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Here, we have a AI based search engine trained using hugging face transformers of LLM. The intention here is basically to detect bugs in the logs. Most often we rely on keywords such as errors, debug or warning prints which can sometimes be misleading . However the approach proposed here is more of a semantic based search.

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Developers and testers often need to analyze multiple data sources, particularly log files. The current standard method involves opening logs in a text editor and searching for specific keywords. Typically, logs with an "Error" or "Warning" level might indicate faults or issues. However, this manual search method relies on specific keywords such as "error," "warning," and so on. This approach can be ineffective if the context appears under different terms or phrases. To address this, we propose an "AI-Enabled Search Engine" that performs searches based on the semantic meaning of the text, rather than specific keywords. For instance, if we need to identify defects or anomalies in the Syslog, a search for "defect" or "anomaly" would work even if those exact keywords are not present in the logs. The AI understands the semantic meaning of the text and classifies it accordingly, unlike regular parsers or manual searches that rely on predefined rules or patterns.

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Here, we have a AI based search engine trained using hugging face transformers of LLM. The intention here is basically to detect bugs in the logs. Most often we rely on keywords such as errors, debug or warning prints which can sometimes be misleading . However the approach proposed here is more of a semantic based search.

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