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Deploy DocsGPT for private document analysis and research knowledge bases
Passed 17 of 22 test cases with the skill, and 11 of 22 without it.
and 8 more in the full report
7 fixed10 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
What default numerical value is assigned to the TEMPERATURE environment variable in DocsGPT base settings?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies TEMPERATURE parameter value of 0.1. | Missed | Passed |
The instructions your model reads when the skill loads.
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 The self-hosted approach ensures that proprietary research data, unpublished manuscripts, and confidential institutional documents never leave your infrastructure.
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
test cases passed, vs 11 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.