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https://github.com/khoj-ai/khoj.git
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69ef6829c1
- Integrate with Ollama or other openai compatible APIs by simply setting `OPENAI_API_BASE' environment variable in docker-compose etc. - Update docs on integrating with Ollama, openai proxies on first run - Auto populate all chat models supported by openai compatible APIs - Auto set vision enabled for all commercial models - Minor - Add huggingface cache to khoj_models volume. This is where chat models and (now) sentence transformer models are stored by default - Reduce verbosity of yarn install of web app. Otherwise hit docker log size limit & stops showing remaining logs after web app install - Suggest `ollama pull <model_name>` to start it in background
78 lines
3.5 KiB
Text
78 lines
3.5 KiB
Text
# Ollama
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```mdx-code-block
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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```
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:::info
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This is only helpful for self-hosted users. If you're using [Khoj Cloud](https://app.khoj.dev), you can use our first-party supported models.
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:::
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:::info
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Khoj can directly run local LLMs [available on HuggingFace in GGUF format](https://huggingface.co/models?library=gguf). The integration with Ollama is useful to run Khoj on Docker and have the chat models use your GPU or to try new models via CLI.
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:::
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Ollama allows you to run [many popular open-source LLMs](https://ollama.com/library) locally from your terminal.
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For folks comfortable with the terminal, Ollama's terminal based flows can ease setup and management of chat models.
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Ollama exposes a local [OpenAI API compatible server](https://github.com/ollama/ollama/blob/main/docs/openai.md#models). This makes it possible to use chat models from Ollama with Khoj.
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## Setup
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:::info
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Restart your Khoj server after first run or update to the settings below to ensure all settings are applied correctly.
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:::
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<Tabs groupId="type" queryString>
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<TabItem value="first-run" label="First Run">
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<Tabs groupId="server" queryString>
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<TabItem value="docker" label="Docker">
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1. Setup Ollama: https://ollama.com/
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2. Download your preferred chat model with Ollama. For example,
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```bash
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ollama pull llama3.1
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```
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3. Uncomment `OPENAI_API_BASE` environment variable in your downloaded Khoj [docker-compose.yml](https://github.com/khoj-ai/khoj/blob/master/docker-compose.yml#:~:text=OPENAI_API_BASE)
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4. Start Khoj docker for the first time to automatically integrate and load models from the Ollama running on your host machine
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```bash
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# run below command in the directory where you downloaded the Khoj docker-compose.yml
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docker-compose up
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```
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</TabItem>
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<TabItem value="pip" label="Pip">
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1. Setup Ollama: https://ollama.com/
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2. Download your preferred chat model with Ollama. For example,
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```bash
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ollama pull llama3.1
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```
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3. Set `OPENAI_API_BASE` environment variable to `http://localhost:11434/v1` in your shell before starting Khoj for the first time
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```bash
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export OPENAI_API_BASE="http://localhost:11434/v1"
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khoj --anonymous-mode
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```
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</TabItem>
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</Tabs>
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</TabItem>
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<TabItem value="update" label="Update">
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1. Setup Ollama: https://ollama.com/
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2. Download your preferred chat model with Ollama. For example,
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```bash
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ollama pull llama3.1
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```
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3. Create a new [OpenAI Processor Conversation Config](http://localhost:42110/server/admin/database/openaiprocessorconversationconfig/add) on your Khoj admin panel
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- Name: `ollama`
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- Api Key: `any string`
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- Api Base Url: `http://localhost:11434/v1/` (default for Ollama)
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4. Create a new [Chat Model Option](http://localhost:42110/server/admin/database/chatmodeloptions/add) on your Khoj admin panel.
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- Name: `llama3.1` (replace with the name of your local model)
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- Model Type: `Openai`
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- Openai Config: `<the ollama config you created in step 3>`
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- Max prompt size: `20000` (replace with the max prompt size of your model)
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5. Go to [your config](http://localhost:42110/settings) and select the model you just created in the chat model dropdown.
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If you want to add additional models running on Ollama, repeat step 4 for each model.
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</TabItem>
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</Tabs>
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That's it! You should now be able to chat with your Ollama model from Khoj.
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