OpenViking

OpenViking 是面向 AI Agent 的上下文数据库，本 Skill 提供初始化脚本与 MCP 服务器使用说明：用 query 完成检索增强问答、search 做语义检索、add_resource 把文件、目录或 URL 加入向量记忆。依赖 Volcengine Ark 的 embedding 与 VLM API Key，服务器默认监听本地 2033 端口。

zaynjarvis

热度 74.34

最近更新 2026-05-11 15:57:11

分类：知识依赖：MCP 服务 / 第三方 API / 读写文件 / 代码执行应用场景：研究 / 笔记整理 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add openviking-skill

Metadata

name: openviking
description: RAG and semantic search via OpenViking Context Database MCP server. Query documents, search knowledge base, add files/URLs to vector memory. Use for document Q&A, knowledge management, AI agent memory, file search, semantic retrieval. Triggers on "openviking", "search documents", "semantic search", "knowledge base", "vector database", "RAG", "query pdf", "document query", "add resource".

OpenViking - Context Database for AI Agents

OpenViking is ByteDance's open-source Context Database designed for AI Agents — a next-generation RAG system that replaces flat vector storage with a filesystem paradigm for managing memories, resources, and skills.

Key Features:

Filesystem paradigm: Organize context like files with URIs (viking://resources/...)
Tiered context (L0/L1/L2): Abstract → Overview → Full content, loaded on demand
Directory recursive retrieval: Better accuracy than flat vector search
MCP server included: Full RAG pipeline via Model Context Protocol

Quick Check: Is It Set Up?

test -f ~/code/openviking/examples/mcp-query/ov.conf && echo "Ready" || echo "Needs setup"
curl -s http://localhost:2033/mcp && echo "Running" || echo "Not running"

If Not Set Up → Initialize

Run the init script (one-time):

bash ~/.openclaw/skills/openviking-mcp/scripts/init.sh

This will:

Clone OpenViking from https://github.com/volcengine/OpenViking
Install dependencies with uv sync
Create ov.conf template
Pause for you to add API keys (embedding.dense.api_key, vlm.api_key)

Required: Volcengine/Ark API Keys

Config Key	Purpose
`embedding.dense.api_key`	Semantic search embeddings
`vlm.api_key`	LLM for answer generation

Get keys from: https://console.volcengine.com/ark

Start the Server

cd ~/code/openviking/examples/mcp-query
uv run server.py

Options:

--port 2033 - Listen port
--host 127.0.0.1 - Bind address
--data ./data - Data directory

Server will be at: http://127.0.0.1:2033/mcp

Connect to Claude

claude mcp add --transport http openviking http://localhost:2033/mcp

Or add to ~/.mcp.json:

{
  "mcpServers": {
    "openviking": {
      "type": "http",
      "url": "http://localhost:2033/mcp"
    }
  }
}

Tools Available

Tool	Description
`query`	Full RAG pipeline — search + LLM answer
`search`	Semantic search only, returns docs
`add_resource`	Add files, directories, or URLs

Example Usage

Once connected via MCP:

"Query: What is OpenViking?"
"Search: machine learning papers"
"Add https://example.com/article to knowledge base"
"Add ~/documents/report.pdf"

Troubleshooting

Issue	Fix
Port in use	`uv run server.py --port 2034`
Auth errors	Check API keys in ov.conf
Server not found	Ensure it's running: `curl localhost:2033/mcp`

Files

ov.conf - Configuration (API keys, models)
data/ - Vector database storage
server.py - MCP server implementation