https://github.com/ashad001/ultimaterag
In Development
https://github.com/ashad001/ultimaterag
ai embeddings gemini jina llama-index llms machine-learning openai pdf-search rag vector-store web-search
Last synced: 5 months ago
JSON representation
In Development
- Host: GitHub
- URL: https://github.com/ashad001/ultimaterag
- Owner: Ashad001
- Created: 2024-07-01T07:41:30.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-30T07:06:59.000Z (almost 2 years ago)
- Last Synced: 2025-04-02T08:26:40.348Z (about 1 year ago)
- Topics: ai, embeddings, gemini, jina, llama-index, llms, machine-learning, openai, pdf-search, rag, vector-store, web-search
- Language: Python
- Homepage:
- Size: 1.81 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# UltimateRAG
This project is an AI-based application that leverages Gemini/OpenAI and JinaAI embeddings on a FastAPI backend. The application is capable of reading PDFs, maintaining webpage memory, and facilitating interactive chat with websites, webpages, and PDFs.
## Project Description
The AI-based application aims to provide an interactive and intelligent interface for users to engage with various digital content. The key features of the application include:
- **PDF Reading**: The application can read and understand the content of PDF documents.
- **Webpage Memory**: The application retains memory of interactions on webpages, allowing for a seamless user experience.
- **Interactive Chat**: Users can chat with the application about websites, webpages, and PDFs, receiving intelligent and context-aware responses.
## Project Structure
The project is divided into two main parts: the backend and the frontend.
### Backend
- **Framework**: FastAPI
- **AI Models**: Gemini/OpenAI, JinaAI embeddings
- **Capabilities**: Reading PDFs, webpage memory, interactive chat
### Frontend
- **Framework**: Svelte
- **UI/UX**: Responsive and user-friendly interface for interacting with the AI-based functionalities
## To-Do List
### Backend
1. **Set up FastAPI backend**
- Install FastAPI and create the initial project structure
2. **Integrate AI Models**
- Configure and integrate Gemini/OpenAI models
- Set up JinaAI embeddings
3. **Implement PDF Reading Capability**
- Develop endpoints to upload and read PDF documents
4. **Develop Webpage Memory Feature**
- Create mechanisms to store and retrieve webpage interactions
5. **Enable Interactive Chat**
- Implement chat functionality using AI models
### Frontend
1. **Set up Svelte Frontend**
- Install Svelte and create the initial project structure
2. **Design User Interface**
- Create a responsive and intuitive UI for interacting with the backend functionalities
3. **Implement PDF Interaction**
- Develop frontend components to upload and display PDF content
4. **Develop Webpage Memory Interface**
- Create frontend components to visualize and interact with stored webpage memories
5. **Enable Chat Interface**
- Implement a chat interface to communicate with the backend
## Getting Started
### Prerequisites
- **Backend**: Python, FastAPI, Gemini/OpenAI, JinaAI
- **Frontend**: Node.js, Svelte