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fe0fd9dd70
Related to https://github.com/langchain-ai/langchain/issues/30344 https://github.com/langchain-ai/langchain/pull/30542 introduced an erroneous test for token counts for o-series models. tiktoken==0.8 does not support o-series models in `tiktoken.encoding_for_model(model_name)`, and this is the version of tiktoken we had in the lock file. So we would default to `cl100k_base` for o-series, which is the wrong encoding model. The test tested against this wrong encoding (so it passed with tiktoken 0.8). Here we update tiktoken to 0.9 in the lock file, and fix the expected counts in the test. Verified that we are pulling [o200k_base](https://github.com/openai/tiktoken/blob/main/tiktoken/model.py#L8), as expected.
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