• In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.

• You can try the project live here

• This project showcase the implementation of an advanced RAG system that uses groq as an llm to retrieve information about langsmith.

Steps I followed:

1. I have used the WebBaseLoader from the langchain_community document loader to load the data from the https://docs.smith.langchain.com/ webpage.
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 HuggingFaceInstructEmbeddings using the FAISS vector store.
4. setup the llm ChatGroq with the model name mixtral-8x7b-32768
5. Setup ChatPromptTemplate
6. finally created the document_chain and retrieval_chain for chaining llm to prompt and retriever to document_chain respectively

• langchain==0.1.20
• langchain-community==0.0.38
• langchain-core==0.1.52
• langchain-groq==0.1.3
• faiss-cpu==1.8.0
• python-dotenv

1. Prerequisites
   • Git
   • Command line familiarity
2. Clone the Repository: git clone https://github.com/NebeyouMusie/End-To-End-Advanced-RAG-Project-using-Open-Source-LLM-Models-And-Groq-Inferencing.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-Advanced-RAG-Project-using-Open-Source-LLM-Models-And-Groq-Inferencing using your terminal
5. Install Libraries: pip install -r requirements.txt
6. run streamlit run app.py
7. open the link displayed in the terminal on your preferred browser

• Collaborations are welcomed ❤️

• I would like to thank Krish Naik

• LinkedIn: Nebeyou Musie
• Gmail: [email protected]
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