chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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from __future__ import annotations
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import asyncio
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import json
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import time
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from collections.abc import Sequence
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from datetime import datetime, timezone
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from pydantic import BaseModel
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from rich.console import Console
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from agents import Runner, RunResult, RunResultStreaming, custom_span, gen_trace_id, trace
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from examples.web_search_utils import extract_url_citations, extract_web_search_source_urls
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from .agents.financials_agent import financials_agent
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from .agents.planner_agent import FinancialSearchItem, FinancialSearchPlan, planner_agent
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from .agents.risk_agent import risk_agent
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from .agents.search_agent import FinancialSearchSummary, search_agent
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from .agents.verifier_agent import VerificationResult, verifier_agent
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from .agents.writer_agent import REVISION_PROMPT, FinancialReportData, writer_agent
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from .printer import Printer
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class FinancialSource(BaseModel):
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title: str
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url: str
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class FinancialSearchEvidence(BaseModel):
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query: str
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reason: str
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summary: str
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sources: list[FinancialSource]
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retrieved_at: str
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def _extract_financial_sources(items: Sequence[object]) -> list[FinancialSource]:
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sources: list[FinancialSource] = []
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seen: set[str] = set()
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for citation in extract_url_citations(items):
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if citation.url in seen:
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continue
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seen.add(citation.url)
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sources.append(FinancialSource(title=citation.title, url=citation.url))
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for url in extract_web_search_source_urls(items):
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if url in seen:
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continue
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seen.add(url)
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sources.append(FinancialSource(title=url, url=url))
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return sources
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async def _summary_extractor(run_result: RunResult | RunResultStreaming) -> str:
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"""Custom output extractor for sub‑agents that return an AnalysisSummary."""
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# The financial/risk analyst agents emit an AnalysisSummary with a `summary` field.
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# We want the tool call to return just that summary text so the writer can drop it inline.
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return str(run_result.final_output.summary)
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class FinancialResearchManager:
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"""
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Orchestrates the full flow: planning, searching, sub‑analysis, writing, and verification.
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"""
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def __init__(self) -> None:
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self.console = Console()
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self.printer = Printer(self.console)
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self.research_cutoff = datetime.now(timezone.utc).date().isoformat()
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async def run(self, query: str) -> None:
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trace_id = gen_trace_id()
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try:
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with trace("Financial research trace", trace_id=trace_id):
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self.printer.update_item(
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"trace_id",
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f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}",
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is_done=True,
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hide_checkmark=True,
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)
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self.printer.update_item("start", "Starting financial research...", is_done=True)
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search_plan = await self._plan_searches(query)
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search_results = await self._perform_searches(search_plan)
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report, verification = await self._produce_verified_report(query, search_results)
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final_report = f"Report summary\n\n{report.short_summary}"
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self.printer.update_item("final_report", final_report, is_done=True)
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finally:
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self.printer.end()
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# Print to stdout
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print("\n\n=====REPORT=====\n\n")
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print(f"Report:\n{report.markdown_report}")
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print("\n\n=====FOLLOW UP QUESTIONS=====\n\n")
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print("\n".join(report.follow_up_questions))
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print("\n\n=====VERIFICATION=====\n\n")
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print(verification)
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async def _produce_verified_report(
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self,
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query: str,
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search_results: Sequence[FinancialSearchEvidence],
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) -> tuple[FinancialReportData, VerificationResult]:
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report = await self._write_report(query, search_results)
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verification = await self._verify_report(query, report, search_results)
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if verification.verified:
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return report, verification
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report = await self._revise_report(query, report, search_results, verification)
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verification = await self._verify_report(query, report, search_results)
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if not verification.verified:
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raise RuntimeError(
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"Financial report failed evidence verification after one revision: "
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f"{verification.model_dump_json()}"
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)
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return report, verification
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async def _plan_searches(self, query: str) -> FinancialSearchPlan:
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self.printer.update_item("planning", "Planning searches...")
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result = await Runner.run(planner_agent, f"Query: {query}")
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self.printer.update_item(
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"planning",
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f"Will perform {len(result.final_output.searches)} searches",
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is_done=True,
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)
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return result.final_output_as(FinancialSearchPlan)
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async def _perform_searches(
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self, search_plan: FinancialSearchPlan
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) -> Sequence[FinancialSearchEvidence]:
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with custom_span("Search the web"):
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self.printer.update_item("searching", "Searching...")
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tasks = [asyncio.create_task(self._search(item)) for item in search_plan.searches]
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results: list[FinancialSearchEvidence] = []
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num_completed = 0
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num_succeeded = 0
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num_failed = 0
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for task in asyncio.as_completed(tasks):
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result = await task
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if result is not None:
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results.append(result)
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num_succeeded += 1
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else:
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num_failed += 1
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num_completed += 1
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status = f"Searching... {num_completed}/{len(tasks)} finished"
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if num_failed:
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status += f" ({num_succeeded} succeeded, {num_failed} failed)"
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self.printer.update_item(
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"searching",
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status,
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)
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summary = f"Searches finished: {num_succeeded}/{len(tasks)} succeeded"
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if num_failed:
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summary += f", {num_failed} failed"
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self.printer.update_item("searching", summary, is_done=True)
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return results
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async def _search(self, item: FinancialSearchItem) -> FinancialSearchEvidence | None:
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input_data = f"Search term: {item.query}\nReason: {item.reason}"
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try:
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result = await Runner.run(search_agent, input_data)
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search_summary = result.final_output_as(FinancialSearchSummary)
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sources = _extract_financial_sources(result.new_items)
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if not sources:
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return None
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return FinancialSearchEvidence(
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query=item.query,
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reason=item.reason,
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summary=search_summary.summary,
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sources=sources,
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retrieved_at=self.research_cutoff,
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)
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except Exception:
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return None
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async def _write_report(
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self,
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query: str,
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search_results: Sequence[FinancialSearchEvidence],
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) -> FinancialReportData:
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# Expose the specialist analysts as tools so the writer can invoke them inline
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# and still produce the final FinancialReportData output.
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fundamentals_tool = financials_agent.as_tool(
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tool_name="fundamentals_analysis",
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tool_description="Use to get a short write‑up of key financial metrics",
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custom_output_extractor=_summary_extractor,
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)
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risk_tool = risk_agent.as_tool(
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tool_name="risk_analysis",
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tool_description="Use to get a short write‑up of potential red flags",
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custom_output_extractor=_summary_extractor,
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)
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writer_with_tools = writer_agent.clone(tools=[fundamentals_tool, risk_tool])
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self.printer.update_item("writing", "Thinking about report...")
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input_data = self._report_input(query, search_results)
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result = Runner.run_streamed(writer_with_tools, input_data)
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update_messages = [
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"Planning report structure...",
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"Writing sections...",
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"Finalizing report...",
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]
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last_update = time.time()
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next_message = 0
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async for _ in result.stream_events():
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if time.time() - last_update > 5 and next_message < len(update_messages):
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self.printer.update_item("writing", update_messages[next_message])
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next_message += 1
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last_update = time.time()
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self.printer.mark_item_done("writing")
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return result.final_output_as(FinancialReportData)
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async def _revise_report(
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self,
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query: str,
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report: FinancialReportData,
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search_results: Sequence[FinancialSearchEvidence],
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verification: VerificationResult,
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) -> FinancialReportData:
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self.printer.update_item("revising", "Revising report from verification feedback...")
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revision_agent = writer_agent.clone(instructions=REVISION_PROMPT)
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input_data = (
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f"{self._report_input(query, search_results)}\n"
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f"Existing report:\n{report.model_dump_json()}\n"
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f"Verification feedback:\n{verification.model_dump_json()}"
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)
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result = await Runner.run(revision_agent, input_data)
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self.printer.mark_item_done("revising")
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return result.final_output_as(FinancialReportData)
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async def _verify_report(
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self,
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query: str,
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report: FinancialReportData,
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search_results: Sequence[FinancialSearchEvidence],
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) -> VerificationResult:
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self.printer.update_item("verifying", "Verifying report...")
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input_data = json.dumps(
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{
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"original_query": query,
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"research_cutoff": self.research_cutoff,
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"report": report.model_dump(mode="json"),
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"evidence": [item.model_dump(mode="json") for item in search_results],
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},
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ensure_ascii=False,
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)
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result = await Runner.run(verifier_agent, input_data)
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self.printer.mark_item_done("verifying")
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return result.final_output_as(VerificationResult)
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def _report_input(
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self,
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query: str,
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search_results: Sequence[FinancialSearchEvidence],
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) -> str:
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return json.dumps(
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{
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"original_query": query,
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"research_cutoff": self.research_cutoff,
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"evidence": [item.model_dump(mode="json") for item in search_results],
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},
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ensure_ascii=False,
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)
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