Exa AI’s Mission

• The Internet contains humanity’s collective knowledge, but the current search experience is more like navigating a garbage dump than wandering through a library of knowledge. The core problem is that Internet knowledge is buried under a large amount of information.

• Exa’s mission is to organize the world’s knowledge, filter information and extract real knowledge through better search algorithms.

Technical Advantages of Exa AI

• Exa is the first web-scale neural search engine that uses end-to-end Transformer technology (the same technology as ChatGPT) to filter by meaning rather than keywords.

• For example, searching for “startups working on climate change” on Exa will return startups that are actually working on climate change, rather than irrelevant pages that are optimized for the keyword.

Model Training of Exa AI

• Exa’s model training data set includes shared links on web pages, rather than simple text and sentences. This allows its search engine to better understand and predict the relevance of web page links.

• Exa’s search engine doesn’t just predict the next word, but the next related link. This means that its model training is not based on the continuous word sequence of natural language, but on the relationship and structure of web page links.

• In other words, its model learns how to navigate from one link to the next related link, rather than generating coherent text.

• Exa’s training method focuses on predicting the most relevant links, avoiding SEO spam in traditional search engines and low-quality AI-generated content.

Main Features of Exa AI

1. Semantic Search: Exa’s search engine is able to understand semantic meaning, not just keyword matching, to provide more relevant search results.

2. Content crawling: You can crawl complete and cleaned content from any web page to provide high-quality data for AI.

3. Similarity search: Find similar results by URL or long text, making the search more accurate.

4. Large-scale data processing: Able to process up to 1 million search results, meeting the needs of AI large-scale data processing.

5. Real-time updates: crawl new URLs every minute to ensure that the AI always has the latest data.

6. Powerful filtering capabilities: You can search by domain name, date range, or data category, providing a highly customized search experience.

7. Simple API integration: Exa provides a simple and easy-to-use API, developers can integrate and use Exa’s search function with just a few lines of code.

Technical Principles of Exa AI

1. Embedding Model:

• Definition: Embedding models are models that convert text into high-dimensional numerical vectors (embeddings). These vectors mathematically represent the content of the text so that similar content is closer in the vector space.

• Implementation: Exa uses the same technology as ChatGPT to train an embedding model to convert web page content into vector representations, making the search process smarter and more precise.

2. End-to-end Transformer model

• Definition: Transformer is a neural network architecture widely used in natural language processing tasks. It captures the relationship between various parts of the text through a self-attention mechanism.

• Application: Exa uses an end-to-end Transformer model to filter Internet information based on the actual meaning of the query rather than keywords. This approach enables Exa to better understand complex queries and provide more accurate search results.

3. Efficient information filtering

• Problem: Traditional search engines (such as Google) rely on keyword matching and are easily interfered with by SEO optimized content, returning a large amount of irrelevant information.

• Solution: Exa uses embedded models and Transformer technology to filter information based on the actual meaning of the query, avoid irrelevant and low-quality content, and return truly relevant knowledge.

4. Real-time content extraction

• Definition: Exa’s “highlights” feature can instantly extract web page content from search results and customize the length and amount of content according to user needs.

• Implementation: Exa chunks and embeds full web pages in the background, using a paragraph prediction model to extract content. This enables Exa to provide high-quality search results instantly when users query.

5. Long query processing

• Capabilities: Exa is able to process long queries, including sentences, paragraphs, and even entire web pages. This means that users can ask more complex and specific questions, and Exa will still be able to return accurate results.

• Applications: This is very useful for research work, writing assistants, learning tools, and other application scenarios that require detailed information.

6. High-quality retrieval

• Requirements: Large language models (LLMs) require high-quality retrieval results to ensure the quality of output content.

• Implementation: Exa provides high-quality web retrieval for LLMs, filters out low-quality and irrelevant information, and ensures that the output content of LLMs is of high quality. This makes Exa play an important role in AI applications.

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