[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/chat/report_chat.py

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+from fastapi import Body, Request
+from sse_starlette.sse import EventSourceResponse
+from fastapi.concurrency import run_in_threadpool
+from configs import (LLM_MODELS,
+                     VECTOR_SEARCH_TOP_K,
+                     SCORE_THRESHOLD,
+                     TEMPERATURE,
+                     USE_RERANKER,
+                     RERANKER_MODEL,
+                     RERANKER_MAX_LENGTH,
+                     MODEL_PATH,
+                     MAX_TOKENS,
+                     MAX_CUT_TOKENS)
+from server.utils import wrap_done, get_ChatOpenAI
+from server.utils import BaseResponse, get_prompt_template, get_format_template
+from server.utils import get_strategy_prompt_template
+from langchain.chains import LLMChain
+from langchain.callbacks import AsyncIteratorCallbackHandler
+from typing import AsyncIterable, List, Optional
+import asyncio
+from langchain.prompts.chat import ChatPromptTemplate
+from server.chat.utils import History
+from server.knowledge_base.kb_service.base import KBServiceFactory
+import json
+from urllib.parse import urlencode
+from server.knowledge_base.kb_doc_api import search_docs
+from server.reranker.reranker import LangchainReranker
+from server.utils import embedding_device
+from server.chat.policy_fun import add_summary_retrieved_results, get_llm_model_response
+import json
+
+async def report_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
+        fileName: List = Body([], description="文件名称", examples=[[]]),
+        knowledge_base_name: str = Body(..., description="知识库名称",
+                                        examples=["t_strategy_report_bge_v1"]),
+        top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
+        score_threshold: float = Body(
+            SCORE_THRESHOLD,
+            description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右",
+            ge=0,
+            le=2
+        ),
+        history: List[History] = Body(
+            [],
+            description="历史对话",
+            examples=[[]]
+        ),
+        stream: bool = Body(False, description="流式输出"),
+        model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
+        temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
+        max_tokens: Optional[int] = Body(
+            MAX_TOKENS,
+            description="限制LLM生成Token数量，默认None代表模型最大值"
+        ),
+        prompt_name: str = Body(
+            "default",
+            description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
+        ),
+        request: Request = None,
+        use_summary = True,
+        chunk_size: int = 20000,
+        min_chunk_size: int = 2000,
+        summary_model_name = LLM_MODELS[0],
+        query_rewrite_model_name = LLM_MODELS[0]
+        ):
+    
+    
+    kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
+    if kb is None:
+        return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
+
+    history = [History.from_data(h) for h in history]
+
+    async def knowledge_base_chat_iterator(
+            query: str,
+            top_k: int,
+            history: Optional[List[History]],
+            model_name: str = model_name,
+            prompt_name: str = prompt_name,
+    ) -> AsyncIterable[str]:
+        nonlocal max_tokens
+        callback = AsyncIteratorCallbackHandler()
+
+        if isinstance(max_tokens, int) and max_tokens <= 0:
+            max_tokens = None
+
+        model = get_ChatOpenAI(
+            model_name=model_name,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            callbacks=[callback],
+        )
+        # print('-------------- debug', query)
+        search_query = get_llm_model_response(
+                        strategy_name="query rewrite",
+                        llm_model_name=query_rewrite_model_name,
+                        template_prompt_name="query_rewrite_report",
+                        prompt_param_dict={"query": query},
+                        temperature=0.01,
+                        max_tokens=512
+                        )
+        # print('search query', search_query)
+        json_string = search_query.strip("```json\n").strip("```")
+        # print('search query----json string', json_string)
+        try: # 防止json格式错误
+            # 读取改写后的query
+            data = json.loads(json_string)
+            policies = data['report']
+            search_query = ''
+            for policy in policies:
+                search_query += policy
+        except:
+            search_query = query
+
+        print('search query', search_query)
+        
+        docs = await run_in_threadpool(search_docs,
+                                       fileName=fileName,
+                                       query=search_query,
+                                       knowledge_base_name=knowledge_base_name,
+                                       top_k=top_k,
+                                       score_threshold=score_threshold)
+        # print(docs)
+        # doc加入metadata的summary字段
+        if use_summary:
+            docs = await add_summary_retrieved_results(docs, query, 512,chunk_size,min_chunk_size,summary_model_name)
+            print(docs)
+
+        # context = "\n".join([doc.page_content for doc in docs])
+        # 需要规范格式的prompt_name
+        # 默认default，即为空，不用管
+        format_list = ["Abstract Assistant", "Outline Assistant"]
+        if prompt_name in format_list:
+            format_template = get_format_template("knowledge_base_chat", "abstract_format")
+        else:
+            format_template = get_format_template("knowledge_base_chat", "default")
+
+        # 政策知识库
+        # 相关信息把标题和内容进行整合
+        if knowledge_base_name == 't_strategy_report_bge_v1':
+            knowledge = []
+            newdocs =[]
+            for inum,doc in enumerate(docs):
+                if use_summary :
+                    if len(doc.metadata['summary'])>15:
+                        knowledge.append(f"""参考报告[{len(knowledge) + 1}] 报告来源： {doc.metadata['source']} \n报告内容： {doc.metadata['summary']}""")
+                        newdocs.append(doc)
+                    else:
+                        pass
+                else:
+                    knowledge.append(f"""参考报告[{inum + 1}] 报告来源:  {doc.metadata['source']} \n报告内容:  {doc.page_content}""")
+            context = "\n\n".join(knowledge)
+            docs = newdocs
+            
+        # 非报告知识库
+        else:
+            context = "\n".join([doc.page_content for doc in docs])
+
+
+        if len(docs) == 0 and fileName:  # 如果没有找到相关文档，使用empty模板
+            prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
+        elif len(docs) == 0 and not fileName and prompt_name != "Abstract Assistant":
+            prompt_template = get_prompt_template("knowledge_base_chat", "empty")
+        elif prompt_name == 'iast_report_chat' or (knowledge_base_name == "t_strategy_report_bge_v1" and prompt_name == 'default'):
+            print("use report prompt_template")
+            prompt_template = get_strategy_prompt_template("knowledge_base_chat", 'iast_report_chat')
+        else:
+            prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
+        
+        print("prompt_template", prompt_template)
+        
+        input_msg = History(role="user", content=prompt_template).to_msg_template(False)
+        chat_prompt = ChatPromptTemplate.from_messages(
+            [i.to_msg_template() for i in history] + [input_msg])
+
+        chain = LLMChain(prompt=chat_prompt, llm=model)
+
+        print(
+            f"\n知识库问答开始调用：参数：\nkb:{knowledge_base_name}\nquery:{query}\nhistory:{history}\ncontext:{context}\nfile_name:{fileName}\nformat_template:{format_template}\n\n")
+        query = query.replace("原文", "")
+        task = asyncio.create_task(wrap_done(
+            chain.acall({"context": context,
+                         "history": history,
+                         "question": query,
+                         "file_name": str(fileName),
+                         "format_template": format_template}),
+            callback.done),
+        )
+        source_documents = []
+        
+        
+        # 报告知识库
+        if knowledge_base_name == 't_strategy_report_bge_v1':
+            for inum, doc in enumerate(docs):
+                filename = doc.metadata.get("source")
+                print("filename", filename)
+                if filename:
+                    text = f"""[{inum + 1}] 报告出处: [{filename}]\n\n{doc.metadata['summary']}\n\n"""
+                else:
+                    text = f"""[{inum + 1}] \n\n{doc.metadata['summary']}\n\n"""
+                source_documents.append(text)
+        
+        # 非报告知识库
+        else:
+            for inum, doc in enumerate(docs):
+                filename = doc.metadata.get("source")
+                parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
+                base_url = request.base_url
+                url = f"{base_url}knowledge_base/download_doc?" + parameters
+                if filename:
+                    text = f"""出处: [{filename}]({url}) \n\n"""
+                else:
+                    text = f"""出处: [{"原文地址"}]({url}) \n\n"""
+                source_documents.append(text)
+
+        if len(source_documents) == 0:  # 没有找到相关文档
+            source_documents.append(f"<span style='color:red'>未找到相关文档,该回答为大模型自身能力解答！</span>")
+
+        if stream:
+            async for token in callback.aiter():
+                # Use server-sent-events to stream the response
+                yield json.dumps({"answer": token}, ensure_ascii=False)
+        else:
+            answer = ""
+            async for token in callback.aiter():
+                answer += token
+            yield json.dumps({"answer": answer})
+        await task
+        yield json.dumps({"docs": source_documents}, ensure_ascii=False)
+
+    return EventSourceResponse(knowledge_base_chat_iterator(query, top_k, history, model_name, prompt_name))