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b344f34635
When calling `embed_documents` and providing a `chunk_size` argument, that argument is ignored when `OpenAIEmbeddings` is instantiated with its default configuration (where `check_embedding_ctx_length=True`). `_get_len_safe_embeddings` specifies a `chunk_size` parameter but it's not being passed through in `embed_documents`, which is its only caller. This appears to be an oversight, especially given that the `_get_len_safe_embeddings` docstring states it should respect "the set embedding context length and chunk size." Developers typically expect method parameters to take effect (also, take precedence) when explicitly provided, especially when instantiating using defaults. I was confused as to why my API calls were being rejected regardless of the chunk size I provided. This bug also exists in langchain_community package. I can add that to this PR if requested otherwise I will create a new one once this passes.
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