Exa 是一个完全为大型语言模型（LLM）设计的搜索引擎。它允许用户使用自然语言查询在互联网上搜索文档，然后检索所需文档的干净 HTML 内容。 与基于关键词的搜索（如 Google）不同，Exa 的神经网络搜索能力使其能够语义理解查询并返回相关文档。例如，我们可以搜索“关于猫的精彩文章”，并比较 Google 和 Exa 的搜索结果。Google 会根据关键词“精彩”给出 SEO 优化的列表文章。Exa 则直接返回相关结果。 本笔记本将介绍如何在 LangChain 中使用 Exa Search。
安装 LangChain Exa 集成包
pip install -qU langchain-exa
# and some deps for this notebook
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
# Initialize the ExaSearchResults tool
search_tool = ExaSearchResults(exa_api_key=os.environ["EXA_API_KEY"])
# Perform a search query
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)
ExaSearchResults 的高级功能
您可以使用高级搜索选项，例如控制搜索类型、实时抓取和内容过滤
# Perform a search query with advanced options
search_results = search_tool._run(
query="Latest AI research papers",
num_results=10, # Number of results (1-100)
type="auto", # Can be "neural", "keyword", or "auto"
livecrawl="always", # Can be "always", "fallback", or "never"
text_contents_options={"max_characters": 2000}, # Limit text length
summary={"query": "generate one liner"}, # Custom summary prompt
)
print("Advanced Search Results:")
print(search_results)
from langchain_exa import ExaFindSimilarResults
# Initialize the ExaFindSimilarResults tool
find_similar_tool = ExaFindSimilarResults(exa_api_key=os.environ["EXA_API_KEY"])
# Find similar results based on a URL
similar_results = find_similar_tool._run(
url="http://espn.com", num_results=5, text_contents_options=True, highlights=True
)
print("Similar Results:", similar_results)
在 Agent 中使用
我们可以将 ExaSearchResults 和 ExaFindSimilarResults 工具与 LangGraph 代理一起使用。这使得代理能够根据用户的查询动态搜索信息并查找相似内容。 首先，让我们设置语言模型。您需要提供您的 OpenAI API 密钥：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
# Initialize the language model
model = init_chat_model(model="gpt-4o", model_provider="openai", temperature=0)
# Initialize Exa Tools
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,
)
# Create agent with both tools
agent = create_agent(model, [exa_search, exa_find_similar])
# Example 1: Basic search
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()
使用 ExaSearchRetriever
ExaSearchRetriever 是一个使用 Exa Search 检索相关文档的检索器。
TextContentsOptions 的 max_characters 参数以前称为 max_length，现已弃用。请务必改用 max_characters。
基本用法
以下是使用 ExaSearchRetriever 的一个简单示例
from langchain_exa import ExaSearchRetriever
# Create a new instance of the ExaSearchRetriever
exa = ExaSearchRetriever(exa_api_key=os.environ["EXA_API_KEY"])
# Search for a query and save the results
results = exa.invoke("What is the capital of France?")
# Print the results
print(results)
高级功能
您可以使用高级功能，例如控制结果数量、搜索类型、实时抓取、摘要和文本内容选项
from langchain_exa import ExaSearchRetriever
# Create a new instance with advanced options
exa = ExaSearchRetriever(
exa_api_key=os.environ["EXA_API_KEY"],
k=20, # Number of results (1-100)
type="auto", # Can be "neural", "keyword", or "auto"
livecrawl="always", # Can be "always", "fallback", or "never"
text_contents_options={"max_characters": 3000}, # Limit text length
# Custom prompt for an LLM generated summary of page content
summary={"query": "generate one line summary in simple words."},
)
# Search with advanced options
results = exa.invoke("Latest developments in quantum computing")
print(f"Found {len(results)} results")
for result in results[:3]: # Print first 3 results
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)
API 参考
有关所有 Exa API 功能和配置的详细文档，请访问 Exa API 文档。
