In artificial intelligence and natural language processing, the collaboration between Microsoft Azure and OpenAI has brought forth a powerful synergy, allowing developers to harness cutting-edge language models for a wide array of applications. The Azure OpenAI Service offers a way to integrate OpenAI's advanced language capabilities into various projects, enabling tasks such as text generation, language translation, and more. However, like any sophisticated technology, this service comes with its own set of complications and potential challenges.
One common hurdle that developers may encounter when working with the Azure OpenAI Service is the "InvalidRequestError: Too many inputs. The max number of inputs is 1". This error message can appear at first, leaving developers wondering about its root cause and how to effectively address it. In this article, we delve into the depths of this error to provide a comprehensive understanding of why it occurs and offer practical troubleshooting steps to overcome it.
What is Embedding?
An embedding is a vector representation of a word, phrase, or sentence. It is a way of capturing the semantic meaning of a piece of text in a numerical format that can be used by machine learning models.
Embeddings are used in a variety of natural language processing (NLP) tasks, such as text classification, machine translation, and question answering. They are also used in other machine learning tasks, such as image classification and speech recognition.
There are many different ways to generate embeddings. One common approach is to use a neural network to learn the embedding representations. This is the approach that is used by the Azure OpenAI Service.
Why generate embeddings using the Azure OpenAI Service?
There are a few reasons why someone might want to generate embeddings using the Azure OpenAI Service.
The Azure OpenAI Service uses a large language model that has been trained on a massive dataset of text and code. This means that the embeddings generated by the service are likely to be more accurate and informative than embeddings generated by other methods.
The Azure OpenAI Service is a cloud-based service, which means that it is easy to use and scale. You can generate embeddings for as many inputs as you need, without having to worry about the hardware requirements.
The Azure OpenAI Service is a secure service, which means that your data is protected.
However, during the process of generating embeddings using the Azure OpenAI Service, an unexpected error can surface, potentially exhausting the spirits of developers. In the below section, we will see what causes this error - its origins, underlying causes, and most importantly, the possible remedies poised to restore the developers' drive.
Azure OpenAI Service: InvalidRequestError: Too many inputs. The max number of inputs is 1
The error message Azure OpenAI Service: InvalidRequestError: Too many inputs. The max number of inputs is 1 indicates that you are trying to generate embeddings for more than one input at a time. The Azure OpenAI Service currently limits the number of inputs to 1 per request.
This error can cause two problems:
The request will fail.
You will be charged for multiple requests, even though only one request was successful.
The different ways to work around the limitation of one input per request when generating embeddings with the Azure OpenAI Service:
Solution 1: Sending Multiple Requests
One way to generate embeddings for multiple inputs is to send multiple requests to the Azure OpenAI Service, each with a single input. Here’s an example that demonstrates this approach:
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
# Set up the Azure credentials
key = "YOUR_AZURE_KEY"
endpoint = "YOUR_AZURE_ENDPOINT"
credential = AzureKeyCredential(key)
client = TextAnalyticsClient(endpoint=endpoint, credential=credential)
# Define the input texts
input_texts = ["This is the first input.", "This is the second input.", "This is the third input."]
# Generate the embeddings for each input text
embeddings = []
for input_text in input_texts:
response = client.generate_embeddings(documents=[input_text])
embeddings.append(response[0].embeddings)
# Print the embeddingsprint(embeddings) In this example, we define a list of input texts and iterate over it, sending a request to generate embeddings for each input text. The resulting embeddings are stored in a list and printed to the console.
Solution 2: Using Batching
Another way to generate embeddings for multiple inputs is to emulate batching by modifying the AzureTextEmbeddings.GenerateEmbeddingsAsync() method. This approach involves sending multiple requests to the Azure OpenAI Service in batches. Here’s an example that demonstrates this approach:
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
# Set up the Azure credentials
key = "YOUR_AZURE_KEY"
endpoint = "YOUR_AZURE_ENDPOINT"
credential = AzureKeyCredential(key)
client = TextAnalyticsClient(endpoint=endpoint, credential=credential)
# Define the input texts
input_texts = ["This is the first input.", "This is the second input.", "This is the third input."]
# Define the batch size
batch_size = 2
# Generate the embeddings in batches
embeddings = []
for i in range(0, len(input_texts), batch_size):
batch = input_texts[i:i+batch_size]
response = client.generate_embeddings(documents=batch)
embeddings.extend([result.embeddings for result in response])
# Print the embeddings
print(embeddings) In this example, we define a list of input texts and a batch size. We then iterate over the input texts in batches, sending a request to generate embeddings for each batch. The resulting embeddings are stored in a list and printed to the console.
Solution 3: Contacting Azure Support
If you have further questions or concerns about generating embeddings with the Azure OpenAI Service, you can contact Azure support through an Azure support request. To do so, sign in to the Azure portal, select Help + support in the left navigation pane, and then select New Support Request.
Conclusion
You now possess an understanding of the underlying triggers behind this error and have familiarized yourself with effective solutions to navigate and surmount its limitations.