Document Summarization

I hope you’ve gained a basic understanding of setting up a simple chatbot and Ollama ecosystem. In this blog, we’ll take it a step further by exploring document summarization concepts using a simple RAG-based application. To get started, we’ll need to cover the basics of RAG, and then we’ll dive into a practical example. In this example, we’ll learn how to summarize documents and engage in conversations with uploaded documents using RAG.

Let’s break it down:

We’ll start with a brief introduction to RAG and its key concepts.
Then, we’ll move on to a hands-on example where we’ll apply RAG to summarize documents and chat with uploaded documents.
By the end of this blog, you’ll have a solid understanding of how to use RAG for document summarization and conversational AI.

Let’s get started and explore the power of RAG in document summarization and chatbots!”

RAG and its key concepts

RAG-Retrieval Agumented Generation

Installation & Dev Setup

For installation and code setup please refer previous blog.

Hope you got the better understanding of the basic RAG and advanced RAG from the above post. In this article we are implementing advanced RAG to chat with documents application.

Start coding…

1.Project Setup

We are using the following dependinceis

As we are using pyTorch dependencies, and it has support for python till 3.10, and if have you the latest version, please setup the env with 3.10, to that please execute the following command

python3.9 -m venv docusum source docusum/bin/activate

Then run the following commands manuallay, if still get any issues with requirments.txt

pip install flask-cors
pip install tiktoken
pip install unstructured
pip install Flask==2.0.1
pip install Werkzeug==2.2.2
pip install llama-index
pip install llama-index-vector-stores-qdrant
pip install llama-index-llms-ollama
pip install llama-index-embeddings-ollama
pip install torch sentence-transformers
pip install qdrant-client
pip install fastembed

2. Implementations

Ollama for running the LLMs locally. If you want to learn more about Ollama and how to get started with it locally, visit this article first.
Lllama Model: Use ollama list to check if it is installed on your system, or else use the command ollama pull llama3.2 to download the latest default manifest of the model.
Qdrant vector database to store the PDF document’s vector embeddings.
Flask: