Dev (#1902)

修改Agent的内容

server/agent/tools/__init__.py

@@ -1,11 +1,11 @@
 ## 导入所有的工具类
 from .search_knowledge_simple import knowledge_search_simple
-from .search_all_knowledge_once import knowledge_search_once
+from .search_all_knowledge_once import knowledge_search_once, KnowledgeSearchInput
-from .search_all_knowledge_more import knowledge_search_more
+from .search_all_knowledge_more import knowledge_search_more, KnowledgeSearchInput
-from .calculate import calculate
+from .calculate import calculate, CalculatorInput
-from .translator import translate
+from .translator import translate, TranslateInput
-from .weather import weathercheck
+from .weather import weathercheck, WhetherSchema
-from .shell import shell
+from .shell import shell, ShellInput
-from .search_internet import search_internet
+from .search_internet import search_internet, SearchInternetInput
-from .wolfram import wolfram
+from .wolfram import wolfram, WolframInput
-from .youtube import youtube_search
+from .youtube import youtube_search, YoutubeInput

server/agent/tools/calculate.py

@@ -1,6 +1,7 @@
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMMathChain
 from server.agent import model_container
+from pydantic import BaseModel, Field
 
 _PROMPT_TEMPLATE = """
 将数学问题翻译成可以使用Python的numexpr库执行的表达式。使用运行此代码的输出来回答问题。
@@ -58,6 +59,9 @@ PROMPT = PromptTemplate(
 )
 
 
+class CalculatorInput(BaseModel):
+    query: str = Field()
+
 def calculate(query: str):
     model = model_container.MODEL
     llm_math = LLMMathChain.from_llm(model, verbose=True, prompt=PROMPT)
@@ -68,3 +72,5 @@ if __name__ == "__main__":
     result = calculate("2的三次方")
     print("答案:",result)
 
+
+

server/agent/tools/search_all_knowledge_more.py

@@ -14,7 +14,7 @@ from server.chat.knowledge_base_chat import knowledge_base_chat
 from configs import VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, MAX_TOKENS
 import asyncio
 from server.agent import model_container
-
+from pydantic import BaseModel, Field
 
 async def search_knowledge_base_iter(database: str, query: str) -> str:
     response = await knowledge_base_chat(query=query,
@@ -264,6 +264,8 @@ def knowledge_search_more(query: str):
     ans = llm_knowledge.run(query)
     return ans
 
+class KnowledgeSearchInput(BaseModel):
+    location: str = Field(description="知识库查询的内容")
 
 if __name__ == "__main__":
     result = knowledge_search_more("机器人和大数据在代码教学上有什么区别")

server/agent/tools/search_all_knowledge_once.py

@@ -23,7 +23,7 @@ from configs import VECTOR_SEARCH_TOP_K, SCORE_THRESHOLD, MAX_TOKENS
 
 import asyncio
 from server.agent import model_container
-
+from pydantic import BaseModel, Field
 
 async def search_knowledge_base_iter(database: str, query: str):
     response = await knowledge_base_chat(query=query,
@@ -225,6 +225,10 @@ def knowledge_search_once(query: str):
     return ans
 
 
+class KnowledgeSearchInput(BaseModel):
+    location: str = Field(description="知识库查询的内容")
+
+
 if __name__ == "__main__":
     result = knowledge_search_once("大数据的男女比例")
     print(result)

server/agent/tools/search_internet.py

@@ -3,6 +3,7 @@ from server.chat.search_engine_chat import search_engine_chat
 from configs import VECTOR_SEARCH_TOP_K, MAX_TOKENS
 import asyncio
 from server.agent import model_container
+from pydantic import BaseModel, Field
 
 async def search_engine_iter(query: str):
     response = await search_engine_chat(query=query,
@@ -25,9 +26,11 @@ async def search_engine_iter(query: str):
     return contents
 
 def search_internet(query: str):
-
     return asyncio.run(search_engine_iter(query))
 
+class SearchInternetInput(BaseModel):
+    location: str = Field(description="需要查询的内容")
+
 
 if __name__ == "__main__":
     result = search_internet("今天星期几")

server/agent/tools/shell.py

@@ -1,6 +1,9 @@
 # LangChain 的 Shell 工具
+from pydantic import BaseModel, Field
 from langchain.tools import ShellTool
 def shell(query: str):
     tool = ShellTool()
     return tool.run(tool_input=query)
 
+class ShellInput(BaseModel):
+    query: str = Field(description="一个能在Linux命令行运行的Shell命令")

server/agent/tools/translator.py

@@ -1,6 +1,7 @@
 from langchain.prompts import PromptTemplate
 from langchain.chains import LLMChain
 from server.agent import model_container
+from pydantic import BaseModel, Field
 
 _PROMPT_TEMPLATE = '''
 # 指令
@@ -29,6 +30,9 @@ def translate(query: str):
     ans = llm_translate.run(query)
     return ans
 
+class TranslateInput(BaseModel):
+    location: str = Field(description="需要被翻译的内容")
+
 if __name__ == "__main__":
     result = translate("Can Love remember the question and the answer? 这句话如何诗意的翻译成中文")
     print("答案:",result)

server/agent/tools/weather.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 ## 单独运行的时候需要添加
 import sys
 import os
+
 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
 
 import re
@@ -23,11 +24,11 @@ from typing import List, Any, Optional
 from datetime import datetime
 from langchain.prompts import PromptTemplate
 from server.agent import model_container
+from pydantic import BaseModel, Field
 
 ## 使用和风天气API查询天气
 KEY = "ac880e5a877042809ac7ffdd19d95b0d"
-#key长这样，这里提供了示例的key，这个key没法使用，你需要自己去注册和风天气的账号，然后在这里填入你的key
+# key长这样，这里提供了示例的key，这个key没法使用，你需要自己去注册和风天气的账号，然后在这里填入你的key
-
 
 
 _PROMPT_TEMPLATE = """
@@ -95,7 +96,7 @@ def get_city_info(location, adm, key):
     return data
 
 
-def format_weather_data(data,place):
+def format_weather_data(data, place):
     hourly_forecast = data['hourly']
     formatted_data = f"\n 这是查询到的关于{place}未来24小时的天气信息: \n"
     for forecast in hourly_forecast:
@@ -141,7 +142,7 @@ def format_weather_data(data,place):
     return formatted_data
 
 
-def get_weather(key, location_id,place):
+def get_weather(key, location_id, place):
     url = "https://devapi.qweather.com/v7/weather/24h?"
     params = {
         'location': location_id,
@@ -149,18 +150,20 @@ def get_weather(key, location_id,place):
     }
     response = requests.get(url, params=params)
     data = response.json()
-    return format_weather_data(data,place)
+    return format_weather_data(data, place)
 
 
 def split_query(query):
     parts = query.split()
     adm = parts[0]
+    if len(parts) == 1:
+        return adm, adm
     location = parts[1] if parts[1] != 'None' else adm
     return location, adm
 
 
 def weather(query):
-    location, adm= split_query(query)
+    location, adm = split_query(query)
     key = KEY
     if key == "":
         return "请先在代码中填入和风天气API Key"
@@ -169,17 +172,19 @@ def weather(query):
         location_id = city_info['location'][0]['id']
         place = adm + "市" + location + "区"
 
-        weather_data = get_weather(key=key, location_id=location_id,place=place)
+        weather_data = get_weather(key=key, location_id=location_id, place=place)
-        return weather_data  + "以上是查询到的天气信息，请你查收\n"
+        return weather_data + "以上是查询到的天气信息，请你查收\n"
     except KeyError:
         try:
             city_info = get_city_info(location=adm, adm=adm, key=key)
             location_id = city_info['location'][0]['id']
             place = adm + "市"
-            weather_data = get_weather(key=key, location_id=location_id,place=place)
+            weather_data = get_weather(key=key, location_id=location_id, place=place)
             return weather_data + "重要提醒：用户提供的市和区中，区的信息不存在，或者出现错别字，因此该信息是关于市的天气，请你查收\n"
         except KeyError:
             return "输入的地区不存在，无法提供天气预报"
+
+
 class LLMWeatherChain(Chain):
     llm_chain: LLMChain
     llm: Optional[BaseLanguageModel] = None
@@ -319,12 +324,15 @@ class LLMWeatherChain(Chain):
         return cls(llm_chain=llm_chain, **kwargs)
 
 
-
 def weathercheck(query: str):
     model = model_container.MODEL
     llm_weather = LLMWeatherChain.from_llm(model, verbose=True, prompt=PROMPT)
     ans = llm_weather.run(query)
     return ans
 
+
+class WhetherSchema(BaseModel):
+    location: str = Field(description="应该是一个地区的名称，用空格隔开，例如：上海 浦东，如果没有区的信息，可以只输入上海")
+
 if __name__ == '__main__':
-    result = weathercheck("苏州姑苏区今晚热不热？")
+    result = weathercheck("苏州姑苏区今晚热不热？")

server/agent/tools/wolfram.py

@@ -1,7 +1,11 @@
 # Langchain 自带的 Wolfram Alpha API 封装
 from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
+from pydantic import BaseModel, Field
 wolfram_alpha_appid = "your key"
 def wolfram(query: str):
     wolfram = WolframAlphaAPIWrapper(wolfram_alpha_appid=wolfram_alpha_appid)
     ans = wolfram.run(query)
-    return ans
+    return ans
+
+class WolframInput(BaseModel):
+    location: str = Field(description="需要运算的具体问题")

server/agent/tools/youtube.py

@@ -1,5 +1,9 @@
 # Langchain 自带的 YouTube 搜索工具封装
 from langchain.tools import YouTubeSearchTool
+from pydantic import BaseModel, Field
 def youtube_search(query: str):
     tool = YouTubeSearchTool()
-    return tool.run(tool_input=query)
+    return tool.run(tool_input=query)
+
+class YoutubeInput(BaseModel):
+    location: str = Field(description="要搜索视频关键字")

server/agent/tools_select.py

@@ -1,60 +1,20 @@
 from langchain.tools import Tool
 from server.agent.tools import *
 
-## 请注意，如果你是为了使用AgentLM，在这里，你应该使用英文版本，下面的内容是英文版本。
+## 请注意，如果你是为了使用AgentLM，在这里，你应该使用英文版本。
-# tools = [
-#     Tool.from_function(
-#         func=calculate,
-#         name="Simple Calculator Tool",
-#         description="Perform simple mathematical operations, Just simple, Use Wolfram Math Tool for more complex operations"
-#     ),
-#     Tool.from_function(
-#         func=translate,
-#         name="Translation Tool",
-#         description="Use this tool if you can't access the internet and need to translate various languages"
-#     ),
-#     Tool.from_function(
-#         func=weathercheck,
-#         name="Weather Checking Tool",
-#         description="Check the weather for various places in China for the next 24 hours without needing internet access"
-#     ),
-#     Tool.from_function(
-#         func=shell,
-#         name="Shell Tool",
-#         description="Use command line tool output"
-#     ),
-#     Tool.from_function(
-#         func=knowledge_search_more,
-#         name="Knowledge Base Tool",
-#         description="Prioritize accessing the knowledge base to get answers"
-#     ),
-#     Tool.from_function(
-#         func=search_internet,
-#         name="Internet Tool",
-#         description="If you can't access the internet, this tool can help you access Bing to answer questions"
-#     ),
-#     Tool.from_function(
-#         func=wolfram,
-#         name="Wolfram Math Tool",
-#         description="Use this tool to perform more complex mathematical operations"
-#     ),
-#     Tool.from_function(
-#         func=youtube_search,
-#         name="Youtube Search Tool",
-#         description="Use this tool to search for videos on Youtube"
-#     )
-# ]
 
 tools = [
     Tool.from_function(
         func=calculate,
         name="计算器工具",
         description="进行简单的数学运算, 只是简单的, 使用Wolfram数学工具进行更复杂的运算",
+        args_schema=CalculatorInput,
     ),
     Tool.from_function(
         func=translate,
         name="翻译工具",
-        description="如果你无法访问互联网，并且需要翻译各种语言，应该使用这个工具"
+        description="如果你无法访问互联网，并且需要翻译各种语言，应该使用这个工具",
+        args_schema=TranslateInput,
     ),
     Tool.from_function(
         func=weathercheck,
@@ -79,13 +39,15 @@ tools = [
     Tool.from_function(
         func=wolfram,
         name="Wolfram数学工具",
-        description="高级的数学运算工具，能够完成非常复杂的数学问题"
+        description="高级的数学运算工具，能够完成非常复杂的数学问题",
+        args_schema=WolframInput,
     ),
     Tool.from_function(
         func=youtube_search,
         name="Youtube搜索工具",
-        description="使用这个工具在Youtube上搜索视频"
+        description="使用这个工具在Youtube上搜索视频",
-    )
+        args_schema=YoutubeInput,
+    ),
 ]
 
 tool_names = [tool.name for tool in tools]

server/chat/agent_chat.py

@@ -1,7 +1,7 @@
 from langchain.memory import ConversationBufferWindowMemory
 from server.agent.tools_select import tools, tool_names
 from server.agent.callbacks import CustomAsyncIteratorCallbackHandler, Status
-from langchain.agents import AgentExecutor, LLMSingleActionAgent
+from langchain.agents import AgentExecutor, LLMSingleActionAgent, initialize_agent, BaseMultiActionAgent
 from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
 from fastapi import Body
 from fastapi.responses import StreamingResponse

server/knowledge_base/kb_cache/faiss_cache.py

@@ -1,8 +1,9 @@
+from configs import CACHED_VS_NUM
 from server.knowledge_base.kb_cache.base import *
 from server.knowledge_base.utils import get_vs_path
 from langchain.vectorstores import FAISS
 import os
-
+from langchain.schema import Document
 
 class ThreadSafeFaiss(ThreadSafeObject):
     def __repr__(self) -> str: