Mistral

The UC AI Mistral package integrates Mistral AI’s models (La Plateforme) into your Oracle database applications.

Features

• Support for the latest Mistral models (Mistral Large, Medium, Small, Magistral, Codestral, Ministral)
• Full function calling (tools) support
• Reasoning models (Magistral)
• Multi-modal support (text, images)
• Structured output support
• Embeddings support (mistral-embed, codestral-embed)

Prerequisites

1. Mistral API key
2. Oracle database with internet access to Mistral’s API endpoints (see Network Setup (ACL & Wallet))
3. UC AI package installed
4. Set up API key (guide)

You can get an API key by signing up at Mistral La Plateforme.

Creating a Web Credential

To use your Mistral key with APEX Web Credentials, create a new web credential in your APEX workspace (Workspace Utilities → Web Credentials) with the following configuration:

• Authentication Type: HTTP Header
• Credential Name: Authorization
• Credential Secret: Bearer <your_api_key>

Also set Valid for URLs to the Mistral API (https://api.mistral.ai/) to limit the scope of the credential.

To use the web credential in your PL/SQL code, set the package variable uc_ai_mistral.g_apex_web_credential to the static ID of your web credential before calling any UC AI functions:

uc_ai_mistral.g_apex_web_credential := 'MISTRAL';

Models

Mistral offers a range of models optimized for different use cases. The UC AI Mistral package provides constants for the current models:

Generalist Models

• uc_ai_mistral.c_model_mistral_large - Mistral Large
• uc_ai_mistral.c_model_mistral_medium - Mistral Medium, a multimodal model
• uc_ai_mistral.c_model_mistral_small - Mistral Small, a multimodal model
• uc_ai_mistral.c_model_zai_glm_5_2 - Z.ai GLM 5.2, served on the Mistral platform

Edge Models

• uc_ai_mistral.c_model_ministral_3_14b - Ministral 3 14B
• uc_ai_mistral.c_model_ministral_8b - Ministral 8B
• uc_ai_mistral.c_model_ministral_3b - Ministral 3B

Specialized Models

• uc_ai_mistral.c_model_codestral - Optimized for code generation tasks

Embedding Models

• uc_ai_mistral.c_model_mistral_embed - General purpose text embeddings
• uc_ai_mistral.c_model_codestral_embed - Code-specific embeddings

Most of these constants hold a -latest name, such as mistral-small-latest. Mistral resolves such a name to the newest version of that model family. The two embedding constants hold a fixed name. c_model_ministral_3_14b and c_model_zai_glm_5_2 hold a fixed name too, because Mistral documents no -latest alias for them.

Retired Models

Mistral marks the Magistral, Devstral, and Pixtral models as deprecated. The package keeps the constants c_model_magistral_medium, c_model_magistral_small, c_model_devstral_medium, c_model_devstral_small, and c_model_pixtral_large for code that still references them.

See Mistral’s models documentation for the latest model information and capabilities.

Tip

If a new model is not yet available as a constant, you can pass the model name directly as a string. See the FAQ for details.

Usage Examples

Basic Text Generation

declare
  l_result json_object_t;
begin
  l_result := uc_ai.generate_text(
    p_user_prompt => 'What is Oracle APEX?',
    p_provider => uc_ai.c_provider_mistral,
    p_model => uc_ai_mistral.c_model_mistral_small
  );

  dbms_output.put_line('AI Response: ' || l_result.get_string('final_message'));
end;
/

With System Prompt

declare
  l_result json_object_t;
begin
  l_result := uc_ai.generate_text(
    p_user_prompt => 'I have tomatoes, salad, potatoes, olives, and cheese. What can I cook with that?',
    p_system_prompt => 'You are an assistant helping users to get recipes. Please answer in short sentences.',
    p_provider => uc_ai.c_provider_mistral,
    p_model => uc_ai_mistral.c_model_mistral_small
  );

  dbms_output.put_line('Recipe: ' || l_result.get_string('final_message'));
end;
/

Using Tools/Function Calling

Mistral models support tools/function calling. You can define tools in your application and the model will call them as needed.

declare
  l_result json_object_t;
begin
  -- Ensure tools are set up in UC_AI_TOOLS table
  uc_ai.g_enable_tools := true;

  l_result := uc_ai.generate_text(
    p_user_prompt => 'What is the email address of Jim?',
    p_system_prompt => 'You are an assistant to a time tracking system. Your tools give you access to user, project and timetracking information. Answer concise and short.',
    p_provider => uc_ai.c_provider_mistral,
    p_model => uc_ai_mistral.c_model_mistral_small
  );

  dbms_output.put_line('AI Response: ' || l_result.get_string('final_message'));
end;
/

See the tools guide for details on how to set up and use tools.

Multi-modal Analysis

Mistral’s multimodal models (Mistral Small/Medium, Pixtral) support vision capabilities for image analysis.

Image Analysis

declare
  l_messages json_array_t := json_array_t();
  l_content json_array_t := json_array_t();
  l_result json_object_t;
begin
  l_messages.append(uc_ai_message_api.create_system_message(
    'You are an image analysis assistant.'));

  l_content.append(uc_ai_message_api.create_file_content(
    p_media_type => 'image/webp',
    p_data_blob => your_image_blob,
    p_filename => 'image.webp'
  ));

  l_content.append(uc_ai_message_api.create_text_content(
    'What is depicted in the attached image?'
  ));

  l_messages.append(uc_ai_message_api.create_user_message(l_content));

  l_result := uc_ai.generate_text(
    p_messages => l_messages,
    p_provider => uc_ai.c_provider_mistral,
    p_model => uc_ai_mistral.c_model_mistral_small
  );

  dbms_output.put_line('Analysis: ' || l_result.get_string('final_message'));
end;
/

Reasoning / Thinking

Mistral handles reasoning through dedicated models instead of a request parameter: pick a Magistral model and it reasons by default. There is no reasoning effort setting for Mistral — uc_ai.g_enable_reasoning and uc_ai.g_reasoning_level are ignored for this provider.

declare
  l_result json_object_t;
begin
  l_result := uc_ai.generate_text(
    p_user_prompt => 'Answer in one sentence. If there is a great filter, are we before or after it and why.',
    p_provider => uc_ai.c_provider_mistral,
    p_model => uc_ai_mistral.c_model_magistral_small
  );

  dbms_output.put_line('AI Response: ' || l_result.get_string('final_message'));
end;
/

Structured Output

Mistral supports structured output with JSON schemas:

declare
  l_result json_object_t;
  l_schema json_object_t;
  l_final_message clob;
  l_structured_output json_object_t;
begin
  -- Define your JSON schema
  l_schema := json_object_t('{
    "name": "confidence_response",
    "strict": true,
    "schema": {
      "type": "object",
      "properties": {
        "response": {
          "type": "string",
          "description": "The answer to the question"
        },
        "confidence": {
          "type": "number",
          "description": "Confidence level between 0 and 1"
        }
      },
      "required": ["response", "confidence"],
      "additionalProperties": false
    }
  }');

  l_result := uc_ai.generate_text(
    p_user_prompt => 'What is the capital of France? Please respond with confidence.',
    p_system_prompt => 'You are a helpful assistant that provides accurate information.',
    p_provider => uc_ai.c_provider_mistral,
    p_model => uc_ai_mistral.c_model_mistral_small,
    p_response_json_schema => l_schema
  );

  l_final_message := l_result.get_clob('final_message');
  l_structured_output := json_object_t(l_final_message);

  dbms_output.put_line('Response: ' || l_structured_output.get_string('response'));
  dbms_output.put_line('Confidence: ' || l_structured_output.get_number('confidence'));
end;
/

Embeddings

Mistral offers embedding models for semantic search and retrieval use cases:

declare
  l_result json_array_t;
begin
  l_result := uc_ai.generate_embeddings(
    p_input => json_array_t('["Oracle APEX is a low-code development platform."]'),
    p_provider => uc_ai.c_provider_mistral,
    p_model => uc_ai_mistral.c_model_mistral_embed
  );

  dbms_output.put_line('Embedding dimensions: ' ||
    treat(l_result.get(0) as json_array_t).get_size);
end;
/