110 lines
No EOL
3.8 KiB
Python
110 lines
No EOL
3.8 KiB
Python
from fastapi import APIRouter, HTTPException
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from app.services.openai_client import OpenAIClient, AIFactChecker
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from app.config import OPENAI_API_KEY
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from app.models.fact_check_models import (
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AIFactCheckRequest,
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AIFactCheckResponse,
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VerificationResult,
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TokenUsage,
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ErrorResponse
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)
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from urllib.parse import urlparse
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import asyncio
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# Initialize router and OpenAI client
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aifact_check_router = APIRouter()
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openai_client = OpenAIClient(api_key=OPENAI_API_KEY)
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fact_checker = AIFactChecker(openai_client=openai_client)
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@aifact_check_router.post(
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"/aicheck-facts",
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response_model=AIFactCheckResponse,
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responses={
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400: {"model": ErrorResponse},
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500: {"model": ErrorResponse}
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}
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)
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async def ai_fact_check(request: AIFactCheckRequest):
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"""
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Endpoint to fact-check a given statement based on multiple webpage URLs.
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Input:
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- urls: List of webpage URLs to analyze (with or without http/https)
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- content: The fact statement to verify
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Response:
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- JSON response with verification results per URL, sources, and token usage
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"""
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try:
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results = {}
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all_sources = set()
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all_contexts = []
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total_prompt_tokens = 0
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total_completion_tokens = 0
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total_tokens = 0
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# Process all URLs concurrently
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tasks = [
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fact_checker.check_fact(url=url, query=request.content)
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for url in request.urls
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]
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fact_check_results = await asyncio.gather(*tasks, return_exceptions=True)
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# Process results
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for url, result in zip(request.urls, fact_check_results):
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if isinstance(result, Exception):
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# Handle failed URL checks
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results[url] = VerificationResult(
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verdict="Error",
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confidence="Low",
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evidence=f"Error checking URL: {str(result)}",
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reasoning="URL processing failed",
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missing_info="Could not access or process the URL"
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)
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continue
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verification_result = VerificationResult(
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verdict=result["verification_result"]["verdict"],
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confidence=result["verification_result"]["confidence"],
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evidence=result["verification_result"]["evidence"],
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reasoning=result["verification_result"]["reasoning"],
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missing_info=result["verification_result"].get("missing_info", None)
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)
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results[url] = verification_result
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all_sources.update(result["sources"])
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# Accumulate token usage
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total_prompt_tokens += result["token_usage"]["prompt_tokens"]
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total_completion_tokens += result["token_usage"]["completion_tokens"]
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total_tokens += result["token_usage"]["total_tokens"]
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token_usage = TokenUsage(
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prompt_tokens=total_prompt_tokens,
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completion_tokens=total_completion_tokens,
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total_tokens=total_tokens
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)
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return AIFactCheckResponse(
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query=request.content,
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verification_result=results,
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sources=list(all_sources),
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token_usage=token_usage
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)
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except ValueError as e:
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raise HTTPException(
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status_code=400,
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detail=ErrorResponse(
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detail=str(e),
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error_code="INVALID_URL",
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path="/aicheck-facts"
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).dict()
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)
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=ErrorResponse(
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detail=f"Error processing fact-check request: {str(e)}",
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error_code="PROCESSING_ERROR",
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path="/aicheck-facts"
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).dict()
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) |