Table of contents
Exit editor mode
Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
Azure OpenAI stored completions & distillation (preview) (classic)
Feedback
Summarize this article for me
Applies only to: 
Foundry (classic) portal. This article isn't available for the new Foundry portal. Learn more about the new portal.
Stored completions allow you to capture the conversation history from chat completions sessions to use as datasets for evaluations and fine-tuning.
Stored completions support
Model & region availability
As long as you're using the Chat Completions API for inferencing, you can leverage stored completions. It is supported for all Azure OpenAI models, and in all supported regions (including global-only regions).
Configure stored completions
To enable stored completions for your Azure OpenAI deployment set the store parameter to True. Use the metadata parameter to enrich your stored completion dataset with additional information.
import os
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
token_provider = get_bearer_token_provider(
DefaultAzureCredential(), "https://ai.azure.com/.default"
)
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=token_provider,
)
completion = client.chat.completions.create(
model="gpt-4o", # replace with model deployment name
store= True,
metadata = {
"user": "admin",
"category": "docs-test",
},
messages=[
{"role": "system", "content": "Provide a clear and concise summary of the technical content, highlighting key concepts and their relationships. Focus on the main ideas and practical implications."},
{"role": "user", "content": "Ensemble methods combine multiple machine learning models to create a more robust and accurate predictor. Common techniques include bagging (training models on random subsets of data), boosting (sequentially training models to correct previous errors), and stacking (using a meta-model to combine base model predictions). Random Forests, a popular bagging method, create multiple decision trees using random feature subsets. Gradient Boosting builds trees sequentially, with each tree focusing on correcting the errors of previous trees. These methods often achieve better performance than single models by reducing overfitting and variance while capturing different aspects of the data."}
]
)
print(completion.choices[0].message)
Important
Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If you use an API key, store it securely in Azure Key Vault. For more information about using API keys securely in your apps, see API keys with Azure Key Vault.
For more information about AI services security, see Authenticate requests to Azure AI services.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/"
)
completion = client.chat.completions.create(
model="gpt-4o", # replace with model deployment name
store= True,
metadata = {
"user": "admin",
"category": "docs-test",
},
messages=[
{"role": "system", "content": "Provide a clear and concise summary of the technical content, highlighting key concepts and their relationships. Focus on the main ideas and practical implications."},
{"role": "user", "content": "Ensemble methods combine multiple machine learning models to create a more robust and accurate predictor. Common techniques include bagging (training models on random subsets of data), boosting (sequentially training models to correct previous errors), and stacking (using a meta-model to combine base model predictions). Random Forests, a popular bagging method, create multiple decision trees using random feature subsets. Gradient Boosting builds trees sequentially, with each tree focusing on correcting the errors of previous trees. These methods often achieve better performance than single models by reducing overfitting and variance while capturing different aspects of the data."}
]
)
print(completion.choices[0].message)
Microsoft Entra ID
curl $AZURE_OPENAI_ENDPOINT/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AZURE_OPENAI_AUTH_TOKEN" \
-d '{
"model": "gpt-4o",
"store": true,
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello!"
}
]
}'
API Key
curl $AZURE_OPENAI_ENDPOINT/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "api-key: $AZURE_OPENAI_API_KEY" \
-d '{
"model": "gpt-4o",
"store": true,
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello!"
}
]
}'
{
"id": "chatcmpl-B4eQ716S5wGUyFpGgX2MXnJEC5AW5",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "Ensemble methods enhance machine learning performance by combining multiple models to create a more robust and accurate predictor. The key techniques include:
1. **Bagging (Bootstrap Aggregating)**: Involves training multiple models on random subsets of the data to reduce variance and overfitting. A popular method within bagging is Random Forests, which build numerous decision trees using random subsets of features and data samples.
2. **Boosting**: Focuses on sequentially training models, where each new model attempts to correct the errors made by previous ones. Gradient Boosting is a common boosting technique that builds trees sequentially, concentrating on the mistakes of earlier trees to improve accuracy.
3. **Stacking**: Uses a meta-model to combine predictions from various base models, leveraging their strengths to enhance overall predictions.
These ensemble methods generally outperform individual models because they effectively handle overfitting, reduce variance, and capture diverse aspects of the data. In practical applications, they are valued for their ability to improve model accuracy and stability.",
"refusal": null,
"role": "assistant",
"audio": null,
"function_call": null,
"tool_calls": null
},
"content_filter_results": {
"hate": {
"filtered": false,
"severity": "safe"
},
"protected_material_code": {
"filtered": false,
"detected": false
},
"protected_material_text": {
"filtered": false,
"detected": false
},
"self_harm": {
"filtered": false,
"severity": "safe"
},
"sexual": {
"filtered": false,
"severity": "safe"
},
"violence": {
"filtered": false,
"severity": "safe"
}
}
}
],
"created": 1740448387,
"model": "gpt-4o-2024-08-06",
"object": "chat.completion",
"service_tier": null,
"system_fingerprint": "fp_b705f0c291",
"usage": {
"completion_tokens": 205,
"prompt_tokens": 157,
"total_tokens": 362,
"completion_tokens_details": {
"accepted_prediction_tokens": 0,
"audio_tokens": 0,
"reasoning_tokens": 0,
"rejected_prediction_tokens": 0
},
"prompt_tokens_details": {
"audio_tokens": 0,
"cached_tokens": 0
}
},
"prompt_filter_results": [
{
"prompt_index": 0,
"content_filter_results": {
"hate": {
"filtered": false,
"severity": "safe"
},
"jailbreak": {
"filtered": false,
"detected": false
},
"self_harm": {
"filtered": false,
"severity": "safe"
},
"sexual": {
"filtered": false,
"severity": "safe"
},
"violence": {
"filtered": false,
"severity": "safe"
}
}
}
]
}
Once stored completions are enabled for an Azure OpenAI deployment, they'll begin to show up in the Microsoft Foundry portal in the Stored Completions pane.
Distillation
Distillation allows you to turn your stored completions into a fine-tuning dataset. A common use case is to use stored completions with a larger more powerful model for a particular task and then use the stored completions to train a smaller model on high quality examples of model interactions.
Distillation requires a minimum of 10 stored completions, though it's recommended to provide hundreds to thousands of stored completions for the best results.
From the Stored Completions pane in the Foundry portal use the Filter options to select the completions you want to train your model with.
To begin distillation, select Distill
Pick which model you would like to fine-tune with your stored completion dataset.
Confirm which version of the model you want to fine-tune:
A
.jsonlfile with a randomly generated name will be created as a training dataset from your stored completions. Select the file > Next.Note
Stored completion distillation training files cannot be accessed directly and cannot be exported externally/downloaded.
The rest of the steps correspond to the typical Azure OpenAI fine-tuning steps. To learn more, see our fine-tuning getting started guide.
Evaluation
The evaluation of large language models is a critical step in measuring their performance across various tasks and dimensions. This is especially important for fine-tuned models, where assessing the performance gains (or losses) from training is crucial. Thorough evaluations can help your understanding of how different versions of the model may impact your application or scenario.
Stored completions can be used as a dataset for running evaluations.
From the Stored Completions pane in the Foundry portal use the Filter options to select the completions you want to be part of your evaluation dataset.
To configure the evaluation, select Evaluate
This launches the Evaluations pane with a prepopulated
.jsonlfile with a randomly generated name that is created as an evaluation dataset from your stored completions.Note
Stored completion evaluation data files cannot be accessed directly and cannot be exported externally/downloaded.
To learn more about evaluation see, getting started with evaluations
Stored completions API
To access the stored completions API commands you may need to upgrade your version of the OpenAI library.
pip install --upgrade openai
List stored completions
Additional parameters:
metadata: Filter by the key/value pair in the stored completionsafter: Identifier for the last stored completion message from the previous pagination request.limit: Number of stored completions messages to retrieve.order: Order of the results by index (ascending or descending).
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
token_provider = get_bearer_token_provider(
DefaultAzureCredential(), "https://ai.azure.com/.default"
)
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=token_provider,
)
response = client.chat.completions.list()
print(response.model_dump_json(indent=2))
from openai import OpenAI
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
)
response = client.chat.completions.list()
print(response.model_dump_json(indent=2))
Microsoft Entra ID
curl https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AZURE_OPENAI_AUTH_TOKEN" \
API Key
curl https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "api-key: $AZURE_OPENAI_API_KEY" \
{
"data": [
{
"id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u",
"choices": [
{
"finish_reason": null,
"index": 0,
"logprobs": null,
"message": {
"content": "Ensemble methods enhance machine learning performance by combining multiple models to create a more robust and accurate predictor. The key techniques include:
1. **Bagging (Bootstrap Aggregating):** This involves training models on random subsets of the data to reduce variance and prevent overfitting. Random Forests, a popular bagging method, build multiple decision trees using random feature subsets, leading to robust predictions.
2. **Boosting:** This sequential approach trains models to correct the errors of their predecessors, thereby focusing on difficult-to-predict data points. Gradient Boosting is a common implementation that sequentially builds decision trees, each improving upon the prediction errors of the previous ones.
3. **Stacking:** This technique uses a meta-model to combine the predictions of multiple base models, leveraging their diverse strengths to enhance overall prediction accuracy.
The practical implications of ensemble methods include achieving superior model performance compared to single models by capturing various data patterns and reducing overfitting and variance. These methods are widely used in applications where high accuracy and model reliability are critical.",
"refusal": null,
"role": "assistant",
"audio": null,
"function_call": null,
"tool_calls": null
}
}
],
"created": 1740447656,
"model": "gpt-4o-2024-08-06",
"object": null,
"service_tier": null,
"system_fingerprint": "fp_b705f0c291",
"usage": {
"completion_tokens": 208,
"prompt_tokens": 157,
"total_tokens": 365,
"completion_tokens_details": null,
"prompt_tokens_details": null
},
"request_id": "0000aaaa-11bb-cccc-dd22-eeeeee333333",
"seed": -430976584126747957,
"top_p": 1,
"temperature": 1,
"presence_penalty": 0,
"frequency_penalty": 0,
"metadata": {
"user": "admin",
"category": "docs-test"
}
}
],
"has_more": false,
"object": "list",
"total": 1,
"first_id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u",
"last_id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u"
}
Get stored completion
Get stored completion by ID.
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
token_provider = get_bearer_token_provider(
DefaultAzureCredential(), "https://ai.azure.com/.default"
)
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=token_provider
)
response = client.chat.completions.retrieve("chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u")
print(response.model_dump_json(indent=2))
from openai import OpenAI
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
)
response = client.chat.completions.retrieve("chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u")
print(response.model_dump_json(indent=2))
Microsoft Entra ID
curl https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AZURE_OPENAI_AUTH_TOKEN" \
API Key
curl https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u \
-H "Content-Type: application/json" \
-H "api-key: $AZURE_OPENAI_API_KEY" \
{
"id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u",
"choices": [
{
"finish_reason": null,
"index": 0,
"logprobs": null,
"message": {
"content": "Ensemble methods enhance machine learning performance by combining multiple models to create a more robust and accurate predictor. The key techniques include:
1. **Bagging (Bootstrap Aggregating):** This involves training models on random subsets of the data to reduce variance and prevent overfitting. Random Forests, a popular bagging method, build multiple decision trees using random feature subsets, leading to robust predictions.
2. **Boosting:** This sequential approach trains models to correct the errors of their predecessors, thereby focusing on difficult-to-predict data points. Gradient Boosting is a common implementation that sequentially builds decision trees, each improving upon the prediction errors of the previous ones.
3. **Stacking:** This technique uses a meta-model to combine the predictions of multiple base models, leveraging their diverse strengths to enhance overall prediction accuracy.
The practical implications of ensemble methods include achieving superior model performance compared to single models by capturing various data patterns and reducing overfitting and variance. These methods are widely used in applications where high accuracy and model reliability are critical.",
"refusal": null,
"role": "assistant",
"audio": null,
"function_call": null,
"tool_calls": null
}
}
],
"created": 1740447656,
"model": "gpt-4o-2024-08-06",
"object": "chat.completion",
"service_tier": null,
"system_fingerprint": "fp_b705f0c291",
"usage": {
"completion_tokens": 208,
"prompt_tokens": 157,
"total_tokens": 365,
"completion_tokens_details": null,
"prompt_tokens_details": null
},
"request_id": "0000aaaa-11bb-cccc-dd22-eeeeee333333",
"seed": -430976584126747957,
"top_p": 1,
"temperature": 1,
"presence_penalty": 0,
"frequency_penalty": 0,
"metadata": {
"user": "admin",
"category": "docs-test"
}
}
Get stored chat completion messages
Additional parameters:
after: Identifier for the last stored completion message from the previous pagination request.limit: Number of stored completions messages to retrieve.order: Order of the results by index (ascending or descending).
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
token_provider = get_bearer_token_provider(
DefaultAzureCredential(), "https://ai.azure.com/.default"
)
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=token_provider,
)
response = client.chat.completions.messages.list("chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u", limit=2)
print(response.model_dump_json(indent=2))
from openai import OpenAI
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
)
response = client.chat.completions.messages.list("chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u", limit=2)
print(response.model_dump_json(indent=2))
Microsoft Entra ID
curl https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u/messages \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AZURE_OPENAI_AUTH_TOKEN" \
API Key
curl https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u/messages \
-H "Content-Type: application/json" \
-H "api-key: $AZURE_OPENAI_API_KEY" \
{
"data": [
{
"content": "Provide a clear and concise summary of the technical content, highlighting key concepts and their relationships. Focus on the main ideas and practical implications.",
"refusal": null,
"role": "system",
"audio": null,
"function_call": null,
"tool_calls": null,
"id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u-0"
},
{
"content": "Ensemble methods combine multiple machine learning models to create a more robust and accurate predictor. Common techniques include bagging (training models on random subsets of data), boosting (sequentially training models to correct previous errors), and stacking (using a meta-model to combine base model predictions). Random Forests, a popular bagging method, create multiple decision trees using random feature subsets. Gradient Boosting builds trees sequentially, with each tree focusing on correcting the errors of previous trees. These methods often achieve better performance than single models by reducing overfitting and variance while capturing different aspects of the data.",
"refusal": null,
"role": "user",
"audio": null,
"function_call": null,
"tool_calls": null,
"id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u-1"
}
],
"has_more": false,
"object": "list",
"total": 2,
"first_id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u-0",
"last_id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u-1"
}
Update stored chat completion
Add metadata key:value pairs to an existing stored completion.
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
token_provider = get_bearer_token_provider(
DefaultAzureCredential(), "https://ai.azure.com/.default"
)
client = AzureOpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=token_provider,
)
response = client.chat.completions.update(
"chatcmpl-C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1w",
metadata={"fizz": "buzz"}
)
print(response.model_dump_json(indent=2))
from openai import OpenAI
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=os.getenv("AZURE_OPENAI_API_KEY")
)
response = client.chat.completions.update(
"chatcmpl-C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1w",
metadata={"fizz": "buzz"}
)
print(response.model_dump_json(indent=2))
Microsoft Entra ID
curl -X https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AZURE_OPENAI_AUTH_TOKEN"
-d '{
"metadata": {
"fizz": "buzz"
}
}'
API Key
curl -X https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u \
-H "Content-Type: application/json" \
-H "api-key: $AZURE_OPENAI_API_KEY"
-d '{
"metadata": {
"fizz": "buzz"
}
}'
"id": "chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u",
"choices": [
{
"finish_reason": null,
"index": 0,
"logprobs": null,
"message": {
"content": "Ensemble methods enhance machine learning performance by combining multiple models to create a more robust and accurate predictor. The key techniques include:
1. **Bagging (Bootstrap Aggregating):** This involves training models on random subsets of the data to reduce variance and prevent overfitting. Random Forests, a popular bagging method, build multiple decision trees using random feature subsets, leading to robust predictions.
2. **Boosting:** This sequential approach trains models to correct the errors of their predecessors, thereby focusing on difficult-to-predict data points. Gradient Boosting is a common implementation that sequentially builds decision trees, each improving upon the prediction errors of the previous ones.
3. **Stacking:** This technique uses a meta-model to combine the predictions of multiple base models, leveraging their diverse strengths to enhance overall prediction accuracy.
The practical implications of ensemble methods include achieving superior model performance compared to single models by capturing various data patterns and reducing overfitting and variance. These methods are widely used in applications where high accuracy and model reliability are critical.",
"refusal": null,
"role": "assistant",
"audio": null,
"function_call": null,
"tool_calls": null
}
}
],
"created": 1740447656,
"model": "gpt-4o-2024-08-06",
"object": "chat.completion",
"service_tier": null,
"system_fingerprint": "fp_b705f0c291",
"usage": {
"completion_tokens": 208,
"prompt_tokens": 157,
"total_tokens": 365,
"completion_tokens_details": null,
"prompt_tokens_details": null
},
"request_id": "0000aaaa-11bb-cccc-dd22-eeeeee333333",
"seed": -430976584126747957,
"top_p": 1,
"temperature": 1,
"presence_penalty": 0,
"frequency_penalty": 0,
"metadata": {
"user": "admin",
"category": "docs-test"
"fizz": "buzz"
}
}
Delete stored chat completion
Delete stored completion by completion ID.
Microsoft Entra ID
from openai import OpenAI
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
token_provider = get_bearer_token_provider(
DefaultAzureCredential(), "https://ai.azure.com/.default"
)
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=token_provider
)
response = client.chat.completions.delete("chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u")
print(response.model_dump_json(indent=2))
from openai import OpenAI
client = OpenAI(
base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/",
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
)
response = client.chat.completions.delete("chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u")
print(response.model_dump_json(indent=2))
curl -X DELETE -D - https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $AZURE_OPENAI_AUTH_TOKEN"
API Key
curl -X DELETE -D - https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/chat/completions/chatcmpl-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u \
-H "Content-Type: application/json" \
-H "api-key: $AZURE_OPENAI_API_KEY"
"id"• "chatcmp1-A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u",
"deleted": true,
"object": "chat. completion. deleted"
Troubleshooting
Do I need special permissions to use stored completions?
Stored completions access is controlled via two DataActions:
Microsoft.CognitiveServices/accounts/OpenAI/stored-completions/readMicrosoft.CognitiveServices/accounts/OpenAI/stored-completions/action
By default Azure AI User role has access to both these permissions:
How do I delete stored data?
Data can be deleted by deleting the associated Azure OpenAI resource. If you wish to only delete stored completion data you must open a case with customer support.
How much stored completion data can I store?
You can store a maximum 10 GB of data.
Can I prevent stored completions in my project?
Users of Azure OpenAI resources can disable stored completions within the Azure portal. Within the Azure OpenAI resource, navigate to the Stored Completions panel in the Resource Management view. Toggle the Stored Completions control to Disabled and click Save.
Users of Foundry resources must open a case with customer support to disable Stored Completions at the Azure subscription level.
TypeError: Completions.create() got an unexpected argument 'store'
This error occurs when you're running an older version of the OpenAI client library that predates the stored completions feature being released. Run pip install openai --upgrade.
Feedback
Was this page helpful?
Yes
No
No
Need help with this topic?
Want to try using Ask Learn to clarify or guide you through this topic?
Ask Learn
Ask Learn
Suggest a fix?
Additional resources
- Last updated on 2026-02-27
