(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')# <...
from langchain_aws import ChatBedrockConverse@@ -64,6 +65,44 @@ class ClassifyQuery(BaseModel): assert isinstance(chunk, ClassifyQuery)def test_structured_output_streaming() -> None: model = ChatBedrockConverse( model="anthropic.claude-3-5-sonnet-20240620-v1:0", temperature=0...
Python版本：3.9.6(默认， Sep 26 2023, 21:46:56)[GCC 11.4.0]
langchain 库合作来实现文档分析应用程序。 具体来说,我想使用此文档中描述的路由技术。我想按照该示例进行操作,但我的环境仅限于 AWS,并且由于部署的限制,我使用 ChatBedrock 而不是 ChatOpenAI。 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...
我正在经历同样的问题。
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。
I have a problem for which routing as it is described in the langchain documentation. I am restricted to AWS and Bedrock cannot use the ChatOpenAI class and instead have to procceed with ChatBedrock. According to this overview the with_structured_output method, which I need, is not (yet)...
return 'chain in python_docs' elif "js_docs" in result.datasource: return 'chain in js_docs' else: return 'chain in golang_docs'llm = ChatOpenAI(model="gpt-3.5-turbo-16k", temperature=5) structured_llm = llm.with_structured_output(RouteQuery)prompt...
from langchain_core.pydantic_v1import BaseModel, Field # Define a Pydantic model to parse the model's output classFruit(BaseModel): name:str = Field(description="The name of the fruit shown in the image") color:str = Field(description="The color of the fruit shown in the image") ...
a model must be trained to detect when to call a function and output a structured response like JSON with the function and its arguments. The model is then optimized as a NIM microservice for NVIDIA infrastructure and easy deployment, making it compatible with frameworks like LangChain’sLangGra...