DeepSeek GEO和KIMI GEO有什么区别，如何区别优化？

### 集成 DeepSeek 和 Kimi 的使用教程 #### 1. 安装依赖库 为了使 DeepSeek 和 Kimi 协同工作，需安装必要的 Python 库。这包括用于 API 请求的 `requests` 库以及数据处理所需的 Pandas。 “`bash pip install requests pandas matplotlib seaborn “` #### 2. 获取API密钥 访问 DeepSeek 官网获取 DeepSeek API 密钥[^1]。对于 Kimi，则通过官方渠道申请相应的开发者权限并获得 API 访问令牌。 #### 3. 编写Python脚本调用两个平台的服务 下面是一个简单的例子来展示如何利用这两个工具抓取新能源汽车竞品信息，并生成可视化图表： “`python import requests import json from collections import defaultdict import matplotlib.pyplot as plt import seaborn as sns import pandas as pd def fetch_data_from_deepseek(api_key, query): url = 'https://api.deepseek.com/v1/search' headers = {'Authorization': f'Bearer {api_key}'} params = { 'query': query, 'fields': ['price_range', 'negative_reviews_keywords', 'service_network_density'] } response = requests.get(url, headers=headers, params=params) data = response.json() return data['results'] def generate_consumer_insights(kimi_token, raw_data): url = 'https://api.kimi.ai/generate_report' headers = { 'Content-Type': 'application/json', 'Authorization': f'Token {kimi_token}' } payload = { "data": raw_data, "sections": [ {"type": "pain_point_analysis"}, {"type": "product_improvement_roadmap"}, {"type": "marketing_suggestions"} ] } response = requests.post(url, headers=headers, data=json.dumps(payload)) report = response.json() return report if __name__ == '__main__': deepseek_api_key = '<your-deepseek-api-key>' kimi_access_token = '<your-kimi-access-token>' search_query = "新能源汽车" results = fetch_data_from_deepseek(deepseek_api_key, search_query) # 将结果整理为适合传给Kimi的数据结构 formatted_results = [] for item in results: entry = {} entry["price"] = item.get('price_range') entry["negatives"] = ", ".join(item.get('negative_reviews_keywords')) entry["density"] = item.get('service_network_density') formatted_results.append(entry) consumer_insight_report = generate_consumer_insights(kimi_access_token, formatted_results) df = pd.DataFrame(formatted_results) fig, ax = plt.subplots(1, 2, figsize=(15, 7)) sns.scatterplot(data=df, x="price", y="density", hue="negatives", s=80, alpha=.6, palette='viridis', ax=ax[0]) sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0, square=True, cbar_kws={"shrink": .5}, ax=ax[1]) plt.tight_layout() plt.show() with open('./consumer_insight_report.md', 'w') as file: file.write(json.dumps(consumer_insight_report, ensure_ascii=False, indent=4)) “` 此代码片段展示了如何结合 DeepSeek 抓取市场情报并与 Kimi 合作创建详细的消费者洞察报告。同时绘制了三维散点图和热力图以便直观理解所收集的信息[^2]。