fashion-rag

Tutorial Followed: https://www.youtube.com/watch?v=de6_BjEVWZo

The first step into the RAG world via fashion context. The "assistant" answers the user's question using the data from the fashion PDF file.

I used a model from HuggingFace and an open-source vector store.

I created a RAG chain using the following:

• For embedding, the model: sentence-transformers/all-MiniLM-L6-v2
• For VectorDB: ChromaDB (pip install chromadb)
• As the LLM: mistralai/Mixtral-8x7B-Instruct-v0.1

Short and simple, I create the vector database from the PDF file containing fashion data and use the LLM to query it and answer a question. I kept it simple, but you can extend it, correct it, and even add a UI (with Streamlit like in the video).

To start:

(Optional) You can create and activate a virtual environment first.

1. install the requirements: pip install -r requirements.txt
2. Create a .env file at the project's root.
3. Place your HUGGINGFACEHUB_API_TOKEN inside.

You're good to go from there. Good luck!