Configuration File

Configuration

OpenViking uses a JSON configuration file (~/.openviking/ov.conf) for settings.

Configuration File

Create ~/.openviking/ov.conf in your project directory:

{
  "embedding": {
    "dense": {
      "provider": "volcengine",
      "api_key": "your-api-key",
      "model": "doubao-embedding-vision-250615",
      "dimension": 1024
    }
  },
  "vlm": {
    "provider": "volcengine",
    "api_key": "your-api-key",
    "model": "doubao-seed-1-8-251228"
  },
  "rerank": {
    "provider": "volcengine",
    "api_key": "your-api-key",
    "model": "doubao-rerank-250615"
  },
  "storage": {
    "agfs": {
      "backend": "local",
      "path": "./data"
    }
  }
}

Configuration Examples

{
  "embedding": {
    "dense": {
      "api_base" : "https://ark.cn-beijing.volces.com/api/v3",
      "api_key"  : "your-volcengine-api-key",
      "provider" : "volcengine",
      "dimension": 1024,
      "model"    : "doubao-embedding-vision-250615",
      "input": "multimodal"
    }
  },
  "vlm": {
    "api_base" : "https://ark.cn-beijing.volces.com/api/v3",
    "api_key"  : "your-volcengine-api-key",
    "provider" : "volcengine",
    "model"    : "doubao-seed-1-8-251228"
  }
}

{
  "embedding": {
    "dense": {
      "api_base" : "https://api.openai.com/v1",
      "api_key"  : "your-openai-api-key",
      "provider" : "openai",
      "dimension": 3072,
      "model"    : "text-embedding-3-large"
    }
  },
  "vlm": {
    "api_base" : "https://api.openai.com/v1",
    "api_key"  : "your-openai-api-key",
    "provider" : "openai",
    "model"    : "gpt-4-vision-preview"
  }
}

Configuration Sections

embedding

Embedding model configuration for vector search, supporting dense, sparse, and hybrid modes.

Dense Embedding

{
  "embedding": {
    "dense": {
      "provider": "volcengine",
      "api_key": "your-api-key",
      "model": "doubao-embedding-vision-250615",
      "dimension": 1024,
      "input": "multimodal"
    }
  }
}

Parameters

Available Models

With input: "multimodal", OpenViking can embed text, images (PNG, JPG, etc.), and mixed content.

Supported providers:

• openai: OpenAI Embedding API
• volcengine: Volcengine Embedding API
• vikingdb: VikingDB Embedding API
• jina: Jina AI Embedding API

vikingdb provider example:

{
  "embedding": {
    "dense": {
      "provider": "vikingdb",
      "model": "bge_large_zh",
      "ak": "your-access-key",
      "sk": "your-secret-key",
      "region": "cn-beijing",
      "dimension": 1024
    }
  }
}

jina provider example:

{
  "embedding": {
    "dense": {
      "provider": "jina",
      "api_key": "jina_xxx",
      "model": "jina-embeddings-v5-text-small",
      "dimension": 1024
    }
  }
}

Available Jina models:

• jina-embeddings-v5-text-small: 677M params, 1024 dim, max seq 32768 (default)
• jina-embeddings-v5-text-nano: 239M params, 768 dim, max seq 8192

Get your API key at https://jina.ai

Local deployment (GGUF/MLX): Jina embedding models are open-weight and available in GGUF and MLX formats on Hugging Face. You can run them locally with any OpenAI-compatible server (e.g. llama.cpp, MLX, vLLM) and point the api_base to your local endpoint:

{
  "embedding": {
    "dense": {
      "provider": "jina",
      "api_key": "local",
      "api_base": "http://localhost:8080/v1",
      "model": "jina-embeddings-v5-text-nano",
      "dimension": 768
    }
  }
}

Sparse Embedding

{
  "embedding": {
    "sparse": {
      "provider": "volcengine",
      "api_key": "your-api-key",
      "model": "bm25-sparse-v1"
    }
  }
}

Hybrid Embedding

Two approaches are supported:

Option 1: Single hybrid model

{
  "embedding": {
    "hybrid": {
      "provider": "volcengine",
      "api_key": "your-api-key",
      "model": "doubao-embedding-hybrid",
      "dimension": 1024
    }
  }
}

Option 2: Combine dense + sparse

{
  "embedding": {
    "dense": {
      "provider": "volcengine",
      "api_key": "your-api-key",
      "model": "doubao-embedding-vision-250615",
      "dimension": 1024
    },
    "sparse": {
      "provider": "volcengine",
      "api_key": "your-api-key",
      "model": "bm25-sparse-v1"
    }
  }
}

vlm

Vision Language Model for semantic extraction (L0/L1 generation).

{
  "vlm": {
    "api_key": "your-api-key",
    "model": "doubao-seed-1-8-251228",
    "base_url": "https://ark.cn-beijing.volces.com/api/v3"
  }
}

Parameters

Available Models

When resources are added, VLM generates:

1. L0 (Abstract): ~100 token summary
2. L1 (Overview): ~2k token overview with navigation

If VLM is not configured, L0/L1 will be generated from content directly (less semantic), and multimodal resources may have limited descriptions.

rerank

Reranking model for search result refinement.

{
  "rerank": {
    "provider": "volcengine",
    "api_key": "your-api-key",
    "model": "doubao-rerank-250615"
  }
}

If rerank is not configured, search uses vector similarity only.

storage

Storage backend configuration.

{
  "storage": {
    "agfs": {
      "backend": "local",
      "path": "./data",
      "timeout": 30.0
    },
    "vectordb": {
      "backend": "local",
      "path": "./data"
    }
  }
}

Config Files

OpenViking uses two config files:

When config files are at the default path, OpenViking loads them automatically — no additional setup needed.

If config files are at a different location, there are two ways to specify:

# Option 1: Environment variable
export OPENVIKING_CONFIG_FILE=/path/to/ov.conf
export OPENVIKING_CLI_CONFIG_FILE=/path/to/ovcli.conf

# Option 2: Command-line argument (serve command only)
python -m openviking serve --config /path/to/ov.conf

ov.conf

The config sections documented above (embedding, vlm, rerank, storage) all belong to ov.conf. SDK embedded mode and server share this file.

ovcli.conf

Config file for the HTTP client (SyncHTTPClient / AsyncHTTPClient) and CLI to connect to a remote server:

{
  "url": "http://localhost:1933",
  "api_key": "your-secret-key",
  "output": "table"
}

See Deployment for details.

server Section

When running OpenViking as an HTTP service, add a server section to ov.conf:

{
  "server": {
    "host": "0.0.0.0",
    "port": 1933,
    "api_key": "your-secret-key",
    "cors_origins": ["*"]
  }
}

For startup and deployment details see Deployment, for authentication see Authentication.

Full Schema

{
  "embedding": {
    "dense": {
      "provider": "volcengine",
      "api_key": "string",
      "model": "string",
      "dimension": 1024,
      "input": "multimodal"
    }
  },
  "vlm": {
    "provider": "string",
    "api_key": "string",
    "model": "string",
    "base_url": "string"
  },
  "rerank": {
    "provider": "volcengine",
    "api_key": "string",
    "model": "string"
  },
  "storage": {
    "agfs": {
      "backend": "local|remote",
      "path": "string",
      "url": "string",
      "timeout": 30.0
    },
    "vectordb": {
      "backend": "local|remote",
      "path": "string",
      "url": "string",
      "project": "string"
    }
  },
  "server": {
    "host": "0.0.0.0",
    "port": 1933,
    "api_key": "string",
    "cors_origins": ["*"]
  }
}

Notes:

• storage.vectordb.sparse_weight controls hybrid (dense + sparse) indexing/search. It only takes effect when you use a hybrid index; set it > 0 to enable sparse signals.

Troubleshooting

API Key Error

Error: Invalid API key

Check your API key is correct and has the required permissions.

Vector Dimension Mismatch

Error: Vector dimension mismatch

Ensure the dimension in config matches the model's output dimension.

VLM Timeout

Error: VLM request timeout

• Check network connectivity
• Increase timeout in config
• Try a smaller model

Rate Limiting

Error: Rate limit exceeded

Volcengine has rate limits. Consider batch processing with delays or upgrading your plan.

Related Documentation

• Volcengine Purchase Guide - API key setup
• API Overview - Client initialization
• Server Deployment - Server configuration
• Context Layers - L0/L1/L2