Vane Search

Vane Search API

本地部署的 Vane (Perplexica) AI 搜索引擎，通过 SearxNG 搜索 + Embedding 重排序 + LLM 总结，返回带引用来源的深度回答。

服务地址

• API: http://localhost:3010/api/search (POST)
• Web UI: http://localhost:3010
• 容器名: vane

快速调用

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curl -s -X POST "http://localhost:3010/api/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "你的搜索问题",
    "sources": ["web"],
    "chatModel": {
      "providerId": "00a2a3e5-5949-4938-9842-037ffa97a47a",
      "key": "qwen3.5-plus"
    },
    "embeddingModel": {
      "providerId": "2e10b45d-b8aa-4dfd-8124-c11f86abcba8",
      "key": "text-embedding-v3"
    }
  }'

完整参数说明

模型配置

Chat Model (必填)

当前配置为通义千问 (DashScope OpenAI-compatible API)：

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{
  "providerId": "00a2a3e5-5949-4938-9842-037ffa97a47a",
  "key": "qwen3.5-plus"
}

Embedding Model (必填)

当前配置为 DashScope text-embedding-v3 (中文向量模型)：

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{
  "providerId": "2e10b45d-b8aa-4dfd-8124-c11f86abcba8",
  "key": "text-embedding-v3"
}

sources 搜索来源

可组合使用：["web", "academic"]

optimizationMode 搜索深度

返回格式

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{
  "message": "AI 生成的 Markdown 格式回答（含 [N] 引用标记）",
  "sources": [
    {
      "title": "来源标题",
      "url": "来源URL",
      "content": "来源摘要内容"
    }
  ]
}

使用示例

快速搜索 (speed)

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curl -s -X POST "http://localhost:3010/api/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Claude Code 最新版本是什么",
    "sources": ["web"],
    "chatModel": {"providerId": "00a2a3e5-5949-4938-9842-037ffa97a47a", "key": "qwen3.5-plus"},
    "embeddingModel": {"providerId": "2e10b45d-b8aa-4dfd-8124-c11f86abcba8", "key": "text-embedding-v3"}
  }'

深度调研 (quality)

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curl -s -X POST "http://localhost:3010/api/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "2025年主流 AI Agent 框架对比分析",
    "sources": ["web", "academic"],
    "optimizationMode": "quality",
    "systemInstructions": "用中文回答，重点分析技术架构差异和适用场景",
    "chatModel": {"providerId": "00a2a3e5-5949-4938-9842-037ffa97a47a", "key": "qwen3.5-plus"},
    "embeddingModel": {"providerId": "2e10b45d-b8aa-4dfd-8124-c11f86abcba8", "key": "text-embedding-v3"}
  }'

多轮追问

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curl -s -X POST "http://localhost:3010/api/search" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "其中哪个框架最适合企业级部署？",
    "sources": ["web"],
    "history": [
      {"role": "user", "content": "2025年主流 AI Agent 框架有哪些"},
      {"role": "assistant", "content": "主流框架包括 LangChain、CrewAI、AutoGen..."}
    ],
    "chatModel": {"providerId": "00a2a3e5-5949-4938-9842-037ffa97a47a", "key": "qwen3.5-plus"},
    "embeddingModel": {"providerId": "2e10b45d-b8aa-4dfd-8124-c11f86abcba8", "key": "text-embedding-v3"}
  }'

服务管理

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# 查看状态
docker ps --filter name=vane

# 查看日志
docker logs vane --tail 20

# 重启
docker restart vane

# 停止/启动
docker stop vane
docker start vane