[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/server/custom/AsyncIteratorCallbackHandlerNew.py

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+from __future__ import annotations
+
+import asyncio
+from typing import Any, AsyncIterator, Dict, List, Literal, Union, cast
+
+from langchain_core.outputs import LLMResult
+
+from langchain.callbacks.base import AsyncCallbackHandler
+
+# TODO If used by two LLM runs in parallel this won't work as expected
+
+
+class AsyncIteratorCallbackHandler(AsyncCallbackHandler):
+    """Callback handler that returns an async iterator."""
+
+    queue: asyncio.Queue[str]
+
+    done: asyncio.Event
+
+    @property
+    def always_verbose(self) -> bool:
+        return True
+
+    def __init__(self) -> None:
+        self.queue = asyncio.Queue()
+        self.done = asyncio.Event()
+
+    async def on_llm_start(
+        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+    ) -> None:
+        # If two calls are made in a row, this resets the state
+        self.done.clear()
+
+    async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
+        if token is not None and token != "":
+            self.queue.put_nowait(token)
+
+    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
+        self.done.set()
+
+    async def on_llm_error(self, error: BaseException, **kwargs: Any) -> None:
+        self.done.set()
+
+    # TODO implement the other methods
+    def get_queue(self):
+        return self.queue._queue
+
+    async def aiter(self) -> AsyncIterator[str]:
+        while not self.queue.empty() or not self.done.is_set():
+            # Wait for the next token in the queue,
+            # but stop waiting if the done event is set
+            done, other = await asyncio.wait(
+                [
+                    # NOTE: If you add other tasks here, update the code below,
+                    # which assumes each set has exactly one task each
+                    asyncio.ensure_future(self.queue.get()),
+                    asyncio.ensure_future(self.done.wait()),
+                ],
+                return_when=asyncio.FIRST_COMPLETED,
+            )
+
+            # Cancel the other task
+            if other:
+                other.pop().cancel()
+
+            # Extract the value of the first completed task
+            token_or_done = cast(Union[str, Literal[True]], done.pop().result())
+
+            # If the extracted value is the boolean True, the done event was set
+            if token_or_done is True:
+                break
+
+            # Otherwise, the extracted value is a token, which we yield
+            yield token_or_done

langchain-chat/server/custom/abstract_search.py

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+import asyncio
+import json
+import logging
+from typing import AsyncIterable, List, Optional
+from fastapi import Body, Request, HTTPException
+from fastapi.concurrency import run_in_threadpool
+from sse_starlette.sse import EventSourceResponse
+from server.knowledge_base.kb_doc_api import search_docs
+from configs import (
+    TEMPERATURE,
+    VECTOR_SEARCH_TOP_K,
+    SCORE_THRESHOLD,
+    LLM_MODELS,
+    JOURNAL_KNOWLEDGE_BASE,
+    MAX_TOKENS,
+)
+from server.utils import BaseResponse, get_prompt_template
+from server.chat.policy_fun_iast import get_llm_model_response
+from server.knowledge_base.kb_service.base import KBServiceFactory
+
+async def abstract_search(
+                        query: str = Body(..., description="用户输入", examples=["人工智能领域相关的文献有啥"]),
+                        knowledge_base_name_list: List[str] = Body(..., description="知识库名称", examples=[["t_journal_article_bge_v1"]]),
+                        # top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数", ge=1),
+                        # fileName: List[str] = Body([], description="文件名称", examples=[["123.txt"]]),
+                        # 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"),
+                        # 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=0.0,
+                        #     le=1.0
+                        # ),
+                        # request: Request = None,
+                        # query_rewrite_model_name: str = Body(
+                        #     'Qwen2-72B-Instruct',
+                        #     description="Query rewrite model name."
+                        # ),
+) -> EventSourceResponse:
+
+    async def search_iterator() -> AsyncIterable[str]:
+        try:
+            # 重写查询
+            search_query_response = await run_in_threadpool(
+                get_llm_model_response,
+                strategy_name="query rewrite",
+                llm_model_name=LLM_MODELS[0],
+                template_prompt_name="query_rewrite",
+                prompt_param_dict={"query": query, "history": []},
+                temperature=0.01,
+                max_tokens=512
+            )
+
+            # 解析重写后的查询
+            try:
+                data = json.loads(search_query_response.strip("```json\n").strip("```"))
+                queries = data.get('query', [])
+                search_query = ' '.join(queries)
+            except json.JSONDecodeError:
+                search_query = query
+
+            logging.info(f'段落推荐输入问题: {search_query}')
+
+            # 执行文档搜索（这里只搜索期刊知识库）
+            journaldocs = await run_in_threadpool(
+                search_docs,
+                query=search_query,
+                fileName=[],
+                knowledge_base_name=JOURNAL_KNOWLEDGE_BASE,
+                top_k=VECTOR_SEARCH_TOP_K,
+                score_threshold=SCORE_THRESHOLD
+            )
+            logging.info(f"段落推荐召回资料: {journaldocs}")
+            # 处理搜索结果，去重并整理
+            seen_ids = set()
+            journallist = []
+            # journaldocs = []
+            for doc in journaldocs:
+                doc_id = doc.metadata.get('ID')
+                content = doc.metadata.get('abstract', '').strip()
+                #处理content不是完整段落的情况    
+                if content:
+                    if not content.endswith('。'):
+                        last_period_index = content.rfind('。')
+                        if last_period_index != -1:
+                            content = content[:last_period_index + 1]
+                        else:
+                            content += '...'
+                    
+                    if doc_id and content and doc_id not in seen_ids:
+                        journallist.append({
+                            'id': doc_id,
+                            'title': doc.metadata.get('title', ''),
+                            'content': content
+                        })
+                        seen_ids.add(doc_id)
+
+            search_results = {'journal_article': journallist}
+            logging.info(f"段落推荐召回资料整理结果：{journallist}")
+
+            # 如果没有找到相关文档，返回默认响应
+            # if not journallist:
+            #     search_results['journal_article'].append({
+            #         'id': 'empty',
+            #         'title': 'empty',
+            #         'content': '暂未找到相关资料。'
+            #     })
+
+            yield json.dumps(search_results, ensure_ascii=False)
+
+        except Exception as e:
+            # 捕获并返回错误信息
+            error_response = BaseResponse(code=500, msg=str(e))
+            yield json.dumps(error_response.dict(), ensure_ascii=False)
+
+    return EventSourceResponse(search_iterator())

langchain-chat/server/custom/article_overview.py

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+import asyncio
+import json
+from typing import AsyncIterable, List, Optional
+from urllib.parse import urlencode
+
+from fastapi import Body, Request
+from fastapi.concurrency import run_in_threadpool
+from langchain.callbacks import AsyncIteratorCallbackHandler
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.prompts.chat import ChatPromptTemplate
+from sse_starlette.sse import EventSourceResponse
+
+from configs import (TEMPERATURE,
+                     USE_RERANKER,
+                     RERANKER_MODEL,
+                     RERANKER_MAX_LENGTH,
+                     LLM_MODELS,
+                     MODEL_PATH,
+                     MAX_TOKENS)
+from server.chat.utils import History
+from server.knowledge_base.kb_service.base import KBServiceFactory
+from server.reranker.reranker import LangchainReranker
+from server.utils import BaseResponse, get_prompt_template
+from server.utils import embedding_device
+from server.utils import wrap_done, get_ChatOpenAI
+
+
+async def article_overview(query: str = Body("你好", description="用户输入", examples=["你好"]),
+                           knowledge_base_name: str = Body(..., description="知识库名称", examples=["t_policy_total_bce_v1"]),
+                           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(
+                               "Article Overview",
+                               description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
+                           ),
+                           source_name_list: List[str] = Body([], description="资源列表"),
+                           request: Request = None,
+                           ):
+    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}")
+    query = "帮我对以下文件进行总结 ：" + ",".join(source_name_list)
+    if len(source_name_list) > 1:
+        prompt_name = "Article Overview2"
+    else:
+        prompt_name = "Article Overview"
+    async def article_overview_iterator(
+            query: str,
+            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],
+        )
+        docs = []
+        docs = await run_in_threadpool(kb.get_doc_by_sources_name,
+                                       source_name_list=source_name_list)
+
+        # 加入reranker
+        if USE_RERANKER:
+            reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL, "BAAI/bge-reranker-large")
+            print("-----------------model path------------------")
+            print(reranker_model_path)
+            reranker_model = LangchainReranker(top_n=3,
+                                               device=embedding_device(),
+                                               max_length=RERANKER_MAX_LENGTH,
+                                               model_name_or_path=reranker_model_path
+                                               )
+            print("---------before rerank------------------")
+            print(docs)
+            docs = reranker_model.compress_documents(documents=docs,
+                                                     query=query)
+            print("---------after rerank------------------")
+            print(docs)
+        # context = "\n".join([doc.page_content for doc in docs])
+        # 相关信息把标题和内容进行整合
+        if knowledge_base_name == 't_policy_total_bce_v1':
+            knowledge = []
+            for doc in docs:
+                if doc.metadata["_type"] == "title":
+                    knowledge.append(doc.page_content + "\n" + doc.metadata['content'])
+                if doc.metadata["_type"] == "content":
+                    knowledge.append(doc.metadata['title'] + "\n" + doc.page_content)
+            context = "\n\n".join(knowledge)
+        # 非政策知识库
+        else:
+            context = "\n".join([doc.page_content for doc in docs])
+        print(f"context:{context}\n")
+        if len(docs) == 0:  # 如果没有找到相关文档，使用empty模板
+            prompt_template = get_prompt_template("knowledge_base_chat", "empty")
+        else:
+            prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
+        input_msg = History(role="user", content=prompt_template).to_msg_template(False)
+        chat_prompt = ChatPromptTemplate.from_messages([input_msg])
+        print(f"chat_prompt:{chat_prompt}\n")
+        chain = LLMChain(prompt=chat_prompt, llm=model)
+
+        # Begin a task that runs in the background.
+        task = asyncio.create_task(wrap_done(
+            chain.acall({"context": context, "question": query}),
+            callback.done),
+        )
+
+        source_documents = []
+        # 政策知识库
+        if knowledge_base_name == 't_policy_total_bce_v1':
+            for inum, doc in enumerate(docs):
+                # 获取标题以及详情地址（url）
+                filename = doc.metadata.get("title")
+                detail_url = 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
+                # text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\n\n"""
+                if filename:
+                    text = f"""出处: [{filename}]({detail_url}) \n\n"""
+                else:
+                    text = f"""出处: [{"原文地址"}]({detail_url}) \n\n"""
+                source_documents.append(text)
+        # 非政策知识库
+        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)
+            yield json.dumps({"docs": source_documents}, ensure_ascii=False)
+        else:
+            answer = ""
+            async for token in callback.aiter():
+                answer += token
+            yield json.dumps({"answer": answer,
+                              "docs": source_documents},
+                             ensure_ascii=False)
+        await task
+
+    return EventSourceResponse(article_overview_iterator(query, model_name=model_name, prompt_name=prompt_name))
+
+
+class ArticleOverview:
+    query = "请给我对文件进行一下总结"
+
+    def __init__(self):
+        self._PROMPT_TEMPLATE = """
+            '<角色> 你是由浪潮开发的知冶大模型中所选定的文件综述助手。</角色> \n\n'
+            'Your task is to write a detailed summary of the provided {{context}} file. Ensure that your summary is '
+            'longer than 300 words and captures the essence of the content. Focus on the main points, '
+            'key findings, and any important implications or conclusions. Maintain an unbiased tone and avoid relying '
+            'on stereotypes. Organize the summary in a clear and coherent manner, using appropriate headings or '
+            'bullet points if necessary. Remember to keep the summary concise while preserving the core information. '
+            'Let\'s start with a brief overview of the file\'s main topic and then delve into the specifics.'
+            'PLEASE ALWAYS RESPOND IN CHINESE!\n'
+            '<已知信息>{{ context }}</已知信息>\n'
+            '<问题>{{ question }}</问题>\n',
+        """
+        self.PROMPT = PromptTemplate(
+            input_variables=["question", "database_names"],
+            template=self._PROMPT_TEMPLATE,
+        )
+
+    def query_out(self, knowledge_base_name: str, source_name_list: list):
+        self.query = "帮我对以下文件进行总结 ：" + ",".join(source_name_list)
+        return article_overview(self.query,
+                                knowledge_base_name=knowledge_base_name,
+                                source_name_list=source_name_list
+                                )

langchain-chat/server/custom/chapter_overview.py

@@ -0,0 +1,113 @@
+import asyncio
+import json
+from typing import AsyncIterable, List, Optional
+from urllib.parse import urlencode
+
+from fastapi import Body, Request
+from fastapi.concurrency import run_in_threadpool
+from langchain.callbacks import AsyncIteratorCallbackHandler
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.prompts.chat import ChatPromptTemplate
+from sse_starlette.sse import EventSourceResponse
+
+from configs import (TEMPERATURE,
+                     USE_RERANKER,
+                     RERANKER_MODEL,
+                     RERANKER_MAX_LENGTH,
+                     MODEL_PATH,
+                     MAX_TOKENS,
+                     MAX_CUT_TOKENS, LLM_MODELS)
+from server.chat.utils import History
+from server.knowledge_base.kb_service.base import KBServiceFactory
+from server.reranker.reranker import LangchainReranker
+from server.utils import BaseResponse, get_prompt_template
+from server.utils import embedding_device
+from server.utils import wrap_done, get_ChatOpenAI
+from collections import defaultdict
+from server.custom.custom_fun import chapter_overview_summary
+
+async def task(param):
+    contextk = param["contextk"]
+    i = param["i"]
+    model_name = param["model_name"]
+    temperature = param["temperature"]
+    max_tokens = param["max_tokens"]
+    chat_prompt = param["chat_prompt"]
+    print(f"i:{i}，len_context:{len(contextk)}\n")
+    callback_temp = AsyncIteratorCallbackHandler()
+    model_temp = get_ChatOpenAI(
+        model_name=model_name,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        callbacks=[callback_temp],
+    )
+    chain_temp = LLMChain(prompt=chat_prompt, llm=model_temp)
+    task_temp = wrap_done(chain_temp.acall({"context": contextk,
+                                            "question": "对该部分内容进行总结"}),
+                          callback_temp.done)
+
+    await task_temp
+    async for token in callback_temp.aiter():
+        yield token
+
+
+# 使用多线程执行任务
+async def run_tasks_concurrently(params):
+    result = []
+    async for data in asyncio.as_completed([task(param) async for param in params]):
+        result.append(''.join([token async for token in data]))
+    return result
+
+
+async def chapter_overview(query: str = Body("为我总结这些内容", description="用户输入", examples=["你好"]),
+                           knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
+                           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(
+                               "Chapter Overview",
+                               description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
+                           ),
+                           source_name_list: List[str] = Body([], description="资源列表"),
+                           request: Request = None,
+                           ):
+   
+    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}")
+    async def chapter_overview_iterator(
+            model_name: str = model_name,
+    ) -> AsyncIterable[str]:
+        nonlocal max_tokens
+        if isinstance(max_tokens, int) and max_tokens <= 0:
+            max_tokens = None
+
+        docs = await run_in_threadpool(kb.get_doc_by_sources_name,source_name_list=source_name_list)
+        
+        chapter_summaries, global_summary = await chapter_overview_summary(docs, model_name, temperature, max_tokens)
+        
+        if stream:
+            for h1, summaries in chapter_summaries.items():
+                for summary in summaries:
+                    yield json.dumps({"chapter_title": h1, "summary": summary}, ensure_ascii=False)
+            yield json.dumps({"global_summary": global_summary}, ensure_ascii=False)
+            # yield json.dumps({"docs": source_documents}, ensure_ascii=False)
+        else:
+            result = {
+                "chapter_summaries": chapter_summaries,
+                "global_summary": global_summary,
+            }
+            yield json.dumps(result, ensure_ascii=False)
+
+    return EventSourceResponse(chapter_overview_iterator(model_name=model_name))
+
+
+
+
+    
+          

langchain-chat/server/custom/custom_fun.py

@@ -0,0 +1,74 @@
+"""
+Custom functions for document processing.
+"""
+
+
+async def chapter_overview_summary(docs, model_name, temperature, max_tokens):
+    """
+    为文档生成章节概述摘要的占位函数。
+
+    Args:
+        docs: 文档列表
+        model_name: 模型名称
+        temperature: 温度参数
+        max_tokens: 最大token数
+
+    Returns:
+        chapter_summaries: 章节摘要字典
+        global_summary: 全局摘要字符串
+    """
+    chapter_summaries = {}
+    global_summary = ""
+
+    # 简单的占位实现：直接使用文档内容
+    for doc in docs:
+        h1 = doc.metadata.get("head1", "默认章节") if hasattr(doc, 'metadata') else "默认章节"
+        if h1 not in chapter_summaries:
+            chapter_summaries[h1] = []
+        content = doc.page_content if hasattr(doc, 'page_content') else str(doc)
+        chapter_summaries[h1].append(content[:500] if len(content) > 500 else content)
+
+    # 生成全局摘要
+    if docs:
+        all_content = " ".join([doc.page_content if hasattr(doc, 'page_content') else str(doc) for doc in docs[:3]])
+        global_summary = all_content[:1000] if len(all_content) > 1000 else all_content
+
+    return chapter_summaries, global_summary
+
+
+async def chi_translation(docs, prompt_template, model_name, temperature, max_tokens):
+    """
+    中文翻译占位函数。
+
+    Args:
+        docs: 文档列表
+        prompt_template: 提示模板
+        model_name: 模型名称
+        temperature: 温度参数
+        max_tokens: 最大token数
+
+    Yields:
+        翻译结果字符串
+    """
+    for doc in docs:
+        content = doc.page_content if hasattr(doc, 'page_content') else str(doc)
+        yield content
+
+
+async def eng_translation(docs, prompt_template, model_name, temperature, max_tokens):
+    """
+    英文翻译占位函数。
+
+    Args:
+        docs: 文档列表
+        prompt_template: 提示模板
+        model_name: 模型名称
+        temperature: 温度参数
+        max_tokens: 最大token数
+
+    Yields:
+        翻译结果字符串
+    """
+    for doc in docs:
+        content = doc.page_content if hasattr(doc, 'page_content') else str(doc)
+        yield content

langchain-chat/server/custom/paper_translation.py

@@ -0,0 +1,78 @@
+import asyncio
+import json
+from typing import AsyncIterable, List, Optional
+from urllib.parse import urlencode
+
+from fastapi import Body, Request
+from fastapi.concurrency import run_in_threadpool
+from langchain.callbacks import AsyncIteratorCallbackHandler
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+from langchain.prompts.chat import ChatPromptTemplate
+from sse_starlette.sse import EventSourceResponse
+
+from configs import (TEMPERATURE,
+                     USE_RERANKER,
+                     RERANKER_MODEL,
+                     RERANKER_MAX_LENGTH,
+                     MODEL_PATH,
+                     MAX_TOKENS,
+                     MAX_CUT_TOKENS, LLM_MODELS)
+from server.chat.utils import History
+from server.knowledge_base.kb_service.base import KBServiceFactory
+from server.reranker.reranker import LangchainReranker
+from server.utils import BaseResponse, get_prompt_template
+from server.utils import embedding_device
+from server.utils import wrap_done, get_ChatOpenAI
+from collections import defaultdict
+from server.custom.custom_fun import chi_translation,eng_translation
+
+async def paper_translation(query: str = Body("为我总结这些内容", description="用户输入", examples=["你好"]),
+                           knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
+                           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(
+                               "eng_chi",
+                               description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
+                           ),
+                           source_name_list: List[str] = Body([], description="资源列表"),
+                           request: Request = None,
+                           ):
+    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}")
+    async def translation_iterator(
+            query: str,
+            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
+
+        docs = []
+        docs = await run_in_threadpool(kb.get_doc_by_sources_name,
+                                    source_name_list=source_name_list)
+        
+        if len(docs) == 0:  # 如果没有找到相关文档，使用empty模板
+            prompt_template = get_prompt_template("knowledge_base_chat", "empty")
+        else:
+            prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
+        # input_msg = History(role="user", content=prompt_template).to_msg_template(False)
+        # chat_prompt = ChatPromptTemplate.from_messages([input_msg])
+        # chain = LLMChain(prompt=chat_prompt, llm=model)
+        if prompt_name == "chi_eng":
+            async for chunk in chi_translation(docs, prompt_template, model_name, temperature, max_tokens):
+                yield chunk
+        else:
+            async for chunk in eng_translation(docs, prompt_template, model_name, temperature, max_tokens):
+                yield chunk
+       
+
+    return EventSourceResponse(translation_iterator(query, model_name=model_name, prompt_name=prompt_name))