Comments (2)
Passing in model & engine when creating OpenAIEmbeddings as shown below is working for me:
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY, model=OPENAI_EMBEDDING_MODEL_NAME, engine=OPENAI_EMBEDDING_DEPLOYMENT_NAME, client="azure", chunk_size=1)
OPENAI_EMBEDDING_MODEL_NAME is "text-embedding-ada-002" and OPENAI_EMBEDDING_DEPLOYMENT_NAME is the name of my specific model deployment in Azure OpenAI.
from azure_openai_langchain_sample.
Hello. I am trying to use your examples. Thanks for sharing.
In the PDf notebook, we have this gist:
# Use FAISS to index the embeddings. This will allow us to perform a similarity search on the texts using the embeddings. # https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/faiss.html pdfDocSearch = FAISS.from_texts(textTexts, embeddings)
I am getting this error: InvalidRequestError:
"Must provide an 'engine' or 'deployment_id' parameter to create a".
I am using OPENAI_EMBEDDING_MODEL_NAME = "text-embedding-ada-002 (Version 2)" but I think that this is wrong. I should specify a deployment that is based on a embedding model such as this above, correct?Please advice.
Thank you.
Got the same error. Any hint? Thanks!
from azure_openai_langchain_sample.
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from azure_openai_langchain_sample.