What You Will Learn

• Integrate Mistral AI models with the JavaScript SDK
• Build chat completions and implement streaming responses
• Create embeddings and perform semantic search with Supabase
• Design Retrieval-Augmented Generation (RAG) pipelines
• Implement function calling to build AI agents
• Run Mistral models locally using Ollama

Study Guide

Introduction

Welcome to the comprehensive course on Mistral AI and its integration with JavaScript. This course is designed to equip you with the skills needed to build intelligent applications using Mistral AI’s powerful models and platform. From understanding the basics of Mistral AI to advanced concepts like Retrieval Augmented Generation (RAG) and function calling, this course covers it all. Whether you're a seasoned JavaScript developer or just starting, this course will enhance your ability to create sophisticated AI-driven applications.

Introduction to Mistral AI

Mistral AI is a pioneering company in the field of artificial intelligence, known for developing foundational AI models. In recent times, they have gained significant recognition for launching small, open-source models that rival some of the best closed-source models available. This course will delve into why Mistral AI is a critical tool for AI engineers and how you can leverage it to build intelligent applications.

To kick off our journey, Sophia Yang, Head of Developer Relations at Mistral AI, provides an official introduction to the company. She highlights Mistral AI's commitment to high-performing, accessible models, which are crucial for developers aiming to stay at the forefront of AI technology.

Mistral AI's Models and Platform

Mistral AI offers a diverse range of models, including both open-source and commercial options. The open-source models, such as Mistral 7B and Mixtral 8x7B, are available under the Apache 2.0 license, allowing developers to experiment and innovate freely. These models are ideal for those who wish to explore AI without incurring significant costs.

For enterprise-grade applications, Mistral provides commercial models like Mistral Small, Mistral Medium, and Mistral Large. Each model caters to different needs: Mistral Small is optimized for low latency use cases, Mistral Medium is suited for language-based tasks, and Mistral Large is designed for the most sophisticated requirements.

Additionally, Mistral offers an embedding model for working with vector databases, facilitating tasks like semantic search and data retrieval.

Users can interact with Mistral's models through the chat assistant "Le Chat" at chat.mistral.ai, and the platform provides various API endpoints for seamless integration. This course will focus on how to use the Mistral API for a range of tasks, providing you with the flexibility to choose between cloud service integration and self-deployment options.

API Basics and JavaScript SDK

The heart of this course lies in utilizing Mistral AI's JavaScript SDK. To start interacting with the API, you need an API key, which can be obtained through the Mistral platform after adding payment information. The course operates on a pay-as-you-go model, ensuring that you only pay for what you use.

One of the key features covered is the chat completion API, which facilitates conversational interactions with the models. This API is designed to handle back-and-forth conversations, allowing you to feed it a prompt and a series of messages to generate a completion or continuation of the conversation.

Key parameters for the client.chat method include:

• Model: Specify the model to use, such as "mistral-tiny".
• Messages: An array of message objects with roles like "user" or "system" to provide instructions.
• Temperature: Controls the creativity of the generated text, ranging from 0 to 1.

Through practical examples, the course demonstrates how to personalize AI responses and use system prompts to guide model behavior, enhancing the user experience.

Streaming Responses

For a more interactive user experience, the course explores streaming responses. By using client.chatStream, developers can implement streaming, which returns an async iterable. This allows for processing the response token by token, providing real-time feedback to users.

An asynchronous for...of loop is employed to handle the incoming chunks of data, enabling developers to access the content of each chunk via chunk.choices[0].delta.content. This technique is particularly useful for applications requiring dynamic, real-time interactions.

JSON Response Format

Mistral's API can be configured to return responses in JSON format, which is crucial for integrating AI capabilities into applications that require structured data. By setting response_format: { type: "json_object" } and specifying this in the prompt (e.g., "reply with JSON"), developers can facilitate easier data handling and manipulation.

This structured approach is beneficial for applications that rely on data consistency and need to integrate AI-generated insights seamlessly.

Model Selection

Choosing the right model is critical for optimizing performance and cost. The course provides an overview of Mistral's different models and their capabilities, helping developers make informed decisions based on their specific needs.

Open-weight models like Mistral 7B and Mixtral 8x7B can be downloaded and run locally, offering cost-effective solutions for experimentation and development. On the other hand, commercial models provide higher performance but require API usage or cloud/self-hosting arrangements.

Performance comparisons using benchmarks like MMLU (Massive Multitask Language Understanding) are presented, highlighting the trade-off between model performance and cost per million tokens. Developers are advised to choose the most suitable model for their tasks, balancing performance and cost considerations.

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is a powerful technique that enhances AI applications by providing domain-specific knowledge beyond the model's training data. The process involves two main steps:

• Retrieval: Fetching relevant data from a knowledge source, such as a vector database containing embeddings of company data or real-time information.
• Generation: Using the retrieved information as context to generate a more informed and accurate response to the user.

RAG is crucial for giving AI applications domain knowledge, improving the relevance and factuality of their responses, and enabling them to answer questions based on proprietary or up-to-date information.

Embeddings

Embeddings are a fundamental concept in AI, representing text as numerical vectors. These vectors capture the semantic meaning of the text, allowing AI models to understand and process language effectively.

The course uses Mistral's embedding model (mistral-embed) to create these numerical representations. Mathematically similar words and phrases have embeddings that are closer in the vector space, enabling powerful semantic search capabilities.

By passing text through an AI model, developers can generate embeddings that serve as the language AI understands, facilitating advanced tasks like semantic search and data retrieval.

Text Splitting

When dealing with large documents, it's essential to split them into smaller chunks before creating embeddings. This ensures meaningful semantic representation and prevents the loss of context.

The Langchain library is introduced as a tool for text splitting, specifically using the RecursiveCharacterTextSplitter. Parameters like chunk_size and chunk_overlap are important for effective splitting, aiming to create chunks that deal with a single subject or theme.

Balancing chunk size and context is crucial to maintain the quality of embeddings and ensure accurate semantic search results.

Vector Databases with Supabase

Supabase, an open-source backend platform with a PostgreSQL database, is used to manage the vector database. The PG Vector extension for PostgreSQL enables storing and querying vector embeddings.

The course guides users through setting up a Supabase project, enabling the PG Vector extension, and creating a table (handbook_docs) with columns for id, content, and embedding (a vector of 128 dimensions). Mistral-generated embeddings are then uploaded to this Supabase table.

This setup provides a robust foundation for performing semantic search and retrieval tasks, leveraging the power of vector databases.

Performing Similarity Search (Retrieval)

Supabase offers a SQL function for performing vector similarity searches using cosine distance. The rpc method in the Supabase JavaScript client is used to call this function, allowing developers to find information based on semantic similarity rather than exact keyword matches.

Parameters for the function include query_embedding (the embedding of the user's question) and match_threshold (to control the similarity of results). This approach enables developers to retrieve relevant information efficiently and accurately.

Generating Chat Responses with Retrieved Context

Once the relevant content is retrieved from the vector database, it is combined with the user's query to form a prompt for the Mistral chat completion API. This allows the AI to generate answers grounded in the provided context, enhancing the accuracy and relevance of responses.

By leveraging the power of RAG and vector databases, developers can create AI applications that deliver informed and contextually accurate responses, improving user satisfaction and engagement.

Function Calling for AI Agents

Function calling is a revolutionary paradigm that allows AI models to instruct applications to take specific actions based on user prompts. Instead of just generating text, the AI can determine that a particular function needs to be executed to fulfil the user's request.

The process involves sending a list of available tools (functions described in a specific schema) to the Mistral API along with the user's prompt. The model can then return instructions in the tool_calls property, specifying the function to call and its arguments.

Developers need to implement logic to execute these functions and send the results back to the model for further processing and response generation. The course demonstrates how to define tool schemas (name, description, parameters) and how to handle the tool_calls response from the API.

A loop structure is used to manage the back-and-forth between the user, the AI (requesting function calls), and the execution of those functions until a final response is generated. This capability significantly enhances the potential of AI to provide more dynamic and helpful user experiences.

Running Mistral Models Locally with Ollama

Ollama is introduced as a tool to run large language models locally on a computer. Users can download and run Mistral models (e.g., ollama run mistral) directly on their machine, offering benefits like free token usage and 100% data privacy.

The course demonstrates a simple Node.js/Express.js application that uses the Ollama SDK to interact with a locally running Mistral model via an API endpoint. The Ollama SDK interface is noted to be similar to the Mistral API SDK, providing a seamless transition for developers familiar with the Mistral platform.

Conclusion

Congratulations on completing the course! You are now equipped with the knowledge and skills to leverage Mistral AI in your JavaScript applications, from basic chat completions to advanced AI engineering paradigms like RAG and function calling. The ability to run AI models locally further enhances your capability to create sophisticated and private AI solutions.

As you apply these skills, remember the importance of thoughtful application and continuous exploration. The field of AI is ever-evolving, and staying informed about the latest developments will ensure your success in building intelligent, impactful applications.

Podcast

There'll soon be a podcast available for this course.

Frequently Asked Questions

Welcome to the FAQ section for the 'Video Course: Learn Mistral AI – JavaScript Tutorial.' This comprehensive resource is designed to answer all your questions about leveraging Mistral AI's capabilities in JavaScript. Whether you're a beginner or an advanced user, this guide will help you navigate the intricacies of Mistral AI and its practical applications in business.

What is Mistral AI and why should I learn about it?

Mistral AI is a company that develops foundational AI models. They gained significant attention for launching small, open-source models that rival leading closed-source alternatives. Understanding Mistral's technology is valuable for building intelligent applications and staying at the forefront of AI development, making it crucial for AI engineers.

What will I learn in this Mistral AI course?

This course will teach you how to build intelligent applications using Mistral AI, starting with basic chat completions and advancing to Retrieval Augmented Generation (RAG) and function calling. Hands-on experience with Mistral's models will enable you to create sophisticated conversational user experiences and run AI models locally.

Is prior programming experience required for this course?

Yes, a foundational understanding of JavaScript is beneficial as the course focuses on using Mistral AI with JavaScript. If you're new to JavaScript or AI engineering, it's recommended to explore introductory courses to build a solid foundation.

What are the different types of models offered by Mistral AI?

Mistral AI offers various models: Open-Source Models like Mistral 7B and Mixtral 8x7B, Commercial Models for different applications, and an Embedding Model for generating text embeddings. Each serves distinct business needs, from local deployment to API access.

How can I access and use Mistral AI models?

Access Mistral AI models via the Platform API, Cloud Services, or Self-Deployment. The JavaScript SDK is used to integrate models into applications, while cloud services offer fast deployment. Self-deployment provides maximum control and flexibility.

What is Retrieval Augmented Generation (RAG) and why is it important?

RAG enhances AI applications by providing domain-specific information they weren't trained on. It involves Retrieval of relevant data and Generation of informed responses. RAG improves AI's relevance and factuality, allowing answers based on proprietary or current information.

What is function calling and how can it revolutionise user experiences?

Function calling allows AI models to instruct applications to perform specific actions based on user prompts. This creates AI agents that interact with the real world, enhancing user experiences by performing tasks like scheduling appointments or fetching data through API calls.

Can I run Mistral AI models on my own computer?

Yes, Mistral AI's open-source models can be run locally. The course introduces Ollama, a tool that simplifies running large language models on your computer. This ensures data privacy and free usage, apart from hardware and electricity costs.

How do embeddings work and why are they important?

Embeddings convert text into numerical vectors, capturing semantic meaning and relationships. This facilitates semantic search and enables AI applications to understand and process language more effectively, making them crucial for tasks like recommendation systems and information retrieval.

What are the key components of the Mistral API?

The Mistral API's key components include API endpoints for model interaction, parameters like model and messages for chat completions, and authentication via API keys. These components enable seamless integration and usage of Mistral models in applications.

How do I use the Mistral JavaScript SDK?

The Mistral JavaScript SDK provides tools and libraries for integrating Mistral AI models into JavaScript applications. It simplifies API interactions and supports tasks like chat completions and function calls. Documentation and examples guide developers in effectively using the SDK.

What is the role of the "temperature" parameter in the Mistral API?

The "temperature" parameter controls the randomness and creativity of generated text. A value closer to 1 results in more random and creative outputs, while a value closer to 0 produces focused and deterministic responses, allowing customization of the AI's behavior.

How does Retrieval Augmented Generation (RAG) enhance AI applications?

RAG enhances AI applications by grounding responses in external knowledge. It retrieves relevant data and uses it as context to generate accurate responses. This improves the AI's ability to provide informed answers, especially in domains with proprietary or up-to-date information.

What are the benefits of using Mistral AI models locally?

Running Mistral AI models locally offers benefits like data privacy, cost savings on API usage, and control over deployment. It allows businesses to process sensitive data on-premises and customize AI models to meet specific needs without relying on external services.

What are the challenges of integrating Mistral AI models into existing systems?

Integrating Mistral AI models can present challenges such as compatibility with existing infrastructure, scalability of deployments, and training requirements for staff. Addressing these challenges involves careful planning, resource allocation, and potentially modifying existing systems to accommodate AI capabilities.

How do I handle outdated information in RAG implementations?

Handling outdated information in RAG involves regularly updating the knowledge base and implementing mechanisms to verify the relevance of retrieved data. Automated updates and validation processes ensure the AI's responses remain accurate and relevant, enhancing the application's reliability.

What are the ethical considerations when using function calling in AI agents?

Ethical considerations include ensuring transparency in AI actions, obtaining user consent for data usage, and preventing misuse of AI capabilities. Developers must implement safeguards to protect user privacy and ensure AI agents act within ethical and legal boundaries.

How do I choose between cloud-based API access and local inference?

Choosing between cloud-based API access and local inference depends on factors like cost, latency, data privacy, and computational resources. Cloud access offers scalability and ease of use, while local inference provides control and privacy. Businesses should evaluate their specific needs to determine the best approach.

What are the main differences between Mistral's open-source and commercial models?

Open-source models, like Mistral 7B, offer flexibility and local deployment under an Apache 2.0 license, while commercial models provide enhanced performance and require API access or cloud integration. Businesses choose based on their needs for control, performance, and deployment options.

How can I leverage Mistral AI for business applications?

Mistral AI can be leveraged for various business applications, including customer service automation, data analysis, and personalized recommendations. By integrating Mistral models into existing systems, businesses can enhance efficiency, improve customer interactions, and gain valuable insights from data.

What security measures should I consider when using Mistral AI?

Security measures include implementing access controls for API keys, ensuring secure data transmission, and regularly updating models to protect against vulnerabilities. Businesses should also conduct security audits and monitor AI interactions to safeguard sensitive information.

How do I get started with using Mistral AI in my projects?

To get started, familiarize yourself with Mistral AI's documentation and SDKs, obtain an API key, and explore sample projects. Begin with basic implementations and gradually incorporate advanced features like RAG and function calling. Experimentation and iteration will help you effectively integrate Mistral AI into your projects.