https://github.com/karaketir16/openwebui-langfuse

This repository provides resources and guidelines to facilitate the integration of Open-WebUI and Langfuse, enabling seamless monitoring and management of AI model usage statistics.
https://github.com/karaketir16/openwebui-langfuse

docker docker-compose langfuse llm llm-webui ollama ollama-webui open-webui pipelines

Last synced: 5 months ago
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This repository provides resources and guidelines to facilitate the integration of Open-WebUI and Langfuse, enabling seamless monitoring and management of AI model usage statistics.

• Host: GitHub
• URL: https://github.com/karaketir16/openwebui-langfuse
• Owner: karaketir16
• License: mit
• Created: 2024-06-23T11:18:29.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2024-06-23T11:39:41.000Z (almost 2 years ago)
• Last Synced: 2024-06-23T12:33:56.851Z (almost 2 years ago)
• Topics: docker, docker-compose, langfuse, llm, llm-webui, ollama, ollama-webui, open-webui, pipelines
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.MD
  • License: LICENSE

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README

          
# OLLAMA + OPEN-WEBUI + PIPELINES + LANGFUSE

## Introduction

This repository provides a setup for integrating [OLLAMA](https://github.com/ollama/ollama), [OPEN-WEBUI](https://github.com/open-webui/open-webui), [PIPELINES](https://github.com/open-webui/pipelines/), and [LANGFUSE](https://github.com/langfuse/langfuse) using Docker. Follow the steps below to get everything up and running.

## Prerequisites

- Docker and required GPU drivers installed on your system.

## Installation

1. **Clone this repository:**

   ```bash

   git clone https://github.com/karaketir16/openwebui-langfuse.git

   cd openwebui-langfuse

   ```

2. **Run the setup script:**

   ```bash

   ./run-compose.sh

   ```

   or

   ```bash

   docker compose -f docker-compose.yaml -f langfuse-v3.yaml up -d

   # default driver is nvidia

   ```

## Configuration

### Langfuse Setup

0. **Documentation**

   - You can find up-to-date documentation [here](https://langfuse.com/docs/integrations/openwebui).

1. **Download the `langfuse_filter_pipeline.py` file (only if offline):**

   - If your setup does **not** have internet access:

     - You can manually download the script from:

       `https://github.com/open-webui/pipelines/blob/main/examples/filters/langfuse_filter_pipeline.py`

     - Or use the local copy provided at: `example/langfuse_filter_pipeline.py`

2. **Access Langfuse:**

   - Open your browser and go to `http://localhost:4000`.

3. **Create an Admin Account and Project:**

   - Create an admin account and then create an organization and a project.

   - Go to Project Settings and create an API key.

   - Retrieve the secret key and public key.

### Open-WebUI Setup

1. **Access Open-WebUI:**

   - Open your browser and go to `http://localhost:3000`.

2. **Create an Admin Account:**

   - Create an admin account.

3. **Upload the Pipeline Script:**

   - Go to `Settings -> Admin Settings -> Pipelines`.

   - If online, paste this URL:

     ```

     https://raw.githubusercontent.com/open-webui/pipelines/refs/heads/main/examples/filters/langfuse_filter_pipeline.py

     ```

     into the `Install from Github URL` field and click the download button.

   - If offline or using a custom script, upload `langfuse_filter_pipeline.py` from your local machine via the `Upload Pipeline` section.

4. **Configure the Script:**

   - After uploading the pipeline, edit its configuration in the UI.

   - Replace the placeholder values as follows:

     - `your-secret-key-here` → your **Langfuse secret key**

     - `your-public-key-here` → your **Langfuse public key**

     - `https://cloud.langfuse.com` → `http://langfuse-web:4000` (**local address**)

5. **Monitor Usage:**

   - You can now monitor Open-WebUI usage statistics from Langfuse.

### Model Downloading

1. **Access Open-WebUI:**

   - Open your browser and go to `http://localhost:3000`.

2. **Create an Admin Account:**

   - Create an admin account if you haven’t already.

3. **Pull Models:**

   - Navigate to `Settings -> Admin Settings -> Models`.

   - Enter a model tag to pull from the Ollama library (e.g., `phi3:mini`).

   - Press the pull button.