This repository provides Jupyter notebooks for accessing and interacting with various Large Language Model (LLM) APIs using the Julia programming language. It demonstrates how to connect to and utilize Anthropic, Cohere, Mistral, and OpenAI APIs in Julia, allowing for experimentation with tasks like text generation, retrieval, and conversation.

• JULIA_ANTHROPIC_API.ipynb: A notebook showing how to connect with Anthropic's API using Julia, including API setup, parameter tuning, and sample API requests.
• JULIA_COHERE_API.ipynb: Provides guidance on using Cohere's API in Julia, with examples of text generation and retrieval tasks.
• JULIA_MISTRAL_API.ipynb: Explains the setup for the Mistral API in Julia, demonstrating API calls and response handling.
• JULIA_OPENAI_API.ipynb: Covers the configuration and usage of OpenAI's API in Julia, including example requests and parameter customization.

To run these notebooks, you will need:

• Jupyter Notebook with Julia kernel (IJulia)
• Julia 1.5 or newer
• API keys for each service (Anthropic, Cohere, Mistral, OpenAI)
• Required dependencies as listed in requirements.txt

1. Clone the repository:

   git clone https://github.com/simonpierreboucher/llm_Julia_notebook.git
   cd llm_Julia_notebook

2. Install the dependencies for Jupyter Notebook and any Python API integrations:

   pip install -r requirements.txt

3. Install Julia Packages: Open a Julia session or include these commands at the beginning of each notebook to install necessary packages:

   using Pkg
   Pkg.add("HTTP")  # for API requests
   Pkg.add("JSON")  # for JSON parsing
   Pkg.add("DataFrames")  # for data manipulation if needed
   Pkg.add("PyCall")  # for Python interoperability if required

1. Start Jupyter Notebook: Launch Jupyter with the Julia kernel by running:

   jupyter notebook

2. Select a Notebook: Open the notebook for the desired API provider (Anthropic, Cohere, Mistral, or OpenAI).
3. Follow Instructions: Each notebook includes setup steps and examples for making API requests using Julia.

• API Interactions: Each notebook provides examples of API calls for generating text, performing retrieval, and other LLM functionalities.
• Parameter Customization: Adjust parameters such as temperature, max tokens, and prompt design to optimize model output.
• Cross-Provider Testing: Easily compare responses from multiple LLM providers within the Julia environment.

We welcome contributions! Feel free to submit issues or pull requests to improve functionality, add features, or fix bugs.

This repository is licensed under the MIT License.