法学硕士不断产生品牌信息的幻觉。他们发明产品功能，引用错误的定价，迷惑竞争对手。通过实时搜索数据进行品牌研究，通过为法学硕士提供经过验证的事实来解决这个问题。本教程构建了一个品牌研究管道，可在 Google 中搜索公司信息，在 Amazon 中搜索产品数据，在 Reddit 中搜索社区情绪，在 YouTube 中搜索内容展示，所有这些均通过 Scavio API 进行，每次搜索 0.005 美元。
前置条件
- 已安装 Python 3.9+
- 请求已安装库
- 来自 scavio.dev 的 Scavio API 密钥
- LLM API 密钥（OpenAI、Anthropic 或本地 Ollama）
操作指南
步骤 1: 建立品牌数据收集管道
搜索多个平台，收集经过验证的品牌数据。每个平台都提供不同的视角：Google 提供官方信息，Amazon 提供产品，Reddit 提供声誉。
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
URL = 'https://api.scavio.dev/api/v1/search'
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def research_brand(brand: str) -> dict:
data = {'brand': brand, 'sources': {}}
# Google: official info, knowledge graph
resp = requests.post(URL, headers=H,
json={'query': f'{brand} company', 'country_code': 'us', 'num_results': 5})
result = resp.json()
data['sources']['google'] = {
'knowledge_graph': result.get('knowledge_graph', {}),
'top_results': [{'title': r['title'], 'snippet': r.get('snippet', ''),
'url': r['link']} for r in result.get('organic_results', [])[:3]],
}
time.sleep(0.3)
# Amazon: product presence
resp = requests.post(URL, headers=H,
json={'query': f'site:amazon.com {brand}', 'country_code': 'us', 'num_results': 5})
data['sources']['amazon'] = [{'title': r['title'], 'url': r['link']}
for r in resp.json().get('organic_results', [])]
time.sleep(0.3)
# Reddit: community sentiment
resp = requests.post(URL, headers=H,
json={'query': f'site:reddit.com {brand} review', 'country_code': 'us', 'num_results': 5})
data['sources']['reddit'] = [{'title': r['title'], 'snippet': r.get('snippet', ''),
'url': r['link']} for r in resp.json().get('organic_results', [])]
return data
brand_data = research_brand('Notion')
kg = brand_data['sources']['google']['knowledge_graph']
print(f'Brand: {brand_data["brand"]}')
print(f'Description: {kg.get("description", "N/A")}')
print(f'Amazon products: {len(brand_data["sources"]["amazon"])}')
print(f'Reddit discussions: {len(brand_data["sources"]["reddit"])}')步骤 2: 将品牌数据格式化为 LLM 上下文
将收集到的数据构建为法学硕士可以有效使用的格式。包含来源 URL，以便法学硕士可以引用其来源。
Python
def format_brand_context(data: dict) -> str:
lines = [f'Verified brand research data for: {data["brand"]}', '']
# Knowledge graph data
kg = data['sources']['google'].get('knowledge_graph', {})
if kg:
lines.append('OFFICIAL INFO (Google Knowledge Graph):')
if kg.get('description'):
lines.append(f' Description: {kg["description"]}')
if kg.get('website'):
lines.append(f' Website: {kg["website"]}')
if kg.get('founded'):
lines.append(f' Founded: {kg["founded"]}')
lines.append('')
# Google search results
lines.append('WEB PRESENCE (Google):')
for i, r in enumerate(data['sources']['google']['top_results'], 1):
lines.append(f' [{i}] {r["title"]}')
lines.append(f' {r["snippet"][:150]}')
lines.append('')
# Amazon products
amazon = data['sources']['amazon']
if amazon:
lines.append(f'PRODUCT PRESENCE (Amazon, {len(amazon)} listings):')
for r in amazon[:3]:
lines.append(f' - {r["title"][:80]}')
lines.append('')
# Reddit sentiment
reddit = data['sources']['reddit']
if reddit:
lines.append(f'COMMUNITY SENTIMENT (Reddit, {len(reddit)} discussions):')
for r in reddit[:3]:
lines.append(f' - {r["title"][:80]}')
if r['snippet']:
lines.append(f' "{r["snippet"][:120]}"')
return '\n'.join(lines)
context = format_brand_context(brand_data)
print(context)步骤 3: 向法学硕士询问有根据的背景
将经过验证的品牌数据发送给法学硕士，并附上仅使用所提供来源的说明。这消除了对品牌事实的幻觉。
Python
def grounded_brand_analysis(brand: str, question: str) -> dict:
data = research_brand(brand)
context = format_brand_context(data)
messages = [
{'role': 'system', 'content': (
'You are a brand research analyst. Answer ONLY based on the verified '
'data provided below. Do NOT make up facts, pricing, features, or '
'statistics. If the data does not contain the answer, say so. '
'Cite sources as [Google], [Amazon], or [Reddit].'
)},
{'role': 'user', 'content': f'{context}\n\nQuestion: {question}'}
]
llm_url = os.environ.get('LLM_URL', 'http://localhost:11434/v1/chat/completions')
resp = requests.post(llm_url, json={
'model': 'llama3', 'messages': messages, 'max_tokens': 512
})
answer = resp.json()['choices'][0]['message']['content']
return {
'brand': brand,
'question': question,
'answer': answer,
'sources_used': 3, # google + amazon + reddit
'cost': 0.015, # 3 searches
}
result = grounded_brand_analysis('Notion', 'What is Notion and what products do they offer?')
print(f'Q: {result["question"]}')
print(f'A: {result["answer"]}')
print(f'\nSources: {result["sources_used"]} platforms, Cost: ${result["cost"]}')步骤 4: 批量研究多个品牌进行比较
研究多个品牌并生成比较报告。法学硕士可以准确地比较品牌，因为每个事实都基于实时数据。
Python
def compare_brands(brands: list[str], question: str) -> str:
all_context = []
for brand in brands:
data = research_brand(brand)
context = format_brand_context(data)
all_context.append(context)
time.sleep(0.5)
combined = '\n\n---\n\n'.join(all_context)
messages = [
{'role': 'system', 'content': (
'You are a brand research analyst. Compare the brands using ONLY '
'the verified data provided. Do not invent facts or features. '
'Cite sources. Be specific about what each brand offers.'
)},
{'role': 'user', 'content': f'{combined}\n\nCompare these brands: {question}'}
]
llm_url = os.environ.get('LLM_URL', 'http://localhost:11434/v1/chat/completions')
resp = requests.post(llm_url, json={
'model': 'llama3', 'messages': messages, 'max_tokens': 1024
})
answer = resp.json()['choices'][0]['message']['content']
cost = len(brands) * 3 * 0.005 # 3 searches per brand
print(f'Compared {len(brands)} brands using {len(brands) * 3} searches')
print(f'Cost: ${cost:.3f}')
return answer
comparison = compare_brands(['Notion', 'Obsidian', 'Coda'],
'Which is best for team collaboration and why?')
print(comparison)Python 示例
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def research_brand(brand):
results = {}
for prefix in ['', 'site:reddit.com ', 'site:amazon.com ']:
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': f'{prefix}{brand}', 'country_code': 'us', 'num_results': 5})
platform = 'reddit' if 'reddit' in prefix else 'amazon' if 'amazon' in prefix else 'google'
results[platform] = resp.json().get('organic_results', [])
time.sleep(0.2)
kg = results.get('google', [{}])[0] if results.get('google') else {}
print(f'{brand}: {len(results["google"])} google, {len(results["reddit"])} reddit, {len(results["amazon"])} amazon')
return results
research_brand('Notion')JavaScript 示例
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function researchBrand(brand) {
const results = {};
for (const [platform, prefix] of [['google',''], ['reddit','site:reddit.com '], ['amazon','site:amazon.com ']]) {
const resp = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: `${prefix}${brand}`, country_code: 'us', num_results: 5 })
});
results[platform] = (await resp.json()).organic_results || [];
}
console.log(`${brand}: google=${results.google.length}, reddit=${results.reddit.length}, amazon=${results.amazon.length}`);
return results;
}
researchBrand('Notion');预期输出
JSON
Brand: Notion
Description: American productivity and note-taking web application
Amazon products: 4
Reddit discussions: 5
Q: What is Notion and what products do they offer?
A: Based on the verified data, Notion is an American productivity and
note-taking web application [Google]. Their Amazon presence includes
Notion-related productivity guides and templates [Amazon]. Reddit
discussions show strong community adoption for team wikis and project
management [Reddit].
Sources: 3 platforms, Cost: $0.015