cloro

pip install -U langchain-cloro

import os
os.environ["CLORO_API_KEY"] = "your-api-key"

from langchain_cloro import CloroGoogleSearch

tool = CloroGoogleSearch()
result = tool.invoke({"query": "best laptops for programming"})

result = tool.invoke({
    "query": "best laptops for programming",
    "include_aioverview": True,
    "aioverview_markdown": True
})

result = tool.invoke({
    "query": "python tutorials",
    "country": "GB",  # 英国结果
    "device": "mobile",  # 或 "desktop"
    "pages": 3  # 页数 (1-20)
})

from langchain_cloro import CloroChatGPT

tool = CloroChatGPT()
result = tool.invoke({"prompt": "What are the best sneakers under $100?"})

result = tool.invoke({
    "prompt": "best running shoes 2024",
    "include_raw_response": True,
    "include_search_queries": True,
    "country": "US"
})

from langchain_cloro import CloroGemini

tool = CloroGemini()
result = tool.invoke({"prompt": "Explain quantum entanglement"})

result = tool.invoke({
    "prompt": "What is machine learning?",
    "include_markdown": True,
    "country": "US"
})

from langchain_cloro import CloroPerplexity

tool = CloroPerplexity()
result = tool.invoke({"prompt": "Best hotels in San Francisco"})

from langchain_cloro import CloroGrok

tool = CloroGrok()
result = tool.invoke({"prompt": "Latest news about AI"})

from langchain_cloro import CloroCopilot

tool = CloroCopilot()
result = tool.invoke({"prompt": "What is the capital of France?"})

from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_cloro import CloroGoogleSearch, CloroChatGPT

# 初始化工具
search_tool = CloroGoogleSearch()
chatgpt_tool = CloroChatGPT()
tools = [search_tool, chatgpt_tool]

# 创建代理
llm = ChatOpenAI(model="gpt-4", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

# 运行代理
result = agent_executor.invoke({"input": "Search for information about AI trends and summarize what you find"})

import json

result = tool.invoke({"query": "python programming"})
data = json.loads(result)

# 访问结构化结果
if "result" in data:
    if "organicResults" in data["result"]:
        for item in data["result"]["organicResults"]:
            print(f"{item['title']}: {item['link']}")