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RAG with Knowledge Graph

Overview

Retrieval Augmented Generation(RAG) is a way of generating reliable answers from LLM using an external knowledge base. This project shows how to use RAG with a knowledge graph using Weaviate as the vector database and the exllamav2 implementation of the mistral orca model.

The following is the pipeline -

  1. Extract text from a PDF
  2. Chunk the data into k size with w overlap.
  3. Extract (source, relation, target) from the chunks and create a knowledge graph
  4. Extract embeddings for the nodes and relationships.
  5. Store the text and vectors in weaviate vector database.
  6. Apply a keyword search on the nodes and retrieve the top k chunks.
  7. Generate the answer from the top k retrieved chunks.
  8. You can also visualize the sub-graph of the nodes used to generate the answer.

Installation

pip install -r requirements.txt

Features

  • Uses the Exllamav2 implementation of the mistral orca model which is extremely fast and memory efficient.
  • Construction of knowledge graph from text to understand the concepts better and retrieve more relevant text chunks.
  • The vector database used is Weaviate for storing the data and applying the keyword search(can also be done for hybrid search).

Usage

  • First # to weaviate free sandbox to get your api key and weaviate instance url.
  • Create a .env file and store your credentials there.

Sample .env

WEAVIATE_API_KEY = 'xxx'
WEAVIATE_CLIENT_URL = 'xxx'
  • Run the main file.
python main.py