如何将可运行对象转换为工具

先决条件

本指南假定读者熟悉以下概念：

在这里，我们将演示如何将 LangChain Runnable 转换为一个可由代理、链或聊天模型使用的工具。

依赖项

注意: 此指南需要 langchain-core >= 0.2.13。我们还将使用 OpenAI 进行嵌入，但任何 LangChain 嵌入均可满足需求。我们将使用一个简单的 LangGraph 代理进行演示。

%%capture --no-stderr
%pip install -U langchain-core langchain-openai langgraph

LangChain 工具是代理、链或聊天模型与世界交互的接口。有关工具调用、内置工具、自定义工具等的使用指南，请参见此处。

LangChain 工具——BaseTool 的实例——是具有额外约束的可运行对象，这些约束使它们能够被语言模型有效调用：

它们的输入被限制为可序列化的，具体来说是字符串和Python dict 对象；
它们包含名称和描述，说明了其使用方法和使用时机；
它们可能包含有关其参数的详细 args_schema。也就是说，尽管一个工具（作为一个 Runnable）可能只接受一个 dict 输入，但填充字典所需的特定键和类型信息应在 args_schema 中指定。

接受字符串或 dict 输入的可运行对象可以使用 as_tool 方法转换为工具，该方法允许指定工具名称、描述以及参数的额外模式信息。

基本用法

带有类型定义的 dict 输入：

from typing import List

from langchain_core.runnables import RunnableLambda
from typing_extensions import TypedDict


class Args(TypedDict):
    a: int
    b: List[int]


def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(f)
as_tool = runnable.as_tool(
    name="My tool",
    description="Explanation of when to use tool.",
)

API 参考：RunnableLambda

print(as_tool.description)

as_tool.args_schema.schema()

Explanation of when to use tool.

{'title': 'My tool',
 'type': 'object',
 'properties': {'a': {'title': 'A', 'type': 'integer'},
  'b': {'title': 'B', 'type': 'array', 'items': {'type': 'integer'}}},
 'required': ['a', 'b']}

as_tool.invoke({"a": 3, "b": [1, 2]})

'6'

无需输入信息，可以通过 arg_types 指定参数类型：

from typing import Any, Dict


def g(x: Dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))


runnable = RunnableLambda(g)
as_tool = runnable.as_tool(
    name="My tool",
    description="Explanation of when to use tool.",
    arg_types={"a": int, "b": List[int]},
)

或者，可以通过直接传递工具所需的 args_schema 来完全指定模式：

from pydantic import BaseModel, Field

class GSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: List[int] = Field(..., description="List of ints")

runnable = RunnableLambda(g)
as_tool = runnable.as_tool(GSchema)

也支持字符串输入：

def f(x: str) -> str:
    return x + "a"


def g(x: str) -> str:
    return x + "z"


runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()

as_tool.invoke("b")

'baz'

在代理中

下面我们将在一个代理应用中集成 LangChain Runnables 作为工具。我们将通过以下示例进行演示：

一个文档检索器;
一个简单的 RAG 链，允许代理将其相关的查询委托给它。

我们首先实例化一个支持工具调用的聊天模型：

选择聊天模型:

pip install -qU "langchain[openai]"

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("gpt-4o-mini", model_provider="openai")

在完成 RAG 教程后，我们首先构建一个检索器：

from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import OpenAIEmbeddings

documents = [
    Document(
        page_content="Dogs are great companions, known for their loyalty and friendliness.",
    ),
    Document(
        page_content="Cats are independent pets that often enjoy their own space.",
    ),
]

vectorstore = InMemoryVectorStore.from_documents(
    documents, embedding=OpenAIEmbeddings()
)

retriever = vectorstore.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 1},
)

API 参考：文档 |InMemoryVectorStore | OpenAIEmbeddings

接下来，我们创建并使用一个简单的预构建 LangGraph 代理，并为其提供工具：

from langgraph.prebuilt import create_react_agent

tools = [
    retriever.as_tool(
        name="pet_info_retriever",
        description="Get information about pets.",
    )
]
agent = create_react_agent(llm, tools)

API 参考：create_react_agent

for chunk in agent.stream({"messages": [("human", "What are dogs known for?")]}):
    print(chunk)
    print("----")

{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD', 'function': {'arguments': '{"__arg1":"dogs"}', 'name': 'pet_info_retriever'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 60, 'total_tokens': 79}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-d7f81de9-1fb7-4caf-81ed-16dcdb0b2ab4-0', tool_calls=[{'name': 'pet_info_retriever', 'args': {'__arg1': 'dogs'}, 'id': 'call_W8cnfOjwqEn4cFcg19LN9mYD'}], usage_metadata={'input_tokens': 60, 'output_tokens': 19, 'total_tokens': 79})]}}
----
{'tools': {'messages': [ToolMessage(content="[Document(id='86f835fe-4bbe-4ec6-aeb4-489a8b541707', page_content='Dogs are great companions, known for their loyalty and friendliness.')]", name='pet_info_retriever', tool_call_id='call_W8cnfOjwqEn4cFcg19LN9mYD')]}}
----
{'agent': {'messages': [AIMessage(content='Dogs are known for being great companions, known for their loyalty and friendliness.', response_metadata={'token_usage': {'completion_tokens': 18, 'prompt_tokens': 134, 'total_tokens': 152}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-9ca5847a-a5eb-44c0-a774-84cc2c5bbc5b-0', usage_metadata={'input_tokens': 134, 'output_tokens': 18, 'total_tokens': 152})]}}
----

查看上面运行的 LangSmith 跟踪。

更进一步，我们可以创建一个简单的 RAG 链，它接受一个额外的参数——在这里是答案的“风格”。

from operator import itemgetter

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

system_prompt = """
You are an assistant for question-answering tasks.
Use the below context to answer the question. If
you don't know the answer, say you don't know.
Use three sentences maximum and keep the answer
concise.

Answer in the style of {answer_style}.

Question: {question}

Context: {context}
"""

prompt = ChatPromptTemplate.from_messages([("system", system_prompt)])

rag_chain = (
    {
        "context": itemgetter("question") | retriever,
        "question": itemgetter("question"),
        "answer_style": itemgetter("answer_style"),
    }
    | prompt
    | llm
    | StrOutputParser()
)

API 参考：StrOutputParser | ChatPromptTemplate | RunnablePassthrough

请注意，我们的链的输入模式包含必需的参数，因此可直接转换为工具，无需进一步指定：

rag_chain.input_schema.schema()

{'title': 'RunnableParallel<context,question,answer_style>Input',
 'type': 'object',
 'properties': {'question': {'title': 'Question'},
  'answer_style': {'title': 'Answer Style'}}}

rag_tool = rag_chain.as_tool(
    name="pet_expert",
    description="Get information about pets.",
)

下面我们再次调用代理。请注意，代理会将其所需的参数填充到 tool_calls 中：

agent = create_react_agent(llm, [rag_tool])

for chunk in agent.stream(
    {"messages": [("human", "What would a pirate say dogs are known for?")]}
):
    print(chunk)
    print("----")

{'agent': {'messages': [AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_17iLPWvOD23zqwd1QVQ00Y63', 'function': {'arguments': '{"question":"What are dogs known for according to pirates?","answer_style":"quote"}', 'name': 'pet_expert'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 28, 'prompt_tokens': 59, 'total_tokens': 87}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-7fef44f3-7bba-4e63-8c51-2ad9c5e65e2e-0', tool_calls=[{'name': 'pet_expert', 'args': {'question': 'What are dogs known for according to pirates?', 'answer_style': 'quote'}, 'id': 'call_17iLPWvOD23zqwd1QVQ00Y63'}], usage_metadata={'input_tokens': 59, 'output_tokens': 28, 'total_tokens': 87})]}}
----
{'tools': {'messages': [ToolMessage(content='"Dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages."', name='pet_expert', tool_call_id='call_17iLPWvOD23zqwd1QVQ00Y63')]}}
----
{'agent': {'messages': [AIMessage(content='According to pirates, dogs are known for their loyalty and friendliness, making them great companions for pirates on long sea voyages.', response_metadata={'token_usage': {'completion_tokens': 27, 'prompt_tokens': 119, 'total_tokens': 146}, 'model_name': 'gpt-4o-mini', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-5a30edc3-7be0-4743-b980-ca2f8cad9b8d-0', usage_metadata={'input_tokens': 119, 'output_tokens': 27, 'total_tokens': 146})]}}
----

查看上面运行的 LangSmith 跟踪。

依赖项​

基本用法​

在代理中​

依赖项

基本用法

在代理中