Project Title

localGPT — Private, On-Premise Document Intelligence Platform with GPT Models

Overview

LocalGPT is a private, on-premise Document Intelligence platform that allows users to chat with their documents using GPT models, ensuring that no data leaves the user's device. It features a hybrid search engine, smart router, and contextual enrichment to provide accurate and relevant insights from files. The platform is modular, lightweight, and easy to deploy and maintain.

Key Features

Utmost Privacy: Data remains on your computer, ensuring 100% security.
Versatile Model Support: Seamlessly integrate a variety of open-source models via Ollama.
Diverse Embeddings: Choose from a range of open-source embeddings.
Reuse Your LLM: Once downloaded, reuse your LLM without the need for repeated downloads.
API: LocalGPT has an API that you can use for building RAG Applications.

Use Cases

Researchers and analysts who need to extract insights from large volumes of documents without compromising data privacy.
Enterprises that require a secure, on-premise solution for document analysis and summarization.
Individuals who want to chat with their documents and extract relevant information without sharing their data online.

Advantages

100% private and secure, as no data leaves the user's device.
Modular and lightweight architecture, enabling easy deployment and maintenance.
Supports a variety of open-source models and embeddings, providing flexibility in document analysis.
Smart router for automatic selection between RAG and direct LLM answering, improving accuracy and efficiency.

Limitations / Considerations

The platform may require significant computational resources, especially when handling large volumes of documents or using complex models.
As an on-premise solution, it may not be suitable for users who prefer cloud-based document analysis tools.
The platform's performance may be affected by the quality and relevance of the documents being analyzed.

DocArray: A library for building document-based AI applications, but without the focus on privacy and on-premise deployment.
Haystack: An open-source NLP framework for building search systems, but with a more general focus rather than privacy and on-premise deployment.
LangChain: A framework for building language model applications, but without the emphasis on privacy and document analysis.