$ pip show langchain
Name: langchain
Version: 0.3.7
Summary: Building applications with LLMs through composability
Home-page: https://github.com/langchain-ai/langchain
Author: 
Author-email: 
License: MIT
Location: /home/xjg/.conda/envs/langchain/lib/python3.10/site-packages
Requires: aiohttp, async-timeout, langchain-core, langchain-text-splitters, langsmith, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity
Required-by: langchain-community

from langchain_community.utilities import SerpAPIWrapper
import os
# 刪除all_proxy環(huán)境變量
if 'all_proxy' in os.environ:
    del os.environ['all_proxy']
# 刪除ALL_PROXY環(huán)境變量
if 'ALL_PROXY' in os.environ:
    del os.environ['ALL_PROXY']
os.environ["SERPAPI_API_KEY"] = "xxx"
params = {
    "engine": "bing",
    "gl": "us",
    "hl": "en",
}
search = SerpAPIWrapper(params=params)
result = search.run("Obama's first name?")
print(result)

from langchain_community.utilities import SerpAPIWrapper
from langchain.agents import create_self_ask_with_search_agent, AgentType,Tool,AgentExecutor
from langchain import hub
from langchain_openai import ChatOpenAI
import os
from dotenv import load_dotenv, find_dotenv
# 刪除all_proxy環(huán)境變量
if 'all_proxy' in os.environ:
    del os.environ['all_proxy']
# 刪除ALL_PROXY環(huán)境變量
if 'ALL_PROXY' in os.environ:
    del os.environ['ALL_PROXY']
_ = load_dotenv(find_dotenv())
os.environ["SERPAPI_API_KEY"] = "xxx"
chat_model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
# 實(shí)例化查詢工具
search = SerpAPIWrapper()
tools = [
    Tool(
        name="Intermediate Answer",
        func=search.run,
        description="useful for when you need to ask with search",
    )
]
prompt = hub.pull("hwchase17/self-ask-with-search")
self_ask_with_search = create_self_ask_with_search_agent(
     chat_model,tools,prompt
)
agent_executor = AgentExecutor(agent=self_ask_with_search, tools=tools,verbose=True,handle_parsing_errors=True)
reponse = agent_executor.invoke({"input": "成都舉辦的大運(yùn)會(huì)是第幾屆大運(yùn)會(huì)？2023年大運(yùn)會(huì)舉辦地在哪里？"})
print(reponse)
print(chat_model.invoke("成都舉辦的大運(yùn)會(huì)是第幾屆大運(yùn)會(huì)？").content)
print(chat_model.invoke("2023年大運(yùn)會(huì)舉辦地在哪里？").content)

from langchain.agents import create_react_agent, AgentType,Tool,AgentExecutor
from langchain import hub
from langchain_community.agent_toolkits.load_tools import load_tools
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv, find_dotenv
from langchain_openai import ChatOpenAI
import os
# 刪除all_proxy環(huán)境變量
if 'all_proxy' in os.environ:
    del os.environ['all_proxy']
# 刪除ALL_PROXY環(huán)境變量
if 'ALL_PROXY' in os.environ:
    del os.environ['ALL_PROXY']
_ = load_dotenv(find_dotenv())
os.environ["SERPAPI_API_KEY"] = "xxx"
chat_model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = hub.pull("hwchase17/react")
tools = load_tools(["serpapi", "llm-math"], llm=chat_model)
agent = create_react_agent(chat_model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools,verbose=True)
reponse =agent_executor.invoke({"input": "誰(shuí)是萊昂納多·迪卡普里奧的女朋友？她現(xiàn)在年齡的0.43次方是多少?"})
print(reponse)

from langchain_openai import ChatOpenAI
from  langchain.agents import tool,AgentExecutor,create_openai_functions_agent
from langchain import hub
import os
from dotenv import load_dotenv, find_dotenv
# 刪除all_proxy環(huán)境變量
if 'all_proxy' in os.environ:
    del os.environ['all_proxy']
# 刪除ALL_PROXY環(huán)境變量
if 'ALL_PROXY' in os.environ:
    del os.environ['ALL_PROXY']
_ = load_dotenv(find_dotenv())
chat_model = ChatOpenAI(model="gpt-4o-mini",temperature=0)
@tool
def get_word_length(word: str) -> int:
    """Returns the length of a word."""
    return len(word)
tools = [get_word_length]
prompt = hub.pull("hwchase17/openai-functions-agent")
agent = create_openai_functions_agent(llm=chat_model, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True,handle_parsing_errors=True)
agent_executor.invoke({"input": "單詞“educati”中有多少個(gè)字母?"})

from langchain_openai import ChatOpenAI
from  langchain.agents import tool,AgentExecutor,create_openai_functions_agent
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain.prompts import MessagesPlaceholder
from langchain.memory import ConversationBufferMemory
from langchain import hub
import os
from dotenv import load_dotenv, find_dotenv
# 刪除all_proxy環(huán)境變量
if 'all_proxy' in os.environ:
    del os.environ['all_proxy']
# 刪除ALL_PROXY環(huán)境變量
if 'ALL_PROXY' in os.environ:
    del os.environ['ALL_PROXY']
_ = load_dotenv(find_dotenv())
chat_model = ChatOpenAI(model="gpt-4o-mini",temperature=0)
@tool
def get_word_length(word: str) -> int:
    """Returns the length of a word."""
    return len(word)
tools = [get_word_length]
prompt = ChatPromptTemplate.from_messages(
    [
        ("placeholder", "{chat_history}"),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}"),
    ]
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
agent = create_openai_functions_agent(llm=chat_model, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True)
agent_executor.invoke({"input":"單詞“educati”中有多少個(gè)字母?"})
agent_executor.invoke({"input":"那是一個(gè)真實(shí)的單詞嗎？"})