OpenViking MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@OpenViking MCPsearch the knowledge base for Vestas grounding technical specifications"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
OpenViking MCP
OpenViking MCP Server - 为AI助手提供OpenViking知识库访问能力
功能
语义搜索 - 基于向量嵌入的智能检索
文档读取 - 读取PDF、MD等文档内容
模式匹配 - 使用glob模式搜索文件
摘要/概览 - 获取L0摘要和L1概览
全文搜索 - 关键字全文检索
Related MCP server: RAG Documentation MCP Server
快速开始
1. 安装依赖
pip install openviking mcp2. 配置Ollama
确保Ollama服务运行中,并安装嵌入模型:
ollama serve
ollama pull qwen3-embedding:0.6b3. 配置OpenCode
编辑 C:\Users\fenci\.config\opencode\opencode.json:
{
"mcp": {
"openviking": {
"type": "local",
"command": ["python", "path\\to\\openviking_mcp_server.py"],
"environment": {
"OPENVIKING_DATA_PATH": "path\\to\\viking_data",
"OPENVIKING_CONFIG_FILE": "path\\to\\ov.conf"
},
"enabled": true,
"timeout": 20000
}
}
}4. 使用
在OpenCode中直接搜索:
使用 openviking 搜索"维斯塔斯接地"项目结构
openviking-mcp/
├── openviking_mcp_server.py # MCP服务器实现
├── OPENVIKING_MCP_MANUAL.md # 详细文档
├── example_usage.py # 使用示例
├── ov.conf # 配置文件示例
├── viking_data/ # 数据目录(可选)
└── docs/ # 文档目录(可选)可用工具
工具 | 功能 |
| 语义搜索 |
| 读取内容 |
| 模式匹配 |
| L0摘要 |
| L1概览 |
| 全文搜索 |
文档
详细使用说明请参考 OPENVIKING_MCP_MANUAL.md
相关链接
版本
OpenViking: 0.1.17
MCP SDK: 1.25.0
Python: 3.13+
许可证
MIT License
This server cannot be deployed
Maintenance
Related MCP Connectors
Versioned documentation registry and semantic search for AI tools and coding assistants.
Personal wiki and memory layer for AI assistants. Persistent, structured memory across sessions.
Search your knowledge bases from any AI assistant using hybrid RAG.
Search and reason over your Obsidian-style Markdown vault, right from ChatGPT.
Related MCP Servers
- AlicenseNot gradedqualityFmaintenanceEnables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.6 npm64MIT
- AlicenseAqualityDmaintenanceProvides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.76 npm1MIT
- AlicenseNot gradedqualityCmaintenanceProvides AI assistants with semantic search and read access to local files and directories, enabling knowledge retrieval from indexed content.8 npm17MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI assistants with direct access to local Markdown documentation libraries, enabling them to list, read, and search through docs on demand.MIT