1. **Privacy**: Your data will never have to leave your private network. You can even use Khoj without an internet connection if deployed on your personal computer.
2. **Customization**: You can customize Khoj to your liking, from models, to host URL, to feature enablement.
If you want to use the offline chat model and you have a GPU, you should use Installation Option 2 - local setup via the Python package directly. Our Docker image doesn't currently support running the offline chat model on GPU, making inference times really slow.
Use the sample docker-compose [in Github](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml) to run Khoj in Docker. Start by configuring all the environment variables to your choosing. Your admin account will automatically be created based on the admin credentials in that file, so pay attention to those. To start the container, run the following command in the same directory as the docker-compose.yml file. This will automatically setup the database and run the Khoj server.
```shell
docker-compose up
```
Khoj should now be running at http://localhost:42110. You can see the web UI in your browser.
1. Use the [recommended installer](https://www.postgresql.org/download/windows/).
2. Follow instructions to [Install PgVector](https://github.com/pgvector/pgvector#windows) in case you need to manually install it. Windows support is experimental for pgvector currently, so we recommend using Docker.
</TabItem>
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From [official instructions](https://wiki.postgresql.org/wiki/Apt)
</TabItem>
<TabItem value="source" label="From Source">
1. Follow instructions to [Install Postgres](https://www.postgresql.org/download/)
2. Follow instructions to [Install PgVector](https://github.com/pgvector/pgvector#installation) in case you need to manually install it.
Make sure to update your environment variables to match your Postgres configuration if you're using a different name. The default values should work for most people. When prompted for a password, you can use the default password `postgres`, or configure it to your preference. Make sure to set the environment variable `POSTGRES_PASSWORD` to the same value as the password you set here.
- Check [llama-cpp-python setup](https://python.langchain.com/docs/integrations/llms/llamacpp#installation) if you hit any llama-cpp issues with the installation
`--anonymous-mode` allows you to run the server without setting up Google credentials for login. This allows you to use any of the clients without a login wall. If you want to use Google login, you can skip this flag, but you will have to add your Google developer credentials.
On the first run, you will be prompted to input credentials for your admin account and do some basic configuration for your chat model settings. Once created, you can go to http://localhost:42110/server/admin and login with the credentials you just created.
Note: To start Khoj automatically in the background use [Task scheduler](https://www.windowscentral.com/how-create-automated-task-using-task-scheduler-windows-10) on Windows or [Cron](https://en.wikipedia.org/wiki/Cron) on Mac, Linux (e.g with `@reboot khoj`)
You can use Khoj with a custom domain as well. To do so, you need to set the `KHOJ_DOMAIN` environment variable to your domain (e.g., `export KHOJ_DOMAIN=my-khoj-domain.com` or add it to your `docker-compose.yml`). By default, the Khoj server you set up will not be accessible outside of `localhost` or `127.0.0.1`.
To expose Khoj on a custom domain over the public internet, use of an SSL certificate is strongly recommended. You can use [Let's Encrypt](https://letsencrypt.org/) to get a free SSL certificate for your domain.
To disable HTTPS, set the `KHOJ_NO_HTTPS` environment variable to `True`. This can be useful if Khoj is only accessible behind a secure, private network.
##### Configure OpenAI or a custom OpenAI-compatible proxy server
1. Go to the [OpenAI settings](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/) in the server admin settings to add an OpenAI processor conversation config. This is where you set your API key and server API base URL. The API base URL is optional - it's only relevant if you're using another OpenAI-compatible proxy server.
2. Go over to configure your [chat model options](http://localhost:42110/server/admin/database/chatmodeloptions/). Set the `chat-model` field to a supported chat model[^1] of your choice. For example, you can specify `gpt-4-turbo-preview` if you're using OpenAI.
- Make sure to set the `model-type` field to `OpenAI`.
- The `tokenizer` and `max-prompt-size` fields are optional. Set them only if you're sure of the tokenizer or token limit for the model you're using. Contact us if you're unsure what to do here.
##### Configure Offline Chat
1. No need to setup a conversation processor config!
2. Go over to configure your [chat model options](http://localhost:42110/server/admin/database/chatmodeloptions/). Set the `chat-model` field to a supported chat model[^1] of your choice. For example, we recommend `NousResearch/Hermes-2-Pro-Mistral-7B-GGUF`, but [any gguf model on huggingface](https://huggingface.co/models?library=gguf) should work.
- Make sure to set the `model-type` to `Offline`. Do not set `openai config`.
- The `tokenizer` and `max-prompt-size` fields are optional. Set them only when using a non-standard model (i.e not mistral, gpt or llama2 model) when you know the token limit.
1. Select files and folders to index [using the desktop client](/get-started/setup#2-download-the-desktop-client). When you click 'Save', the files will be sent to your server for indexing.
[^1]: Khoj, by default, can use [OpenAI GPT3.5+ chat models](https://platform.openai.com/docs/models/overview) or [GGUF chat models](https://huggingface.co/models?library=gguf). See [this section](/miscellaneous/advanced#use-openai-compatible-llm-api-server-self-hosting) on how to locally use OpenAI-format compatible proxy servers.
You can use our desktop executables to select file paths and folders to index. You can simply select the folders or files, and they'll be automatically uploaded to the server. Once you specify a file or file path, you don't need to update the configuration again; it will grab any data diffs dynamically over time.
**To download the latest desktop client, go to https://download.khoj.dev** and the correct executable for your OS will automatically start downloading. You can also go to https://khoj.dev/downloads to explicitly download your image of choice. Once downloaded, you can configure your folders for indexing using the settings tab. To set your chat configuration, you'll have to use the web interface for the Khoj server you setup in the previous step.
To use the desktop client, you need to go to your Khoj server's settings page (http://localhost:42110/config) and copy the API key. Then, paste it into the desktop client's settings page. Once you've done that, you can select files and folders to index. Set the desktop client settings to use `http://127.0.0.1:42110` as the host URL.
To configure your host URL on your clients when self-hosting, use `http://127.0.0.1:42110`. This is the default port for the Khoj server. Note that `localhost` will not work.
From the same directory where you have your `docker-compose` file, run the command below to remove the server to delete its containers, networks, images and volumes.
```shell
docker-compose down --volumes
```
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<TabItem value="emacs" label="Emacs">
Uninstall the khoj Emacs, or desktop client in the standard way from Emacs or your OS respectively
You can also `rm -rf ~/.khoj` to remove the Khoj data directory if did a local install.
</TabItem>
<TabItem value="obsidian" label="Obsidian">
Uninstall the khoj Obisidan, or desktop client in the standard way from Obsidian or your OS respectively
You can also `rm -rf ~/.khoj` to remove the Khoj data directory if did a local install.
#### Dependency conflict when trying to install Khoj python package with pip
- **Reason**: When conflicting dependency versions are required by Khoj vs other python packages installed on your system
- **Fix**: Install Khoj in a python virtual environment using [venv](https://docs.python.org/3/library/venv.html) or [pipx](https://pypa.github.io/pipx) to avoid this dependency conflicts
#### Install fails while building Tokenizer dependency
- **Details**: `pip install khoj-assistant` fails while building the `tokenizers` dependency. Complains about Rust.
- **Fix**: Install Rust to build the tokenizers package. For example on Mac run:
```shell
brew install rustup
rustup-init
source ~/.cargo/env
```
- **Refer**: [Issue with Fix](https://github.com/khoj-ai/khoj/issues/82#issuecomment-1241890946) for more details
#### Khoj in Docker errors out with \"Killed\" in error message
- **Fix**: Increase RAM available to Docker Containers in Docker Settings
- **Refer**: [StackOverflow Solution](https://stackoverflow.com/a/50770267), [Configure Resources on Docker for Mac](https://docs.docker.com/desktop/mac/#resources)
