DocsGPT

The platform ingests documents in formats including PDF, DOCX, CSV, XLSX, EPUB, Markdown, HTML, and JSON, and creates a queryable knowledge base that users can interact with through a conversational interface.

Unlike closed-source documentation chatbots that require sending content to external servers, DocsGPT can be deployed fully on-premises with local language models, keeping sensitive documentation private.

The platform targets engineering teams, technical writers, and enterprises that want to make internal documentation and knowledge bases searchable through natural language without building the retrieval and generation infrastructure from scratch.

A software company with thousands of pages of technical documentation can deploy DocsGPT to let engineers ask questions in plain language and receive answers drawn from the specific documentation rather than searching manually or waiting for human support.

The open-source model allows teams to inspect the code, customize the pipeline, and contribute improvements.

DocsGPT supports multiple LLM backends including OpenAI, Google Gemini, Anthropic, and local models through Ollama and llama_cpp. This multi-model support gives deployment teams the flexibility to choose their model provider based on cost, privacy, and capability requirements.

Organizations in regulated industries that cannot send document contents to external APIs can run DocsGPT entirely on internal infrastructure with a locally hosted model. Organizations that want the latest capabilities can connect to OpenAI or Anthropic APIs without changing the platform.