61 lines
2.3 KiB
Python
61 lines
2.3 KiB
Python
from fastapi import Body
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from configs import LLM_MODELS, TEMPERATURE, MAX_TOKENS
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from server.chat.policy_fun_iast import get_llm_model_response
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from typing import Optional
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from langchain.chains import LLMChain
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from langchain.prompts import ChatPromptTemplate
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from server.chat.utils import History
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from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
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from langchain.callbacks import AsyncIteratorCallbackHandler
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import asyncio
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from server.knowledge_base.kb_service.base import TextRank
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from configs.basic_config import *
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async def gen_paragraph(
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data: dict = Body(..., description="包含文件全文内容的JSON对象", example={"context": "文件的全文内容"})
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):
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"""
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接收文件的全文内容生成章节速览。
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"""
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context: Optional[str] = data.get("context")
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if not context:
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return {"error": "缺少必要参数 'context'"}
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logger.info(f"开始生成章节速览...")
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# 定义生成摘要的函数
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def generate_summary(text: str) -> str:
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"""使用 TextRank 生成文本摘要"""
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summary = TextRank(text, num_sentences=1) # 生成80句话的摘要
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return summary
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# 根据正文长度决定是否生成摘要
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# if len(context) >= 30000:
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# context_summary = generate_summary(context)
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# logger.info(f"生成撰写文稿的摘要: %s", context_summary)
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# else:
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# context_summary = context # 直接使用原文
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# logger.info(f"撰写文稿小于30000字符，使用原文")
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if len(context) >= 40000:
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context_summary = context[:40000]
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else:
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context_summary = context
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# 调用模型生成生成摘要内容
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try:
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article_paragraph = get_llm_model_response(
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strategy_name="gen_paragraph",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="gen_paragraph",
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prompt_param_dict={
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"context": context_summary, # 使用摘要或原文
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},
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temperature=TEMPERATURE,
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max_tokens=MAX_TOKENS
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)
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except Exception as e:
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logger.error("生成章节速览时出错: %s", e)
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return {"error": "生成章节速览时发生错误，请重试"}
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logger.info("生成章节速览内容: %s", article_paragraph)
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return article_paragraph
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