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Quick Start

docs/en/getting-started/02-quickstart.md

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Quick Start

Get started with OpenViking in 5 minutes.

Prerequisites

Before using OpenViking, ensure your environment meets the following requirements:

  • Python Version: 3.10 or higher
  • Operating System: Linux, macOS, Windows
  • Network Connection: Stable network connection required (for downloading dependencies and accessing model services)

Installation & Startup

OpenViking can be installed via a Python Package to be used as a local library, or you can quickly launch it as an independent service using Docker.

Option 1: Install via pip (As a local library)

bash
pip install openviking --upgrade --force-reinstall

Option 2: Start via Docker (As an independent service)

If you prefer to run OpenViking as a standalone service, Docker is recommended.

  1. Prepare Configuration Directory Create the OpenViking directory on your host and prepare the ov.conf configuration file (see the "Configuration" section below for details). All persistent state — config and workspace data — lives under this single directory:

    bash
    mkdir -p ~/.openviking
touch ~/.openviking/ov.conf

  2. Start with Docker Compose Create a docker-compose.yml file:

    yaml
    services:
  openviking:
    image: ghcr.io/volcengine/openviking:latest
    container_name: openviking
    ports:
      - "1933:1933"
      - "8020:8020"
    volumes:
      - ~/.openviking:/app/.openviking
    restart: unless-stopped

    Then run the following command in the same directory:

    bash
    docker-compose up -d

    By default, the container starts the OpenViking API server on 1933, the Console UI on 8020, and the bundled vikingbot gateway. If you need to disable vikingbot, add either command: ["--without-bot"] or environment: ["OPENVIKING_WITH_BOT=0"].

    On platforms that don't allow bind mounts, set OPENVIKING_CONF_CONTENT to the full config JSON to bootstrap on first start, or docker exec in and run openviking-server init after the container is up. See Deployment Guide for details.

💡 Mac Local Network Access Tip (Connection reset error):

By default, OpenViking only listens to 127.0.0.1 for security reasons. If you are using Docker on a Mac, your host machine may not be able to access it directly via localhost:1933.

Recommended Solution: Use socat for port forwarding (No config changes needed): Override the default startup command in your docker-compose.yml to use socat for internal port forwarding:

yaml
services:
  openviking:
    image: ghcr.io/volcengine/openviking:latest
    ports:
      - "8020:8020"
      - "1933:1934" # Map host 1933 to container 1934
    volumes:
      - ~/.openviking:/app/.openviking
    command: /bin/sh -c "apt-get update && apt-get install -y socat && socat TCP-LISTEN:1934,fork,reuseaddr TCP:127.0.0.1:1933 & openviking-server"

This perfectly solves the access issue for Mac host machines.

Model Preparation

OpenViking requires the following model capabilities:

  • VLM Model: For image and content understanding
  • Embedding Model: For vectorization and semantic retrieval

OpenViking supports multiple model services:

  • Volcengine (Doubao Models): Recommended, cost-effective with good performance, free quota for new users. For purchase and activation, see: Volcengine Purchase Guide
  • OpenAI Models: Supports GPT-4V and other VLM models, plus OpenAI Embedding models
  • OpenAI Codex: Supports Codex as the VLM provider through ChatGPT/Codex OAuth
  • Other Custom Model Services: Supports model services compatible with OpenAI API format

Configuration

Configuration File Template

Recommended first-time setup:

bash
openviking-server init
openviking-server doctor

If you prefer manual setup, create ~/.openviking/ov.conf:

json
{
  "embedding": {
    "dense": {
      "api_base" : "<api-endpoint>",
      "api_key"  : "<your-api-key>",
      "provider" : "<provider-type>",
      "dimension": 1024,
      "model"    : "<model-name>"
    }
  },
  "vlm": {
    "api_base" : "<api-endpoint>",
    "api_key"  : "<your-api-key>",
    "provider" : "<provider-type>",
    "model"    : "<model-name>"
  }
}

provider, model, api_base, and api_key depend on the VLM service you choose. Some providers may use local OAuth state instead of a manually copied API key.

For complete examples for each model provider, see Configuration Guide - Examples.

For first-time setup, openviking-server init is the recommended path. It helps you pick a provider and writes a working config template for the selected setup.

Environment Variables

When the config file is at the default path ~/.openviking/ov.conf, no additional setup is needed — OpenViking loads it automatically.

If the config file is at a different location, specify it via environment variable:

bash
export OPENVIKING_CONFIG_FILE=/path/to/your/ov.conf

Run Your First Example

Create Python Script

Create example.py:

python
import openviking as ov

# Initialize OpenViking client with data directory
client = ov.OpenViking(path="./data")

try:
    # Initialize the client
    client.initialize()

    # Add resource (supports URL, file, or directory)
    # Local directory scans respect .gitignore by default.
    add_result = client.add_resource(
        path="https://raw.githubusercontent.com/volcengine/OpenViking/refs/heads/main/README.md"
    )
    root_uri = add_result['root_uri']

    # Explore the resource tree structure
    ls_result = client.ls(root_uri)
    print(f"Directory structure:\n{ls_result}\n")

    # Use glob to find markdown files
    glob_result = client.glob(pattern="**/*.md", uri=root_uri)
    if glob_result['matches']:
        content = client.read(glob_result['matches'][0])
        print(f"Content preview: {content[:200]}...\n")

    # Wait for semantic processing to complete
    print("Wait for semantic processing...")
    client.wait_processed()

    # Get abstract and overview of the resource
    abstract = client.abstract(root_uri)
    overview = client.overview(root_uri)
    print(f"Abstract:\n{abstract}\n\nOverview:\n{overview}\n")

    # Perform semantic search
    results = client.find("what is openviking", target_uri=root_uri)
    print("Search results:")
    for r in results.resources:
        print(f"  {r.uri} (score: {r.score:.4f})")

    # Close the client
    client.close()

except Exception as e:
    print(f"Error: {e}")

Run the Script

bash
python example.py

Expected Output

Directory structure:
...

Content preview: ...

Wait for semantic processing...
Abstract:
...

Overview:
...

Search results:
  viking://resources/... (score: 0.8523)
  ...

Congratulations! You have successfully run OpenViking.

Server Mode

Want to run OpenViking as a shared service? See Quick Start: Server Mode.

Next Steps