_auditor.build_audit_report(data)
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return result.model_dump()
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@agent.tool
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async def build_analysis_report(ctx: RunContext[Deps]):
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"""Calls the analyst subagent to get a full report of the organization's performance across years"""
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data = ctx.deps.extracted_data
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if not data:
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raise ValueError("No extracted data available for analysis.")
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if len(data) == 1:
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logger.info(
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"build_analysis_report called with single-year data; report will still be generated but trends may be limited."
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)
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result = await analyst.build_performance_report(data)
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return result.model_dump()
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@agent.tool_plain
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async def search_web_information(query: str, max_results: int = 5):
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"""Search the web for up-to-date information using Tavily. Use this when you need current information, news, research, or facts not in your knowledge base."""
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result = await web_search.search_web(query=query, max_results=max_results)
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return result.model_dump()
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@router.post("/chat")
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async def chat(request: Request, tema: Annotated[str, Header()]) -> Response:
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extracted_data_service = get_extracted_data_service()
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data = await extracted_data_service.get_by_tema(tema)
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extracted_data = [doc.get_extracted_data() for doc in data]
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logger.info(f"Extracted data amount: {len(extracted_data)}")
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deps = Deps(extracted_data=extracted_data)
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return await VercelAIAdapter.dispatch_request(request, agent=agent, deps=deps)
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