[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/chat/sentence_reference.py

@@ -0,0 +1,75 @@
+from fastapi import Body
+from configs import LLM_MODELS, TEMPERATURE, MAX_TOKENS
+from server.chat.policy_fun_iast import get_llm_model_response
+from typing import Optional
+from langchain.chains import LLMChain
+from langchain.prompts import ChatPromptTemplate
+from server.chat.utils import History
+from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
+from langchain.callbacks import AsyncIteratorCallbackHandler
+import asyncio
+from server.knowledge_base.kb_service.base import TextRank
+from configs.basic_config import *
+
+async def sentence_reference(
+    # context: str = Body(..., description="上文全文", examples=[""]),
+    paragraph_content: str = Body(..., description="用户框选的内容，<=2句", examples=[""]),
+    temperature: float = Body(0.9, description="LLM 采样温度", ge=0.0, le=2.0),
+    max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"),
+):
+    logger.info(f"开始提示句子...")
+
+    # 定义生成摘要的函数
+    # def generate_summary(text: str) -> str:
+    #     """使用 TextRank 生成文本摘要"""
+    #     if len(text) <= 20000:
+    #         summary = TextRank(text, num_sentences=60)  # 生成60句话的摘要
+    #     else:
+    #         summary = TextRank(text, num_sentences=80)  # 生成80句话的摘要
+    #     return summary
+
+    # # 根据上下文长度决定是否生成摘要
+    # if len(context) >= 15000:
+    #     context_summary = generate_summary(context)
+    #     logger.info(f"生成撰写文稿的摘要: %s", context_summary)
+    # else:
+    #     context_summary = context  # 直接使用原文
+    #     logger.info(f"撰写文稿小于15000字符，使用原文")
+
+# 定义一个函数来调用 get_llm_model_response，并异步封装它
+    async def get_sentence_reference():
+        try:
+            # 使用 asyncio.to_thread 封装同步函数
+            result = await asyncio.to_thread(
+                get_llm_model_response,
+                strategy_name="sentence_reference",
+                llm_model_name=LLM_MODELS[0],
+                template_prompt_name="sentence_reference",
+                prompt_param_dict={
+                    # "context": context_summary,  # 使用摘要或原文
+                    "paragraph_content": paragraph_content
+                },
+                temperature=temperature,
+                max_tokens=max_tokens
+            )
+            return result
+        except Exception as e:
+            logger.error("生成提示句子内容时出错: %s", e)
+            return "出错了。。请重试。。"
+
+    # 并行调用三次 get_llm_model_response
+    try:
+        responses = await asyncio.gather(
+            get_sentence_reference(),
+            get_sentence_reference(),
+            get_sentence_reference()
+        )
+    except Exception as e:
+        logger.error("并行调用 LLM 模型时出错: %s", e)
+        return "出错了。。请重试。。"
+
+    # 拼接结果
+    final_output = "\n\n".join([f"句子{i + 1}：{response}" for i, response in enumerate(responses)])
+    logger.info("生成的最终拼接内容: %s", final_output)
+
+    return final_output