PrivateGPT: The Fully Private Solution for Question Answering

Introduction

Private AI has developed a new product, which helps companies safely leverage OpenAI's chatbot without compromising customer or employee privacy. This new model provides a privacy layer for large language models (LLMs) that automatically redacts sensitive information and personally identifiable information (PII) from user prompts. This helps businesses safely leverage generative AI while ensuring data privacy and security.

The major contribution to this project was the development of privateGPT.py, a script that uses a local language model based on GPT4All-J to interact with documents stored in a local vector store. privateGPT.py is a powerful tool that can be used to redact PII from a variety of documents, including text files, PDFs, and images. It's going to be a valuable tool for businesses that want to use generative AI without compromising data privacy. This new model is called "PrivateGPT".

What is Private GPT?

Private GPT is a variant of the GPT language model that enables users to ask questions to their own documents without an internet connection. It has been designed to prioritize user privacy by ensuring that no data is left behind in the execution environment at any point. The underlying technology behind private GPT is based on the LMS developed by OpenAI and specifically, the GPT 3.5 architecture. The model has been adapted to work locally without relying on an internet connection, making it suitable for situations where privacy or limited connectivity is a concern.

Benefits of Private GPT

Enables users to ingest their own documents into the model, allowing them to have a custom knowledge base for their queries.

Keeps all information within the local execution environment, ensuring that data remains private and secured within the application.

Allows users to ask questions and get responses from their own documents without an internet connection, so particularly useful in situations where internet access is limited or restricted.

Enhanced Precision: Private GPT can undergo training on a tailored dataset, resulting in more precise outcomes compared to employing a versatile language model.

Heightened Effectiveness: Private GPT enables task automation, encompassing activities like addressing queries and producing reports.

Augmented Security: Private GPT ensures that all information remains within the confines of the local execution environment, safeguarding data against unauthorized breaches.

By using PrivateGPT, businesses can ensure that their data is protected and that they are in compliance with data privacy regulations.

How does Private GPT work?

The first step is to collect relevant text data. This data can come from a variety of sources, such as PDFs, text documents, and notes. Once the data has been collected, it is ingested into the Private GPT model. The model then uses this data to train itself to answer questions and generate text. The trained model is then deployed in the local execution environment. This ensures that all information remains private and secure.

What are some potential use cases for PrivateGPT in different industries?

PrivateGPT offers a wide range of potential applications across various industries, such as finance, manufacturing, retail, and online chat communications. In the realm of finance, PrivateGPT holds the capability to enhance the accuracy and efficiency of financial operations, mitigating the potential risks associated with human errors. Within the manufacturing sector, finance teams can utilize PrivateGPT to streamline their operations and make well-informed decisions.

One notable advantage of PrivateGPT is its ability to facilitate the creation of personalized GPT-3 models without the need for coding or technical expertise.

Furthermore, PrivateGPT can effectively safeguard sensitive information during online chat conversations.

PrivateGPT represents an experimental initiative aimed at verifying the viability of a completely confidential solution for question answering, employing extensive language models (LLMs) and vector embeddings. Although still in the developmental phase and not yet prepared for widespread application, the project has already exhibited the potential for an entirely secure question answering system.

How to access PrivateGPT?

To access PrivateGPT, Visit the PrivateGPT GitHub repository, where the README file provides detailed instructions on how to install and use the tool. The link is provided under 'source' section at the end of this article.

Conclusion

PrivateGPT introduces a promising technology that has the capacity to transform the manner in which we engage with information. Nevertheless, it is essential to acknowledge that the project is currently under development and faces certain limitations. Notably, PrivateGPT can solely address questions that are based on the indexed documents. It is incapable of answering queries that necessitate knowledge beyond the scope of the available documents. 

In spite of these limitations, PrivateGPT signifies a significant advancement in the progression of confidential question answering systems. The project holds the potential to enable the posing of questions concerning sensitive information without the risk of unauthorized parties gaining access to that data.

source
https://www.githubs.cn/projects/635240594-privategpt