Document management

Ingestion

Ingesting & Managing Documents

The ingestion of documents can be done in different ways:

  • Using the /ingest API
  • Using the Gradio UI
  • Using the Bulk Local Ingestion functionality (check next section)

Bulk Local Ingestion

When you are running PrivateGPT in a fully local setup, you can ingest a complete folder for convenience (containing pdf, text files, etc.) and optionally watch changes on it with the command:

$make ingest /path/to/folder -- --watch

To log the processed and failed files to an additional file, use:

$make ingest /path/to/folder -- --watch --log-file /path/to/log/file.log

Note for Windows Users: Depending on your Windows version and whether you are using PowerShell to execute PrivateGPT API calls, you may need to include the parameter name before passing the folder path for consumption:

$make ingest arg=/path/to/folder -- --watch --log-file /path/to/log/file.log

After ingestion is complete, you should be able to chat with your documents by navigating to http://localhost:8001 and using the option Query documents, or using the completions / chat API.

Ingestion troubleshooting

Running out of memory

To do not run out of memory, you should ingest your documents without the LLM loaded in your (video) memory. To do so, you should change your configuration to set llm.mode: mock.

You can also use the existing PGPT_PROFILES=mock that will set the following configuration for you:

1llm:
2  mode: mock
3embedding:
4  mode: local

This configuration allows you to use hardware acceleration for creating embeddings while avoiding loading the full LLM into (video) memory.

Once your documents are ingested, you can set the llm.mode value back to local (or your previous custom value).

Ingestion speed

The ingestion speed depends on the number of documents you are ingesting, and the size of each document. To speed up the ingestion, you can change the ingestion mode in configuration.

The following ingestion mode exist:

  • simple: historic behavior, ingest one document at a time, sequentially
  • batch: read, parse, and embed multiple documents using batches (batch read, and then batch parse, and then batch embed)
  • parallel: read, parse, and embed multiple documents in parallel. This is the fastest ingestion mode for local setup.
  • pipeline: Alternative to parallel. To change the ingestion mode, you can use the embedding.ingest_mode configuration value. The default value is simple.

To configure the number of workers used for parallel or batched ingestion, you can use the embedding.count_workers configuration value. If you set this value too high, you might run out of memory, so be mindful when setting this value. The default value is 2. For batch mode, you can easily set this value to your number of threads available on your CPU without running out of memory. For parallel mode, you should be more careful, and set this value to a lower value.

The configuration below should be enough for users who want to stress more their hardware:

1embedding:
2  ingest_mode: parallel
3  count_workers: 4

If your hardware is powerful enough, and that you are loading heavy documents, you can increase the number of workers. It is recommended to do your own tests to find the optimal value for your hardware.

If you have a bash shell, you can use this set of command to do your own benchmark:

$# Wipe your local data, to put yourself in a clean state
># This will delete all your ingested documents
>make wipe
>
>time PGPT_PROFILES=mock python ./scripts/ingest_folder.py ~/my-dir/to-ingest/

Supported file formats

privateGPT by default supports all the file formats that contains clear text (for example, .txt files, .html, etc.). However, these text based file formats as only considered as text files, and are not pre-processed in any other way.

It also supports the following file formats:

  • .hwp
  • .pdf
  • .docx
  • .pptx
  • .ppt
  • .pptm
  • .jpg
  • .png
  • .jpeg
  • .mp3
  • .mp4
  • .csv
  • .epub
  • .md
  • .mbox
  • .ipynb
  • .json

Please note the following nuance: while privateGPT supports these file formats, it might require additional dependencies to be installed in your python’s virtual environment. For example, if you try to ingest .epub files, privateGPT might fail to do it, and will instead display an explanatory error asking you to download the necessary dependencies to install this file format.

Other file formats might work, but they will be considered as plain text files (in other words, they will be ingested as .txt files).

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