openviking
通过 OpenViking 上下文数据库 MCP 服务器进行 RAG 和语义搜索。查询文档、搜索知识库、将文件/URL 添加到向量内存。用于文档问答、知识管理、AI 代理记忆、文件搜索、语义检索。触发“openviking”、“搜索文档”、“语义搜索”、“知识库”、“矢量数据库”、“RAG”、“查询 pdf”、“文档查询”、“添加资源”。
安装 / 下载方式
TotalClaw CLI推荐
totalclaw install totalclaw:totalclaw~zaynjarvis-openvikingcURL直接下载,无需登录
curl -fsSL https://skills.taituai.com/api/skills/totalclaw%3Atotalclaw~zaynjarvis-openviking/file -o zaynjarvis-openviking.md## 概述(中文)
通过 OpenViking 上下文数据库 MCP 服务器进行 RAG 和语义搜索。查询文档、搜索知识库、将文件/URL 添加到向量内存。用于文档问答、知识管理、AI 代理记忆、文件搜索、语义检索。触发“openviking”、“搜索文档”、“语义搜索”、“知识库”、“矢量数据库”、“RAG”、“查询 pdf”、“文档查询”、“添加资源”。
## 原文
# 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?
```bash
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
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
## Start the Server
```bash
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
```bash
claude mcp add --transport http openviking http://localhost:2033/mcp
```
Or add to `~/.mcp.json`:
```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