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GPT4All for desktop document chat

Try desktop model chat and search an indexed folder of your own documents.

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Local AIPublished 2 min read

Key Takeaways

  • The desktop app handles model downloads and local conversations.
  • The desktop workflow needs both a compatible chat model and an index of the text you want to query.
  • Build a collection from a few files with known answers.
  • The index reflects the text extracted from the files.

How it works

The desktop app handles model downloads and local conversations. LocalDocs indexes files so relevant text can accompany a question to the generation model.

What you need

The desktop workflow needs both a compatible chat model and an index of the text you want to query. Keep those roles separate when diagnosing slow responses or missing sources.

What to try first

Build a collection from a few files with known answers. Try one question that needs an exact quote and a second whose answer is absent, then check the source snippets the app returns.

Update one indexed file and ask about the changed fact again. Old results can reveal an ingestion or refresh problem even when the generation model behaves as expected.

Where to look for failures

The index reflects the text extracted from the files. Scanned pages, tables and similar filenames can lead to plausible answers from the wrong passage.

Related Franklin pages

Use the same two or three documents in each workspace. Comparing source passages, index updates and data flow reveals differences that a fluent chat answer can hide.

For more on how model size affects deployment decisions, read Franklin’s guide. Small language models

Project documentation

Try it for your workflow

Explore the project’s documentation for setup and usage, or save this page to a collection for later.

Read the project documentation ↗