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...
Writing to self.response_schema, contradicts the "wrapping" definition of langchain. Using a model in multiple threads makes it so that response_schemas are getting mixed up. The description itself says that it should return a runnable w...
(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')# <...
我正在经历同样的问题。
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...
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...
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。
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...
To get started, we will be cloning thisLangChain + Next.js starter templatethat showcases how to use various LangChain modules for diverse use cases, including: Simple chat interactions Structured outputsfrom LLM calls Handling multi-step questions withautonomous AI agents ...
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...