# 引入Qwen大模型
from utils import get_qwen_models

llm , chat, _ = get_qwen_models()

chat.invoke("現(xiàn)在幾點了？")

AIMessage(content='我是一個AI模型，無法實時獲取當(dāng)前時間。請您查看您的設(shè)備或詢問周圍的人來獲取準(zhǔn)確的時間。',
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')

# 定義一個獲取當(dāng)前時間的函數(shù)
def get_datetime() -> str:
    """
        跟時期或時間查詢相關(guān)的問題，請調(diào)用此方法
        注意：
            - 此方法沒有入?yún)?            - 返參是字符串形式的日期
    """
# 調(diào)用該函數(shù)
get_datetime()

'2024-08-29 20:39:34'

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}
""")

from langgraph.prebuilt import create_react_agent
from langchain_core.messages importHumanMessage

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

# 調(diào)用代理
try:
    response = agent.invoke({"messages":[HumanMessage(content="現(xiàn)在幾點了？")]})
print(response)
exceptKeyErroras e:
print(f"KeyError: {e}")
exceptExceptionas e:
print(f"發(fā)生錯誤: {e}")

{'messages':
[
HumanMessage(content='現(xiàn)在幾點了？',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='現(xiàn)在的時間是14點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')
]
}

import datetime
from langchain.prompts importPromptTemplate
from langgraph.prebuilt import create_react_agent
from langchain_core.messages importHumanMessage
from utils import get_qwen_models

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

# 定義調(diào)用函數(shù)
defget_datetime()->str:
"""
    獲取當(dāng)前時間
    """
    now = datetime.datetime.now()
    formatted_date = now.strftime("%Y-%m-%d %H:%M:%S")

return formatted_date

# 給大模型綁定工具
bined_chat = chat.bind_tools(tools=[get_datetime])

# 創(chuàng)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}
""")

# 創(chuàng)建Agent
agent = create_react_agent(model=chat, tools=[get_datetime])

# 調(diào)用Agent
try:
    response = agent.invoke({"messages":[HumanMessage(content="現(xiàn)在幾點了？")]})
print(response)
exceptKeyErroras e:
print(f"KeyError: {e}")
exceptExceptionas e:
print(f"發(fā)生錯誤: {e}")

# 連接大模型
from utils import get_qwen_models

llm, chat, embed = get_qwen_models()

# 連接數(shù)據(jù)庫
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_uri("sqlite:///博金杯比賽數(shù)據(jù).db")

from langchain_community.agent_toolkits import SQLDatabaseToolkit

# 初始化數(shù)據(jù)庫工具包，傳入數(shù)據(jù)庫連接對象 db 和語言模型 llm
toolkit = SQLDatabaseToolkit(db=db, llm=llm)

# 從工具包中獲取可用的工具，并將其存儲在 tools 變量中
tools = toolkit.get_tools()

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)

from langgraph.prebuilt import create_react_agent

# 創(chuàng)建Agent，傳入 chat、工具 tools 和 第四步的 prompt
agent_executor = create_react_agent(chat, tools, messages_modifier=system_message)

# 查詢
example_query = "請幫我查詢出20210415日，建筑材料一級行業(yè)漲幅超過5%（不包含）的股票數(shù)量"

# 流式處理事件
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()

================================ HumanMessage=================================

請幫我查詢出20210415日，建筑材料一級行業(yè)漲幅超過5%（不包含）的股票數(shù)量
==================================AiMessage==================================
ToolCalls:
  sql_db_list_tables (call_c14a5fc51d324381926311)
Call ID: call_c14a5fc51d324381926311
Args:
    tool_input:
=================================ToolMessage=================================
Name: sql_db_list_tables

A股公司行業(yè)劃分表, A股票日行情表,基金份額持有人結(jié)構(gòu),基金債券持倉明細(xì),基金可轉(zhuǎn)債持倉明細(xì),基金基本信息,基金日行情表,基金股票持倉明細(xì),基金規(guī)模變動表,港股票日行情表
==================================AiMessage==================================
ToolCalls:
  sql_db_schema (call_f9acd6019db64e93a74987)
Call ID: call_f9acd6019db64e93a74987
Args:
    table_names: A股公司行業(yè)劃分表, A股票日行情表
=================================ToolMessage=================================
Name: sql_db_schema


CREATE TABLE "A股公司行業(yè)劃分表"(
"股票代碼" TEXT,
...
[(74,)]
==================================AiMessage==================================

在2021年04月15日，建筑材料一級行業(yè)漲幅超過5%（不包含）的股票數(shù)量為74只。

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

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

# 連接數(shù)據(jù)庫
db =SQLDatabase.from_uri("sqlite:///博金杯比賽數(shù)據(jù).db")

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

# 構(gòu)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)

# 創(chuàng)建Agent
agent_executor = create_react_agent(chat, tools, messages_modifier=system_message)

# 查詢
example_query ="請幫我查詢出20210415日，建筑材料一級行業(yè)漲幅超過5%（不包含）的股票數(shù)量"

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

# 查看工具調(diào)用過程
for event in events:
    event["messages"][-1].pretty_print()

pip install wikipedia

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

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


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

# 構(gòu)建Prompt
wiki_PREFIX ="""你是一個搜索專家，可以根據(jù)用戶提出的問題，為用戶搜索相應(yīng)的內(nèi)容.
你可以使用維基百科工具來搜索相關(guān)的內(nèi)容。

如果搜索不到對應(yīng)的內(nèi)容，你可以對問題進(jìn)行相關(guān)性引申3個問題，來豐富查詢過程。
搜索后的結(jié)果，請使用markdown的方式返回。
"""

system_message =SystemMessage(cnotallow=wiki_PREFIX)

# 創(chuàng)建Agent
agent_executor = create_react_agent(chat, tools, messages_modifier=system_message)

# 查詢
example_query ="請幫我查詢出美國林肯號航母的相關(guān)信息"

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

# 查看工具調(diào)用過程
for event in events:
    event["messages"][-1].pretty_print()

請幫我查詢出美國林肯號航母的相關(guān)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亞伯拉罕·林肯號（USS AbrahamLincoln，舷號CVN-72）是美國海軍第五艘尼米茲級核動力航空母艦，也是第三艘以美國第16任總統(tǒng)亞伯拉罕·林肯命名的軍艦。她的母港位于加利福尼亞州圣迭戈的北島海軍航空站，隸屬于太平洋艦隊。作為航母打擊群3(CSG-3)的旗艦，她搭載了第9艦載機聯(lián)隊(CarrierAirWingNine)。林肯號在完成于紐波特紐斯船廠的燃料補給及復(fù)雜大修(RCOH)后，于2017年5月12日重新歸隊。截至2024年8月10日，亞伯拉罕·林肯號及其打擊群被部署至中東地區(qū)，作為美國對伊朗和以色列之間緊張局勢升級應(yīng)對的一部分。

另外，還有兩艘以前總統(tǒng)亞伯拉罕·林肯命名的艦艇：
- USS亞伯拉罕·林肯號（SSBN-602），一艘1961年至1981年間在役的彈道導(dǎo)彈潛艇；
- USS亞伯拉罕·林肯號（CVN-72），即目前仍在服役的航空母艦。

而航母打擊群9(CSG-9)曾是亞伯拉罕·林肯號所屬的打擊群，但目前該打擊群的旗艦為另一艘尼米茲級航母USS西奧多·羅斯福號（USS TheodoreRoosevelt，CVN-71）。