■ RunnableLambda 클래스의 as_tool 메소드에서 BaseModel 객체를 설정해 StructuredTool 객체를 만드는 방법을 보여준다.
▶ main.py
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from typing import Dict from typing import Any from pydantic import BaseModel from pydantic import Field from typing import List from langchain_core.runnables import RunnableLambda def test(x : Dict[str, Any]) -> str: return str(x["a"] * max(x["b"])) runnableLambda = RunnableLambda(test) class TestArgument(BaseModel): """Apply a function to an integer and list of integers.""" a : int = Field(..., description = "Integer" ) b : List[int] = Field(..., description = "List of ints") structuredTool = runnableLambda.as_tool(TestArgument) responseString = structuredTool.invoke({"a" : 3, "b" : [1, 2]}) print(responseString) """ 6 """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.3 aiohttp==3.10.8 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.6.0 async-timeout==4.0.3 attrs==24.2.0 certifi==2024.8.30 charset-normalizer==3.3.2 exceptiongroup==1.2.2 frozenlist==1.4.1 greenlet==3.1.1 h11==0.14.0 httpcore==1.0.5 httpx==0.27.2 idna==3.10 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.1 langchain-core==0.3.7 langchain-text-splitters==0.3.0 langsmith==0.1.129 multidict==6.1.0 numpy==1.26.4 orjson==3.10.7 packaging==24.1 pydantic==2.9.2 pydantic_core==2.23.4 PyYAML==6.0.2 requests==2.32.3 sniffio==1.3.1 SQLAlchemy==2.0.35 tenacity==8.5.0 typing_extensions==4.12.2 urllib3==2.2.3 yarl==1.13.1 |
※ pip install langchain 명령을 실행했다.