如何将Runnables转换为工具
在这里，我们将演示如何将LangChain的Runnable转换为可以被代理、链或聊天模型使用的工具。
依赖项
注意: 本指南需要 langchain-core >= 0.2.13。我们还将使用 OpenAI 进行嵌入，但任何 LangChain 嵌入都应该足够。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、链或聊天模型可以用来与世界交互的接口。请参阅 这里 获取关于工具调用、内置工具、自定义工具等更多信息的操作指南。
LangChain工具——BaseTool的实例——是具有额外约束的Runnables，这些约束使它们能够被语言模型有效地调用：
- 它们的输入被限制为可序列化的，特别是字符串和Python
dict对象； - 它们包含名称和描述，指示应如何使用以及何时使用它们；
- 它们可能包含一个详细的args_schema用于其参数。也就是说，虽然一个工具（作为
Runnable）可能接受一个单一的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Runnables可以使用as_tool方法转换为工具，该方法允许为参数指定名称、描述和额外的模式信息。
基本用法
使用类型化的 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 Reference: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'
没有类型信息时，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的所需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
下面我们将把LangChain Runnables作为工具集成到一个代理应用程序中。我们将通过以下方式演示：
我们首先实例化一个支持工具调用的聊天模型：
Select chat model:
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_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o-mini")
按照RAG教程，让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},
)
接下来，我们使用一个简单的预构建的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 Reference: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})]}}
----
查看上述运行的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()
)
请注意，我们的链的输入模式包含所需的参数，因此无需进一步说明即可转换为工具：
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，代理在其tool_calls中填充了所需的参数：
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})]}}
----
查看上述运行的LangSmith 跟踪。