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- Stream JSON string content. Final chunk includes parsed representation (following OpenAI [docs](https://platform.openai.com/docs/guides/structured-outputs#streaming)). - Mildly (?) breaking change: if you were using streaming with `response_format` before, usage metadata will disappear unless you set `stream_usage=True`. ## Response format Before:  After:  ## with_structured_output For pydantic output, behavior of `with_structured_output` is unchanged (except for warning disappearing), because we pluck the parsed representation straight from OpenAI, and OpenAI doesn't return it until the stream is completed. Open to alternatives (e.g., parsing from content or intermediate dict chunks generated by OpenAI). Before:  After: 
langchain-openai
This package contains the LangChain integrations for OpenAI through their openai SDK.
Installation and Setup
- Install the LangChain partner package
pip install langchain-openai
- Get an OpenAI api key and set it as an environment variable (
OPENAI_API_KEY)
LLM
See a usage example.
from langchain_openai import OpenAI
If you are using a model hosted on Azure, you should use different wrapper for that:
from langchain_openai import AzureOpenAI
For a more detailed walkthrough of the Azure wrapper, see here
Chat model
See a usage example.
from langchain_openai import ChatOpenAI
If you are using a model hosted on Azure, you should use different wrapper for that:
from langchain_openai import AzureChatOpenAI
For a more detailed walkthrough of the Azure wrapper, see here
Text Embedding Model
See a usage example
from langchain_openai import OpenAIEmbeddings
If you are using a model hosted on Azure, you should use different wrapper for that:
from langchain_openai import AzureOpenAIEmbeddings
For a more detailed walkthrough of the Azure wrapper, see here