(BaseModel):answer:strclassAnswer2(BaseModelV2):"""The answer."""answer:strfromlangchain_openaiimportChatOpenAImodel=ChatOpenAI()model.with_structured_output(Answer).invoke('the answer is foo')# <-- Returns pydantic objectmodel.with_structured_output(Answer2).invoke('the answer is foo')# <...
根据此概述,我需要的 with_structured_output方法尚未针对 AWS Bedrock上的模型实现,这就是为什么我正在寻找解决方法或任何方法来复制此功能。 有人找到解决此问题的方法吗?python langchain amazon-bedrock claude 1个回答 1投票 我在这两篇博文中找到了解决方案:here和here。关键是使用 instructor 包,它是 py...
经过进一步的调查，我发现ChatMistralAI期望使用Pydantic V1模型，而langchain默认安装了Pydantic V2。可以...
我正在经历同样的问题。
with_structured_output( { "title": "ID Extraction", "description": "Extracts IDs from the input text.", "type": "array", "items": { "type": "object", "properties": { "type": { "type": "string", "description": ("The type"), "enum": [ "ORDER_ID", "PO_ID", "INVOICE_...
是的，我遇到了和你相同的问题。即使没有使用with_structured_output,with_config也无法传播参数。
structured_llm=llm.with_structured_output(Search) query_analyzer={"question":RunnablePassthrough()}|prompt|structured_llm API Reference:ChatPromptTemplate|RunnablePassthrough|ChatOpenAI We can see that if we spell the name exactly correctly, it knows how to handle it ...
with_structured_output( Disambiguate ) Next, we integrate the LLM prompt with the structured output to create a chain using LangChain Expression Language (LCEL) syntax and encapsulate it within a disambiguate function.extraction_chain = extraction_prompt | extraction_llm def entity_resolution(entities...
Structured Output with OpenAI Functions The second example in the template shows how to have a model return output according to a specific schema usingOpenAI Functions. For context, OpenAI Functions is a novel feature that allows developers to make their models more interactive and dynamic by enabli...
Note that LLMListwiseRerank requires a model with the with_structured_output method implemented. from langchain.retrievers.document_compressors import LLMListwiseRerankfrom langchain_openai import ChatOpenAIllm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)_filter = LLMListwiseRerank.from_ll...