A Python framework to implement Retrieval-Augmented Generation (RAG) using a Vector-store Database. This framework utilizes the Vector-Store repository to store and retrieve embeddings for effective RAG implementation.
- main.py - The main RAG pipeline framework, processing input and orchestrating the RAG flow.
- llm.py - A module that interfaces with the Google Gemini API, simulating a Large Language Model (LLM) for generating responses.
- get_embedding.py - A script for generating embeddings from
.txt
files using the SentenceTransformer model. - comm-Vectorstore.py - A communication module that uploads embeddings to the Vector Store for storage and retrieval.
- Clone the Vector-Store repository and set it up according to the instructions.
- Configure the
main.py
andget_embedding.py
to use your Vector-store Database. - Run
main.py
to execute the RAG pipeline with any input text.
- Python 3.x
- Sentence-Transformers - For generating text embeddings
- nltk - For sentence tokenization
- Google Gemini API credentials for
llm.py
(or any compatible LLM API for inference)
pip install -r requirements.txt