https://github.com/simonpierreboucher/llm_julia_notebook
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.
https://github.com/simonpierreboucher/llm_julia_notebook
anthropic cohere julia large-language-models llm mistral openai
Last synced: 3 months ago
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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.
- Host: GitHub
- URL: https://github.com/simonpierreboucher/llm_julia_notebook
- Owner: simonpierreboucher
- Created: 2024-11-13T22:33:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-13T22:37:04.000Z (over 1 year ago)
- Last Synced: 2025-01-20T22:29:15.772Z (over 1 year ago)
- Topics: anthropic, cohere, julia, large-language-models, llm, mistral, openai
- Language: Jupyter Notebook
- Homepage:
- Size: 22.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# LLM Julia Notebook
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.
## Repository Structure
- **[JULIA_ANTHROPIC_API.ipynb](https://github.com/simonpierreboucher/llm_Julia_notebook/blob/main/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](https://github.com/simonpierreboucher/llm_Julia_notebook/blob/main/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](https://github.com/simonpierreboucher/llm_Julia_notebook/blob/main/JULIA_MISTRAL_API.ipynb)**: Explains the setup for the Mistral API in Julia, demonstrating API calls and response handling.
- **[JULIA_OPENAI_API.ipynb](https://github.com/simonpierreboucher/llm_Julia_notebook/blob/main/JULIA_OPENAI_API.ipynb)**: Covers the configuration and usage of OpenAI's API in Julia, including example requests and parameter customization.
## Getting Started
### Prerequisites
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`
### Installation
1. Clone the repository:
```bash
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:
```bash
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:
```julia
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
```
### Running the Notebooks
1. **Start Jupyter Notebook**: Launch Jupyter with the Julia kernel by running:
```bash
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.
## Use Cases
- **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.
## Contributing
We welcome contributions! Feel free to submit issues or pull requests to improve functionality, add features, or fix bugs.
## License
This repository is licensed under the MIT License.