完成如何为搜索结果构建代理上下文桥教程需要多长时间？

大多数开发者在15到30分钟内完成本教程。您需要一个Scavio API密钥（免费套餐即可）和可用的Python或JavaScript环境。

开始前需要准备什么？

Python 3.8+. 请求库. 来自 scavio.dev 的 Scavio API 密钥. 代理框架（LangChain、CrewAI 或自定义）. Scavio API密钥注册即送50个免费积分。

我可以用免费套餐运行本教程吗？

可以。免费套餐注册即送50个积分，完全足够完成本教程并构建一个可运行的原型解决方案。

这支持哪些框架？

Scavio提供原生LangChain包（langchain-scavio）、MCP服务器以及适用于任何HTTP客户端的REST API。本教程使用 the raw REST API, 但您可以根据需要适配您选择的框架。

搜索结果的代理上下文桥 (2026)

多步骤代理工作流程在步骤之间传递搜索结果，但原始 API 响应会在不相关的字段上浪费上下文窗口令牌。上下文桥将搜索结果序列化为每个代理步骤所需的最小结构化 JSON，从而将令牌使用量减少 60-80%，同时保留重要数据。本教程构建了一个桥梁，可以压缩和格式化搜索结果以供代理使用。

前置条件

Python 3.8+
请求库
来自 scavio.dev 的 Scavio API 密钥
代理框架（LangChain、CrewAI 或自定义）

操作指南

步骤 1: 定义上下文压缩策略

为不同的代理上下文窗口大小创建压缩级别。

Python

import os, requests, json

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}

def compress_results(results, level='medium'):
    """Compress search results for agent context windows."""
    if level == 'minimal':
        # ~20 tokens per result (for 4K context models)
        return [{'t': r.get('title', '')[:40], 'u': r.get('link', '').split('/')[2] if r.get('link') else ''}
                for r in results[:3]]
    elif level == 'medium':
        # ~60 tokens per result (for 8K-16K context)
        return [{'title': r.get('title', '')[:60], 'domain': r.get('link', '').split('/')[2] if r.get('link') else '',
                 'snippet': r.get('snippet', '')[:100]} for r in results[:5]]
    else:  # full
        # ~120 tokens per result (for 32K+ context)
        return [{'title': r.get('title', ''), 'link': r.get('link', ''),
                 'snippet': r.get('snippet', ''), 'position': r.get('position', 0)}
                for r in results[:10]]

data = requests.post('https://api.scavio.dev/api/v1/search',
    headers=SH, json={'query': 'best serp api 2026', 'country_code': 'us'}).json()
results = data.get('organic_results', [])
for level in ['minimal', 'medium', 'full']:
    compressed = compress_results(results, level)
    tokens_est = len(json.dumps(compressed)) // 4
    print(f'{level:8}: {len(compressed)} results, ~{tokens_est} tokens')

步骤 2: 构建上下文桥类

创建一个桥来管理跨代理步骤的搜索上下文。

Python

class ContextBridge:
    def __init__(self, max_tokens=4000):
        self.max_tokens = max_tokens
        self.context = []
        self.token_count = 0
        self.searches = 0
        self.cost = 0.0

    def search(self, query, platform=None):
        body = {'query': query, 'country_code': 'us'}
        if platform: body['platform'] = platform
        data = requests.post('https://api.scavio.dev/api/v1/search',
            headers=SH, json=body).json()
        self.searches += 1
        self.cost += 0.005
        results = data.get('organic_results', [])
        # Auto-select compression based on remaining budget
        remaining = self.max_tokens - self.token_count
        if remaining < 500: level = 'minimal'
        elif remaining < 2000: level = 'medium'
        else: level = 'full'
        compressed = compress_results(results, level)
        entry = {'query': query, 'platform': platform or 'google',
                 'results': compressed, 'level': level}
        tokens = len(json.dumps(entry)) // 4
        self.context.append(entry)
        self.token_count += tokens
        return entry

    def get_context(self):
        return json.dumps(self.context, indent=None)

    def stats(self):
        return f'{self.searches} searches, ~{self.token_count} tokens, ${self.cost:.3f}'

bridge = ContextBridge(max_tokens=4000)
bridge.search('best serp api 2026')
bridge.search('serp api user reviews', platform='reddit')
print(f'Stats: {bridge.stats()}')

步骤 3: 添加SERP特征提取

提取人工智能概述和代理上下文的特色片段数据。

Python

def extract_features(data):
    """Extract SERP features into agent-friendly format."""
    features = {}
    if data.get('ai_overview'):
        ao = data['ai_overview']
        features['ai_overview'] = {
            'present': True,
            'text': json.dumps(ao)[:200] if isinstance(ao, dict) else str(ao)[:200]
        }
    if data.get('answer_box'):
        ab = data['answer_box']
        features['answer_box'] = {
            'title': ab.get('title', '')[:60],
            'answer': ab.get('answer', ab.get('snippet', ''))[:150]
        }
    paa = data.get('related_questions', [])
    if paa:
        features['people_also_ask'] = [q.get('question', '')[:80] for q in paa[:4]]
    return features

def search_with_features(query):
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': query, 'country_code': 'us', 'include_ai_overview': True}).json()
    results = compress_results(data.get('organic_results', []), 'medium')
    features = extract_features(data)
    return {'query': query, 'results': results, 'features': features}

context = search_with_features('best python framework 2026')
print(json.dumps(context, indent=2)[:500])

步骤 4: 序列化多步骤工作流程的上下文

格式化累积的上下文以便在代理步骤之间进行切换。

Python

def format_for_agent(bridge, task_description):
    """Format accumulated search context for the next agent step."""
    context_str = bridge.get_context()
    prompt = f"""Based on the following search results gathered across {bridge.searches} searches:

{context_str}

Task: {task_description}

Provide your analysis based solely on the search data above. Note any gaps."""
    token_est = len(prompt) // 4
    print(f'Context prompt: ~{token_est} tokens ({bridge.stats()})')
    return prompt

# Example multi-step workflow
bridge = ContextBridge(max_tokens=6000)
bridge.search('serp api pricing comparison 2026')
bridge.search('serp api developer reviews', platform='reddit')
bridge.search('serp api python tutorial', platform='youtube')

prompt = format_for_agent(bridge,
    'Compare the top 3 SERP APIs by pricing, developer experience, and community sentiment.')
print(f'\nPrompt preview:\n{prompt[:300]}...')
print(f'\nTotal search cost: ${bridge.cost:.3f}')

Python 示例

Python

import os, requests, json
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}

def bridge_search(query, max_results=3):
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': query, 'country_code': 'us'}).json()
    compressed = [{'title': r['title'][:50], 'snippet': r.get('snippet', '')[:80]}
                  for r in data.get('organic_results', [])[:max_results]]
    tokens = len(json.dumps(compressed)) // 4
    print(f'{query}: {len(compressed)} results, ~{tokens} tokens. Cost: $0.005')
    return compressed

bridge_search('best serp api 2026')

JavaScript 示例

JavaScript

const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
async function bridgeSearch(query, maxResults = 3) {
  const data = await fetch('https://api.scavio.dev/api/v1/search', {
    method: 'POST', headers: SH,
    body: JSON.stringify({ query, country_code: 'us' })
  }).then(r => r.json());
  const compressed = (data.organic_results || []).slice(0, maxResults)
    .map(r => ({ title: r.title.slice(0, 50), snippet: (r.snippet || '').slice(0, 80) }));
  const tokens = Math.ceil(JSON.stringify(compressed).length / 4);
  console.log(`${query}: ${compressed.length} results, ~${tokens} tokens`);
  return compressed;
}
await bridgeSearch('best serp api 2026');

预期输出

JSON

minimal :  3 results, ~45 tokens
medium  :  5 results, ~190 tokens
full    : 10 results, ~520 tokens

Stats: 2 searches, ~380 tokens, $0.010

Context prompt: ~450 tokens (3 searches, ~520 tokens, $0.015)

Prompt preview:
Based on the following search results gathered across 3 searches:
[{"query": "serp api pricing comparison 2026", "platform": "google", "results": [...

Total search cost: $0.015

前置条件

Python 3.8+
请求库
来自 scavio.dev 的 Scavio API 密钥
代理框架（LangChain、CrewAI 或自定义）

操作指南

步骤 1: 定义上下文压缩策略

为不同的代理上下文窗口大小创建压缩级别。

Python

import os, requests, json

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}

def compress_results(results, level='medium'):
    """Compress search results for agent context windows."""
    if level == 'minimal':
        # ~20 tokens per result (for 4K context models)
        return [{'t': r.get('title', '')[:40], 'u': r.get('link', '').split('/')[2] if r.get('link') else ''}
                for r in results[:3]]
    elif level == 'medium':
        # ~60 tokens per result (for 8K-16K context)
        return [{'title': r.get('title', '')[:60], 'domain': r.get('link', '').split('/')[2] if r.get('link') else '',
                 'snippet': r.get('snippet', '')[:100]} for r in results[:5]]
    else:  # full
        # ~120 tokens per result (for 32K+ context)
        return [{'title': r.get('title', ''), 'link': r.get('link', ''),
                 'snippet': r.get('snippet', ''), 'position': r.get('position', 0)}
                for r in results[:10]]

data = requests.post('https://api.scavio.dev/api/v1/search',
    headers=SH, json={'query': 'best serp api 2026', 'country_code': 'us'}).json()
results = data.get('organic_results', [])
for level in ['minimal', 'medium', 'full']:
    compressed = compress_results(results, level)
    tokens_est = len(json.dumps(compressed)) // 4
    print(f'{level:8}: {len(compressed)} results, ~{tokens_est} tokens')

步骤 2: 构建上下文桥类

创建一个桥来管理跨代理步骤的搜索上下文。

Python

class ContextBridge:
    def __init__(self, max_tokens=4000):
        self.max_tokens = max_tokens
        self.context = []
        self.token_count = 0
        self.searches = 0
        self.cost = 0.0

    def search(self, query, platform=None):
        body = {'query': query, 'country_code': 'us'}
        if platform: body['platform'] = platform
        data = requests.post('https://api.scavio.dev/api/v1/search',
            headers=SH, json=body).json()
        self.searches += 1
        self.cost += 0.005
        results = data.get('organic_results', [])
        # Auto-select compression based on remaining budget
        remaining = self.max_tokens - self.token_count
        if remaining < 500: level = 'minimal'
        elif remaining < 2000: level = 'medium'
        else: level = 'full'
        compressed = compress_results(results, level)
        entry = {'query': query, 'platform': platform or 'google',
                 'results': compressed, 'level': level}
        tokens = len(json.dumps(entry)) // 4
        self.context.append(entry)
        self.token_count += tokens
        return entry

    def get_context(self):
        return json.dumps(self.context, indent=None)

    def stats(self):
        return f'{self.searches} searches, ~{self.token_count} tokens, ${self.cost:.3f}'

bridge = ContextBridge(max_tokens=4000)
bridge.search('best serp api 2026')
bridge.search('serp api user reviews', platform='reddit')
print(f'Stats: {bridge.stats()}')

步骤 3: 添加SERP特征提取

提取人工智能概述和代理上下文的特色片段数据。

Python

def extract_features(data):
    """Extract SERP features into agent-friendly format."""
    features = {}
    if data.get('ai_overview'):
        ao = data['ai_overview']
        features['ai_overview'] = {
            'present': True,
            'text': json.dumps(ao)[:200] if isinstance(ao, dict) else str(ao)[:200]
        }
    if data.get('answer_box'):
        ab = data['answer_box']
        features['answer_box'] = {
            'title': ab.get('title', '')[:60],
            'answer': ab.get('answer', ab.get('snippet', ''))[:150]
        }
    paa = data.get('related_questions', [])
    if paa:
        features['people_also_ask'] = [q.get('question', '')[:80] for q in paa[:4]]
    return features

def search_with_features(query):
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': query, 'country_code': 'us', 'include_ai_overview': True}).json()
    results = compress_results(data.get('organic_results', []), 'medium')
    features = extract_features(data)
    return {'query': query, 'results': results, 'features': features}

context = search_with_features('best python framework 2026')
print(json.dumps(context, indent=2)[:500])

步骤 4: 序列化多步骤工作流程的上下文

格式化累积的上下文以便在代理步骤之间进行切换。

Python

def format_for_agent(bridge, task_description):
    """Format accumulated search context for the next agent step."""
    context_str = bridge.get_context()
    prompt = f"""Based on the following search results gathered across {bridge.searches} searches:

{context_str}

Task: {task_description}

Provide your analysis based solely on the search data above. Note any gaps."""
    token_est = len(prompt) // 4
    print(f'Context prompt: ~{token_est} tokens ({bridge.stats()})')
    return prompt

# Example multi-step workflow
bridge = ContextBridge(max_tokens=6000)
bridge.search('serp api pricing comparison 2026')
bridge.search('serp api developer reviews', platform='reddit')
bridge.search('serp api python tutorial', platform='youtube')

prompt = format_for_agent(bridge,
    'Compare the top 3 SERP APIs by pricing, developer experience, and community sentiment.')
print(f'\nPrompt preview:\n{prompt[:300]}...')
print(f'\nTotal search cost: ${bridge.cost:.3f}')

Python 示例

Python

import os, requests, json
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}

def bridge_search(query, max_results=3):
    data = requests.post('https://api.scavio.dev/api/v1/search',
        headers=SH, json={'query': query, 'country_code': 'us'}).json()
    compressed = [{'title': r['title'][:50], 'snippet': r.get('snippet', '')[:80]}
                  for r in data.get('organic_results', [])[:max_results]]
    tokens = len(json.dumps(compressed)) // 4
    print(f'{query}: {len(compressed)} results, ~{tokens} tokens. Cost: $0.005')
    return compressed

bridge_search('best serp api 2026')

JavaScript 示例

JavaScript

const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
async function bridgeSearch(query, maxResults = 3) {
  const data = await fetch('https://api.scavio.dev/api/v1/search', {
    method: 'POST', headers: SH,
    body: JSON.stringify({ query, country_code: 'us' })
  }).then(r => r.json());
  const compressed = (data.organic_results || []).slice(0, maxResults)
    .map(r => ({ title: r.title.slice(0, 50), snippet: (r.snippet || '').slice(0, 80) }));
  const tokens = Math.ceil(JSON.stringify(compressed).length / 4);
  console.log(`${query}: ${compressed.length} results, ~${tokens} tokens`);
  return compressed;
}
await bridgeSearch('best serp api 2026');

预期输出

JSON

minimal :  3 results, ~45 tokens
medium  :  5 results, ~190 tokens
full    : 10 results, ~520 tokens

Stats: 2 searches, ~380 tokens, $0.010

Context prompt: ~450 tokens (3 searches, ~520 tokens, $0.015)

Prompt preview:
Based on the following search results gathered across 3 searches:
[{"query": "serp api pricing comparison 2026", "platform": "google", "results": [...

Total search cost: $0.015

如何为搜索结果构建代理上下文桥

前置条件

操作指南

步骤 1: 定义上下文压缩策略

步骤 2: 构建上下文桥类

步骤 3: 添加SERP特征提取

步骤 4: 序列化多步骤工作流程的上下文

Python 示例

JavaScript 示例

预期输出

相关教程

常见问题

完成如何为搜索结果构建代理上下文桥教程需要多长时间？

开始前需要准备什么？

我可以用免费套餐运行本教程吗？

这支持哪些框架？

相关资源

2026 年最佳 AI 生产力应用程序

2026年最佳SerpAPI替代方案

搜索 API 供应商格局（2026）

搜索付费墙时代（2026）

2026 开源 AI 网络访问方案

DataForSEO vs Serper

开始构建

如何为搜索结果构建代理上下文桥

前置条件

操作指南

步骤 1: 定义上下文压缩策略

步骤 2: 构建上下文桥类

步骤 3: 添加SERP特征提取

步骤 4: 序列化多步骤工作流程的上下文

Python 示例

JavaScript 示例

预期输出

相关教程

常见问题

完成如何为搜索结果构建代理上下文桥教程需要多长时间？

开始前需要准备什么？

我可以用免费套餐运行本教程吗？

这支持哪些框架？

相关资源

2026 年最佳 AI 生产力应用程序

2026年最佳SerpAPI替代方案

搜索 API 供应商格局（2026）

搜索付费墙时代（2026）

2026 开源 AI 网络访问方案

DataForSEO vs Serper

开始构建