跟新了langchain 0.1.x需要的依赖和修改的代码

requirements.txt

@@ -5,8 +5,10 @@ torch>=2.1.2
 torchvision>=0.16.2
 torchaudio>=2.1.2
 
-langchain>=0.0.352
-langchain-experimental>=0.0.47
+langchain>=0.1.0
+langchain_openai>=0.0.2
+langchain-community>=1.0.0
+
 pydantic==1.10.13
 fschat==0.2.35
 openai==1.9.0

server/agent/agent_factory/glm3_agent.py

@@ -1,8 +1,8 @@
 """
 This file is a modified version for ChatGLM3-6B the original glm3_agent.py file from the langchain repo.
 """
-from __future__ import annotations
 
+from __future__ import annotations
 import json
 import logging
 from typing import Any, List, Sequence, Tuple, Optional, Union
@@ -21,9 +21,7 @@ from langchain.agents.agent import AgentExecutor
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.tools.base import BaseTool
-from langchain_core.callbacks import Callbacks
 
-HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
 logger = logging.getLogger(__name__)
 
 
@@ -148,7 +146,6 @@ class StructuredGLM3ChatAgent(Agent):
         formatted_tools = formatted_tools.replace("'", "\\'").replace("{", "{{").replace("}", "}}")
         template = prompt.format(tool_names=tool_names,
                                  tools=formatted_tools,
-                                 history="None",
                                  input="{input}",
                                  agent_scratchpad="{agent_scratchpad}")
 
@@ -169,7 +166,6 @@ class StructuredGLM3ChatAgent(Agent):
             prompt: str = None,
             callbacks: List[BaseCallbackHandler] = [],
             output_parser: Optional[AgentOutputParser] = None,
-            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
             input_variables: Optional[List[str]] = None,
             memory_prompts: Optional[List[BasePromptTemplate]] = None,
             **kwargs: Any,
@@ -229,6 +225,5 @@ def initialize_glm3_agent(
         callbacks=callbacks,
         memory=memory,
         tags=tags_,
-        intermediate_steps=[],
         **kwargs,
     )

server/agent/agent_instruct.md

@@ -0,0 +1,32 @@
+# What tools should be used 
+
+# search_internet
+
+使用这个工具是因为用户需要在联网进行搜索。这些问题通常是你不知道的，这些问题具有特点，
+
+例如：
+
++ 联网帮我查询 xxx
++ 我想知道最新的新闻
+
+或者，用户有明显的意图，需要获取事实的信息。
+
+返回字段如下
+
+```
+search_internet
+```
+
+# search_local_knowledge
+
+使用这个工具是希望用户能够获取本地的知识，这些知识通常是你自身能力不具备的专业问题，或者用户指定了某个任务的。
+
+例如：
+
++ 告诉我 关于 xxx 的 xxx 信息
++ xxx 中 xxx 的 xxx 是什么
+
+返回字段如下
+```
+search_local_knowledge
+```

server/agent/tools_factory/calculate.py

@@ -11,7 +11,7 @@ def calculate(a: float, b: float, operator: str) -> float:
         if b != 0:
             return a / b
         else:
-            return float('inf')  # 防止除以零
+            return float('inf')
     elif operator == "^":
         return a ** b
     else:

server/agent/tools_factory/search_youtube.py

@@ -1,5 +1,4 @@
-# Langchain 自带的 YouTube 搜索工具封装
-from langchain.tools import YouTubeSearchTool
+from langchain_community.tools import YouTubeSearchTool
 from pydantic import BaseModel, Field
 def search_youtube(query: str):
     tool = YouTubeSearchTool()

server/agent/tools_factory/shell.py

@@ -1,6 +1,6 @@
 # LangChain 的 Shell 工具
 from pydantic import BaseModel, Field
-from langchain.tools import ShellTool
+from langchain_community.tools import ShellTool
 def shell(query: str):
     tool = ShellTool()
     return tool.run(tool_input=query)

server/chat/chat.py

@@ -5,7 +5,8 @@ from typing import List, Union, AsyncIterable, Dict
 from fastapi import Body
 from fastapi.responses import StreamingResponse
 
-from langchain.agents import initialize_agent, AgentType
+from langchain.agents import initialize_agent, AgentType, create_structured_chat_agent, AgentExecutor
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain_core.output_parsers import StrOutputParser
 from langchain.chains import LLMChain
 from langchain.prompts.chat import ChatPromptTemplate
@@ -83,7 +84,6 @@ def create_models_chains(history, history_len, prompts, models, tools, callbacks
                 llm=models["action_model"],
                 tools=tools,
                 prompt=prompts["action_model"],
-                memory=memory,
                 callbacks=callbacks,
                 verbose=True,
             )
@@ -92,7 +92,6 @@ def create_models_chains(history, history_len, prompts, models, tools, callbacks
                 llm=models["action_model"],
                 tools=tools,
                 prompt=prompts["action_model"],
-                memory=memory,
                 callbacks=callbacks,
                 verbose=True,
             )
@@ -102,7 +101,6 @@ def create_models_chains(history, history_len, prompts, models, tools, callbacks
                 tools=tools,
                 callbacks=callbacks,
                 agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
-                memory=memory,
                 verbose=True,
             )
 
@@ -111,7 +109,7 @@ def create_models_chains(history, history_len, prompts, models, tools, callbacks
         #     chain
         # )
         # full_chain = ({"topic": classifier_chain, "input": lambda x: x["input"]} | branch)
-        full_chain = ({"input": lambda x: x["input"]} | agent_executor)
+        full_chain = ({"input": lambda x: x["input"], } | agent_executor)
     else:
         chain.llm.callbacks = callbacks
         full_chain = ({"input": lambda x: x["input"]} | chain)
@@ -146,14 +144,9 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
 
         callback = AgentExecutorAsyncIteratorCallbackHandler()
         callbacks = [callback]
-
-        # 从配置中选择模型
         models, prompts = create_models_from_config(callbacks=[], configs=model_config, stream=stream)
-
-        # 从配置中选择工具
         tools = [tool for tool in all_tools if tool.name in tool_config]
         tools = [t.copy(update={"callbacks": callbacks}) for t in tools]
-        # 构建完整的Chain
         full_chain = create_models_chains(prompts=prompts,
                                           models=models,
                                           conversation_id=conversation_id,
@@ -163,6 +156,7 @@ async def chat(query: str = Body(..., description="用户输入", examples=["恼
                                           history_len=history_len,
                                           metadata=metadata)
         task = asyncio.create_task(wrap_done(full_chain.ainvoke({"input": query}), callback.done))
+
         async for chunk in callback.aiter():
             data = json.loads(chunk)
             data["message_id"] = message_id

server/knowledge_base/utils.py

@@ -17,8 +17,9 @@ from langchain.text_splitter import TextSplitter
 from pathlib import Path
 from server.utils import run_in_thread_pool, get_model_worker_config
 import json
-from typing import List, Union,Dict, Tuple, Generator
+from typing import List, Union, Dict, Tuple, Generator
 import chardet
+from langchain_community.document_loaders import JSONLoader
 
 
 def validate_kb_name(knowledge_base_id: str) -> bool:
@@ -122,15 +123,13 @@ def _new_json_dumps(obj, **kwargs):
     kwargs["ensure_ascii"] = False
     return _origin_json_dumps(obj, **kwargs)
 
+
 if json.dumps is not _new_json_dumps:
     _origin_json_dumps = json.dumps
     json.dumps = _new_json_dumps
 
 
-class JSONLinesLoader(langchain.document_loaders.JSONLoader):
-    '''
-    行式 Json 加载器，要求文件扩展名为 .jsonl
-    '''
+class JSONLinesLoader(JSONLoader):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._json_lines = True

server/utils.py

@@ -9,8 +9,8 @@ from configs import (LLM_MODEL_CONFIG, LLM_DEVICE, EMBEDDING_DEVICE,
                      FSCHAT_MODEL_WORKERS, HTTPX_DEFAULT_TIMEOUT)
 import os
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import OpenAI
+from langchain_openai.chat_models import ChatOpenAI
+from langchain_community.llms import OpenAI
 import httpx
 from typing import (
     TYPE_CHECKING,

update_requirements.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+python -m pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+while read requirement; do
+    python -m pip install --upgrade "$requirement" -i https://pypi.tuna.tsinghua.edu.cn/simple
+done < requirements.txt