Mistral AI Model Integration Project

Overview

This project is designed to provide hands-on experience with Mistral AI's open-source and commercial models, including the Mixtral 8x7B and Mixtral 8x22B. Throughout the course, you'll explore various tasks such as effective prompting, function calling, JSON mode usage, and building Retrieval-Augmented Generation (RAG) systems. By the end, you'll be equipped with the knowledge to select appropriate AI models for different levels of complexity and integrate them into practical applications.

Features

1. API Access to Mistral Models

   • Learn to access Mistral's models via API calls.
   • Select the right model based on task complexity:
     • Simple tasks: Classification, text summarization.
     • Medium tasks: Email writing, content generation.
     • Advanced tasks: Code generation, complex reasoning.
   • Understand the trade-offs between model speed and task complexity.

2. Effective Prompting Techniques

   • Develop prompts to maximize model performance.
   • Customize prompts for classification, text generation, and more.

3. Function Calling in Mistral AI

   • Use Mistral's function calling to offload tasks better suited for traditional code, such as querying a database.
   • Implement tools for numerical data retrieval or other external services within the LLM environment.

4. Building a Retrieval-Augmented Generation (RAG) System

   • Build a basic RAG system from scratch.
   • Learn to chunk data properly and create embeddings for efficient similarity search.
   • Integrate RAG as a function in your Mistral-powered chat system to retrieve information from documents and databases.

5. Document Interaction via Chat Interface

   • Build an interactive chat interface.
   • Upload documents and ask questions, with the system retrieving relevant data using the RAG system.

Project Structure

/project-root
  ├── /docs                 # Documentation for setup and usage
  ├── /src                  # Source code for the project
  │   ├── /api               # API integration with Mistral models
  │   ├── /rag_system        # RAG system implementation
  │   └── /chat_interface    # Code for the chat-based document interaction system
  ├── /tests                # Unit and integration tests
  ├── README.md             # Project overview and setup instructions
  └── requirements.txt      # Python dependencies

Setup Instructions

Prerequisites

• Python 3.8+
• Mistral AI account and API key
• Basic knowledge of Python, APIs, and LLMs

Usage

1. API Access to Mistral Models

Use the api/ module to access the Mistral models. Specify the task complexity and performance requirements to select the right model for your use case.

2. Function Calling

Incorporate native function calling to offload tasks that traditional code can handle more effectively, such as database querying.

3. Building a RAG System

Follow the rag_system/ module to implement similarity search, data chunking, and embedding creation. Use this tool in your chat system for document interaction.

4. Chat Interface

The chat_interface/ module enables you to upload documents and interact with them by asking questions. The system will retrieve relevant information using RAG.

Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

License

This project is licensed under the MIT License.