Private document chat

Private document chat tools let you ask questions about your own files, such as contracts, research papers, manuals or years of notes, with a model running on your machine. The documents are read, indexed and searched locally, so nothing has to be uploaded to answer a question about them.

Under the hood most of these tools follow the same pattern, usually called retrieval. They split your files into passages, turn each passage into a numeric fingerprint, and store those in a local index. When you ask a question, the tool finds the most relevant passages and hands them to the language model along with your question. The model writes an answer based on what it was given.

That design explains both the strengths and the weak spots. Answers can cite the passages they used, which makes checking easy. But if the right passage is not retrieved, the model cannot use it, and a small local model may fill the gap with something plausible. Tools that show their sources make this visible; prefer them for anything that matters.

File handling is where tools differ most. Plain text and simple PDFs are easy. Scanned pages, tables, slides and complex layouts are hard, and some tools put real effort into parsing them while others read only the text layer. Test with your messiest real documents, not a clean sample.

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8 local AI tools in private document chat

AnythingLLM

by Mintplex Labs

A desktop and self-hosted app for chatting with your documents through a local model, with workspaces per project.

  • Open source
  • Runs on macOS
  • Runs on Windows

PrivateGPT

by Zylon

An open-source project for asking questions about documents with a local model, with an API for building on top.

  • Open source
  • Works offline after setup
  • Local API server

Khoj

An open-source assistant that searches and chats over your notes and documents and can be self-hosted.

  • Open source

h2oGPT

by H2O.ai

An open-source app for private question answering over documents, images and audio with local models.

  • Open source

Kotaemon

by Cinnamon AI

A clean web interface for chatting with documents that shows the source passages behind each answer.

  • Open source

RAGFlow

by InfiniFlow

An open-source engine for document question answering that parses complex layouts such as tables and scanned pages.

  • Open source

LocalGPT

by PromtEngineer

A project for chatting with documents entirely on your own machine, with no data leaving the computer.

  • Open source
  • Works offline after setup

ChatRTX

by NVIDIA

An NVIDIA demo app for Windows PCs with RTX graphics that answers questions about your local files.

  • Works offline after setup
  • Runs on Windows

Think about where the index lives and who can reach it. A desktop tool keeps everything in one user's folder. A server version can serve a team, which is useful, but then access control matters: someone who can query the index can read what is in it. Keep sensitive collections in separate workspaces with separate permissions.

Pay attention to how the index is kept up to date. Some tools watch a folder and re-index files as they change, while others need you to add or refresh documents by hand. An index that drifts out of date will answer confidently from old versions of a file, which is worse than no answer, so make updating part of the routine whenever the source documents change.

Finally, set expectations with a short test. Write ten questions you already know the answers to, run them, and check both the answers and the cited passages. It takes an hour and tells you more than any feature list.

Questions about private document chat

Do my documents leave my computer?

Not when both the tool and the model run locally. Some tools can also use hosted models, and then the retrieved passages are sent to that service with your question. Check which model each workspace uses.

Why does the tool miss information I know is in a file?

Usually the relevant passage was not retrieved, often because of how the file was split or parsed. Try rephrasing the question, adjusting chunk settings, or converting scanned PDFs to text first.

How large a document collection can I use?

Indexing large collections works on most machines; it just takes time and disk space. The limit is more often answer quality, since more documents mean more chances to retrieve the wrong passage. Split big collections by topic.

Can I trust the answers for legal or medical documents?

Use them to find and summarize passages, then read the source yourself. Local models can misread or overstate what a document says, and they are not a substitute for professional advice.

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