Vector Store

Summary

This is a simple python implementation of a vector store that showcase the ability to use an embedding model to store documents in an high dimensional vector space, and do similarity search to retrieve the most relevant documents. Furthermore it showcase how this can then be used to do RAG with an LLM like mistral without the need of libraries like Langchain or LlamaIndex

Description of the directory

Everything relevant to the implementation is explained and implemented inside a .ipn ... file -> this

Installation

• Install Ollama for the RAG

• Install the model we are using:

ollama pull mistral

Clone the repo and move inside it

git clone https://github.com/Jac-Zac/IR_Vector_Store_RAG && cd https://github.com/Jac-Zac/IR_Vector_Store_RA

Set up a Python virtual environment

python -m venv .env
source .env/bin/activate

If you are having problems with jupyter ipython kernel install --name "local-venv-kernel" --user

Install dependencies

pip install -r requirements.txt

Data for RAG

• https://arxiv.org/pdf/2201.02177.pdf
• https://arxiv.org/pdf/1706.03762.pdf