Effortlessly Communicate with PDFs Using CLI + Streamlit + Ollama

Author(s): Stefan Pietrusky

TL;DR: Learn how to communicate with PDFs using CLI and Streamlit, without relying on proprietary models. Use Ollama and Meta AI’s LLaMA model to create a free, locally executed PDF chat app with no API restrictions.

Disclaimer: This post has been created automatically using generative AI. Including DALL-E, Gemini, OpenAI and others. Please take its contents with a grain of salt. For feedback on how we can improve, please email us

Introduction

PDF files are a common format for sharing documents, but they can often be difficult to interact with. Many tools for working with PDFs require proprietary models or APIs, making it challenging for users to access and manipulate these files. However, there is a solution that allows for easy communication with PDF files without the use of proprietary models or APIs. In this blog post, we will discuss how to talk to a PDF file using CLI and Streamlit, with the help of Ollama and Meta AI’s LLaMA model.

Understanding CLI and Streamlit

CLI (Command Line Interface) is a text-based interface that allows users to interact with a computer through commands. It is a powerful tool for developers and advanced users, but can be intimidating for beginners. On the other hand, Streamlit is a user-friendly web application framework that allows for the creation of interactive data apps with just a few lines of code. By combining these two tools, we can create a user-friendly interface for interacting with PDF files without the need for proprietary models or APIs.

Introducing Ollama and LLaMA

Ollama is an open-source library that provides a simple and intuitive interface for interacting with PDF files. It uses CLI commands to perform actions such as extracting text, images, and metadata from PDFs. On the other hand, LLaMA (Local Language Model API) is a machine learning model developed by Meta AI that can understand and generate natural language. By using LLaMA, we can enhance the capabilities of Ollama and create a more interactive experience with PDF files.

Creating a Locally Executed PDF Chat App

With the help of Streamlit and Ollama, we can create a locally executed PDF chat app that allows users to communicate with PDF files using natural language. The app will use LLaMA to understand user input and perform actions on the PDF file accordingly. This eliminates the need for API limitations and allows for a more seamless and personalized experience with PDF files.

Advantages of Using CLI, Streamlit, Ollama, and LLaMA

By using CLI and Streamlit, we can create a user-friendly interface for interacting with PDF files without the need for complex coding. Ollama and LLaMA provide powerful capabilities for working with PDFs, making it easier to extract information and perform actions on these files. Additionally, by creating a locally executed app, we eliminate the need for API limitations, allowing for a more customizable and efficient experience with PDFs.

In conclusion, by combining the power of CLI and Streamlit with the innovative Ollama and LLaMA models, we have developed a free and locally executed PDF chat app that eliminates the need for proprietary models and API limitations. This contribution opens up new possibilities for seamless communication with PDF files, making it easier for users to interact with and extract information from these documents. With this tool, talking to a PDF file has never been more accessible and efficient.

Crafted using generative AI from insights found on Towards Data Science.

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