mirror of
https://github.com/hwchase17/langchain.git
synced 2026-06-09 18:50:33 +00:00
b1a7f4e106
Resolves https://github.com/langchain-ai/langchain/issues/29003, https://github.com/langchain-ai/langchain/issues/27264 Related: https://github.com/langchain-ai/langchain-redis/issues/52 ```python from langchain.chat_models import init_chat_model from langchain.globals import set_llm_cache from langchain_community.cache import SQLiteCache from pydantic import BaseModel cache = SQLiteCache() set_llm_cache(cache) class Temperature(BaseModel): value: int city: str llm = init_chat_model("openai:gpt-4o-mini") structured_llm = llm.with_structured_output(Temperature) ``` ```python # 681 ms response = structured_llm.invoke("What is the average temperature of Rome in May?") ``` ```python # 6.98 ms response = structured_llm.invoke("What is the average temperature of Rome in May?") ```
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)
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
LLM (Legacy)
LLM refers to the legacy text-completion models that preceded chat models. 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