Ollama Web Search：让 AI 模型实时联网搜索

export OLLAMA_API_KEY="your_api_key"

curl https://ollama.com/api/web_search \
  --header "Authorization: Bearer $OLLAMA_API_KEY" \
  -d '{
    "query": "what is ollama?"
  }'

{
  "results": [
    {
      "title": "Ollama",
      "url": "https://ollama.com/",
      "content": "Cloud models are now available..."
    },
    {
      "title": "What is Ollama? Introduction to the AI model management tool",
      "url": "https://www.hostinger.com/tutorials/what-is-ollama",
      "content": "Ariffud M. 6min Read..."
    }
  ]
}

pip install 'ollama>=0.6.0'

import ollama

response = ollama.web_search("What is Ollama?")
print(response)

results = [
    {
        "title": "Ollama",
        "url": "https://ollama.com/",
        "content": "Cloud models are now available in Ollama..."
    },
    {
        "title": "What is Ollama? Features, Pricing, and Use Cases - Walturn",
        "url": "https://www.walturn.com/insights/what-is-ollama-features-pricing-and-use-cases",
        "content": "Our services..."
    }
]

npm install 'ollama@>=0.6.0'

import { Ollama } from "ollama";

const client = new Ollama();
const results = await client.webSearch({ query: "what is ollama?" });
console.log(JSON.stringify(results, null, 2));

ollama pull qwen3:4b

from ollama import chat, web_fetch, web_search

# 定义可用工具
available_tools = {'web_search': web_search, 'web_fetch': web_fetch}

# 用户问题
messages = [{'role': 'user', 'content': "what is ollama's new engine"}]

while True:
    # 调用模型
    response = chat(
        model='qwen3:4b',
        messages=messages,
        tools=[web_search, web_fetch],
        think=True  # 启用思考过程
    )
    
    # 输出思考过程
    if response.message.thinking:
        print('Thinking: ', response.message.thinking)
    
    # 输出回答内容
    if response.message.content:
        print('Content: ', response.message.content)
    
    messages.append(response.message)
    
    # 处理工具调用
    if response.message.tool_calls:
        print('Tool calls: ', response.message.tool_calls)
        for tool_call in response.message.tool_calls:
            function_to_call = available_tools.get(tool_call.function.name)
            if function_to_call:
                args = tool_call.function.arguments
                result = function_to_call(**args)
                print('Result: ', str(result)[:200]+'...')
                # 将结果添加到消息历史（限制长度以适应上下文）
                messages.append({
                    'role': 'tool', 
                    'content': str(result)[:2000 * 4], 
                    'tool_name': tool_call.function.name
                })
            else:
                messages.append({
                    'role': 'tool', 
                    'content': f'Tool {tool_call.function.name} not found', 
                    'tool_name': tool_call.function.name
                })
    else:
        break

Thinking: Okay, the user is asking about Ollama's new engine. I need to figure out what they're referring to...

Tool calls: [ToolCall(function=Function(name='web_search', arguments={'max_results': 3, 'query': 'Ollama new engine'}))]

Result: results=[WebSearchResult(content='# New model scheduling\n\n## September 23, 2025\n\nOllama now includes a significantly improved model scheduling system...

Content: Ollama has introduced two key updates to its engine, both released in 2025:

1. **Enhanced Model Scheduling (September 23, 2025)**
   - **Precision Memory Management**: Exact memory allocation reduces out-of-memory crashes
   - **Performance Gains**: Significant speed improvements (e.g., 85.54 tokens/s vs 52.02 tokens/s)
   - **Multi-GPU Support**: Improved efficiency across multiple GPUs
   - **Supported Models**: Includes gemma3, llama4, qwen3, mistral-small3.2, and more

2. **Multimodal Engine (May 15, 2025)**
   - **Vision Support**: First-class support for vision models
   - **Multimodal Tasks**: Image identification, video analysis, document scanning

from ollama import web_fetch

result = web_fetch('https://ollama.com')
print(result)

WebFetchResponse(
    title='Ollama',
    content='[Cloud models](https://ollama.com/blog/cloud-models) are now available in Ollama\n\n**Chat & build with open models**...',
    links=['https://ollama.com/', 'https://ollama.com/models', 'https://github.com/ollama/ollama']
)

import { Ollama } from "ollama";

const client = new Ollama();
const fetchResult = await client.webFetch({ url: "https://ollama.com" });
console.log(JSON.stringify(fetchResult, null, 2));

curl --request POST \
  --url https://ollama.com/api/web_fetch \
  --header "Authorization: Bearer $OLLAMA_API_KEY" \
  --header 'Content-Type: application/json' \
  --data '{
      "url": "ollama.com"
  }'

{
  "mcpServers": {
    "web_search_and_fetch": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "path/to/web-search-mcp.py"],
      "env": { "OLLAMA_API_KEY": "your_api_key_here" }
    }
  }
}

[mcp_servers.web_search]
command = "uv"
args = ["run", "path/to/web-search-mcp.py"]
env = { "OLLAMA_API_KEY" = "your_api_key_here" }

response = ollama.web_search(
    query="what is ollama?",
    max_results=5  # 默认 5，最大 10
)

tools = [web_search, web_fetch]
response = chat(
    model='qwen3:4b',
    messages=messages,
    tools=tools
)

{
  "results": [
    {
      "title": "网页标题",
      "url": "网页 URL",
      "content": "相关内容片段"
    }
  ]
}

{
  "title": "网页标题",
  "content": "网页内容（Markdown 格式）",
  "links": ["链接1", "链接2", "链接3"]
}

messages = [{'role': 'user', 'content': "最新的 Spring Boot 版本有什么新特性？"}]
response = chat(model='qwen3:4b', messages=messages, tools=[web_search])

# 获取网页内容
doc_content = web_fetch('https://docs.spring.io/spring-boot/index.html')

# 基于文档内容回答问题
messages = [
    {'role': 'system', 'content': f'参考文档：{doc_content.content}'},
    {'role': 'user', 'content': '如何配置数据源？'}
]
response = chat(model='qwen3:4b', messages=messages)