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Introduction to Exa Python SDK Guide

The Exa Python SDK Guide provides a comprehensive toolkit for integrating and interacting with the Exa Search API, which was formerly known as Metaphor. Exa is a neural search engine that utilizes advanced machine learning models to retrieve highly relevant content from the web. The SDK is designed to help developers easily implement Exa's search capabilities into their own Python applications. It supports a variety of search options, including keyword-based and neural searches, and offers functions to retrieve not just search results, but also the content and highlights of the web pages found. The SDK also includes utilities for optimizing search queries using Exa's 'Autoprompt' feature, which converts traditional search queries into Exa-optimized formats. This makes it particularly useful for applications that require highly specific or contextually relevant search results. The SDK is built with the flexibility to handle different search scenarios, from general content discovery to finding similar content based on a given URL.

Main Functions of Exa Python SDK

  • search

    Example Example

    exa.search(query='Best practices for Python testing', use_autoprompt=True, num_results=5)

    Example Scenario

    This function performs a search query using Exa's neural search capabilities. It can be tailored with various options such as the number of results, specific domains to include or exclude, and date filters. For instance, a developer looking for the latest best practices in Python testing could use this function to retrieve up-to-date, high-quality articles. By setting `use_autoprompt=True`, the query is automatically optimized for better relevance.

  • find_similar

    Example Example

    exa.find_similar(url='https://example.com/article-on-ai', num_results=3)

    Example Scenario

    The find_similar function retrieves content that is semantically similar to the content of a specified URL. This is particularly useful for content curators or marketers who want to explore related content to a popular or trending article. For example, a user who found an interesting article on AI can use this function to find other articles with similar themes or content.

  • contents

    Example Example

    exa.contents(ids=['doc1', 'doc2'], text=True, highlights=True)

    Example Scenario

    This function retrieves the content and highlights of web pages based on document IDs. It is useful when a developer has already performed a search or find_similar query and wants to fetch detailed content for analysis or display purposes. For instance, after obtaining search results, the developer could use this function to pull the full text and key highlights from the top results to present a summarized view in an application.

  • search_and_contents

    Example Example

    exa.search_and_contents(query='Latest trends in machine learning', text={'max_characters': 1000}, highlights={'num_sentences': 3})

    Example Scenario

    This combined function allows for performing a search and retrieving the content and highlights in a single API call. It's efficient for scenarios where both search results and their content are needed immediately. For example, a data scientist might use this function to quickly find and extract concise summaries of the latest machine learning trends from multiple sources, with both the content and highlights provided.

  • find_similar_and_contents

    Example Example

    exa.find_similar_and_contents(url='https://example.com/tech-news', text=True, highlights={'query': 'summary'})

    Example Scenario

    Similar to the search_and_contents function, this function finds similar content and retrieves their text and highlights in one go. It's ideal for users who want to explore related content to a given URL and simultaneously access the full text and summarized insights. For example, a content strategist could use this to find articles similar to a key piece of tech news and immediately obtain their most relevant portions.

Ideal Users of Exa Python SDK

  • Data Scientists and Researchers

    Data scientists and researchers can leverage Exa's neural search capabilities to find relevant academic papers, articles, and other content that aligns closely with their research topics. The ability to retrieve not only search results but also detailed page contents and highlights enables these users to quickly access and analyze the most pertinent information without needing to sift through extraneous content.

  • Content Curators and Marketers

    Content curators and marketers benefit from Exa's ability to find and recommend content that resonates with their audience. The find_similar function, in particular, allows them to discover related articles and media that can be used to enhance content strategies or suggest additional readings to users. The detailed retrieval of page contents and highlights also aids in content creation, allowing curators to extract key insights and repurpose content effectively.

  • Developers and Product Managers

    Developers and product managers who are building applications that require advanced search functionalities can use Exa's SDK to integrate powerful, neural-based search and content retrieval into their products. This is particularly beneficial for creating content discovery platforms, recommendation systems, or search engines that need to provide users with high-quality, contextually relevant results.

Steps for Using the Exa Python SDK

  • 1

    Visit aichatonline.org for a free trial without login, no need for ChatGPT Plus.

  • 2

    Install the Exa Python SDK by running `pip install exa-py` in your terminal. Ensure you have Python 3.7+ installed.

  • 3

    Obtain your API key from the Exa platform and initialize the SDK in your Python environment with `Exa(key='YOUR_API_KEY')`.

  • 4

    Construct Exa-style search queries for optimal results. Use `use_autoprompt=True` for standard searches or craft precise Exa queries for specific needs.

  • 5

    Leverage the `search`, `find_similar`, and `contents` methods to retrieve search results, find similar content, and get page details efficiently.

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Exa Python SDK Guide Q&A

  • What is the main purpose of the Exa Python SDK?

    The Exa Python SDK allows developers to interact with the Exa Search API, enabling them to perform neural search, retrieve content, and find similar pages programmatically within their Python applications.

  • How do I create an effective search query with the Exa SDK?

    For standard searches, enable `use_autoprompt=True` to convert traditional queries into Exa-optimized ones. For more specific searches, craft your queries in a way that mimics how users would share content online, focusing on key descriptions.

  • Can I customize the search results by domain or date?

    Yes, the SDK supports customization of search results by specifying domains to include or exclude, as well as setting date ranges for crawled or published content using options like `include_domains`, `start_crawl_date`, and `end_published_date`.

  • How can I retrieve the full content of a webpage using Exa?

    Use the `contents` method by passing document IDs or URLs. You can also specify options such as `text` for the full text and `highlights` for key snippets, ensuring you get the most relevant information from the page.

  • What are some common use cases for the Exa SDK?

    Common use cases include academic research, content curation, media monitoring, SEO analysis, and trend tracking. The SDK’s ability to perform high-quality neural searches makes it ideal for any application needing precise and relevant content discovery.