• Written by Azhar Aliyar, Director

When a user submits a query to AsscherAi, the system generates a numeric representation of the query, known as an embedding, which lets it retrieve relevant information from the organisation's data by meaning, rather than exact keyword matching. This process allows the system to capture nuances in language and provide more accurate results. Embeddings are a type of vector that represents words, phrases, or documents in a high-dimensional space, where similar items are mapped to nearby points. The embedding is then used to search for similar vectors in the organisation's data, returning relevant results to the user.

Definition of Embeddings

An embedding is a dense vector representation of a word, phrase, or document that captures its semantic meaning. This representation is learned from large amounts of text data and can be used to compare the similarity between different pieces of text. The embedding is typically generated using a neural network-based algorithm that takes into account the context in which the word or phrase is used. This allows the system to distinguish between words with multiple meanings and capture subtle differences in language.

The use of embeddings in AsscherAi enables the system to provide more accurate and relevant results to user queries. By representing text as vectors in a high-dimensional space, the system can capture complex relationships between different pieces of text and provide results that are more nuanced and accurate.

Difference from Other Techniques

Embeddings are often confused with other techniques such as keyword extraction or topic modeling. However, these techniques are distinct and serve different purposes. Keyword extraction involves identifying the most important words or phrases in a piece of text, while topic modeling involves identifying the underlying themes or topics in a large corpus of text. Embeddings, on the other hand, provide a more fine-grained representation of text that captures its semantic meaning.

For example, a shift supervisor checking the maintenance log may use AsscherAi to query the system for information on equipment maintenance schedules. The system generates an embedding for the query and uses it to retrieve relevant information from the log, returning results that are more accurate and relevant than a simple keyword search.

Applications in a Large Organisation

Embeddings have a wide range of applications in a large organisation, from improving search functionality to enabling more accurate analysis of text data. For instance, a category manager comparing two suppliers may use AsscherAi to query the system for information on supplier performance. The system generates an embedding for the query and uses it to retrieve relevant information from the supplier data, returning results that are more accurate and relevant than a simple keyword search.

Organisations can also use embeddings to analyse large amounts of text data, such as customer feedback or employee surveys. By generating embeddings for the text data, organisations can identify patterns and trends that may not be immediately apparent, and gain a deeper understanding of their customers or employees.

Limitations of Embeddings

While embeddings are a powerful technique for representing text data, they are not a panacea. One of the main limitations of embeddings is that they can be sensitive to the quality of the training data. If the training data is biased or incomplete, the embeddings generated may not accurately capture the semantic meaning of the text. Additionally, embeddings can be computationally intensive to generate, which can make them difficult to use in real-time applications.

For more information on how AsscherAi uses embeddings to provide accurate and relevant results, please visit our website. If you have any questions or would like to learn more about how AsscherAi can be used in your organisation, please contact us.

When Not to Use Embeddings

While embeddings are a powerful technique for representing text data, there are certain situations where they may not be the best choice. For example, if the text data is very structured and keyword-based, such as a database of product information, a simple keyword search may be more effective. Additionally, if the text data is very small, such as a short paragraph of text, a simple keyword search may be sufficient.

In general, embeddings are most effective when dealing with large amounts of unstructured text data, such as customer feedback or employee surveys. In these cases, the use of embeddings can provide a more nuanced and accurate understanding of the text data, and enable organisations to gain deeper insights into their customers or employees.