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 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
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"
Run the init script (one-time):
bash ~/.openclaw/skills/openviking-mcp/scripts/init.sh
This will:
1. Clone OpenViking from https://github.com/volcengine/OpenViking
2. Install dependencies with uv sync
3. Create ov.conf template
4. 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
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
claude mcp add --transport http openviking http://localhost:2033/mcp
Or add to ~/.mcp.json:
{
"mcpServers": {
"openviking": {
"type": "http",
"url": "http://localhost:2033/mcp"
}
}
}
| Tool | Description |
|---|---|
query |
Full RAG pipeline — search + LLM answer |
search |
Semantic search only, returns docs |
add_resource |
Add files, directories, or URLs |
Once connected via MCP:
"Query: What is OpenViking?"
"Search: machine learning papers"
"Add https://example.com/article to knowledge base"
"Add ~/documents/report.pdf"
| 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 |
ov.conf - Configuration (API keys, models)小蔥技能7w4.net持續更新中。
data/ - Vector database storageserver.py - MCP server implementation這個 Skill 質量較好,安裝設定有中文說明,步驟清晰易懂,初始化指令碼能自動完成大部分工作。風險提示比較充分,使用起來比較放心。不足之處是示例和案例偏少,新手可能需要額外查閱資料才能快速上手。