---
title: "Linkup 搜尋"
url: "/docs/search/linkup"
canonical_url: "https://docs.litellm.ai/docs/search/linkup"
type: "docs"
last_updated: "2026-10-08"
related:
  - "/docs/search/searxng"
  - "/docs/search/serper"
---
# Linkup 搜尋

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


**取得 API 金鑰：** [https://linkup.so](https://linkup.so)

## LiteLLM Python SDK {#litellm-python-sdk}

```python showLineNumbers title="Linkup Search"
import os
from litellm import search

os.environ["LINKUP_API_KEY"] = "..."

response = search(
    query="latest AI developments",
    search_provider="linkup",
    max_results=5
)
```

## LiteLLM AI 閘道 {#litellm-ai-gateway}

### 1. 設定 config.yaml {#1-setup-configyaml}

```yaml showLineNumbers title="config.yaml"
model_list:
  - model_name: gpt-5.6-terra
    litellm_params:
      model: gpt-5.6-terra
      api_key: os.environ/OPENAI_API_KEY

search_tools:
  - search_tool_name: linkup-search
    litellm_params:
      search_provider: linkup
      api_key: os.environ/LINKUP_API_KEY
```

### 2. 啟動 proxy {#2-start-the-proxy}

```bash
litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000
```

### 3. 測試搜尋端點 {#3-test-the-search-endpoint}

```bash showLineNumbers title="Test Request"
curl http://0.0.0.0:4000/v1/search/linkup-search \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "latest AI developments",
    "max_results": 5
  }'
```

## Provider-specific 參數 {#provider-specific-parameters}

```python showLineNumbers title="Linkup Search with Provider-specific Parameters"
import os
from litellm import search

os.environ["LINKUP_API_KEY"] = "..."

response = search(
    query="machine learning research",
    search_provider="linkup",
    max_results=10,
    # Linkup-specific parameters
    depth="deep",                      # "standard" (faster) or "deep" (more comprehensive)
    outputType="searchResults",        # "searchResults", "sourcedAnswer", or "structured"
    includeSources=True,               # Include sources in response
    includeImages=True,                # Include images in results
    fromDate="2024-01-01",             # Start date filter (YYYY-MM-DD)
    toDate="2024-12-31",               # End date filter (YYYY-MM-DD)
    includeDomains=["arxiv.org", "nature.com"],  # Domains to search (max 100)
    excludeDomains=["wikipedia.com"],  # Domains to exclude
    includeInlineCitations=True,       # Include inline citations in sourcedAnswer
)
```

## 功能 {#features}

Linkup 提供強大的網頁搜尋與內容擷取能力：

### 搜尋深度 {#search-depth}
控制您搜尋的精確度與速度：
- `standard` - 回傳結果較快
- `deep` - 耗時較長，但回傳更完整的結果

### 輸出類型 {#output-types}
選擇結果的格式：
- `searchResults` - 回傳包含 URL 與內容的搜尋結果清單
- `sourcedAnswer` - 回傳附帶來源的 AI 生成答案
- `structured` - 以自訂 JSON schema 格式回傳結果

### 日期篩選 {#date-filtering}
依日期範圍篩選結果：
```python
response = search(
    query="AI developments",
    search_provider="linkup",
    fromDate="2024-06-01",
    toDate="2024-12-31"
)
```

### 網域篩選 {#domain-filtering}
包含或排除特定網域：
```python
response = search(
    query="research papers",
    search_provider="linkup",
    includeDomains=["arxiv.org", "nature.com", "ieee.org"],
    excludeDomains=["wikipedia.com"]
)
```

### 結構化輸出 {#structured-output}
以自訂 JSON schema 格式取得結果：
```python
response = search(
    query="Microsoft 2024 revenue",
    search_provider="linkup",
    outputType="structured",
    structuredOutputSchema='{"type": "object", "properties": {"revenue": {"type": "string"}, "year": {"type": "string"}}}'
)
```

## 回應格式 {#response-format}

Linkup 會以以下格式回傳結果：

```json
{
  "results": [
    {
      "type": "text",
      "name": "Microsoft 2024 Annual Report",
      "url": "https://www.microsoft.com/investor/reports/ar24/index.html",
      "content": "Highlights from fiscal year 2024..."
    }
  ]
}
```

LiteLLM 會將其轉換為標準的 `SearchResponse` 格式：
- `results[].name` → `SearchResult.title`
- `results[].url` → `SearchResult.url`
- `results[].content` → `SearchResult.snippet`

## Related pages

- [SearXNG 搜尋](https://docs.litellm.ai/docs/search/searxng.md)
- [Serper 搜尋](https://docs.litellm.ai/docs/search/serper.md)