https://github.com/iguptashubham/chatrag-api

Welcome to the Custom Trained Gemini Flash (CTGF) project! This project aims to create a sophisticated conversational AI application that integrates the Gemini Flash large language model using FastAPI and Streamlit.
https://github.com/iguptashubham/chatrag-api

fastapi gemini-api llm python rag streamlit

Last synced: 2 months ago
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Welcome to the Custom Trained Gemini Flash (CTGF) project! This project aims to create a sophisticated conversational AI application that integrates the Gemini Flash large language model using FastAPI and Streamlit.

• Host: GitHub
• URL: https://github.com/iguptashubham/chatrag-api
• Owner: iguptashubham
• Created: 2024-11-03T20:36:59.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-11-03T20:44:19.000Z (over 1 year ago)
• Last Synced: 2025-07-03T14:56:28.584Z (12 months ago)
• Topics: fastapi, gemini-api, llm, python, rag, streamlit
• Language: Python
• Homepage: https://www.linkedin.com/in/shubhai/
• Size: 15.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: Readme.md

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README

          
# ChatRAG API

Welcome to the Custom Trained Gemini Flash (CTGF) project! This project aims to create a sophisticated conversational AI application that integrates the Gemini Flash large language model using FastAPI and Streamlit.

# Workflow

![ChatRAG-API](https://raw.githubusercontent.com/iguptashubham/ChatRAG-API/refs/heads/main/workflow/chatrag-workflow.png)

## Project Overview

Custom Trained Gemini Flash (CTGF) helps in Chat with your custom data, providing a seamless, interactive user experience for querying and retrieving data, leveraging advanced AI capabilities for contextual and engaging conversations.

## Key Components

### FastAPI Backend

- **Endpoints**: Handles file uploads, data retrieval, and conversational queries.

- **Data Management**: Manages data storage and vector databases using FAISS.

- **Asynchronous Processing**: Efficiently manages asynchronous tasks to provide real-time responses.

### Streamlit Frontend

- **User Interface**: A dynamic and user-friendly interface for uploading files, initiating chat sessions, and viewing conversation history.

- **Real-time Interactivity**: Enables users to interact with the AI in real-time, providing instant feedback and response.

### Gemini Flash Model Integration

- **Conversational AI**: Utilizes the advanced capabilities of the Gemini Flash model to generate context-aware, relevant responses.

- **RAG Chain**: Implements a Retrieval-Augmented Generation (RAG) chain to enhance the AI's ability to retrieve accurate information and generate meaningful responses based on user queries.

## Features

- **File Upload**: Users can upload PDF files, which are processed and stored using FastAPI.

- **Query Handling**: FastAPI endpoints manage user queries, retrieve relevant data from the vector store, and provide responses using the Gemini Flash model.

- **Chat History Management**: Stores and retrieves chat history, allowing users to view past interactions.

- **Seamless Integration**: Ensures smooth communication between the backend and frontend, providing a cohesive user experience.

## Installation

To install the necessary dependencies, run:

```bash

pip install -r requirements.txt