Gemma Model Document Q&A

This project demonstrates a Streamlit application for querying documents using the Gemma Model, powered by ChatGroq's Llama3-8b-8192 model and Google Generative AI embeddings for vector-based retrieval. The application allows users to load PDF documents, generate embeddings, and query the documents to receive accurate responses based on the provided context.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/gemma-model-qa.git
   cd gemma-model-qa

2. Install the required packages:

   pip install -r requirements.txt

3. Create a .env file in the root directory with your Groq and Google API keys:

   GROQ_API_KEY=your_groq_api_key
   GOOGLE_API_KEY=your_google_api_key

4. Ensure you have your PDF documents in a directory named Files in the root directory.

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your browser and navigate to the local URL provided by Streamlit (usually http://localhost:8501).

Streamlit Application Code

import os
import streamlit as st
from langchain_groq import ChatGroq
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS # Vector store DB
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_google_genai import GoogleGenerativeAIEmbeddings # Vector embedding technique
from dotenv import load_dotenv

load_dotenv()

# Load the Groq and Google API keys from the .env file
groq_api_key = os.getenv("GROQ_API_KEY")
os.environ["GOOGLE_API_KEY"] = os.getenv("GOOGLE_API_KEY")

st.title("Gemma Model Document Q&A")

llm = ChatGroq(groq_api_key=groq_api_key, model_name="Llama3-8b-8192")

prompt = ChatPromptTemplate.from_template("""
Answer the questions based on the provided context only. Please provide the most accurate response to the questions below. <context> {context} <context> Question: {input}
""")

def vector_embedding():
    if "vectors" not in st.session_state:
        st.session_state.embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
        st.session_state.loader = PyPDFDirectoryLoader("./Files")  # Data ingestion step
        st.session_state.docs = st.session_state.loader.load()  # Document loadings
        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)  # Text splitter
        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)  # Splitting the documents
        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector store

prompt1 = st.text_input("What do you want to ask from the document?")

if st.button("Document Embedding"):
    vector_embedding()
    st.write("Vector Store DB is Ready")

import time

if prompt1:
    document_chain = create_stuff_documents_chain(llm, prompt)
    retriever = st.session_state.vectors.as_retriever()
    retrieval_chain = create_retrieval_chain(retriever, document_chain)

    start = time.process_time()
    response = retrieval_chain.invoke({'input': prompt1})
    st.write(response['answer'])

    # With a Streamlit expander
    with st.expander("Document Similarity Search"):
        # Find the relevant chunks
        for i, doc in enumerate(response["context"]):
            st.write(doc.page_content)
            st.write("---------------------------------")

Contribution

We welcome contributions to improve this project! To get started, follow these steps:

1. Fork the repository: Click the "Fork" button at the top right of the repository page on GitHub.

2. Clone the forked repository:

   git clone https://github.com/HimanshuGitCode/Gemma-Model-Q-A-Pdf-Project.git
   cd Gemma-Model-Q-A-Pdf-Project

3. Create a new branch: Make sure you create a new branch for your changes.

   git checkout -b my-feature-branch

4. Make your changes: Implement your feature or fix the bug.

5. Commit your changes: Write a clear and descriptive commit message.

   git add .
   git commit -m "Description of the changes"

6. Push to your forked repository:

   git push origin my-feature-branch

7. Open a Pull Request: Go to the original repository on GitHub and open a Pull Request from your forked repository.

Guidelines

• Ensure your code follows the existing style and conventions.
• Write clear and concise commit messages.
• Update documentation if necessary.
• Test your changes thoroughly.

We will review your Pull Request and provide feedback. Thank you for contributing!

License

This project is licensed under the MIT License. See the LICENSE file for more details.

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.