Metadata-Version: 2.4
Name: exa-py
Version: 1.13.0
Summary: Python SDK for Exa API.
Home-page: https://github.com/exa-labs/exa-py
Author: Exa
Author-email: Exa AI <hello@exa.ai>
License: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: requests>=2.32.3
Requires-Dist: typing-extensions>=4.12.2
Requires-Dist: openai>=1.48
Requires-Dist: pydantic>=2.10.6
Requires-Dist: pytest-mock>=3.14.0
Requires-Dist: httpx>=0.28.1
Dynamic: author
Dynamic: home-page

# 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

```bash
pip install exa_py
```

## Usage

Import the package and initialize the Exa client with your API key:

```python
from exa_py import Exa

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

## Common requests
```python

  # 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)

```

