一些细节优化 (#1891)

Co-authored-by: zR <zRzRzRzRzRzRzR>

configs/model_config.py.example

@@ -232,3 +232,14 @@ VLLM_MODEL_DICT = {
     "agentlm-70b":"THUDM/agentlm-70b",
 
 }
+
+## 你认为支持Agent能力的模型，可以在这里添加，添加后不会出现可视化界面的警告
+SUPPORT_AGENT_MODEL = [
+    "Azure-OpenAI",
+    "OpenAI",
+    "Anthropic",
+    "Qwen",
+    "qwen-api",
+    "baichuan-api",
+    "agentlm"
+]

configs/prompt_config.py.example

@@ -47,6 +47,11 @@ PROMPT_TEMPLATES["knowledge_base_chat"] = {
         <已知信息>{{ context }}</已知信息>、
         <问题>{{ question }}</问题>
         """,
+    "Empty":  # 搜不到内容的时候调用，此时没有已知信息，这个Empty可以更改，但不能删除，会影响程序使用
+        """
+        <指令>请根据用户的问题，进行简洁明了的回答</指令>
+        <问题>{{ question }}</问题>
+        """,
 }
 PROMPT_TEMPLATES["search_engine_chat"] = {
     "default":
@@ -55,13 +60,17 @@ PROMPT_TEMPLATES["search_engine_chat"] = {
             <已知信息>{{ context }}</已知信息>、
             <问题>{{ question }}</问题>
             """,
-
     "search":
         """
         <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”，答案请使用中文。 </指令>
         <已知信息>{{ context }}</已知信息>、
         <问题>{{ question }}</问题>
         """,
+    "Empty":  # 搜不到内容的时候调用，此时没有已知信息，这个Empty可以更改，但不能删除，会影响程序使用
+        """
+        <指令>请根据用户的问题，进行简洁明了的回答</指令>
+        <问题>{{ question }}</问题>
+        """,
 }
 PROMPT_TEMPLATES["agent_chat"] = {
     "default":

document_loaders/FilteredCSVloader.py

@@ -0,0 +1,80 @@
+## 指定制定列的csv文件加载器
+
+from langchain.document_loaders import CSVLoader
+import csv
+from io import TextIOWrapper
+from typing import Dict, List, Optional
+from langchain.docstore.document import Document
+from langchain.document_loaders.helpers import detect_file_encodings
+
+
+class FilteredCSVLoader(CSVLoader):
+    def __init__(
+            self,
+            file_path: str,
+            columns_to_read: List[str],
+            source_column: Optional[str] = None,
+            metadata_columns: List[str] = [],
+            csv_args: Optional[Dict] = None,
+            encoding: Optional[str] = None,
+            autodetect_encoding: bool = False,
+    ):
+        super().__init__(
+            file_path=file_path,
+            source_column=source_column,
+            metadata_columns=metadata_columns,
+            csv_args=csv_args,
+            encoding=encoding,
+            autodetect_encoding=autodetect_encoding,
+        )
+        self.columns_to_read = columns_to_read
+
+    def load(self) -> List[Document]:
+        """Load data into document objects."""
+
+        docs = []
+        try:
+            with open(self.file_path, newline="", encoding=self.encoding) as csvfile:
+                docs = self.__read_file(csvfile)
+        except UnicodeDecodeError as e:
+            if self.autodetect_encoding:
+                detected_encodings = detect_file_encodings(self.file_path)
+                for encoding in detected_encodings:
+                    try:
+                        with open(
+                            self.file_path, newline="", encoding=encoding.encoding
+                        ) as csvfile:
+                            docs = self.__read_file(csvfile)
+                            break
+                    except UnicodeDecodeError:
+                        continue
+            else:
+                raise RuntimeError(f"Error loading {self.file_path}") from e
+        except Exception as e:
+            raise RuntimeError(f"Error loading {self.file_path}") from e
+
+        return docs
+    def __read_file(self, csvfile: TextIOWrapper) -> List[Document]:
+        docs = []
+        csv_reader = csv.DictReader(csvfile, **self.csv_args)  # type: ignore
+        for i, row in enumerate(csv_reader):
+            if self.columns_to_read[0] in row:
+                content = row[self.columns_to_read[0]]
+                # Extract the source if available
+                source = (
+                    row.get(self.source_column, None)
+                    if self.source_column is not None
+                    else self.file_path
+                )
+                metadata = {"source": source, "row": i}
+
+                for col in self.metadata_columns:
+                    if col in row:
+                        metadata[col] = row[col]
+
+                doc = Document(page_content=content, metadata=metadata)
+                docs.append(doc)
+            else:
+                raise ValueError(f"Column '{self.columns_to_read[0]}' not found in CSV file.")
+
+        return docs

requirements.txt

@@ -1,4 +1,4 @@
-langchain>=0.0.319
+langchain>=0.0.324
 langchain-experimental>=0.0.30
 fschat[model_worker]==0.2.31
 xformers>=0.0.22.post4

requirements_api.txt

@@ -1,4 +1,4 @@
-langchain>=0.0.319
+langchain>=0.0.324
 langchain-experimental>=0.0.30
 fschat[model_worker]==0.2.31
 xformers>=0.0.22.post4

requirements_lite.txt

@@ -1,4 +1,4 @@
-langchain>=0.0.319
+langchain>=0.0.324
 fschat>=0.2.31
 openai
 # sentence_transformers

server/agent/custom_template.py

@@ -3,29 +3,22 @@ from langchain.agents import Tool, AgentOutputParser
 from langchain.prompts import StringPromptTemplate
 from typing import List
 from langchain.schema import AgentAction, AgentFinish
+
+from configs import SUPPORT_AGENT_MODEL
 from server.agent import model_container
 class CustomPromptTemplate(StringPromptTemplate):
-    # The template to use
     template: str
-    # The list of tools available
     tools: List[Tool]
 
     def format(self, **kwargs) -> str:
-        # Get the intermediate steps (AgentAction, Observation tuples)
-        # Format them in a particular way
         intermediate_steps = kwargs.pop("intermediate_steps")
         thoughts = ""
         for action, observation in intermediate_steps:
             thoughts += action.log
             thoughts += f"\nObservation: {observation}\nThought: "
-            # Set the agent_scratchpad variable to that value
         kwargs["agent_scratchpad"] = thoughts
-        # Create a tools variable from the list of tools provided
         kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
-        # Create a list of tool names for the tools provided
         kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
-        # Return the formatted templatepr
-        # print( self.template.format(**kwargs), end="\n\n")
         return self.template.format(**kwargs)
 
 
@@ -36,9 +29,7 @@ class CustomOutputParser(AgentOutputParser):
         self.begin = True
 
     def parse(self, llm_output: str) -> AgentFinish | tuple[dict[str, str], str] | AgentAction:
-        # Check if agent should finish
-        support_agent = ["Azure-OpenAI", "OpenAI", "Anthropic", "Qwen", "qwen-api", "baichuan-api","agentlm"]  # 目前支持agent的模型
-        if not any(agent in model_container.MODEL for agent in support_agent) and self.begin:
+        if not any(agent in model_container.MODEL for agent in SUPPORT_AGENT_MODEL) and self.begin:
             self.begin = False
             stop_words = ["Observation:"]
             min_index = len(llm_output)
@@ -54,8 +45,6 @@ class CustomOutputParser(AgentOutputParser):
                 return_values={"output": llm_output.split("Final Answer:", 1)[-1].strip()},
                 log=llm_output,
             )
-
-        # Parse out the action and action input
         parts = llm_output.split("Action:")
         if len(parts) < 2:
             return AgentFinish(
@@ -66,7 +55,7 @@ class CustomOutputParser(AgentOutputParser):
         action = parts[1].split("Action Input:")[0].strip()
         action_input = parts[1].split("Action Input:")[1].strip()
 
-        # 原来的正则化检查方式
+        # 原来的正则化检查方式，更严格，但是成功率更低
         # regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
         # print("llm_output",llm_output)
         # match = re.search(regex, llm_output, re.DOTALL)
@@ -80,7 +69,6 @@ class CustomOutputParser(AgentOutputParser):
         # action_input = match.group(2)
 
         # Return the action and action input
-
         try:
             ans = AgentAction(
                 tool=action,

server/chat/knowledge_base_chat.py

@@ -55,8 +55,10 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
         )
         docs = search_docs(query, knowledge_base_name, top_k, score_threshold)
         context = "\n".join([doc.page_content for doc in docs])
-
-        prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
+        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(
             [i.to_msg_template() for i in history] + [input_msg])
@@ -76,6 +78,10 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
             url = f"/knowledge_base/download_doc?" + parameters
             text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\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
@@ -88,7 +94,6 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
             yield json.dumps({"answer": answer,
                               "docs": source_documents},
                              ensure_ascii=False)
-
         await task
 
     return StreamingResponse(knowledge_base_chat_iterator(query=query,

server/chat/search_engine_chat.py

@@ -176,6 +176,9 @@ async def search_engine_chat(query: str = Body(..., description="用户输入",
             for inum, doc in enumerate(docs)
         ]
 
+        if len(source_documents) == 0:  # 没有找到相关资料（不太可能）
+            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

server/knowledge_base/kb_doc_api.py

@@ -31,7 +31,6 @@ def search_docs(query: str = Body(..., description="用户输入", examples=["
         return []
     docs = kb.search_docs(query, top_k, score_threshold)
     data = [DocumentWithScore(**x[0].dict(), score=x[1]) for x in docs]
-
     return data
 
 

server/knowledge_base/utils.py

@@ -1,4 +1,7 @@
 import os
+import sys
+
+sys.path.append("/home/congyin/Code/Project_Langchain_0814/Langchain-Chatchat")
 from transformers import AutoTokenizer
 from configs import (
     EMBEDDING_MODEL,
@@ -25,7 +28,6 @@ import io
 from typing import List, Union, Callable, Dict, Optional, Tuple, Generator
 import chardet
 
-
 def validate_kb_name(knowledge_base_id: str) -> bool:
     # 检查是否包含预期外的字符或路径攻击关键字
     if "../" in knowledge_base_id:
@@ -72,6 +74,7 @@ LOADER_DICT = {"UnstructuredHTMLLoader": ['.html'],
                "UnstructuredMarkdownLoader": ['.md'],
                "CustomJSONLoader": [".json"],
                "CSVLoader": [".csv"],
+               # "FilteredCSVLoader": [".csv"], # 需要自己指定，目前还没有支持
                "RapidOCRPDFLoader": [".pdf"],
                "RapidOCRLoader": ['.png', '.jpg', '.jpeg', '.bmp'],
                "UnstructuredFileLoader": ['.eml', '.msg', '.rst',
@@ -88,12 +91,12 @@ class CustomJSONLoader(langchain.document_loaders.JSONLoader):
     '''
 
     def __init__(
-        self,
-        file_path: Union[str, Path],
-        content_key: Optional[str] = None,
-        metadata_func: Optional[Callable[[Dict, Dict], Dict]] = None,
-        text_content: bool = True,
-        json_lines: bool = False,
+            self,
+            file_path: Union[str, Path],
+            content_key: Optional[str] = None,
+            metadata_func: Optional[Callable[[Dict, Dict], Dict]] = None,
+            text_content: bool = True,
+            json_lines: bool = False,
     ):
         """Initialize the JSONLoader.
 
@@ -150,7 +153,7 @@ def get_loader(loader_name: str, file_path_or_content: Union[str, bytes, io.Stri
     根据loader_name和文件路径或内容返回文档加载器。
     '''
     try:
-        if loader_name in ["RapidOCRPDFLoader", "RapidOCRLoader"]:
+        if loader_name in ["RapidOCRPDFLoader", "RapidOCRLoader","FilteredCSVLoader"]:
             document_loaders_module = importlib.import_module('document_loaders')
         else:
             document_loaders_module = importlib.import_module('langchain.document_loaders')
@@ -168,10 +171,11 @@ def get_loader(loader_name: str, file_path_or_content: Union[str, bytes, io.Stri
         # 自动识别文件编码类型，避免langchain loader 加载文件报编码错误
         with open(file_path_or_content, 'rb') as struct_file:
             encode_detect = chardet.detect(struct_file.read())
-        if encode_detect:
-            loader = DocumentLoader(file_path_or_content, encoding=encode_detect["encoding"])
-        else:
-            loader = DocumentLoader(file_path_or_content, encoding="utf-8")
+        if encode_detect is None:
+            encode_detect = {"encoding": "utf-8"}
+
+        loader = DocumentLoader(file_path_or_content, encoding=encode_detect["encoding"])
+        ## TODO：支持更多的自定义CSV读取逻辑
 
     elif loader_name == "JSONLoader":
         loader = DocumentLoader(file_path_or_content, jq_schema=".", text_content=False)
@@ -187,10 +191,10 @@ def get_loader(loader_name: str, file_path_or_content: Union[str, bytes, io.Stri
 
 
 def make_text_splitter(
-    splitter_name: str = TEXT_SPLITTER_NAME,
-    chunk_size: int = CHUNK_SIZE,
-    chunk_overlap: int = OVERLAP_SIZE,
-    llm_model: str = LLM_MODEL,
+        splitter_name: str = TEXT_SPLITTER_NAME,
+        chunk_size: int = CHUNK_SIZE,
+        chunk_overlap: int = OVERLAP_SIZE,
+        llm_model: str = LLM_MODEL,
 ):
     """
     根据参数获取特定的分词器
@@ -262,6 +266,7 @@ def make_text_splitter(
         text_splitter = TextSplitter(chunk_size=250, chunk_overlap=50)
     return text_splitter
 
+
 class KnowledgeFile:
     def __init__(
             self,
@@ -282,7 +287,7 @@ class KnowledgeFile:
         self.document_loader_name = get_LoaderClass(self.ext)
         self.text_splitter_name = TEXT_SPLITTER_NAME
 
-    def file2docs(self, refresh: bool=False):
+    def file2docs(self, refresh: bool = False):
         if self.docs is None or refresh:
             logger.info(f"{self.document_loader_name} used for {self.filepath}")
             loader = get_loader(self.document_loader_name, self.filepath)
@@ -290,20 +295,21 @@ class KnowledgeFile:
         return self.docs
 
     def docs2texts(
-        self,
-        docs: List[Document] = None,
-        zh_title_enhance: bool = ZH_TITLE_ENHANCE,
-        refresh: bool = False,
-        chunk_size: int = CHUNK_SIZE,
-        chunk_overlap: int = OVERLAP_SIZE,
-        text_splitter: TextSplitter = None,
+            self,
+            docs: List[Document] = None,
+            zh_title_enhance: bool = ZH_TITLE_ENHANCE,
+            refresh: bool = False,
+            chunk_size: int = CHUNK_SIZE,
+            chunk_overlap: int = OVERLAP_SIZE,
+            text_splitter: TextSplitter = None,
     ):
         docs = docs or self.file2docs(refresh=refresh)
         if not docs:
             return []
         if self.ext not in [".csv"]:
             if text_splitter is None:
-                text_splitter = make_text_splitter(splitter_name=self.text_splitter_name, chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+                text_splitter = make_text_splitter(splitter_name=self.text_splitter_name, chunk_size=chunk_size,
+                                                   chunk_overlap=chunk_overlap)
             if self.text_splitter_name == "MarkdownHeaderTextSplitter":
                 docs = text_splitter.split_text(docs[0].page_content)
                 for doc in docs:
@@ -320,12 +326,12 @@ class KnowledgeFile:
         return self.splited_docs
 
     def file2text(
-        self,
-        zh_title_enhance: bool = ZH_TITLE_ENHANCE,
-        refresh: bool = False,
-        chunk_size: int = CHUNK_SIZE,
-        chunk_overlap: int = OVERLAP_SIZE,
-        text_splitter: TextSplitter = None,
+            self,
+            zh_title_enhance: bool = ZH_TITLE_ENHANCE,
+            refresh: bool = False,
+            chunk_size: int = CHUNK_SIZE,
+            chunk_overlap: int = OVERLAP_SIZE,
+            text_splitter: TextSplitter = None,
     ):
         if self.splited_docs is None or refresh:
             docs = self.file2docs()
@@ -359,6 +365,7 @@ def files2docs_in_thread(
     如果传入参数是Tuple，形式为(filename, kb_name)
     生成器返回值为 status, (kb_name, file_name, docs | error)
     '''
+
     def file2docs(*, file: KnowledgeFile, **kwargs) -> Tuple[bool, Tuple[str, str, List[Document]]]:
         try:
             return True, (file.kb_name, file.filename, file.file2text(**kwargs))
@@ -373,8 +380,8 @@ def files2docs_in_thread(
         kwargs = {}
         try:
             if isinstance(file, tuple) and len(file) >= 2:
-                filename=file[0]
-                kb_name=file[1]
+                filename = file[0]
+                kb_name = file[1]
                 file = KnowledgeFile(filename=filename, knowledge_base_name=kb_name)
             elif isinstance(file, dict):
                 filename = file.pop("filename")
@@ -396,10 +403,9 @@ def files2docs_in_thread(
 if __name__ == "__main__":
     from pprint import pprint
 
-    kb_file = KnowledgeFile(filename="test.txt", knowledge_base_name="samples")
+    kb_file = KnowledgeFile(
+        filename="/home/congyin/Code/Project_Langchain_0814/Langchain-Chatchat/knowledge_base/csv1/content/gm.csv",
+        knowledge_base_name="samples")
     # kb_file.text_splitter_name = "RecursiveCharacterTextSplitter"
     docs = kb_file.file2docs()
-    pprint(docs[-1])
-
-    docs = kb_file.file2text()
-    pprint(docs[-1])
+    # pprint(docs[-1])

webui_pages/dialogue/dialogue.py

@@ -4,16 +4,17 @@ from streamlit_chatbox import *
 from datetime import datetime
 import os
 from configs import (TEMPERATURE, HISTORY_LEN, PROMPT_TEMPLATES,
-                     DEFAULT_KNOWLEDGE_BASE, DEFAULT_SEARCH_ENGINE)
+                     DEFAULT_KNOWLEDGE_BASE, DEFAULT_SEARCH_ENGINE, SUPPORT_AGENT_MODEL)
 from typing import List, Dict
 
-
 chat_box = ChatBox(
     assistant_avatar=os.path.join(
         "img",
         "chatchat_icon_blue_square_v2.png"
     )
 )
+
+
 def get_messages_history(history_len: int, content_in_expander: bool = False) -> List[Dict]:
     '''
     返回消息历史。
@@ -55,14 +56,14 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
 
         if is_lite:
             dialogue_modes = ["LLM 对话",
-                            "搜索引擎问答",
-                            ]
+                              "搜索引擎问答",
+                              ]
         else:
             dialogue_modes = ["LLM 对话",
-                            "知识库问答",
-                            "搜索引擎问答",
-                            "自定义Agent问答",
-                            ]
+                              "知识库问答",
+                              "搜索引擎问答",
+                              "自定义Agent问答",
+                              ]
         dialogue_mode = st.selectbox("请选择对话模式：",
                                      dialogue_modes,
                                      index=0,
@@ -102,10 +103,10 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
                                  key="llm_model",
                                  )
         if (st.session_state.get("prev_llm_model") != llm_model
-            and not is_lite
-            and not llm_model in config_models.get("online", {})
-            and not llm_model in config_models.get("langchain", {})
-            and llm_model not in running_models):
+                and not is_lite
+                and not llm_model in config_models.get("online", {})
+                and not llm_model in config_models.get("langchain", {})
+                and llm_model not in running_models):
             with st.spinner(f"正在加载模型： {llm_model}，请勿进行操作或刷新页面"):
                 prev_model = st.session_state.get("prev_llm_model")
                 r = api.change_llm_model(prev_model, llm_model)
@@ -210,17 +211,20 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
 
 
         elif dialogue_mode == "自定义Agent问答":
-            chat_box.ai_say([
-                f"正在思考...",
-                Markdown("...", in_expander=True, title="思考过程", state="complete"),
+            if not any(agent in llm_model for agent in SUPPORT_AGENT_MODEL):
+                chat_box.ai_say([
+                    f"正在思考... \n\n <span style='color:red'>该模型并没有进行Agent对齐，请更换支持Agent的模型获得更好的体验！</span>\n\n\n",
+                    Markdown("...", in_expander=True, title="思考过程", state="complete"),
 
-            ])
+                ])
+            else:
+                chat_box.ai_say([
+                    f"正在思考...",
+                    Markdown("...", in_expander=True, title="思考过程", state="complete"),
+
+                ])
             text = ""
             ans = ""
-            support_agent = ["Azure-OpenAI", "OpenAI", "Anthropic", "Qwen", "qwen-api", "baichuan-api","agentlm"]  # 目前支持agent的模型
-            if not any(agent in llm_model for agent in support_agent):
-                ans += "正在思考... \n\n <span style='color:red'>该模型并没有进行Agent对齐，请更换支持Agent的模型获得更好的体验！</span>\n\n\n"
-                chat_box.update_msg(ans, element_index=0, streaming=False)
             for d in api.agent_chat(prompt,
                                     history=history,
                                     model=llm_model,
@@ -278,7 +282,7 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
                                             model=llm_model,
                                             prompt_name=prompt_template_name,
                                             temperature=temperature,
-                                            split_result=se_top_k>1):
+                                            split_result=se_top_k > 1):
                 if error_msg := check_error_msg(d):  # check whether error occured
                     st.error(error_msg)
                 elif chunk := d.get("answer"):