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The NBA tool which forwards user queries to this solution for article retrieval, also provides product information relevant to the queried asset (such as laptop or hardware commodity). This product information includes the specific asset service tag, and the product hierarchy-related tags such as the “Precision Notebook.” The article search process begins by filtering for articles associated with specified tags. The details of this tagging process are further elaborated in the Data pipeline section.
Once articles are filtered for relevance to the product/asset, they are ranked based on semantic similarity. The embedding model plays a key role, in understanding the query once provided, for example, “Keyboard key stuck.” The model’s function is to transform (or embed) the given text, which may be in one of several supported languages, into numerical vectors which capture the semantic meaning of the text, supporting measurement of semantic similarity between the model and article texts. An Embedding Store (an efficient database for managing embeddings) performs the similarity calculation, leveraging its vector indexing functionality, calculating the cosine distance from the query embedded at runtime and the article summaries each embedded and indexed before runtime.
With articles filtered and ranked based on query relevance, they are shortlisted for inclusion in the final search results.