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Missing Azure OpenAI support for "OpenAIEmbeddings" #1577
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Same issue as #1560 |
FYI I worked around this issue with naming my deployment name "text-embedding-ada-002" using the model of the same name. The next issue I ran into was hitting the rate limiter when using embeddings, as the default is 300(?) requests per minute. I had to add my own retrying functionality to the /embeddings/openai.py code, but I am using an older version (0.088?) and it looks like in more recent commits better retrying is being built in. just in case it helps you, here is my embed_documents function, with modifications at the end (last else case):
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The issue comes when you try to use GPT-3.5-turbo |
Can you create embeddings with GPT-3.5-turbo? |
I'm using Aure will not work unless you have chunk_size=1 |
So this is not really an issue for |
Opened a PR to fix this issue by separating deployment name and model name: |
Hi, @tunayokumus! I'm Dosu, and I'm helping the LangChain team manage their backlog. I wanted to let you know that we are marking this issue as stale. From what I understand, the issue is about the missing Azure OpenAI support for "OpenAIEmbeddings". The model and deployment names are hard-coded and cannot be customized. One user suggested a workaround by naming the deployment "text-embedding-ada-002" using the model of the same name. Another user mentioned hitting the rate limiter when using embeddings and added their own retrying functionality. However, it seems that the original author has addressed the issue by opening a pull request to fix it. Before we close this issue, we wanted to check with you if it is still relevant to the latest version of the LangChain repository. If it is, please let us know by commenting on the issue. Otherwise, feel free to close the issue yourself or it will be automatically closed in 7 days. Thank you for your contribution to the LangChain repository! |
It's currently not possible to pass a custom deployment name as model/deployment names are hard-coded as "text-embedding-ada-002" in variables within the class definition.
In Azure OpenAI, the deployment names can be customized and that doesn't work with OpenAIEmbeddings class.
There is proper Azure support for LLM OpenAI, but it is missing for Embeddings.
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