![]() Many of those terms borrow from common concepts like miles, points, or rewards. Every company has a different term for its customer currencies and rebate programs. Imagine trying to compare reward programs between airlines. Many heterogeneous corpuses present problems for simple keyword matching. When your application is searching a large or ambiguous corpus.Here are some scenarios where semantic search will be particularly helpful: Semantic search is great for disentangling subtleties like this. A semantic language model (like the one used by Google) will embed the two queries in disparate locations of the vector space. The preposition “to” entirely changes the meaning of the query, which is impossible to detect with simple keyword matching. Think of the difference between the queries “why can’t I commit changes” - a perennial problem for the novice Git user - and “why can’t I commit to changes” - a problem for the indecisive. The superiority of semantic search over a keyword-based approach becomes clear if we look at an example. While illegible to humans, the vector-based representation works very well for computers to represent meaning. Texts that are similar in meaning are closer to each other, while unrelated texts are more distant. We can then use similarity measures like cosine similarity to understand how close in meaning two vectors (and their associated texts) are. Like all Transformer-based language models, the models used in semantic search encode text (both the documents and the query) as high-dimensional vectors or embeddings. Thanks to Haystack’s modular setup and the availability of high-quality pre-trained language models, you’ll be able to set up your own semantic search system in less than twenty minutes. In this article, we will show you how to set up a semantic search engine in Python, placing it on top of your document collection of choice, with our open source Haystack framework. Over the last decade or so, Python has become the principal language for machine learning (ML) and natural language processing (NLP). As well as being helpful in its own right, semantic search also forms the basis for many complex tasks, like question answering or text summarization. Powered by the latest Transformer language models, semantic search allows you to access the best matches from your document collection within seconds, and on the basis of meaning rather than keyword matches. Semantic search is the task of retrieving documents from a collection of documents (also known as a 'corpus') in response to a query asked in natural language.
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