DocsGPT Guide

Overview

DocsGPT is an open-source platform for building private AI-powered document analysis and question-answering systems. It uses Retrieval Augmented Generation (RAG) to enable natural language queries against your own document collections, making it particularly valuable for researchers who need to quickly extract information from large corpora of papers, technical reports, and institutional documentation.

Unlike general-purpose chatbots, DocsGPT operates on your specific documents, providing grounded answers with source citations. This is critical in academic settings where hallucinated information can derail research. The platform supports a wide range of document formats including PDF, DOCX, Markdown, HTML, and plain text, covering the formats most commonly encountered in research workflows.

With over 18,000 GitHub stars and an active development community, DocsGPT offers both self-hosted deployment for data-sensitive research environments and a cloud-hosted option for quick evaluation. The self-hosted approach ensures that proprietary research data, unpublished manuscripts, and confidential institutional documents never leave your infrastructure.

Installation and Setup

Deploy DocsGPT using Docker Compose for the simplest setup:

git clone https://github.com/arc53/DocsGPT.git
cd DocsGPT

# Copy and configure environment settings
cp .env_sample .env

Edit the .env file to configure your LLM backend:

# Set your LLM provider credentials
LLM_NAME=openai
API_KEY=$OPENAI_API_KEY

# Or use a local model via Ollama
LLM_NAME=ollama
OLLAMA_API_BASE=http://localhost:11434
MODEL_NAME=llama3

Launch the application:

docker compose up -d

The web interface becomes available at http://localhost:5173. For production deployments behind a reverse proxy, configure the appropriate VITE_API_HOST environment variable.

For development or lightweight usage without Docker:

pip install -r requirements.txt
cd application
python app.py