Exa 搜索集成

pip install -qU langchain-exa

# 以及本笔记本的一些依赖项
pip install -qU langchain langchain-openai langchain-community

import getpass
import os

if not os.environ.get("EXA_API_KEY"):
    os.environ["EXA_API_KEY"] = getpass.getpass("Exa API key:\n")

from langchain_exa import ExaSearchResults

# 初始化 ExaSearchResults 工具
search_tool = ExaSearchResults(exa_api_key=os.environ["EXA_API_KEY"])

# 执行搜索查询
search_results = search_tool._run(
    query="When was the last time the New York Knicks won the NBA Championship?",
    num_results=5,
    text_contents_options=True,
    highlights=True,
)

print("Search Results:", search_results)

# 使用高级选项执行搜索查询
search_results = search_tool._run(
    query="Latest AI research papers",
    num_results=10,  # 结果数量 (1-100)
    type="auto",  # 可以是 "neural"、"keyword" 或 "auto"
    livecrawl="always",  # 可以是 "always"、"fallback" 或 "never"
    text_contents_options={"max_characters": 2000},  # 限制文本长度
    summary={"query": "generate one liner"},  # 自定义摘要提示
)

print("Advanced Search Results:")
print(search_results)

from langchain_exa import ExaFindSimilarResults

# 初始化 ExaFindSimilarResults 工具
find_similar_tool = ExaFindSimilarResults(exa_api_key=os.environ["EXA_API_KEY"])

# 基于 URL 查找类似结果
similar_results = find_similar_tool._run(
    url="http://espn.com", num_results=5, text_contents_options=True, highlights=True
)

print("Similar Results:", similar_results)

import getpass

if not os.environ.get("OPENAI_API_KEY"):
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API key:\n")

pip install -qU langgraph

from langchain.chat_models import init_chat_model
from langchain_exa import ExaFindSimilarResults, ExaSearchResults
from langchain.agents import create_agent


# 初始化语言模型
model = init_chat_model(model="gpt-5.4", model_provider="openai", temperature=0)

# 初始化 Exa 工具
exa_search = ExaSearchResults(
    exa_api_key=os.environ["EXA_API_KEY"],
    max_results=5,
)

exa_find_similar = ExaFindSimilarResults(
    exa_api_key=os.environ["EXA_API_KEY"],
    max_results=5,
)

# 创建包含两个工具的代理
agent = create_agent(model, [exa_search, exa_find_similar])

# 示例 1：基本搜索
user_input = "What are the latest developments in quantum computing?"

for step in agent.stream(
    {"messages": user_input},
    stream_mode="values",
):
    step["messages"][-1].pretty_print()

from langchain_exa import ExaSearchRetriever

# 创建 ExaSearchRetriever 的新实例
exa = ExaSearchRetriever(exa_api_key=os.environ["EXA_API_KEY"])

# 搜索查询并保存结果
results = exa.invoke("What is the capital of France?")

# 打印结果
print(results)

from langchain_exa import ExaSearchRetriever

# 使用高级选项创建新实例
exa = ExaSearchRetriever(
    exa_api_key=os.environ["EXA_API_KEY"],
    k=20,  # 结果数量 (1-100)
    type="auto",  # 可以是 "neural"、"keyword" 或 "auto"
    livecrawl="always",  # 可以是 "always"、"fallback" 或 "never"
    text_contents_options={"max_characters": 3000},  # 限制文本长度
    # 用于 LLM 生成页面内容摘要的自定义提示
    summary={"query": "generate one line summary in simple words."},
)

# 使用高级选项进行搜索
results = exa.invoke("Latest developments in quantum computing")
print(f"Found {len(results)} results")
for result in results[:3]:  # 打印前 3 个结果
    print(f"Title: {result.metadata.get('title', 'N/A')}")
    print(f"URL: {result.metadata.get('url', 'N/A')}")
    print(f"Summary: {result.metadata.get('summary', 'N/A')}")
    print("-" * 80)