from exa_py import Exa

exa = Exa(api_key="your-api-key")

  # 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, using the exa-pro model (sends 2 expanded quries to exa search)
  response = exa.answer("This is a query to answer a question", text=True, model="exa-pro")

  # 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)