This repository provides Jupyter notebooks for exploring and utilizing Cohere's Large Language Models (LLMs) in various applications, including chatbots and retrieval-augmented generation (RAG). These notebooks serve as a practical guide for deploying Cohere models for conversational AI and information retrieval tasks.
- COHERE-CHATBOT.ipynb: Demonstrates the setup of a chatbot using a Cohere model, focusing on creating interactive, conversational exchanges.
- COHERE-RAG.ipynb: Implements Retrieval-Augmented Generation (RAG), combining document retrieval and generation for contextually relevant responses. Ideal for Q&A systems and customer support applications.
To run these notebooks, you need:
- Python 3.8+
- Jupyter Notebook
- The dependencies listed in
requirements.txt
-
Clone the repository:
git clone https://github.com/simonpierreboucher/llm_cohere_notebook.git cd llm_cohere_notebook
-
Install the dependencies:
pip install -r requirements.txt
- Start Jupyter Notebook: Navigate to the repository folder and launch Jupyter:
jupyter notebook
- Open a Notebook: Select either the Chatbot or RAG notebook to explore specific functionalities.
- Follow Instructions: Each notebook contains setup instructions and guidance for working with Cohere models in the specified application.
- COHERE-CHATBOT: Perfect for building conversational agents or virtual assistants that engage users in dialogue.
- COHERE-RAG: Suitable for tasks that require accurate, contextually grounded answers, such as support chat and information retrieval systems.
We welcome contributions! Feel free to submit issues or pull requests to enhance functionality, add features, or address bugs.
This repository is licensed under the MIT License.