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会话（Conversational）

LangChain

本教程演示了如何使用针对对话优化的代理。其他代理通常优化用于查找最佳响应的工具，但在对话环境中这并不理想，因为您可能希望代理能够与用户进行聊天。

这是通过一种特定类型的代理（conversational-react-description）实现的，该代理期望与内存组件一起使用。

from langchain.agents import Tool
from langchain.agents import AgentType
from langchain.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.utilities import SerpAPIWrapper
from langchain.agents import initialize_agent
search = SerpAPIWrapper()
tools = [
    Tool(
        name = "Current Search",
        func=search.run,
        description="useful for when you need to answer questions about current events or the current state of the world"
    ),
]
memory = ConversationBufferMemory(memory_key="chat_history")
llm=OpenAI(temperature=0)
agent_chain = initialize_agent(tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain.run(input="hi, i am bob")
    > Entering new AgentExecutor chain...
    
    Thought: Do I need to use a tool? No
    AI: Hi Bob, nice to meet you! How can I help you today?
    
    > Finished chain.


    'Hi Bob, nice to meet you! How can I help you today?'
agent_chain.run(input="what's my name?")
    > Entering new AgentExecutor chain...
    
    Thought: Do I need to use a tool? No
    AI: Your name is Bob!
    
    > Finished chain.


    'Your name is Bob!'
agent_chain.run("what are some good dinners to make this week, if i like thai food?")
    > Entering new AgentExecutor chain...
    
    Thought: Do I need to use a tool? Yes
    Action: Current Search
    Action Input: Thai food dinner recipes
    Observation: 59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's Thai Spicy ...
    Thought: Do I need to use a tool? No
    AI: Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!
    
    > Finished chain.


    "Here are some great Thai dinner recipes you can try this week: Marion Grasby's Thai Spicy Chilli and Basil Fried Rice, Thai Curry Noodle Soup, Thai Green Curry with Coconut Rice, Thai Red Curry with Vegetables, and Thai Coconut Soup. I hope you enjoy them!"
agent_chain.run(input="tell me the last letter in my name, and also tell me who won the world cup in 1978?")
    > Entering new AgentExecutor chain...
    
    Thought: Do I need to use a tool? Yes
    Action: Current Search
    Action Input: Who won the World Cup in 1978
    Observation: Argentina national football team
    Thought: Do I need to use a tool? No
    AI: The last letter in your name is "b" and the winner of the 1978 World Cup was the Argentina national football team.
    
    > Finished chain.


    'The last letter in your name is "b" and the winner of the 1978 World Cup was the Argentina national football team.'
agent_chain.run(input="whats the current temperature in pomfret?")
    > Entering new AgentExecutor chain...
    
    Thought: Do I need to use a tool? Yes
    Action: Current Search
    Action Input: Current temperature in Pomfret
    Observation: Partly cloudy skies. High around 70F. Winds W at 5 to 10 mph. Humidity41%.
    Thought: Do I need to use a tool? No
    AI: The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.
    
    > Finished chain.


    'The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.'

使用聊天模型​

chat-conversational-react-description 代理类型允许我们使用聊天模型创建对话代理，而不是 LLM。

from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, temperature=0)
agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain.run(input="hi, i am bob")
    > Entering new AgentExecutor chain...
    {
        "action": "Final Answer",
        "action_input": "Hello Bob! How can I assist you today?"
    }
    
    > Finished chain.


    'Hello Bob! How can I assist you today?'
agent_chain.run(input="what's my name?")
    > Entering new AgentExecutor chain...
    {
        "action": "Final Answer",
        "action_input": "Your name is Bob."
    }
    
    > Finished chain.


    'Your name is Bob.'
agent_chain.run("what are some good dinners to make this week, if i like thai food?")
    > Entering new AgentExecutor chain...
    {
        "action": "Current Search",
        "action_input": "Thai food dinner recipes"
    }
    Observation: 64 easy Thai recipes for any night of the week · Thai curry noodle soup · Thai yellow cauliflower, snake bean and tofu curry · Thai-spiced chicken hand pies · Thai ...
    Thought:{
        "action": "Final Answer",
        "action_input": "Here are some Thai food dinner recipes you can try this week: Thai curry noodle soup, Thai yellow cauliflower, snake bean and tofu curry, Thai-spiced chicken hand pies, and many more. You can find the full list of recipes at the source I found earlier."
    }
    
    > Finished chain.


    'Here are some Thai food dinner recipes you can try this week: Thai curry noodle soup, Thai yellow cauliflower, snake bean and tofu curry, Thai-spiced chicken hand pies, and many more. You can find the full list of recipes at the source I found earlier.'
agent_chain.run(input="tell me the last letter in my name, and also tell me who won the world cup in 1978?")
    > Entering new AgentExecutor chain...
    {
        "action": "Final Answer",
        "action_input": "The last letter in your name is 'b'. Argentina won the World Cup in 1978."
    }
    
    > Finished chain.


    "The last letter in your name is 'b'. Argentina won the World Cup in 1978."
agent_chain.run(input="whats the weather like in pomfret?")
    > Entering new AgentExecutor chain...
    {
        "action": "Current Search",
        "action_input": "weather in pomfret"
    }
    Observation: Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.
    Thought:{
        "action": "Final Answer",
        "action_input": "Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%."
    }
    
    > Finished chain.


    'Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.'