一键导入
baidu-search
百度AI搜索 - 基于千帆平台的智能搜索服务 提供AI增强的搜索结果,包含引用来源标注
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
百度AI搜索 - 基于千帆平台的智能搜索服务 提供AI增强的搜索结果,包含引用来源标注
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
智能体工作流:单入口设计,所有任务从 router 开始 TRIGGER when: 开发、修复、规划、分析、审查、调研、实施 DO NOT TRIGGER when: 简单闲聊
完成阶段 - 收尾工作、自反思和状态更新 当前版本对齐 .workflow_state.json 状态管理 v1.2: 新增可选 learn 技能触发建议与总结摘要复用
调试阶段 - 系统化问题定位和修复 5步调试法:闻味道 → 揪头发 → 照镜子 → 执行 → 复盘 包含压力升级机制和7项检查清单 v1.2: 强化 context-aware 激活、memory_hints 复用与失败升级事件记录
执行阶段 - TDD 开发循环和代码实现 当前版本对齐 .specs/<feature>/tasks.md、.contract.json、run_tracker.py 和 step_recorder.py Reflexion 强化: mid-task reflection checkpoint + AgentSys 子 agent schema 验证 v1.3: 新增 memory_hints/memory_query/memory_intent 复用与阶段摘要回流
深度探索阶段 - 苏格拉底式追问挖掘用户深层想法 通过迭代追问发现谬误、局限、潜能和潜意识中的构思
产品咨询阶段 - 在开始编码前重构产品想法 问正确的问题,确保理解用户真正想要什么
| name | baidu-search |
| version | 1.0.0 |
| status | implemented |
| description | 百度AI搜索 - 基于千帆平台的智能搜索服务 提供AI增强的搜索结果,包含引用来源标注 |
| tags | ["search","baidu","qianfan","web-search"] |
| requires | {"tools":["Bash"]} |
| env | ["BAIDU_QIANFAN_API_KEY"] |
百度AI搜索是基于百度千帆平台的智能搜索服务,提供AI增强的搜索结果,支持:
Endpoint: https://qianfan.baidubce.com/v2/ai_search/chat/completions
Authentication: Bearer Token
import os
import requests
api_key = os.environ.get("BAIDU_QIANFAN_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
}
def baidu_search(query: str) -> dict:
"""
Perform Baidu AI Search.
Args:
query: Search query string
Returns:
dict with keys: result, references, is_safe
"""
import os
import requests
# 加载 .env 文件
from dotenv import load_dotenv
load_dotenv()
api_key = os.environ.get("BAIDU_QIANFAN_API_KEY")
if not api_key:
raise ValueError("BAIDU_QIANFAN_API_KEY not set")
url = "https://qianfan.baidubce.com/v2/ai_search/chat/completions"
payload = {
"messages": [{"role": "user", "content": query}],
"model": "ernie-4.5-turbo-32k",
"search_source": "baidu_search_v2",
"enable_corner_markers": True,
"stream": False
}
response = requests.post(url, headers=headers, json=payload, timeout=120)
response.raise_for_status()
return response.json()
| Parameter | Type | Default | Description |
|---|---|---|---|
query | string | required | Search query |
model | string | ernie-4.5-turbo-32k | Model to use |
search_source | string | baidu_search_v2 | Search engine version |
enable_deep_search | bool | False | Enable deep search mode |
enable_corner_markers | bool | True | Enable citation markers |
enable_followup_queries | bool | False | Enable follow-up suggestions |
search_recency_filter | string | None | Time filter: week/month/semiyear/year |
temperature | float | 0.11 | Sampling temperature |
top_p | float | 0.55 | Sampling top_p |
{
"choices": [{
"finish_reason": "stop",
"message": {
"content": "AI生成的搜索答案...",
"role": "assistant"
}
}],
"is_safe": true,
"references": [
{
"id": 1,
"title": "来源标题",
"url": "https://example.com",
"content": "来源内容摘要",
"website": "网站名称",
"date": "2026-03-22"
}
]
}
result = baidu_search("今天北京天气怎么样")
print(result["choices"][0]["message"]["content"])
def baidu_deep_search(query: str) -> dict:
payload = {
"messages": [{"role": "user", "content": query}],
"model": "ernie-4.5-turbo-32k",
"enable_deep_search": True,
"enable_corner_markers": True
}
# ... make request
def baidu_search_recent(query: str, days: int = 7) -> dict:
filter_map = {7: "week", 30: "month", 180: "semiyear", 365: "year"}
recency = filter_map.get(days, "month")
payload = {
"messages": [{"role": "user", "content": query}],
"model": "ernie-4.5-turbo-32k",
"search_recency_filter": recency
}
# ... make request
| Variable | Required | Description |
|---|---|---|
BAIDU_QIANFAN_API_KEY | Yes | Baidu Qianfan API Key |
try:
result = baidu_search("search query")
except requests.exceptions.Timeout:
print("Request timed out")
except requests.exceptions.HTTPError as e:
print(f"HTTP error: {e.response.status_code}")
except ValueError as e:
print(f"Configuration error: {e}")
bce-v3/ALTAK-xxx/xxx