大模型之Agent的初步了解 原创

# 引入Qwen大模型
from utils import get_qwen_models

llm , chat, _ = get_qwen_models()

chat.invoke("现在几点了？")
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AIMessage(content='我是一个AI模型，无法实时获取当前时间。请您查看您的设备或询问周围的人来获取准确的时间。',
response_metadata={'model_name':'qwen-max',
'finish_reason':'stop',
'request_id':'cc11822c-605c-9b94-b443-85d30c9b6c0f',
'token_usage':{'input_tokens':12,'output_tokens':24,'total_tokens':36}},
id='run-bb389bae-6801-4e53-a67c-5d41a53aba8c-0')
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# 定义一个获取当前时间的函数
def get_datetime() -> str:
    """
        跟时期或时间查询相关的问题，请调用此方法
        注意：
            - 此方法没有入参
            - 返参是字符串形式的日期
    """
# 调用该函数
get_datetime()
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'2024-08-29 20:39:34'
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from langchain.prompts import PromptTemplate

prompt = PromptTemplate.from_template("""
Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought:{agent_scratchpad}
""")
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from langgraph.prebuilt import create_react_agent
from langchain_core.messages importHumanMessage

agent = create_react_agent(model=chat, tools=[get_datetime])

# 调用代理
try:
    response = agent.invoke({"messages":[HumanMessage(content="现在几点了？")]})
print(response)
exceptKeyErroras e:
print(f"KeyError: {e}")
exceptExceptionas e:
print(f"发生错误: {e}")
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{'messages':
[
HumanMessage(content='现在几点了？',id='1e807299-fb54-4fd9-ba16-71b2c45dab98'),
AIMessage(content='', additional_kwargs={
'tool_calls':[{
'function':{
'name':'get_datetime',
'arguments':'{}'},
'index':0,
'id':'call_d21bf57fd5df4314941b9e',
'type':'function'
}]},
        response_metadata={
'model_name':'qwen-max',
'finish_reason':'tool_calls',
'request_id':'95c8bf84-3105-91c7-988f-430ef4f3bb84',
'token_usage':{'input_tokens':180,'output_tokens':12,'total_tokens':192}},id='run-9b8c496f-4e2a-4698-bb6d-9fec655c3e37-0',
          tool_calls=[{
'name':'get_datetime',
'args':{},
'id':'call_d21bf57fd5df4314941b9e',
'type':'tool_call'}]),
ToolMessage(content='2024-08-30 14:52:29',
      name='get_datetime',
id='ce53e86f-252a-4c6d-b33b-1589732ebbbb',
      tool_call_id='call_d21bf57fd5df4314941b9e'),
AIMessage(content='现在的时间是14点52分29秒。',
      response_metadata={
'model_name':'qwen-max',
'finish_reason':'stop',
'request_id':'adb16577-6a8e-937d-8c13-0d6ba44e5082',
'token_usage':{'input_tokens':220,'output_tokens':17,'total_tokens':237}},
id='run-fd7835ae-b7f2-41d2-b7f9-4a33a51cd67b-0')
]
}
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import datetime
from langchain.prompts importPromptTemplate
from langgraph.prebuilt import create_react_agent
from langchain_core.messages importHumanMessage
from utils import get_qwen_models

# 连接大模型
llm , chat, _ = get_qwen_models()

# 定义调用函数
defget_datetime()->str:
"""
    获取当前时间
    """
    now = datetime.datetime.now()
    formatted_date = now.strftime("%Y-%m-%d %H:%M:%S")

return formatted_date

# 给大模型绑定工具
bined_chat = chat.bind_tools(tools=[get_datetime])

# 创建使用工具的prompt
prompt =PromptTemplate.from_template("""
Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin!

Question: {input}
Thought:{agent_scratchpad}
""")

# 创建Agent
agent = create_react_agent(model=chat, tools=[get_datetime])

# 调用Agent
try:
    response = agent.invoke({"messages":[HumanMessage(content="现在几点了？")]})
print(response)
exceptKeyErroras e:
print(f"KeyError: {e}")
exceptExceptionas e:
print(f"发生错误: {e}")
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# 连接大模型
from utils import get_qwen_models

llm, chat, embed = get_qwen_models()
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# 连接数据库
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_uri("sqlite:///博金杯比赛数据.db")
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from langchain_community.agent_toolkits import SQLDatabaseToolkit

# 初始化数据库工具包，传入数据库连接对象 db 和语言模型 llm
toolkit = SQLDatabaseToolkit(db=db, llm=llm)

# 从工具包中获取可用的工具，并将其存储在 tools 变量中
tools = toolkit.get_tools()
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from langchain_core.messages import SystemMessage

SQL_PREFIX = """You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct SQLite query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the below tools. Only use the information returned by the below tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.

DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.

To start you should ALWAYS look at the tables in the database to see what you can query.
Do NOT skip this step.
Then you should query the schema of the most relevant tables."""

system_message = SystemMessage(cnotallow=SQL_PREFIX)
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from langgraph.prebuilt import create_react_agent

# 创建Agent，传入 chat、工具 tools 和 第四步的 prompt
agent_executor = create_react_agent(chat, tools, messages_modifier=system_message)
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# 查询
example_query = "请帮我查询出20210415日，建筑材料一级行业涨幅超过5%（不包含）的股票数量"

# 流式处理事件
events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)

# 打印流式事件的消息
for event in events:
    event["messages"][-1].pretty_print()
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================================ HumanMessage=================================

请帮我查询出20210415日，建筑材料一级行业涨幅超过5%（不包含）的股票数量
==================================AiMessage==================================
ToolCalls:
  sql_db_list_tables (call_c14a5fc51d324381926311)
Call ID: call_c14a5fc51d324381926311
Args:
    tool_input:
=================================ToolMessage=================================
Name: sql_db_list_tables

A股公司行业划分表, A股票日行情表,基金份额持有人结构,基金债券持仓明细,基金可转债持仓明细,基金基本信息,基金日行情表,基金股票持仓明细,基金规模变动表,港股票日行情表
==================================AiMessage==================================
ToolCalls:
  sql_db_schema (call_f9acd6019db64e93a74987)
Call ID: call_f9acd6019db64e93a74987
Args:
    table_names: A股公司行业划分表, A股票日行情表
=================================ToolMessage=================================
Name: sql_db_schema


CREATE TABLE "A股公司行业划分表"(
"股票代码" TEXT,
...
[(74,)]
==================================AiMessage==================================

在2021年04月15日，建筑材料一级行业涨幅超过5%（不包含）的股票数量为74只。
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from langchain_community.utilities importSQLDatabase
from langchain_community.agent_toolkits importSQLDatabaseToolkit
from langchain_core.messages importSystemMessage
from langgraph.prebuilt import create_react_agent
from utils import get_qwen_models

# 连接大模型
llm, chat, embed = get_qwen_models()

# 连接数据库
db =SQLDatabase.from_uri("sqlite:///博金杯比赛数据.db")

# 初始化SQL工具包
toolkit =SQLDatabaseToolkit(db=db, llm=llm)
tools = toolkit.get_tools()

# 构建Prompt
SQL_PREFIX ="""You are an agent designed to interact with a SQL database.
Given an input question, create a syntactically correct SQLite query to run, then look at the results of the query and return the answer.
Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most 5 results.
You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for the relevant columns given the question.
You have access to tools for interacting with the database.
Only use the below tools. Only use the information returned by the below tools to construct your final answer.
You MUST double check your query before executing it. If you get an error while executing a query, rewrite the query and try again.

DO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.

To start you should ALWAYS look at the tables in the database to see what you can query.
Do NOT skip this step.
Then you should query the schema of the most relevant tables."""

system_message =SystemMessage(cnotallow=SQL_PREFIX)

# 创建Agent
agent_executor = create_react_agent(chat, tools, messages_modifier=system_message)

# 查询
example_query ="请帮我查询出20210415日，建筑材料一级行业涨幅超过5%（不包含）的股票数量"

events = agent_executor.stream(
{"messages":[("user", example_query)]},
    stream_mode="values",
)

# 查看工具调用过程
for event in events:
    event["messages"][-1].pretty_print()
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pip install wikipedia
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from langchain_community.tools importWikipediaQueryRun
from langchain_community.utilities importWikipediaAPIWrapper
from langchain_core.messages importSystemMessage
from langgraph.prebuilt import create_react_agent
from utils import get_qwen_models

# 连接大模型
llm, chat, embed = get_qwen_models()


wikipedia =WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())
tools =[wikipedia]

# 构建Prompt
wiki_PREFIX ="""你是一个搜索专家，可以根据用户提出的问题，为用户搜索相应的内容.
你可以使用维基百科工具来搜索相关的内容。

如果搜索不到对应的内容，你可以对问题进行相关性引申3个问题，来丰富查询过程。
搜索后的结果，请使用markdown的方式返回。
"""

system_message =SystemMessage(cnotallow=wiki_PREFIX)

# 创建Agent
agent_executor = create_react_agent(chat, tools, messages_modifier=system_message)

# 查询
example_query ="请帮我查询出美国林肯号航母的相关信息"

events = agent_executor.stream(
{"messages":[("user", example_query)]},
    stream_mode="values",
)

# 查看工具调用过程
for event in events:
    event["messages"][-1].pretty_print()
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请帮我查询出美国林肯号航母的相关信息
==================================AiMessage==================================
ToolCalls:
  wikipedia (call_78e651d21ea44eafa47741)
Call ID: call_78e651d21ea44eafa47741
Args:
    query: USS AbrahamLincoln(CVN-72)
=================================ToolMessage=================================
Name: wikipedia

Page: USS AbrahamLincoln(CVN-72)
Summary: USS AbrahamLincoln(CVN-72) is the fifth Nimitz-class aircraft carrier in the UnitedStatesNavy.She is the third Navy ship to have been named after the former PresidentAbrahamLincoln.Her home port is NAS NorthIsland,SanDiego,California; she is a member of the UnitedStatesPacificFleet.She is administratively responsible to Commander,NavalAirForcesPacific, and operationally serves as the flagship of CarrierStrikeGroup3 and host to CarrierAirWingNine.She was returned to the fleet on 12May2017, marking the successful completion of her Refueling and ComplexOverhaul(RCOH) carried out at NewportNewsShipyard.As of August10,2024, USS AbrahamLincoln and her strike group are being deployed to the MiddleEast as part of the U.S. response to the escalation of tensions between Iran and Israel.



Page: USS AbrahamLincoln
Summary:Two ships have borne the name AbrahamLincoln,in honor of the 16thPresident of the UnitedStates.

USS AbrahamLincoln(SSBN-602), a ballistic missile submarine in service from 1961 to 1981
USS AbrahamLincoln(CVN-72), an aircraft carrier commissioned in1989 and currently in service

Page:CarrierStrikeGroup9
Summary:CarrierStrikeGroup9(CSG-9 or CARSTRKGRU 9) is a U.S.Navy carrier strike group.CommanderCarrierStrikeGroup9(COMCARSTRKGRU 9 or CCSG 9) is responsible for unit-level training, integrated training, and material readiness for the ships and aviation squadrons assigned to the group.The group reports to Commander, U.S.ThirdFleet,which also supervises its pre-deployment training and certification that includes CompositeUnitTrainingExercises.
It is currently assigned to the U.S.PacificFleet.TheNimitz-class aircraft carrier USS TheodoreRoosevelt(CVN-71) is the group's current flagship. Other group units include Carrier Air Wing 11, the Ticonderoga-class cruiser USS Lake Erie (CG-70), and the Arleigh Burke-class destroyer's USS John S.McCain(DDG-56)USS Halsey(DDG-97), and the USS DanielInouye(DDG-118).
The strike group traces its history to Cruiser-DestroyerGroup3, created on 30June1973, by the re-designation of CruiserDestroyerFlotilla11.From2004, the strike group has made multiple MiddleEast deployments providing air forces over Afghanistan and Iraq, as well as conducting MaritimeSecurityOperations.The strike group received the HumanitarianServiceMedalin recognition of its disaster relief efforts inIndonesia during OperationUnifiedAssistancein2004–05.
==================================AiMessage==================================

USS亚伯拉罕·林肯号（USS AbrahamLincoln，舷号CVN-72）是美国海军第五艘尼米兹级核动力航空母舰，也是第三艘以美国第16任总统亚伯拉罕·林肯命名的军舰。她的母港位于加利福尼亚州圣迭戈的北岛海军航空站，隶属于太平洋舰队。作为航母打击群3(CSG-3)的旗舰，她搭载了第9舰载机联队(CarrierAirWingNine)。林肯号在完成于纽波特纽斯船厂的燃料补给及复杂大修(RCOH)后，于2017年5月12日重新归队。截至2024年8月10日，亚伯拉罕·林肯号及其打击群被部署至中东地区，作为美国对伊朗和以色列之间紧张局势升级应对的一部分。

另外，还有两艘以前总统亚伯拉罕·林肯命名的舰艇：
- USS亚伯拉罕·林肯号（SSBN-602），一艘1961年至1981年间在役的弹道导弹潜艇；
- USS亚伯拉罕·林肯号（CVN-72），即目前仍在服役的航空母舰。

而航母打击群9(CSG-9)曾是亚伯拉罕·林肯号所属的打击群，但目前该打击群的旗舰为另一艘尼米兹级航母USS西奥多·罗斯福号（USS TheodoreRoosevelt，CVN-71）。
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