87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
"""Writer Agent — synthesizes multilingual research tracks into a cohesive report."""
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from __future__ import annotations
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import json
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from typing import Any
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from .base import BaseAgent
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from app.config import settings
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class WriterAgent(BaseAgent):
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name = "writer"
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description = "汇聚多语言/多领域研究成果，撰写完整报告"
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system_prompt = """\
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You are an expert consulting report writer. Your task is to synthesize research
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findings from MULTIPLE parallel tracks (some in English, some in Chinese) into
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ONE cohesive, professional consulting report.
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CRITICAL RULES:
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1. The PRIMARY output language is Chinese (中文) — this is for Chinese clients
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2. For global/international sections, the analysis depth must reflect the English research
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3. For China-specific sections, preserve the precision of Chinese-native research
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4. Maintain professional consulting tone throughout
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5. Every claim should trace back to a research track's findings
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6. Mark chart/table needs: {{CHART:描述}} and {{TABLE:描述}}
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7. If a research track flags "data_gaps", acknowledge uncertainty rather than fabricating
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Output (JSON):
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{
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"title": "报告标题（中文）",
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"title_en": "Report Title (English)",
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"chapters": [
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{
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"title": "章节标题",
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"content": "章节正文（Markdown 格式，中文）",
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"source_tracks": ["引用的研究轨道名称"],
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"charts": ["图表需求"],
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"tables": ["表格需求"]
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}
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],
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"executive_summary": "执行摘要（中文，300-500字）",
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"executive_summary_en": "Executive Summary (English, 200-400 words)"
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}"""
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def __init__(self):
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super().__init__(model=settings.model_for_domain("reasoning"))
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async def run(self, context: dict[str, Any]) -> dict[str, Any]:
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research = context["research"]
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requirement = context["requirement"]
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revision_feedback = context.get("revision_feedback", "")
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# Format multi-track, multilingual research
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tracks_text = ""
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for track in research.get("tracks", []):
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lang_tag = f"[{track.get('native_language', '?').upper()}]"
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domain_tag = f"[{track.get('domain', '?')}]"
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tracks_text += f"\n### {domain_tag} {lang_tag} {track.get('track', '')}\n"
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findings = track.get("findings", {})
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tracks_text += json.dumps(findings, ensure_ascii=False, indent=2)
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synthesis_guide = research.get("synthesis_guide", "")
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prompt = f"""\
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## 原始需求 / Original Requirement
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{requirement}
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## 报告标题
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中文：{research.get("title_zh", "")}
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English: {research.get("title_en", "")}
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## 写作指导 / Synthesis Guide
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{synthesis_guide}
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## 各研究轨道成果 / Research Track Results
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（注意：有些轨道是英文原版 [EN]，有些是中文原版 [ZH]，请综合使用）
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{tracks_text}
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{f"## 审稿反馈 / Review Feedback{revision_feedback}" if revision_feedback else ""}
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请汇聚以上研究成果，撰写完整的中文报告。输出 JSON。"""
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result = await self.call_llm_json(prompt, max_tokens=8192)
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return {"draft": result}
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