End to End RAG Project using ObjectBox and LangChain

• In this end to end project I have built a RAG app using ObjectBox Vector Databse and LangChain. RAG techniques allow us to augment a language model's knowledge base actively, ensuring your AI can access and reason with your data and the very latest information. With ObjectBox you can do that, without the data ever needing to leave the device.

DEMO

• You can check the project live here

Description

• This project showcase the implementation of an advanced RAG system that uses Objectbox vectordatabse and Groq's LLAM3 model as an llm to retrieve information from different PDF documents.

Steps I followed:

1. I have used the PyPdfDirectoryLoader from the langchain_community document loader to load the PDF documents from the us-census-data directory.
2. transformed each text into a chunk of 1000 using the RecursiveCharacterTextSplitter imported from the langchain.text_splitter
3. stored the vector embeddings which were made using the HuggingFaceBgeEmbeddings using the ObjectBox vector store.
4. setup the llm ChatGroq with the model name Llama3-8b-8192
5. Setup ChatPromptTemplate
6. Setup vector_embedding function to enbedd the documents and store them in the ObjectBox vectorstore
7. finally created the document_chain and retrieval_chain for chaining llm to prompt and retriever to document_chain respectively

Libraries Used

• langchain==0.1.20
• langchain-community==0.0.38
• langchain-core==0.1.52
• langchain-groq==0.1.3
• langchain-objectbox
• python-dotenv==1.0.1
• pypdf==4.2.0

Installation

1. Prerequisites
   • Git
   • Command line familiarity
2. Clone the Repository: git clone https://github.com/NebeyouMusie/End-to-End-RAG-Project-using-ObjectBox-and-LangChain.git
3. Create and Activate Virtual Environment (Recommended)
   • python -m venv venv
   • source venv/bin/activate
4. Navigate to the projects directory cd ./End-to-End-RAG-Project-using-ObjectBox-and-LangChain using your terminal
5. Install Libraries: pip install -r requirements.txt
6. Navigate to the app directory cd ./app using your terminal
7. run streamlit run app.py
8. open the link displayed in the terminal on your preferred browser
9. As I have already embedded the documents you don't need to click on the Embedd Documents button/ But, if it's not working then you need to click on the Embedd Documents button and wait until the documnets are processed
10. Enter your question from the PDFs found in the us-census-data directory

Collaboration

• Collaborations are welcomed ❤️

Acknowledgments

• I would like to thank Krish Naik

Contact

• LinkedIn: Nebeyou Musie
• Gmail: nebeyoumusie@gmail.com
• Telegram: Nebeyou Musie