https://github.com/surajsanap/pdfgemini_bot
PDFGem Chat is an interactive chat interface designed for querying information from uploaded PDF files.
https://github.com/surajsanap/pdfgemini_bot
ai faiss gemini genrative-ai google langchain nlp streamlit
Last synced: about 1 year ago
JSON representation
PDFGem Chat is an interactive chat interface designed for querying information from uploaded PDF files.
- Host: GitHub
- URL: https://github.com/surajsanap/pdfgemini_bot
- Owner: SurajSanap
- Created: 2024-01-11T11:33:28.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-14T08:59:56.000Z (over 2 years ago)
- Last Synced: 2025-05-08T19:12:13.401Z (about 1 year ago)
- Topics: ai, faiss, gemini, genrative-ai, google, langchain, nlp, streamlit
- Language: Python
- Homepage:
- Size: 416 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PDFGemini_Bot
## Project Overview
PDFGem Chat is an interactive chat interface designed for querying information from uploaded PDF files. This project utilizes Streamlit, PyPDF2, LangChain, Google Generative AI, and FAISS to create a seamless experience for users to ask questions related to the content of PDF documents.
## Components
### 1. User Interface
- Developed using the Streamlit library for a user-friendly experience.
- Users can ask questions about the content of uploaded PDF files.
### 2. PDF Processing
- Extracts text from PDF files using PyPDF2.
- Splits extracted text into manageable chunks.
### 3. Embedding and Vectorization
- Leverages Google Generative AI Embeddings for converting text into vectors.
- Applies FAISS (Facebook AI Similarity Search) to create a vector store/index of text chunks.
### 4. Conversational Chain
- Implements a conversational chain for question-answering using the Gemini Generative AI model.
- Configures the prompt template for providing context and framing questions.
### 5. Workflow
- Users upload PDF files and ask questions through the interface.
- Text is extracted from PDFs, split into chunks, and converted into vectors.
- The conversational chain processes user input, searches for similar text chunks, and generates responses.
## Code Structure
- **`main()` function**: Sets up the Streamlit interface and handles user input.
- **`get_pdf_text(pdf_docs)` function**: Extracts text from PDF files.
- **`get_text_chunks(text)` function**: Splits text into manageable chunks.
- **`get_vector_store(text_chunks)` function**: Creates a vector store/index from text chunks.
- **`get_conversational_chain()` function**: Configures the conversational chain for question-answering.
- **`user_input(user_question)` function**: Processes user input and generates responses.
- **Environment variables**: Utilizes the `dotenv` library to securely load the Google API key.
## Usage
1. **Upload PDFs**: Use the sidebar to upload one or more PDF files.
2. **Ask a Question**: Enter your question in the provided text input.
3. **Submit & Process**: Click the button to initiate the processing of PDFs and question-answering.
4. **View Response**: The system generates a response based on the input question and the content of the PDFs.
## Dependencies
- Streamlit
- PyPDF2
- LangChain
- Google Generative AI
- FAISS
- Dotenv
## Setup
1. **Install Dependencies**: Ensure the required Python packages are installed.
2. **Set up Google API Key**: Store the Google API key in a secure manner using the `dotenv` file.
3. **Run the Application**: Execute the script to launch the Streamlit interface.
Feel free to explore and enhance the functionalities of this project based on your requirements.
---
**PDFGemini** - Unleash the Power of Conversational PDF Exploration! 💬✨