https://github.com/nebeyoumusie/end-to-end-advanced-rag-project-using-open-source-llm-models-and-groq-inferencing

In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.
https://github.com/nebeyoumusie/end-to-end-advanced-rag-project-using-open-source-llm-models-and-groq-inferencing

chatbot faiss-vector-database groq groq-api huggingface-embeddings langchain llm retrieval-augmented-generation streamlit streamlit-webapp web-scrapping

Last synced: about 2 months ago
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In this project I have built an end to end advanced RAG project using open source llm model, Mistral using groq inferencing engine.

• Host: GitHub
• URL: https://github.com/nebeyoumusie/end-to-end-advanced-rag-project-using-open-source-llm-models-and-groq-inferencing
• Owner: NebeyouMusie
• Created: 2024-05-22T10:33:48.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-06-18T20:15:32.000Z (8 months ago)
• Last Synced: 2024-06-19T02:24:08.886Z (8 months ago)
• Topics: chatbot, faiss-vector-database, groq, groq-api, huggingface-embeddings, langchain, llm, retrieval-augmented-generation, streamlit, streamlit-webapp, web-scrapping
• Language: Python
• Homepage: https://8510-01hwj8ynshjz7spkr595x77ec2.cloudspaces.litng.ai/
• Size: 76.2 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# End To End Advanced RAG Project using Open Source LLM Models And Groq Inferencing

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

![Groq for RAG Image](./images/chatbot-image.png)

## DEMO

 - You can try the project live [here](https://8510-01hwj8ynshjz7spkr595x77ec2.cloudspaces.litng.ai/)

## Description

- 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

## Libraries Used

 - 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

## Installation

 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

## Collaboration

- Collaborations are welcomed ❤️

## Acknowledgments

 - I would like to thank [Krish Naik](https://www.youtube.com/@krishnaik06)

   

## Contact

 - LinkedIn: [Nebeyou Musie](https://www.linkedin.com/in/nebeyou-musie)

 - Gmail: [email protected]

 - Telegram: [Nebeyou Musie](https://t.me/NebeyouMusie)