LibreChat/docs/install/configuration/azure_openai.md

Azure OpenAI

Azure OpenAI Integration for LibreChat

To properly utilize Azure OpenAI within LibreChat, it's crucial to configure the librechat.yaml file according to your specific needs. This document guides you through the essential setup process which allows seamless use of multiple deployments and models with as much flexibility as needed.

Setup

1. Open librechat.yaml for Editing: Use your preferred text editor or IDE to open and edit the librechat.yaml file.

2. Configure Azure OpenAI Settings: Follow the detailed structure outlined below to populate your Azure OpenAI settings appropriately. This includes specifying API keys, instance names, model groups, and other essential configurations.

3. Save Your Changes: After accurately inputting your settings, save the librechat.yaml file.

4. Restart LibreChat: For the changes to take effect, restart your LibreChat application. This ensures that the updated configurations are loaded and utilized.

Here's a working example configured according to the specifications of the Azure OpenAI Endpoint Configuration Docs:

Required Fields

To properly integrate Azure OpenAI with LibreChat, specific fields must be accurately configured in your librechat.yaml file. These fields are validated through a combination of custom and environmental variables to ensure the correct setup. Here are the detailed requirements based on the validation process:

Group-Level Configuration

1. group (String, Required): Unique identifier name for a group of models. Duplicate group names are not allowed and will result in validation errors.

2. apiKey (String, Required): Must be a valid API key for Azure OpenAI services. It could be a direct key string or an environment variable reference (e.g., ${WESTUS_API_KEY}).

3. instanceName (String, Required): Name of the Azure OpenAI instance. This field can also support environment variable references.

4. deploymentName (String, Optional): The deployment name at the group level is optional but required if any model within the group is set to true.

5. version (String, Optional): The version of the Azure OpenAI service at the group level is optional but required if any model within the group is set to true.

6. baseURL (String, Optional): Custom base URL for the Azure OpenAI API requests. Environment variable references are supported. This is optional and can be used for advanced routing scenarios.

7. additionalHeaders (Object, Optional): Specifies any extra headers for Azure OpenAI API requests as key-value pairs. Environment variable references can be included as values.

Model-Level Configuration

Within each group, the models field must contain a mapping of records, or model identifiers to either boolean values or object configurations.

• The key or model identifier must match its corresponding OpenAI model name in order for it to properly reflect its known context limits and/or function in the case of vision. For example, if you intend to use gpt-4-vision, it must be configured like so:

models:
  gpt-4-vision-preview: # matching OpenAI Model name
    deploymentName: "arbitrary-deployment-name"
    version: "2024-02-15-preview" # version can be any that supports vision

• See Model Deployments for more examples.

• If a model is set to true, it implies using the group-level deploymentName and version for this model. Both must be defined at the group level in this case.

• If a model is configured as an object, it can specify its own deploymentName and version. If these are not provided, the model inherits the group's deploymentName and version.

Special Considerations

1. Unique Names: Both model and group names must be unique across the entire configuration. Duplicate names lead to validation failures.

2. Missing Required Fields: Lack of required deploymentName or version either at the group level (for boolean-flagged models) or within the models' configurations (if not inheriting or explicitly specified) will result in validation errors.

3. Environment Variable References: The configuration supports environment variable references (e.g., ${VARIABLE_NAME}). Ensure that all referenced variables are present in your environment to avoid runtime errors. The absence of defined environment variables referenced in the config will cause errors.${INSTANCE_NAME} and ${DEPLOYMENT_NAME} are unique placeholders, and do not correspond to environment variables, but instead correspond to the instance and deployment name of the currently selected model. It is not recommended you use INSTANCE_NAME and DEPLOYMENT_NAME as environment variable names to avoid any potential conflicts.

4. Error Handling: Any issues in the config, like duplicate names, undefined environment variables, or missing required fields, will invalidate the setup and generate descriptive error messages aiming for prompt resolution. You will not be allowed to run the server with an invalid configuration.

Applying these setup requirements thoughtfully will ensure a correct and efficient integration of Azure OpenAI services with LibreChat through the librechat.yaml configuration. Always validate your configuration against the latest schema definitions and guidelines to maintain compatibility and functionality.

Model Deployments

The list of models available to your users are determined by the model groupings specified in your azureOpenAI endpoint config.

For example:

# Example Azure OpenAI Object Structure
endpoints:
  azureOpenAI:
    groups:
      - group: "my-westus" # arbitrary name
        apiKey: "${WESTUS_API_KEY}"
        instanceName: "actual-instance-name" # name of the resource group or instance
        version: "2023-12-01-preview"
        models:
          gpt-4-vision-preview:
            deploymentName: gpt-4-vision-preview
            version: "2024-02-15-preview"
          gpt-3.5-turbo: true
      - group: "my-eastus"
        apiKey: "${EASTUS_API_KEY}"
        instanceName: "actual-eastus-instance-name"
        deploymentName: gpt-4-turbo
        version: "2024-02-15-preview"
        models:
          gpt-4-turbo: true

The above configuration would enable gpt-4-vision-preview, gpt-3.5-turbo and gpt-4-turbo for your users in the order they were defined.

Using Plugins with Azure

To use the Plugins endpoint with Azure OpenAI, you need a deployment supporting function calling. Otherwise, you need to set "Functions" off in the Agent settings. When you are not using "functions" mode, it's recommend to have "skip completion" off as well, which is a review step of what the agent generated.

To use Azure with the Plugins endpoint, make sure the field plugins is set to true in your Azure OpenAI endpoing config:

# Example Azure OpenAI Object Structure
endpoints:
  azureOpenAI:
    plugins: true # <------- Set this
    groups:
    # omitted for brevity

Configuring the plugins field will configure Plugins to use Azure models.

NOTE: The current configuration through librechat.yaml uses the primary model you select from the frontend for Plugin use, which is not usually how it works without Azure, where instead the "Agent" model is used. The Agent model setting can be ignored when using Plugins through Azure.

Using a Specified Base URL with Azure

The base URL for Azure OpenAI API requests can be dynamically configured. This is useful for proxying services such as Cloudflare AI Gateway, or if you wish to explicitly override the baseURL handling of the app.

LibreChat will use the baseURL field for your Azure model grouping, which can include placeholders for the Azure OpenAI API instance and deployment names.

In the configuration, the base URL can be customized like so:

# librechat.yaml file, under an Azure group:
endpoints:
  azureOpenAI:
    groups:
      - group: "group-with-custom-base-url"
      baseURL: "https://example.azure-api.net/${INSTANCE_NAME}/${DEPLOYMENT_NAME}"

# OR
      baseURL: "https://${INSTANCE_NAME}.openai.azure.com/openai/deployments/${DEPLOYMENT_NAME}"

# Cloudflare example
      baseURL: "https://gateway.ai.cloudflare.com/v1/ACCOUNT_TAG/GATEWAY/azure-openai/${INSTANCE_NAME}/${DEPLOYMENT_NAME}"

NOTE: ${INSTANCE_NAME} and ${DEPLOYMENT_NAME} are unique placeholders, and do not correspond to environment variables, but instead correspond to the instance and deployment name of the currently selected model. It is not recommended you use INSTANCE_NAME and DEPLOYMENT_NAME as environment variable names to avoid any potential conflicts.

You can also omit the placeholders completely and simply construct the baseURL with your credentials:

      baseURL: "https://gateway.ai.cloudflare.com/v1/ACCOUNT_TAG/GATEWAY/azure-openai/my-secret-instance/my-deployment"

Lastly, you can specify the entire baseURL through a custom environment variable

      baseURL: "${MY_CUSTOM_BASEURL}"

Enabling Auto-Generated Titles with Azure

To enable titling for Azure, set titleConvo to true.

# Example Azure OpenAI Object Structure
endpoints:
  azureOpenAI:
    titleConvo: true # <------- Set this
    groups:
    # omitted for brevity

You can also specify the model to use for titling, with titleModel provided you have configured it in your group(s).

    titleModel: "gpt-3.5-turbo"

Note: "gpt-3.5-turbo" is the default value, so you can omit it if you want to use this exact model and have it configured. If not configured and titleConvo is set to true, the titling process will result in an error and no title will be generated.

Using GPT-4 Vision with Azure

To use Vision (image analysis) with Azure OpenAI, you need to make sure gpt-4-vision-preview is a specified model in one of your groupings

This will work seamlessly as it does with the OpenAI endpoint (no need to select the vision model, it will be switched behind the scenes)

Generate images with Azure OpenAI Service (DALL-E)

Model ID	Feature Availability	Max Request (characters)
dalle2	East US	1000
dalle3	Sweden Central	4000

• First you need to create an Azure resource that hosts DALL-E
  • At the time of writing, dall-e-3 is available in the SwedenCentral region, dall-e-2 in the EastUS region.
• Then, you need to deploy the image generation model in one of the above regions.
  • Read the Azure OpenAI Image Generation Quickstart Guide for further assistance
• Configure your environment variables based on Azure credentials:

- For DALL-E-3:

DALLE3_AZURE_API_VERSION=the-api-version # e.g.: 2023-12-01-preview
DALLE3_BASEURL=https://<AZURE_OPENAI_API_INSTANCE_NAME>.openai.azure.com/openai/deployments/<DALLE3_DEPLOYMENT_NAME>/
DALLE3_API_KEY=your-azure-api-key-for-dall-e-3

- For DALL-E-2:

DALLE2_AZURE_API_VERSION=the-api-version # e.g.: 2023-12-01-preview
DALLE2_BASEURL=https://<AZURE_OPENAI_API_INSTANCE_NAME>.openai.azure.com/openai/deployments/<DALLE2_DEPLOYMENT_NAME>/
DALLE2_API_KEY=your-azure-api-key-for-dall-e-2

DALL-E Notes:

• For DALL-E-3, the default system prompt has the LLM prefer the "vivid" style parameter, which seems to be the preferred setting for ChatGPT as "natural" can sometimes produce lackluster results.
• See official prompt for reference: DALL-E System Prompt
• You can adjust the system prompts to your liking:

DALLE3_SYSTEM_PROMPT="Your DALL-E-3 System Prompt here"
DALLE2_SYSTEM_PROMPT="Your DALL-E-2 System Prompt here"

• The DALLE_REVERSE_PROXY environment variable is ignored when Azure credentials (DALLEx_AZURE_API_VERSION and DALLEx_BASEURL) for DALL-E are configured.

---

⚠️ Legacy Setup ⚠️

---

Note: The legacy instructions may be used for a simple setup but they are no longer recommended as of v0.7.0 and may break in future versions. This was done to improve upon legacy configuration settings, to allow multiple deployments/model configurations setup with ease: #1390

Use the recommended Setup in the section above.

Required Variables (legacy)

These variables construct the API URL for Azure OpenAI.

• AZURE_API_KEY: Your Azure OpenAI API key.
• AZURE_OPENAI_API_INSTANCE_NAME: The instance name of your Azure OpenAI API.
• AZURE_OPENAI_API_DEPLOYMENT_NAME: The deployment name of your Azure OpenAI API.
• AZURE_OPENAI_API_VERSION: The version of your Azure OpenAI API.

For example, with these variables, the URL for chat completion would look something like:

https://{AZURE_OPENAI_API_INSTANCE_NAME}.openai.azure.com/openai/deployments/{AZURE_OPENAI_API_DEPLOYMENT_NAME}/chat/completions?api-version={AZURE_OPENAI_API_VERSION}

You should also consider changing the AZURE_OPENAI_MODELS variable to the models available in your deployment.

# .env file
AZURE_OPENAI_MODELS=gpt-4-1106-preview,gpt-4,gpt-3.5-turbo,gpt-3.5-turbo-1106,gpt-4-vision-preview

Overriding the construction of the API URL is possible as of implementing Issue #1266

Model Deployments (legacy)

Note: a change will be developed to improve current configuration settings, to allow multiple deployments/model configurations setup with ease: #1390

As of 2023-12-18, the Azure API allows only one model per deployment.

It's highly recommended to name your deployments after the model name (e.g., "gpt-3.5-turbo") for easy deployment switching.

When you do so, LibreChat will correctly switch the deployment, while associating the correct max context per model, if you have the following environment variable set:

AZURE_USE_MODEL_AS_DEPLOYMENT_NAME=TRUE

For example, when you have set AZURE_USE_MODEL_AS_DEPLOYMENT_NAME=TRUE, the following deployment configuration provides the most seamless, error-free experience for LibreChat, including Vision support and tracking the correct max context tokens:

Alternatively, you can use custom deployment names and set AZURE_OPENAI_DEFAULT_MODEL for expected functionality.

• AZURE_OPENAI_MODELS: List the available models, separated by commas without spaces. The first listed model will be the default. If left blank, internal settings will be used. Note that deployment names can't have periods, which are removed when generating the endpoint.

Example use:

# .env file
AZURE_OPENAI_MODELS=gpt-3.5-turbo,gpt-4,gpt-5

• AZURE_USE_MODEL_AS_DEPLOYMENT_NAME: Enable using the model name as the deployment name for the API URL.

Example use:

# .env file
AZURE_USE_MODEL_AS_DEPLOYMENT_NAME=TRUE

Setting a Default Model for Azure (legacy)

This section is relevant when you are not naming deployments after model names as shown above.

Important: The Azure OpenAI API does not use the model field in the payload but is a necessary identifier for LibreChat. If your deployment names do not correspond to the model names, and you're having issues with the model not being recognized, you should set this field to explicitly tell LibreChat to treat your Azure OpenAI API requests as if the specified model was selected.

If AZURE_USE_MODEL_AS_DEPLOYMENT_NAME is enabled, the model you set with AZURE_OPENAI_DEFAULT_MODEL will not be recognized and will not be used as the deployment name; instead, it will use the model selected by the user as the "deployment" name.

• AZURE_OPENAI_DEFAULT_MODEL: Override the model setting for Azure, useful if using custom deployment names.

Example use:

# .env file
# MUST be a real OpenAI model, named exactly how it is recognized by OpenAI API (not Azure)
AZURE_OPENAI_DEFAULT_MODEL=gpt-3.5-turbo # do include periods in the model name here

Using a Specified Base URL with Azure (legacy)

The base URL for Azure OpenAI API requests can be dynamically configured. This is useful for proxying services such as Cloudflare AI Gateway, or if you wish to explicitly override the baseURL handling of the app.

LibreChat will use the AZURE_OPENAI_BASEURL environment variable, which can include placeholders for the Azure OpenAI API instance and deployment names.

In the application's environment configuration, the base URL is set like this:

# .env file
AZURE_OPENAI_BASEURL=https://example.azure-api.net/${INSTANCE_NAME}/${DEPLOYMENT_NAME}

# OR
AZURE_OPENAI_BASEURL=https://${INSTANCE_NAME}.openai.azure.com/openai/deployments/${DEPLOYMENT_NAME}

# Cloudflare example
AZURE_OPENAI_BASEURL=https://gateway.ai.cloudflare.com/v1/ACCOUNT_TAG/GATEWAY/azure-openai/${INSTANCE_NAME}/${DEPLOYMENT_NAME}

The application replaces ${INSTANCE_NAME} and ${DEPLOYMENT_NAME} in the AZURE_OPENAI_BASEURL, processed according to the other settings discussed in the guide.

You can also omit the placeholders completely and simply construct the baseURL with your credentials:

# .env file
AZURE_OPENAI_BASEURL=https://instance-1.openai.azure.com/openai/deployments/deployment-1

# Cloudflare example
AZURE_OPENAI_BASEURL=https://gateway.ai.cloudflare.com/v1/ACCOUNT_TAG/GATEWAY/azure-openai/instance-1/deployment-1

Setting these values will override all of the application's internal handling of the instance and deployment names and use your specified base URL.

Notes:

• You should still provide the AZURE_OPENAI_API_VERSION and AZURE_API_KEY via the .env file as they are programmatically added to the requests.
• When specifying instance and deployment names in the AZURE_OPENAI_BASEURL, their respective environment variables can be omitted (AZURE_OPENAI_API_INSTANCE_NAME and AZURE_OPENAI_API_DEPLOYMENT_NAME) except for use with Plugins.
• Specifying instance and deployment names in the AZURE_OPENAI_BASEURL instead of placeholders creates conflicts with "plugins," "vision," "default-model," and "model-as-deployment-name" support.
• Due to the conflicts that arise with other features, it is recommended to use placeholder for instance and deployment names in the AZURE_OPENAI_BASEURL

Enabling Auto-Generated Titles with Azure (legacy)

The default titling model is set to gpt-3.5-turbo.

If you're using AZURE_USE_MODEL_AS_DEPLOYMENT_NAME and have "gpt-35-turbo" setup as a deployment name, this should work out-of-the-box.

In any case, you can adjust the title model as such: OPENAI_TITLE_MODEL=your-title-model

Using GPT-4 Vision with Azure (legacy)

Currently, the best way to setup Vision is to use your deployment names as the model names, as shown here

This will work seamlessly as it does with the OpenAI endpoint (no need to select the vision model, it will be switched behind the scenes)

Alternatively, you can set the required variables to explicitly use your vision deployment, but this may limit you to exclusively using your vision deployment for all Azure chat settings.

Notes:

• If using AZURE_OPENAI_BASEURL, you should not specify instance and deployment names instead of placeholders as the vision request will fail.
• As of December 18th, 2023, Vision models seem to have degraded performance with Azure OpenAI when compared to OpenAI

Note: a change will be developed to improve current configuration settings, to allow multiple deployments/model configurations setup with ease: #1390

Optional Variables (legacy)

These variables are currently not used by LibreChat

• AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME: The deployment name for completion. This is currently not in use but may be used in future.
• AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME: The deployment name for embedding. This is currently not in use but may be used in future.

These two variables are optional but may be used in future updates of this project.

Using Plugins with Azure

Note: To use the Plugins endpoint with Azure OpenAI, you need a deployment supporting function calling. Otherwise, you need to set "Functions" off in the Agent settings. When you are not using "functions" mode, it's recommend to have "skip completion" off as well, which is a review step of what the agent generated.

To use Azure with the Plugins endpoint, make sure the following environment variables are set:

• PLUGINS_USE_AZURE: If set to "true" or any truthy value, this will enable the program to use Azure with the Plugins endpoint.
• AZURE_API_KEY: Your Azure API key must be set with an environment variable.

Important:

• If using AZURE_OPENAI_BASEURL, you should not specify instance and deployment names instead of placeholders as the plugin request will fail.