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https://github.com/abeed04/rag-based-chat-with-pdf-using-llama3

Turn your PDFs into a conversation with Llama3's RAG-powered chat.
https://github.com/abeed04/rag-based-chat-with-pdf-using-llama3

chunking faiss-vector-database googlegenerativeai groq langchain llama3 pycharm-community python-3 rag streamlit

Last synced: 28 days ago
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Turn your PDFs into a conversation with Llama3's RAG-powered chat.

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README 

        
Chat with PDF using Llama3


Introduction


A simple Streamlit app interface that answers questions about an uploaded PDF document via Llama3.



[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://rag-based-chat-with-pdf-using-llama.streamlit.app/)






 This project implements a chat interface that allows users to ask questions about uploaded PDF documents. It leverages a Retrieval-Augmented Generation (RAG) approach, combining:



- Information Retrieval: Efficiently searching relevant passages in the PDF content using FAISS and text embeddings.

- Generative Language Model (LLM): Answering user questions in a comprehensive and informative way using Llama3, a large language model from Google AI for embeddings.



Features



- Upload multiple PDF documents.

- Ask questions about the content of the uploaded PDFs.

- Get answers generated by Llama3 based on the retrieved information.



Requirements



- Python 3.x

- Streamlit

- PyPDF2

- langchain

- langchain_community

- langchain-groq (Groq API access)

- langchain-google-genai (Google Generative AI access)

- dotenv (for environment variables)



Installation 



1. Install the requirements



   ```

   $ pip install -r requirements.txt

   ```



2.  Set up your Groq API key and Google Cloud project credentials

    ```

    GROQ_API_KEY=your_groq_api_key

    GOOGLE_API_KEY=your_google_api_key

    ```

    

3.  Run the app



    ```

    $ streamlit run streamlit_app.py

    ```



4.  Upload your PDF files in the sidebar.

5.  Click the "Submit & Process" button.

6.  Once processing is complete, enter your question in the text box.

7.  Click "Enter" to receive an answer generated by Llama3 based on the uploaded PDFs.    

    

Use Cases 


Research and Document Summarization:



- Students and researchers can upload research papers, articles, or reports and ask specific questions about the content.

- The chat interface can summarize key findings, identify relevant sections based on the question, or provide supporting evidence from the document.

  

Legal Document Analysis



- Lawyers or legal professionals can upload contracts, agreements, or court documents and ask clarifying questions about terms, clauses, or procedures.

- The chat can highlight relevant sections, identify potential ambiguities, or offer preliminary interpretations based on the document's content.

  

Technical Documentation Exploration



- Software developers, system administrators, or technical support personnel can upload user manuals, technical specifications, or troubleshooting guides.

- The chat can answer questions about specific features, configuration options, or troubleshooting steps based on the information within the documents.

  

Customer Service Chatbots



- Companies can leverage this technology to build chatbots that answer customer questions about product manuals, FAQs, or service policies.

- Users can upload relevant documents and get immediate, context-aware responses based on the retrieved information.



 Educational Materials and Assistive Technologies



- Students with learning disabilities can upload educational materials and ask questions about specific concepts or passages.

- The chat can provide paraphrased explanations, offer alternative learning resources, or highlight key points from the document.