This project demonstrates the integration of advanced AI models and containerised deployment using Open WebUI and Ollama. The project includes the optimization of embedding and instructional models, as well as the integration of personalized financial insights.
- Introduction
- Prerequisites
- Installation
- Usage
- Model Information
- Embedding Books
- References
- Contributing
- License
This repository showcases an AI-powered financial education and planning assistant, utilizing advanced AI models for personalized financial insights. The project leverages Docker for scalable deployment and performance optimization, with Open WebUI as the interface and Ollama for model management.
Before you begin, ensure you have met the following requirements:
-
Clone the repository:
git clone https://github.com/kzhid/AI_Integration_and_Containerised_Deployment.git cd AI_Integration_and_Containerised_Deployment
important: i did not add the modelfile's themselves in this repository, so use your own on ollama, however iv left the modelfile config and other files for learning purposes.
-
Set up Docker container for Open WebUI:
Follow the instructions in the Open WebUI documentation to set up and run the Docker container.
-
Install Ollama:
Install and set up Ollama by following the Ollama documentation.
-
Running Open WebUI:
Start the Open WebUI Docker container:
docker-compose up -d
-
Accessing Open WebUI:
Open your web browser and navigate to
http://localhost:3000
to access the Open WebUI interface. -
Using Ollama:
Ensure Ollama is running and properly configured as per the Ollama documentation.
The project utilizes the default embedder and chatbot models provided by Ollama. No custom model integration is discussed here.
To embed books and provide personalized financial advice:
-
Upload Books:
Use the Open WebUI interface to upload financial books or documents.
-
Embedding Process:
Open WebUI will process the uploaded documents and integrate them with the AI models for personalized insights.
Contributions are welcome! Please fork the repository and create a pull request with your changes. Ensure that your contributions adhere to the coding standards and practices outlined in this project.