fix #1142: 在History中使用jinja2模板代替f-string，避免消息中含有{ }时出错

configs/model_config.py.example

@@ -142,12 +142,12 @@ SEARCH_ENGINE_TOP_K = 5
 # nltk 模型存储路径
 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
 
-# 基于本地知识问答的提示词模版
+# 基于本地知识问答的提示词模版（使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
 PROMPT_TEMPLATE = """【指令】根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题”，不允许在答案中添加编造成分，答案请使用中文。 
 
-【已知信息】{context} 
+【已知信息】{{ context }} 
 
-【问题】{question}"""
+【问题】{{ question }}"""
 
 # API 是否开启跨域，默认为False，如果需要开启，请设置为True
 # is open cross domain

server/chat/chat.py

@@ -21,7 +21,7 @@ def chat(query: str = Body(..., description="用户输入", examples=["恼羞成
                                        ),
          stream: bool = Body(False, description="流式输出"),
          ):
-    history = [History(**h) if isinstance(h, dict) else h for h in history]
+    history = [History.from_data(h) for h in history]
 
     async def chat_iterator(query: str,
                             history: List[History] = [],
@@ -37,8 +37,9 @@ def chat(query: str = Body(..., description="用户输入", examples=["恼羞成
             model_name=LLM_MODEL
         )
 
+        input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
-            [i.to_msg_tuple() for i in history] + [("human", "{input}")])
+            [i.to_msg_template() for i in history] + [input_msg])
         chain = LLMChain(prompt=chat_prompt, llm=model)
 
         # Begin a task that runs in the background.

server/chat/github_chat.py

@@ -1,109 +0,0 @@
-from langchain.document_loaders.github import GitHubIssuesLoader
-from fastapi import Body
-from fastapi.responses import StreamingResponse
-from configs.model_config import (llm_model_dict, LLM_MODEL, SEARCH_ENGINE_TOP_K, PROMPT_TEMPLATE)
-from server.chat.utils import wrap_done
-from server.utils import BaseResponse
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain
-from langchain.callbacks import AsyncIteratorCallbackHandler
-from typing import AsyncIterable
-import asyncio
-from langchain.prompts.chat import ChatPromptTemplate
-from typing import List, Optional, Literal
-from server.chat.utils import History
-from langchain.docstore.document import Document
-import json
-import os
-from functools import lru_cache
-from datetime import datetime
-
-
-GITHUB_PERSONAL_ACCESS_TOKEN = os.environ.get("GITHUB_PERSONAL_ACCESS_TOKEN")
-
-
-@lru_cache(1)
-def load_issues(tick: str):
-    '''
-    set tick to a periodic value to refresh cache
-    '''
-    loader = GitHubIssuesLoader(
-        repo="chatchat-space/langchain-chatglm",
-        access_token=GITHUB_PERSONAL_ACCESS_TOKEN,
-        include_prs=True,
-        state="all",
-    )
-    docs = loader.load()
-    return docs
-
-
-def 
-def github_chat(query: str = Body(..., description="用户输入", examples=["本项目最新进展"]),
-                top_k: int = Body(SEARCH_ENGINE_TOP_K, description="检索结果数量"),
-                include_prs: bool = Body(True, description="是否包含PR"),
-                state: Literal['open', 'closed', 'all'] = Body(None, description="Issue/PR状态"),
-                creator: str = Body(None, description="创建者"),
-                history: List[History] = Body([],
-                                            description="历史对话",
-                                            examples=[[
-                                                {"role": "user",
-                                                "content": "介绍一下本项目"},
-                                                {"role": "assistant",
-                                                "content": "LangChain-Chatchat (原 Langchain-ChatGLM): 基于 Langchain 与 ChatGLM 等大语言模型的本地知识库问答应用实现。"}]]
-                                                ),
-                stream: bool = Body(False, description="流式输出"),
-                ):
-    if GITHUB_PERSONAL_ACCESS_TOKEN is None:
-        return BaseResponse(code=404, msg=f"使用本功能需要 GITHUB_PERSONAL_ACCESS_TOKEN")
-
-    async def chat_iterator(query: str,
-                            search_engine_name: str,
-                            top_k: int,
-                            history: Optional[List[History]],
-                            ) -> AsyncIterable[str]:
-        callback = AsyncIteratorCallbackHandler()
-        model = ChatOpenAI(
-            streaming=True,
-            verbose=True,
-            callbacks=[callback],
-            openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
-            openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
-            model_name=LLM_MODEL
-        )
-
-        docs = lookup_search_engine(query, search_engine_name, top_k)
-        context = "\n".join([doc.page_content for doc in docs])
-
-        chat_prompt = ChatPromptTemplate.from_messages(
-            [i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)])
-
-        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 = [
-            f"""出处 [{inum + 1}] [{doc.metadata["source"]}]({doc.metadata["source"]}) \n\n{doc.page_content}\n\n"""
-            for inum, doc in enumerate(docs)
-        ]
-
-        if stream:
-            async for token in callback.aiter():
-                # Use server-sent-events to stream the response
-                yield json.dumps({"answer": token,
-                                  "docs": source_documents},
-                                 ensure_ascii=False)
-        else:
-            answer = ""
-            async for token in callback.aiter():
-                answer += token
-            yield json.dumps({"answer": token,
-                              "docs": source_documents},
-                             ensure_ascii=False)
-        await task
-
-    return StreamingResponse(search_engine_chat_iterator(query, search_engine_name, top_k, history),
-                             media_type="text/event-stream")

server/chat/knowledge_base_chat.py

@@ -38,7 +38,7 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
     if kb is None:
         return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
 
-    history = [History(**h) if isinstance(h, dict) else h for h in history]
+    history = [History.from_data(h) for h in history]
 
     async def knowledge_base_chat_iterator(query: str,
                                            kb: KBService,
@@ -57,8 +57,9 @@ def knowledge_base_chat(query: str = Body(..., description="用户输入", examp
         docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
         context = "\n".join([doc.page_content for doc in docs])
 
+        input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
-            [i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)])
+            [i.to_msg_template() for i in history] + [input_msg])
 
         chain = LLMChain(prompt=chat_prompt, llm=model)
 

server/chat/search_engine_chat.py

@@ -73,6 +73,8 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
     if search_engine_name not in SEARCH_ENGINES.keys():
         return BaseResponse(code=404, msg=f"未支持搜索引擎 {search_engine_name}")
 
+    history = [History.from_data(h) for h in history]
+
     async def search_engine_chat_iterator(query: str,
                                           search_engine_name: str,
                                           top_k: int,
@@ -91,8 +93,9 @@ def search_engine_chat(query: str = Body(..., description="用户输入", exampl
         docs = lookup_search_engine(query, search_engine_name, top_k)
         context = "\n".join([doc.page_content for doc in docs])
 
+        input_msg = History(role="user", content=PROMPT_TEMPLATE).to_msg_template(False)
         chat_prompt = ChatPromptTemplate.from_messages(
-            [i.to_msg_tuple() for i in history] + [("human", PROMPT_TEMPLATE)])
+            [i.to_msg_template() for i in history] + [input_msg])
 
         chain = LLMChain(prompt=chat_prompt, llm=model)
 

server/chat/utils.py

@@ -1,6 +1,7 @@
 import asyncio
-from typing import Awaitable
+from typing import Awaitable, List, Tuple, Dict, Union
 from pydantic import BaseModel, Field
+from langchain.prompts.chat import ChatMessagePromptTemplate
 
 
 async def wrap_done(fn: Awaitable, event: asyncio.Event):
@@ -28,3 +29,29 @@ class History(BaseModel):
 
     def to_msg_tuple(self):
         return "ai" if self.role=="assistant" else "human", self.content
+
+    def to_msg_template(self, is_raw=True) -> ChatMessagePromptTemplate:
+        role_maps = {
+            "ai": "assistant",
+            "human": "user",
+        }
+        role = role_maps.get(self.role, self.role)
+        if is_raw: # 当前默认历史消息都是没有input_variable的文本。
+            content = "{% raw %}" + self.content + "{% endraw %}"
+        else:
+            content = self.content
+
+        return ChatMessagePromptTemplate.from_template(
+            content,
+            "jinja2",
+            role=role,
+        )
+
+    @classmethod
+    def from_data(cls, h: Union[List, Tuple, Dict]) -> "History":
+        if isinstance(h, (list,tuple)) and len(h) >= 2:
+            h = cls(role=h[0], content=h[1])
+        elif isinstance(h, dict):
+            h = cls(**h)
+
+        return h