Mistral

Introduction

The Mistral Connector allows you to integrate generative AI capabilities into your Mendix application. Since the Mistral API is compatible with OpenAI API, this module mainly focuses on Mistral specific UI while reusing the operations inside of the OpenAI connector.

The current scope covers text generation use cases based on the Chat Completions API and embedding use cases based on the Embeddings API.

Typical Use Cases

The Mistral Connector is commonly used for text generation and embeddings. These use cases are described in more detail below.

Text Generation

• Develop interactive AI chatbots and virtual assistants that can carry out conversations in a natural and engaging manner.
• Use Mistral’s large language models (LLMs) for text comprehension and analysis use cases such as summarization, synthesis, and answering questions about large amounts of text.
• Tailor the Mistral models on a specific task or domain by adding custom data to improve their performance.
• Integrate more easily with Mistral’s platform. By providing text generation models, this allows you to build applications with the following features:
  • Draft documents
  • Write computer code
  • Answer questions about a knowledge base
  • Analyze texts
  • Give software a natural language interface
  • Tutor in a range of subjects
  • Translate languages
  • Simulate characters for games
  • Image to text

For more information, see Mistral models.

Image Generation

Mistral does not currently offer image generation models out of the box. It is possible to equip a Mistral agent with an image generation tool (see Image generation), however, this functionality is not supported by the Mistral Connector.

Knowledge Base

The Mistral connector supports Knowledge bases from providers such as pgVector, Mendix Cloud, Amazon Bedrock, and Azure AI Search to be added to a conversation.

Features

With the current version, Mendix supports the Chat Completions API for text generation and the Embeddings API for vector embeddings.

Prerequisites

To use this connector, you need to sign up for a Mistral account and create an API key. For more information, see the Quickstart guide.

Dependencies

• Mendix Studio Pro version 10.24.0 or above
• GenAI Commons module
• Encryption module
• Community Commons module
• OpenAI connector

Installation

Install all required modules from the Mendix Marketplace as listed in the Dependencies section above.

To import the Mistral Connector into your app, follow the instructions in How to Use Marketplace Content.

Configuration

After you install the Mistral and OpenAI connector, you can find them in the Marketplace Modules section of the App Explorer. The Mistral connector provides a domain model and several pages. You can reuse all activities to connect your app to Mistral from the OpenAI connector. To implement an activity, use it in a microflow. Configure the Encryption module to ensure the connection of your app to Mistral is secure.

General Configuration

1. Add the module roles OpenAIConnector.Administrator and MistralConnector.Administrator to your Administrator User roles in the Security settings of your app.
2. Add the MistralConfiguration_Overview page from the Mistral connector module (USE_ME > MistralConfiguration) to your navigation, or add the Snippet_MistralConfigurations to a page that is already part of your navigation.
3. Continue setting up your Mistral configuration at runtime. For more information, follow the instructions in the Mistral Configuration section below.
4. Configure the models you need to use for your use case.

Mistral Configuration

The following inputs are required for the Mistral configuration:

Configuring the Mistral Deployed Models

A Deployed Model represents a GenAI model instance that can be used by the app to generate text, embeddings, or images. For every model you want to invoke from your app, you need to create a MistralDeployedModel record, a specialization of DeployedModel (and also a specialization of OpenAIDeployedModel). In addition to the model display name and a technical name or identifier, a Mistral deployed model contains a reference to the additional connection details as configured in the previous step.

1. Click the three dots ( ) icon for a Mistral configuration and open Manage Deployed Models. It is possible to use a predefined syncing method, where all available models are retrieved for the specified API key and then filtered according to their capabilities. If you want to use additional models that are made available by Mistral you can add them manually by clicking the New button instead.

2. For every additional model, add a record. The following fields are required:

   Field	Description
   Display name	This is the reference for app users when selecting the appropriate model to use.
   Model name	This is the technical reference of the model. For Mistral, this is equal to the model IDs, for example mistral-medium-2508.
   Output modality	Describes the output of the model. This connector currently supports text, embedding, and image.
   Input modality	Describes the input modalities accepted by the model. This connector currently supports text and image.

3. Close the Manage Deployed Models popup and test the configuration with the newly created deployed models.

Using GenAI Commons Operations

After following the general setup above, you are all set to use the microflow actions under the GenAI (Generate) category from the toolbox. These operations are part of GenAI Commons. Since OpenAI (and therefor Mistral) is compatible with the principles of GenAI Commons, you can pass a MistralDeployedModel to all GenAI Commons operations that expect the generalization of DeployedModel. All actions under GenAI (Generate) will take care of executing the right provider-specific logic, based on the type of specialization passed, in this case, Mistral. From an implementation perspective, it is not needed to required the inner workings of this operation. The input, output, and behavior are described in the GenAICommons documentation. Applicable operations and some Mistral-specific aspects are listed in the sections below.

For more inspiration or guidance on how to use the microflow actions in your logic, Mendix recommends downloading GenAI Showcase App, which demonstrates a variety of examples that cover all the operations mentioned.

You can use the GenAI Commons toolbox actions to create the required Request and handle the Response for your use case.

The internal chat completion logic supports JSON mode, function calling, and vision for Mistral. Make sure to check the actual compatibility of the available models with these functionalities, as this changes over time. The following sections list toolbox actions which are specifically for OpenAI compatible APIs (especially Mistral).

Chat Completions

Operations for chat completions focus on the generation of text based on a certain input. In this context, system prompts and user prompts are two key components that help guide the language model in generating relevant and contextually appropriate responses. For more information on the type of prompts and message roles, see the ENUM_MessageRole enumeration. To learn more about how to create the right prompts for your use case, see the Read More section below

The MistralDeployedModel is compatible with the two Chat Completions operations from GenAI Commons. While developing your custom microflow, you can drag and drop the following operations from the toolbox in Studio Pro. See category GenAI (Generate):

• Chat Completions (with history)
• Chat Completions (without history)

JSON Mode

When JSON mode is used, the model is programmatically instructed to return valid JSON. For Mistral connector, you have to explicitly mention the necessity of a JSON structure in a message in the conversation, e.g. the system prompt. Additionally, after creating the request, but before passing it to the chat completions operation, use the toolbox action Set Response Format to set the required response format to JSON.

Function Calling

Function calling enables LLMs to connect with external tools to gather information, execute actions, convert natural language into structured data, and much more. Function calling thus enables the model to intelligently decide when to let the Mendix app call one or more predefined function microflows to gather additional information to include in the assistant's response.

Mistral does not call the function. The model returns a tool called JSON structure that is used to build the input of the function (or functions) so that they can be executed as part of the chat completions operation. Functions in Mendix are essentially microflows that can be registered within the request to the LLM​. The OpenAI connector takes care of handling the tool call response as well as executing the function microflows until the API returns the assistant's final response for Mistral.

This is all part of the implementation that is executed by the GenAI Commons chat completions operations mentioned before. As a developer, you have to make the system aware of your functions and what these do by registering the function(s) to the request. This is done using the GenAI Commons operation Tools: Add Function to Request once per function before passing the request to the chat completions operation.

Function microflows can have none, a single, or multiple primitive input parameters such as Boolean, Datetime, Decimal, Enumeration, Integer or String. Additionally, they may accept the Request or Tool objects as inputs. The function microflow must return a String value.

For more information, see Function Calling.

Adding Knowledge Bases

Adding knowledge bases to a call enables LLMs to retrieve information when a related topics are mentioned. Including knowledge bases in the request object along with a name and description, enables the model to intelligently decide when to let the Mendix app call one or more predefined knowledge bases. This allows the assistant to include the additional information in its response.

Mistral does not directly connect to the knowledge resources. The model returns a tool call JSON structure that is used to build the input of the retrievals so that they can be executed as part of the chat completions operation. The OpenAI connector takes care of handling the tool call response for Mistral as well as executing the function microflows until the API returns the assistant's final response.

This functionality is part of the implementation executed by the GenAI Commons Chat Completions operations mentioned earlier. As a developer, you need to make the system aware of your indexes and their purpose by registering them with the request. This is done using the GenAI Commons operation Tools: Add Knowledge Base, which must be called once per knowledge resource before passing the request to the Chat Completions operation.

Note that the retrieval process is independent of the model provider and can be used with any model that supports function calling.

Vision

Vision enables models like Mistral Medium 3.1 and Mistral Small 3.2 to interpret and analyze images, allowing them to answer questions and perform tasks related to visual content. This integration of computer vision and language processing enhances the model's comprehension and makes it valuable for tasks involving visual information. To make use of vision with Mistral connector, an optional FileCollection containing one or multiple images must be sent along with a single message.

For Chat Completions without History, FileCollection is an optional input parameter.

For Chat Completions with History, FileCollection can optionally be added to individual user messages using Chat: Add Message to Request.

Use the two microflow actions from the OpenAI specific toolbox Files: Initialize Collection with OpenAI File and Files: Add OpenAIFile to Collection to construct the input with either FileDocuments (for vision, it needs to be of type Image) or URLs. There are similar file operations exposed by the GenAI commons module that can be used for vision requests with the OpenAIConnector for Mistral. However, these generic operations do not support the optional OpenAI API-specific Detail attribute.

For more information on vision, see Mistral documentation.

Document Chat

Document chat enables the model to interpret and analyze PDF documents, allowing it to answer questions and perform tasks based on the document content. Document chat is currently not supported by the Mistral connector as it requires its own API. Check out Document AI documentation if you want to learn about Mistral's OCR capabilities.

Image Generations

Image generation is currently not supported by the Mistral connector. You can learn more about image generation with Mistral in the Image Generation section.

Embeddings Generation

Mistral also provides vector embedding generation capabilities which can be invoked using this connector module. The MistralDeployedModel entity is compatible with the knowledge base operations from the GenAI Commons.

In order to implement embeddings generation into your Mendix application, you can use the Embedding generation microflow actions from GenAI Commons directly. When developing your microflow, you can drag and drop the one you need from the toolbox: find it under the GenAI (Generate) category in the Toolbox in Mendix Studio Pro:

• Generate Embeddings (String)
• Generate Embeddings (Chunk Collection)

Depending on the operation you use in the microflow, an InputText String or a ChunkCollection needs to be provided. The current version of this operation only supports the float representation of the resulting vector.

The microflow action Generate Embeddings (String) supports scenarios where the vector embedding of a single string must be generated, e.g. to use for a nearest neighbor search across an existing knowledge base. This input string can be passed directly as the InputText parameter of this microflow. Additionally, EmbeddingsOptions is optional and can be instantiated using Embeddings: Create EmbeddingsOptions from GenAI Commons. Use the GenAI Commons toolbox action Embeddings: Get First Vector from Response to retrieve the generated embeddings vector. Both mentioned operations can be found under GenAI Knowledge Base (Content) in the Toolbox in Mendix Studio Pro.

The microflow action Generate Embeddings (Chunk Collection) supports the more complex scenario where a collection of string inputs is vectorized in a single API call, such as when converting a collection of texts (chunks) into embeddings to be inserted into a knowledge base. Instead of calling the API for each string, executing a single call for a list of strings can significantly reduce HTTP overhead. Use the exposed microflows of GenAI Commons Chunks: Initialize ChunkCollection to create the wrapper and Chunks: Add Chunk to ChunkCollection, or Chunks: Add KnowledgeBaseChunk to ChunkCollection to construct the input. The resulting embedding vectors returned after a successful API call will be stored in the EmbeddingVector attribute in the same Chunk object.
Purely to generate embeddings, it does not matter whether the ChunkCollection contains Chunks or its specialization KnowledgeBaseChunks. However, if the end goal is to store the generated embedding vectors in a knowledge base (e.g. using the PgVector Knowledge Base module), then Mendix recommends adding KnowledgeBaseChunks to the ChunkCollection and using these as an input for the embeddings operations, so they can later be used directly to populate the knowledge base.

Note that, currently, the knowledge base interaction (e.g. inserting or retrieving chunks) is not supported for OpenAI compatible APIs. For more information on possible ways to work with knowledge bases for embedding generation, see PgVector Knowledge Base and setting up a Vector Database.

Exposed Microflow Actions for OpenAI-compatible APIs

T exposed microflow actions used to construct requests via drag-and-drop specifically for OpenAI-compatible APIs are listed below. You can find these microflows in the Toolbox of Studio Pro. Note that these flows are only required if you need to add Mistral-specific options to your requests. For generic functionality, can use the GenAI Commons toolbox actions to create the required Request and handle the Response. These actions are available under the GenAI (Request Building) and GenAI (Response Handling) categories in the Toolbox.

Set Response Format

This microflow changes the ResponseFormat of the OpenAIRequest_Extension object, which will be created for a Request if not already present. This describes the format that the chat completions model must output. By default, models compatible with the OpenAI API return Text. To enable JSON mode, you must set the input value as JSONObject.

Files: Initialize Collection with OpenAI Image

This operation is currently not relevant for Mistral connector.

Files: Add OpenAI Image to Collection

This operation is currently not relevant for Mistral connector.

Image Generation: Set ImageOptions Extension

This operation is currently not relevant for Mistral connector.

Technical Reference

The module includes technical reference documentation for the available entities, enumerations, activities, and other items that you can use in your application. You can view the information about each object in context by using the Documentation pane in Studio Pro.

The Documentation pane displays the documentation for the currently selected element. To view it, perform the following steps:

1. In the View menu of Studio Pro, select Documentation.

2. Click the element for which you want to view the documentation.

   

Tool Choice

Mistral supports the following tool choice types of GenAI Commons for the Tools: Set Tool Choice action are supported. For API mapping reference, see the table below:

Knowledge Base Retrieval

When adding a KnowledgeBaseRetrieval object to your request, there are some optional parameters. Currently, only the MaxNumberOfResults parameter can be added to the search call and the others (MinimumSimilarity and MetadataCollection) are not compatible with the Mistral API.

GenAI Showcase Application

For more inspiration or guidance on how to use those microflows in your logic, Mendix recommends downloading the GenAI Showcase App, which demonstrates a variety of example use cases.

Troubleshooting

Attribute or Reference Required Error Message After Upgrade

If you encounter an error stating that an attribute or a reference is required after an upgrade, first upgrade all modules by right-clicking the error, then upgrade Data Widgets.

Conflicted Lib Error After Module Import

If you encounter an error caused by conflicting Java libraries, such as java.lang.NoSuchMethodError: 'com.fasterxml.jackson.annotation.OptBoolean com.fasterxml.jackson.annotation.JsonProperty.isRequired()', try synchronizing all dependencies (App > Synchronize dependencies) and then restart your application.

Read More

• Prompt Engineering – OpenAI Documentation
• Introduction to Prompt Engineering – Microsoft Azure Documentation
• Prompt Engineering Techniques – Microsoft Azure Documentation
• ChatGPT Prompt Engineering for Developers - DeepLearning.AI
• Function Calling - OpenAI Documentation
• Vision - OpenAI Documentation
• Vision - Azure OpenAI Documentation