Python SDK for Exa API.
Project description
Exa
Exa (formerly Metaphor) API in Python
Note: This API is basically the same as metaphor-python but reflects new
features associated with Metaphor's rename to Exa. New site is https://exa.ai
Installation
pip install exa_py
Usage
Import the package and initialize the Exa client with your API key:
from exa_py import Exa
exa = Exa(api_key="your-api-key")
Common requests
# basic search
results = exa.search("This is a Exa query:")
# keyword search (non-neural)
results = exa.search("Google-style query", type="keyword")
# search with date filters
results = exa.search("This is a Exa query:", start_published_date="2019-01-01", end_published_date="2019-01-31")
# search with domain filters
results = exa.search("This is a Exa query:", include_domains=["www.cnn.com", "www.nytimes.com"])
# search and get text contents
results = exa.search_and_contents("This is a Exa query:")
# search and get contents with contents options
results = exa.search_and_contents("This is a Exa query:",
text={"include_html_tags": True, "max_characters": 1000})
# find similar documents
results = exa.find_similar("https://example.com")
# find similar excluding source domain
results = exa.find_similar("https://example.com", exclude_source_domain=True)
# find similar with contents
results = exa.find_similar_and_contents("https://example.com", text=True)
# get text contents
results = exa.get_contents(["tesla.com"])
# get contents with contents options
results = exa.get_contents(["urls"],
text={"include_html_tags": True, "max_characters": 1000})
# basic answer
response = exa.answer("This is a query to answer a question")
# answer with full text
response = exa.answer("This is a query to answer a question", text=True)
# answer with streaming
response = exa.stream_answer("This is a query to answer:")
# Print each chunk as it arrives when using the stream_answer method
for chunk in response:
print(chunk, end='', flush=True)
# research task example – answer a question with citations
# Example prompt & schema inspired by the TypeScript example.
QUESTION = (
"Summarize the history of San Francisco highlighting one or two major events "
"for each decade from 1850 to 1950"
)
OUTPUT_SCHEMA: Dict[str, Any] = {
"type": "object",
"required": ["timeline"],
"properties": {
"timeline": {
"type": "array",
"items": {
"type": "object",
"required": ["decade", "notableEvents"],
"properties": {
"decade": {
"type": "string",
"description": 'Decade label e.g. "1850s"',
},
"notableEvents": {
"type": "string",
"description": "A summary of notable events.",
},
},
},
},
},
}
resp = exa.research.create_task(
instructions=QUESTION,
model="exa-research",
output_schema=OUTPUT_SCHEMA,
)
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