App: Context Chat — Nextcloud latest Administration Manual latest documentation

# App: Context Chat

Context Chat is an assistant feature that is implemented via an ensemble of two apps:

> - the `context_chat` app, written purely in PHP

> - the `context_chat_backend` ExternalApp written in Python

Together they provide the ContextChat text processing and search tasks accessible via the Nextcloud Assistant app.

The `context_chat` and `context_chat_backend` apps will use the configured text-to-text task processing provider, which is required on a fresh install. It can be configured to run open source models entirely on-premises, see the list of providers here in the “Backend apps” section.

This app supports input and output in the same languages that the currently configured text-to-text task processing provider supports.

## Requirements

- Minimal Nextcloud version: 30

- Nextcloud AIO is supported

- We currently support NVIDIA GPUs and x86_64 CPUs

- CPU that supports AVX and AVX2 instruction

- CUDA >= v12.2 on your host system

- GPU Setup Sizing

  > - A NVIDIA GPU with at least 2GB VRAM

  >     - The requirements for the text-to-text providers should be checked separately for each app here in the “Backend apps” section, as they can vary greatly based on the model used and whether the provider is hosted locally or remotely.

  > - At least 8GB of system RAM

  >     - 2 GB + additional 500MB for each concurrent request made to the backend if configuration parameters are changed

- CPU Setup Sizing

  > - At least 12GB of system RAM

  >     - 2 GB + additional 500MB for each additional concurrent query request

  >     - 8 GB is recommended in the above case for the default settings

  > - This app makes use of the configured text-to-text task processing provider instead of running its own language model by default, thus 4+ cores for the embedding model is needed

- A dedicated machine is recommended

### Space usage

This app employs a bundled DB with Vector support called PostgreSQL. All the users’ textual data is duplicated, chunked and stored on disk in this vector DB along with semantic embedding vectors for the content.

## Installation

1. Make sure the Nextcloud Assistant app is installed

2. Setup a Deploy Daemon in AppAPI Admin settings

3. Install the `context_chat_backend` ExApp via the “Apps” page in Nextcloud, or by executing (checkout the readme at https://github.com/nextcloud/context_chat_backend for manual install steps)

    occ app_api:app:register context_chat_backend

4. Install the `context_chat` app via the “Apps” page in Nextcloud, or by executing

    occ app:enable context_chat

5. Install a text-to-text provider (text generation provider) via the “Apps” page in Nextcloud. A list of providers can be found here in the “Backend apps” section.

6. Optionally but recommended, setup background workers for faster pickup of tasks. See the relevant section in AI Overview for more information.

Note: Both apps need to be installed and both major version and minor version of the two apps must match for the functionality to work (ie. “v1.3.4” and “v1.3.1”; but not “v1.3.4” and “v2.1.6”; and not “v1.3.4” and “v1.4.5”). Keep this in mind when updating.

## Initial loading of data

### Auto-indexing

Context chat will automatically load user data into the Vector DB using asynchronous background jobs.
The initial loading of data can take a long time depending on the number of files and their size.

The indexing jobs are set up to run during the Nextcloud instance’s maintenance window (typically during the night) only. If you have not set a maintenance window, indexing will run 24/7.

You can set up a separate cron job to run every 30 minutes for Context Chat to avoid slowing down normal background job operation on larger instances.
The following command can bypass the maintenance window so it can either be set to run during the day even with a maintenance window set, or it can be set to run during the weekends 24/7 to speed up the indexing process.

    php cron.php "OCA\\ContextChat\\BackgroundJobs\\IndexerJob" "OCA\\ContextChat\\BackgroundJobs\\ActionJob" "OCA\\ContextChat\\BackgroundJobs\\SubmitContentJob" "OCA\\ContextChat\\BackgroundJobs\\StorageCrawlJob" "OCA\\ContextChat\\BackgroundJobs\\InitialContentImportJob"

### Synchronous indexing

To index all the files synchronously, use the following command:
Note: This does not interact with the auto-indexing feature and that list would remain unchanged. However, the indexed files would be skipped when the auto indexer runs.

    occ context_chat:scan <user_id>

Note: The synchronous command could take several days to complete. On larger systems we thus recommend to use auto-indexing.

## Scaling

Listed below are the major parts of the system that can be scaled independently to improve performance:

1. The text-to-text task processing provider (from among the list of providers here in the “Backend apps” section)

   The text-to-text task processing provider can be scaled by using a hosted service using the OpenAI and LocalAI integration (via OpenAI API) like OpenAI or by hosting your own model on powerful hardware.

2. The vector DB performance

   The vector DB performance can be scaled by using a dedicated or cluster setup for PostgreSQL with the pgvector extension.
   The connection string of the external vector DB can be set using the environment variable `EXTERNAL_DB` during deployment in the “Deploy Options”.

3. The embedding model performance

   The embedding model performance can be scaled by using a hosted embedding service, locally or remotely hosted. It should be able to serve an OpenAI-compatible API.
   The embedding service URL can be set using the environment variable `CC_EM_BASE_URL` during deployment in the “Deploy Options”. Other options like the model name, api key, or username and password can be set using the environment variables `CC_EM_MODEL_NAME`, `CC_EM_API_KEY`, `CC_EM_USERNAME`, and `CC_EM_PASSWORD` respectively.

One part of the system that cannot be scaled yet is the parsing of the documents to extract text. This is currently done in a single instance of the `context_chat_backend` ExApp. It is a CPU-bound task so having a powerful CPU will help speed up the parsing process.

If `context_chat_backend` is already deployed, you can change these environment variables by redeploying it with the new values.

1. Go to Apps page -> search for “Context Chat Backend”

2. Disable and remove the app taking care the data is not removed

3. Set the “Deploy Options” with the new environment variables

4. Reinstall the app

## App store

You can also find the `context_chat` app in our app store, where you can write a review: https://apps.nextcloud.com/apps/context_chat

## Repository

You can find the app’s code repository on GitHub where you can report bugs and contribute fixes and features: https://github.com/nextcloud/context_chat and https://github.com/nextcloud/context_chat_backend

Nextcloud customers should file bugs directly with our Customer Support.

## Commands (OCC)

The options for each command can be found like this, using scan as example: `context_chat:scan --help`

- `context_chat:prompt`

    Ask a question about your data, with options for selective context.

- `context_chat:search`

    Perform a semantic (vector DB based) search on your indexed documents, with options for selective context.

- `context_chat:scan`

    Scan and index the user’s documents based on the user ID provided, synchronously.

- `context_chat:stats`
    Shows the time taken to complete the initial indexing of the documents if it has finished,
    and the current no. of items in the indexer and actions queue.
    “Actions” refers to tasks like file deletions, ownership changes through share changes, etc.
    These file and ownership changes are synced with the backed through this actions queue.

## Configuration Options

- `auto_indexing` boolean (default: true)

    To allow/disallow the IndexerJob from running in the background

    occ config:app:set context_chat auto_indexing --value=true --type=boolean

## Logs

Logs for both the `context_chat` PHP app and the `context_chat_backend` ExApp can be found in the admin settings of your Nextcloud GUI as well as in the Context Chat log file, which is usually located in the Nextcloud data directory. The log file is named `context_chat.log`.

The internal PostgreSQL Vector DB logs (in case of non-Kubernetes setups) can be found inside the docker container at `/nc_app_context_chat_backend_data/vector_db_data/pgsql/logfile`. It can be copied to host using `docker cp nc_app_context_chat_backend:/nc_app_context_chat_backend_data/vector_db_data/pgsql/logfile /tmp/vectordb-logfile`.
This might be needed when the automated setting up of the vector db fails with something like: “pg_ctl: could not start server”.

## Troubleshooting

1. If the docker container seems to suddenly restart during indexing or querying, it could be related to RAM/storage filling up, or AVX being unavailable on the system. AVX can be checked using `grep -i avx /proc/cpuinfo` command on the host system. If AVX is not available, the app will not work.

2. Look for issues in the diagnostic logs, the server logs and the docker container `nc_app_context_chat_container` logs. If unsure, open an issue in either of the repositories.

3. Check “Admin settings -> Context Chat” for statistics and information about the indexing process.

## File access control rules not supported

In Nextcloud you can set up file access control rules using the [files_accesscontrol](https://apps.nextcloud.com/apps/files_accesscontrol) app to restrict access to certain files.

Context Chat does not follow these rules

It is thus possible for users who have been denied access to a document via the files_accesscontrol app to still gain access via Context Chat if the document is visible in the files app for the user in question.

## Known Limitations

- Language models are likely to generate false information and should thus only be used in situations that are not critical. It’s recommended to only use AI at the beginning of a creation process and not at the end, so that outputs of AI serve as a draft for example and not as final product. Always check the output of language models before using it and make sure whether it meets your use-case’s quality requirements.

- Customer support is available upon request, however we can’t solve false or problematic output, most performance issues, or other problems caused by the underlying model. Support is thus limited only to bugs directly caused by the implementation of the app (connectors, API, front-end, AppAPI).

- Files larger than 100MB are not supported

- Password protected PDFs or any other files are not supported. There will be error logs mentioning cryptography and AES in the docker container when such files are encountered but it is nothing to worry about, they will be simply ignored and the system will continue to function normally.

- Podman and Kubernetes are currently not supported for the Context Chat Backend ExApp.

- External storages (through `files_external`) may not work as well as the local storage.

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