■ ChatPromptTemplate 클래스의 invoke 메소드를 사용해 ChatPromptValue 객체를 만드는 방법을 보여준다.
▶ main.py
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from langchain_core.prompts import ChatPromptTemplate from langchain_core.prompts import MessagesPlaceholder from langchain_core.messages import HumanMessage chatPromptTemplate = ChatPromptTemplate.from_messages( [ ("system", "You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value."), MessagesPlaceholder("examples"), ("human", "{text}") ] ) chatPromptValue = chatPromptTemplate.invoke({"text" : "this is some text", "examples" : [HumanMessage(content = "testing 1 2 3")]}) print(chatPromptValue) """ messages = [ SystemMessage( content = "You are an expert extraction algorithm. Only extract relevant information from the text. If you do not know the value of an attribute asked to extract, return null for the attribute's value.", additional_kwargs = {}, response_metadata = {} ), HumanMessage( content = 'testing 1 2 3', additional_kwargs = {}, response_metadata = {} ), HumanMessage( content = 'this is some text', additional_kwargs = {}, response_metadata = {} ) ] """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.4 aiohttp==3.11.9 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.6.2.post1 attrs==24.2.0 certifi==2024.8.30 charset-normalizer==3.4.0 frozenlist==1.5.0 greenlet==3.1.1 h11==0.14.0 httpcore==1.0.7 httpx==0.28.0 idna==3.10 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.9 langchain-core==0.3.21 langchain-text-splitters==0.3.2 langsmith==0.1.147 multidict==6.1.0 numpy==2.1.3 orjson==3.10.12 packaging==24.2 propcache==0.2.1 pydantic==2.10.3 pydantic_core==2.27.1 PyYAML==6.0.2 requests==2.32.3 requests-toolbelt==1.0.0 sniffio==1.3.1 SQLAlchemy==2.0.36 tenacity==9.0.0 typing_extensions==4.12.2 urllib3==2.2.3 yarl==1.18.3 |
※ pip install langchain 명령을 실행했다.