大模型从入门到应用——LangChain：代理（Agents）-[工具（Tools）：多输入工具和工具输入模式]_langchain agent多工具

作者：小桥流水78 | 2024-07-19 21:47:25

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langchain agent多工具

LangChain系列文章：

----### 多输入工具
本节演示如何在Agent中使用需要多个输入的工具，推荐的方法是使用StructuredTool类。

import os
os.environ["LANGCHAIN_TRACING"] = "true"
from langchain import OpenAI
from langchain.agents import initialize_agent, AgentType

llm = OpenAI(temperature=0)
from langchain.tools import StructuredTool

def multiplier(a: float, b: float) -> float:
    """Multiply the provided floats."""
    return a * b

tool = StructuredTool.from_function(multiplier)
# Structured tools are compatible with the STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent type. 
agent_executor = initialize_agent([tool], llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
agent_executor.run("What is 3 times 4")
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日志输出：

Entering new AgentExecutor chain...

Thought: I need to multiply 3 and 4
Action:
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{
“action”: “multiplier”,
“action_input”: {“a”: 3, “b”: 4}
}


Observation: 12
Thought: I know what to respond
Action:
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{
“action”: “Final Answer”,
“action_input”: “3 times 4 is 12”
}


Finished chain.
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输出：

'3 times 4 is 12'
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使用字符串格式的多输入工具

与结构化工具相比，另一种方法是使用常规的Tool类，并接受单个字符串作为输入。然后，工具必须处理解析逻辑，从文本中提取相关值，这将使工具表示与Agent提示紧密耦合。如果底层语言模型无法可靠地生成结构化模式，这仍然很有用。

让我们以乘法函数为例。为了使用它，我们将告诉Agent生成以逗号分隔的长度为两个的Action Input。然后，我们编写一个简单的包装器，将字符串分割成两部分（以逗号为界），并将两个解析后的值作为整数传递给乘法函数。

from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
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以下是乘法函数及其字符串解析器的示例：

def multiplier(a, b):
    return a * b

def parsing_multiplier(string):
    a, b = string.split(",")
    return multiplier(int(a), int(b))
llm = OpenAI(temperature=0)
tools = [
    Tool(
        name = "Multiplier",
        func=parsing_multiplier,
        description="useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2."
    )
]
mrkl = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
mrkl.run("What is 3 times 4")
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日志输出：

Entering new AgentExecutor chain...
 I need to multiply two numbers
Action: Multiplier
Action Input: 3,4
Observation: 12
Thought: I now know the final answer
Final Answer: 3 times 4 is 12

Finished chain.
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输出：

'3 times 4 is 12'
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工具输入模式

默认情况下，工具通过检查函数签名来推断参数模式。对于更严格的要求，可以指定自定义输入模式，以及自定义的验证逻辑。

from typing import Any, Dict

from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper
from pydantic import BaseModel, Field, root_validator

llm = OpenAI(temperature=0)
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我们还需安装tldextract：

!pip install tldextract > /dev/null
notice A new release of pip is available: 23.0.1 -> 23.1
notice To update, run: pip install --upgrade pip
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输入：

import tldextract

_APPROVED_DOMAINS = {
    "langchain",
    "wikipedia",
}

class ToolInputSchema(BaseModel):

    url: str = Field(...)
    
    @root_validator
    def validate_query(cls, values: Dict[str, Any]) -> Dict:
        url = values["url"]
        domain = tldextract.extract(url).domain
        if domain not in _APPROVED_DOMAINS:
            raise ValueError(f"Domain {domain} is not on the approved list:"
                             f" {sorted(_APPROVED_DOMAINS)}")
        return values
    
tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())
agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)
# This will succeed, since there aren't any arguments that will be triggered during validation
answer = agent.run("What's the main title on langchain.com?")
print(answer)
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输出：

The main title of langchain.com is "LANG CHAIN 声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/小桥流水78/article/detail/853673

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