• Changelog

• Introduction
• Quickstart
• Documentation Overview
• TIPS for using the tutorial effectively
  • Strategies for quickly using the code examples
  • Use file2knowledge
• DelphiMistralAI functional coverage
• Conversations vs. Chat Completions
• Tips and tricks
  • How to prevent an error when closing an application while requests are still in progress?
• Contributing
• License

Built with Delphi 12 Community Edition (v12.1 Patch 1)
The wrapper itself is MIT-licensed.
You can compile and test it free of charge with Delphi CE; any recent commercial Delphi edition works as well.

Core capabilities

• Unified access to text, vision and audio endpoints
• Agentic workflows via the v1/conversations endpoint, with built-in tools websearch, Code Interpreter, Image generation and Document library.
• Supports state-of-the-art models, including voxtral, devstral, mistral-ocr and the reasoning-centric magistral

Developer tooling

• Ready-made Sync, Async, and Await code snippets (TutorialHUB compatible)
• Batch processing, function calling, file management, and content moderation out of the box
• Built-in DUnit test helpers and a modular JSON configuration for streamlined setup
• Mock-friendly design: the HTTP layer is injected via dependency injection, so you can swap in stubs or fakes for testing

• Account setup
• Obtain an api key
• Getting started with Mistral AI API

To get started, head over to https://console.mistral.ai and either create a new Mistral account or sign in if you already have one. Once you’re in, open your Organization settings at https://admin.mistral.ai – this is where you’ll centralize billing and access control. In the Administration tab, locate the Billing section and enter your payment details to activate billing. From here you can manage all of your Workspaces as well as the broader Organization.

To initialize the API instance, you need to obtain an API key from MistralAI

Once you have a token, you can initialize IMistralAI interface, which is an entry point to the API.

//uses MistralAI, MistralAI.Types; //Declare  // Client: IMistralAI // ClientCoding: IMistralAI; Client := TMistralAIFactory.CreateInstance(api_key); ClientCoding := TMistralAIFactory.CreateInstance(Codestral_api_key);

Obtain the Codestral_api_key

To streamline the use of the API wrapper, the process for declaring units has been simplified. Regardless of the methods being utilized, you only need to reference the following two core units: MistralAI and MistralAI.Types.

Mistral AI’s API lets you plug the models into your apps and production pipelines in just a few lines of code. It’s currently accessible via La Plateforme—just make sure you’ve activated billing on your account so your API keys will work. Within moments of enabling payments, you’ll be able to call our chat endpoint:

• Synchronous code example
• Asynchronous code example

//uses MistralAI, MistralAI.Types; var API_Key := 'MISTRAL_API_KEY'; var MyClient := TMistralAIFactory.CreateInstance(API_KEY); //Synchronous example var Chat := MyClient.Chat.Create( procedure (Params: TChatParams) begin Params .Model('mistral-tiny') .Messages([Payload.User('Explain to me what joual is for Quebecers.')]) .MaxTokens(1024); end); try for var Item in Chat.Choices do Memo1.Text := Item.Message.Content[0].Text; finally Chat.Free; end;

var MyClient: IMistralAI; procedure TForm1.Test; begin var API_Key := 'MISTRAL_API_KEY'; MyClient := TMistralAIFactory.CreateInstance(API_KEY); //Asynchronous example MyClient.Chat.AsyncCreate( procedure (Params: TChatParams) begin Params .Model('mistral-tiny') .Messages([Payload.User('Explain to me what joual is for Quebecers.')]) .MaxTokens(1024); end, function : TAsynChat begin Result.OnStart := procedure (Sender: TObject) begin Memo1.Lines.Text := 'Please wait...'; end; Result.OnSuccess := procedure (Sender: TObject; Value: TChat) begin for var Item in Value.Choices do Memo1.Lines.Text := Item.Message.Content[0].Text; end; Result.OnError := procedure (Sender: TObject; Error: string) begin Memo1.Lines.Text := Error; end; end); end;

Comprehensive Project Documentation Reference

• Discovery / Evolution
  • Changelog
  • Models

• Standard Completions

  • Chat completions
  • Agents completions

• Specialized Completions

  • Fill-in-the-middle

• Experimental Conversations

  • (beta) Conversations

• (beta) Agents API
• Agents completions

• Files and Libraries
  
  • Files
    
  • (beta) Libraries API to create and manage libraries
  • (beta) Libraries API - manage documents in a library
  • (beta) Libraries API - manage access to a library
• Vector Representations
  • Embeddings

• Moderation

• Fine-tuning
• Batch

• OCR
• Audio

• Strategies for quickly using the code examples
• Use file2knowledge

To streamline the implementation of the code examples provided in this tutorial, two support units have been included in the source code: MistralAI.Tutorial.VCL and MistralAI.Tutorial.FMX Based on the platform selected for testing the provided examples, you will need to initialize either the TVCLTutorialHub or TFMXTutorialHub class within the application's OnCreate event, as illustrated below:

• VCL support with TutorialHUB: TestMistralAI_VCL.zip

• FMX support with TutorialHUB: TestMistralAI_FMX.zip

This project, built with DelphiGenAI , allows you to consult MistralAI documentation and code in order to streamline and accelerate your upskilling.

Below, the table succinctly summarizes all MistralAI endpoints supported by the DelphiMistralAI.

The v1/conversations API is the new core API, designed as an agentic primitive that combines the simplicity of chat completions with the power of action execution. It natively includes several built‑in tools:

• Reasoning
• Web search
• Code interpreter
• Image generation
• Document library

With these integrated capabilities, you can build more autonomous, agent‑oriented applications that not only generate text but also interact with their environment.

The v1/conversations endpoint is intended to gradually replace v1/chat/completions, as it embodies a synthesis of current best practices in AI—especially for those looking to adopt an agentic approach.

To help you get up to speed on both endpoints, the two following documents provide detailed documentation, complete with numerous request examples and use cases:

• v1/chat/completions
• v1/conversations

Starting from version 1.3.0 of DelphiMistralAI, the MistralAI.Monitoring unit is responsible for monitoring ongoing HTTP requests.

The Monitoring interface is accessible by including the MistralAI.Monitoring unit in the uses clause.
Alternatively, you can access it via the HttpMonitoring function, declared in the MistralAI unit.

Usage Example

//uses MistralAI; procedure TForm1.FormCloseQuery(Sender: TObject; var CanClose: Boolean); begin CanClose := not HttpMonitoring.IsBusy; if not CanClose then MessageDLG( 'Requests are still in progress. Please wait for them to complete before closing the application."', TMsgDlgType.mtInformation, [TMsgDlgBtn.mbOK], 0); end; 

Pull requests are welcome. If you're planning to make a major change, please open an issue first to discuss your proposed changes.

This project is licensed under the MIT License.