Mia-Assistant

Mia-Platform Console includes Mia Assistant, an AI-based application that can be interrogated on anything included in the official Mia-Platform Documentation.

MongoDB configuration

Mia-Assistant relies on a MongoDB vector store collection, that is automatically populated during deployment.

However, the Helm Chart is not yet capable of creating the vector store collection and the necessary indexes for it to work; so you have to manually create a collection named assistant-documents and configure an Atlas Vector Search index on it.

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Please mind that Atlas Vector Search indexes are available only on MongoDB Atlas instance with version 6.0.11, 7.0.2 or higher.

Unfortunately, MongoDB's Vector Search indexes are not available on previous versions or on MongoDB Entreprise Server edition. If you don't meet these requirements, unfortunately the service will not work.

Please refer to the official MongoDB official documentation to have more information regarding this.

You can create the index in two ways:

• with a script with the official MongoDB drivers that connects to MongoDB and prepare the embeddings for you, as explained in the MongoDB official documentation
• manually, connecting to your MongoDB Atlas cluster and creating the index from the Atlas web application, as explained in this guide

It is important that the index have this structure:

{
  "fields": [
    {
      "numDimensions": 1536,
      "path": "embedding",
      "similarity": "euclidean",
      "type": "vector"
    },
    {
      "path": "__STATE__",
      "type": "filter"
    }
  ]
}

caution

The structure of the index is mandatory, otherwise the documents cannot be extracted from the collection.

OpenAI Configuration

The Mia-Assistant service can be configured via Helm Chart using the .assistant value.

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At the moment, the only supported models are the ones developed by OpenAI.

The Helm Chart will require including the API key for both the embedding model and the large language model used. For OpenAI models, these two API keys are the same and can be created from the OpenAI API keys page. After logging in with the credentials of your company, you can create the API Key that must be included in the assistant object inside the Helm chart and that will be used by the Mia-Assistant service.

The service is already configured to use the following models:

• text-embedding-3-small as Embedding model
• gpt-3.5-turbo as Large Language Model (LLM)

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Please note that using these models has a cost, which is detailed on the Pricing page of the OpenAI documentation.

When registering with OpenAI, you also have to set up a billing plan in order to use OpenAI services with the Mia-Assistant.

Mia-Assistant Configuration

The configuration regarding the Assistant is included inside the assistant object, which is composed by: In order for the service to correctly start up, please ensure the following properties configured:

Name	Type	Description	Default	Required
enabled	boolean	If set to true, the Mia-Assistant will be enabled	false	❌
keys	object	The configuration for the API Keys and Credentials for specified Models		✅
llm	object	The configuration of the related LLM used under the hood		✅
embeddings	object	The configuration of the related Embeddings used under the hood		✅

LLM and Embeddings Model Configuration

You can choose one or multiple LLMs providers to be used from the Mia-Assistant. The supported ones are:

• azure
• openai
• vertex
• google_anthropic_vertex

As Embedding model you can choose one of the following supported types:

• azure
• openai
• vertex

Note that both vertex and google_anthropic_vertex cannot be configured to use different credentials for LLM and Embeddings models. Credentials for these models is defined in the field keys.vertexAICredentials.

Here an example to configure Mia-Assistant with different LLM providers:

mia-console:
  configurations:
    # ...
    assistant:
      enabled: true,
      keys:
        azureLlmApiKey: "azure-apiKey"
        vertexAICredentials: "vertex-credentials"
      llms:
        - type: "azure", # this model uses keys.azureLlmApiKey
          displayName: "GPT-4o Mini"
          apiVersion": "2025-01-01-preview",
          deploymentName": "gpt-4o-mini",
          name": "gpt-4o-mini",
          url": "https://test.openai.azure.com/"
        - type: "google_anthropic_vertex", # this model uses keys.vertexAICredentials
          name": "claude-sonnet-4@20250514",
          # ...
      embeddings:
        type: "azure",
        apiKey: "embeddings-apiKey"
        apiVersion": "2025-01-01-preview",
        deploymentName": "text-embedding-3-large",
        name": "text-embedding-3-large",
        url": "https://test.openai.azure.com/"

Azure:

mia-console:
  configurations:
    # ...
    assistant:
      enabled: true,
      # ...
      keys:
        azureLlmApiKey: "your-apiKey"
      llms:
        - type": "azure",
          name": "gpt-4o-mini",
          # ...
      embeddings:
        type": "azure",
        apiKey: "azure-embeddings-apiKey"
        name": "text-embedding-3-large",
        # ...

OpenAI:

mia-console:
  configurations:
    # ...
    assistant:
      enabled: true,
      # ...
      keys:
        openaiLlmApiKey: "your-apiKey"
      llms:
        - type": "openai",
          name": "gpt-4o-mini",
          # ...
      embeddings:
        type": "openai",
        apiKey: "openai-embeddings-apiKey"
        name": "text-embedding-3-large",
        # ...

Vertex:

mia-console:
  configurations:
    # ...
    assistant:
      enabled: true,
      # ...
      keys:
        vertexAICredentials: "vertex-ai-credentials"
      llms:
        - type": "vertex",
          name": "gpt-4o-mini",
          # ...
      embeddings:
        type": "vertex",
        name": "text-embedding-004",
        # ...

Anthropic models on Vertex:

mia-console:
  configurations:
    # ...
    assistant:
      enabled: true,
      # ...
      keys:
        vertexAICredentials: "vertex-ai-credentials"
      llms:
        - type": "google_anthropic_vertex",
          name": "claude-sonnet-4@20250514",
          # ...
      embeddings:
        type": "vertex",
        name": "text-embedding-004",
        # ...