Metaphor API Guide - Python SDK-AI-powered search for content discovery.

Example

Using the `search` function, you can submit a Metaphor-style query (or a traditional query with the `use_autoprompt` feature enabled) to retrieve a list of relevant web pages. For example, querying 'This article explains the latest trends in AI' would return articles shared online that match the context of this query.

Scenario

Imagine you're building a content discovery platform where users want to find articles related to recent developments in artificial intelligence. Instead of relying on basic keyword searches, you can leverage the neural understanding of Metaphor to return more nuanced, high-quality results using descriptions of the content users are seeking.

Example

The `find_similar` function allows you to find web pages that are similar to a given URL. For example, given a URL of a specific blog post, you can retrieve a list of articles that are contextually similar.

Scenario

Suppose you're creating a feature for a research tool where users can find related academic papers or articles. If a user provides the URL of a research paper, this function will return articles that cover similar topics or have a similar style, allowing for efficient content discovery in related fields.

Example

The `get_contents` function retrieves the detailed contents of documents given their unique Metaphor IDs. For example, if you have a list of Metaphor result IDs from a previous search, this function will return their full details, including the title, URL, author, and an extract of the content.

Scenario

In a news curation application, after performing a search, you might want to display detailed information about the selected articles. The `get_contents` function can be used to retrieve all necessary metadata and content extracts for the articles identified by their IDs, ensuring you can display or analyze full document details.