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Exa is a neural search engine that provides high-quality web search results optimized for AI applications. The langchain-exa integration provides tools and retrievers for searching the web and finding similar content.
Installation
Install the langchain-exa package:
pip install langchain-exa
Setup
Get an API key from Exa and set it as an environment variable:
export EXA_API_KEY="your-api-key"
Components
The integration provides three main components:
- ExaSearchResults - Tool for neural search
- ExaFindSimilarResults - Tool for finding similar pages
- ExaSearchRetriever - Retriever for RAG applications
The ExaSearchResults tool performs neural search and returns detailed search results.
Basic Usage
from langchain_exa import ExaSearchResults
tool = ExaSearchResults()
results = tool.invoke({"query": "best time to visit japan", "num_results": 5})
Advanced Features
from langchain_exa import ExaSearchResults, TextContentsOptions
tool = ExaSearchResults()
# Search with content options
results = tool.invoke({
"query": "artificial intelligence breakthroughs 2024",
"num_results": 10,
"text_contents_options": {"max_characters": 500},
"highlights": True,
"summary": True,
"type": "neural", # or "keyword" or "auto"
"start_published_date": "2024-01-01",
"include_domains": ["arxiv.org", "nature.com"]
})
Parameters
query - The search query (required)num_results - Number of results to return (1-100, default: 10)text_contents_options - Content extraction options:
True - Return full text{"max_characters": N} - Limit text length
highlights - Include highlighted excerptssummary - Include AI-generated summariestype - Search type: "neural", "keyword", or "auto"include_domains - Filter to specific domainsexclude_domains - Exclude specific domainsstart_published_date - Filter by publish date (YYYY-MM-DD)end_published_date - Filter by publish date (YYYY-MM-DD)start_crawl_date - Filter by crawl date (YYYY-MM-DD)end_crawl_date - Filter by crawl date (YYYY-MM-DD)use_autoprompt - Auto-optimize query for better resultslivecrawl - Crawl live pages: "always", "fallback", or "never"
Find pages similar to a given URL.
from langchain_exa import ExaFindSimilarResults
tool = ExaFindSimilarResults()
results = tool.invoke({
"url": "https://example.com/article",
"num_results": 5,
"exclude_source_domain": True
})
ExaSearchRetriever
The retriever is designed for RAG (Retrieval-Augmented Generation) applications.
Basic Retriever
from langchain_exa import ExaSearchRetriever
retriever = ExaSearchRetriever(k=10)
documents = retriever.invoke("best time to visit japan")
for doc in documents:
print(f"Title: {doc.metadata['title']}")
print(f"URL: {doc.metadata['url']}")
print(f"Content: {doc.page_content[:200]}...")
Retriever with Highlights
from langchain_exa import ExaSearchRetriever
retriever = ExaSearchRetriever(
k=5,
highlights=True,
text_contents_options={"max_characters": 1000}
)
documents = retriever.invoke("machine learning research")
for doc in documents:
highlights = doc.metadata.get("highlights", [])
print(f"Highlights: {highlights}")
Retriever with Summary
from langchain_exa import ExaSearchRetriever
retriever = ExaSearchRetriever(
k=3,
summary=True,
type="auto"
)
documents = retriever.invoke("climate change solutions")
for doc in documents:
print(f"Summary: {doc.metadata['summary']}")
Configuration Options
The retriever supports the same parameters as ExaSearchResults:
retriever = ExaSearchRetriever(
k=10, # Number of results
include_domains=["edu", "gov"],
exclude_domains=["example.com"],
start_published_date="2023-01-01",
use_autoprompt=True,
type="neural", # Search type
highlights=True,
summary=True,
livecrawl="fallback"
)
Using with Agents
Integrate Exa tools with LangChain agents:
from langchain_exa import ExaSearchResults, ExaFindSimilarResults
from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
# Initialize tools
search_tool = ExaSearchResults()
similar_tool = ExaFindSimilarResults()
tools = [search_tool, similar_tool]
# Create agent
model = ChatOpenAI(model="gpt-4")
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful research assistant."),
("human", "{input}"),
("placeholder", "{agent_scratchpad}")
])
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)
# Run agent
result = agent_executor.invoke({
"input": "Search for recent AI research papers and find similar articles"
})
Using in RAG Chains
Combine with LangChain for retrieval-augmented generation:
from langchain_exa import ExaSearchRetriever
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
# Set up retriever
retriever = ExaSearchRetriever(
k=5,
text_contents_options={"max_characters": 1000},
summary=True
)
# Create RAG chain
prompt = ChatPromptTemplate.from_template(
"""Answer the question based on the following context:
{context}
Question: {question}
Answer:"""
)
model = ChatOpenAI(model="gpt-4")
def format_docs(docs):
return "\n\n".join([doc.page_content for doc in docs])
chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
# Use the chain
answer = chain.invoke("What are the latest developments in quantum computing?")
print(answer)
API Reference
For detailed API documentation, see:
Resources