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Vector Search for Practitioners with Elastic
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Log vectorization is the process of transforming logs into embeddings. This process requires a couple of steps, such as generating logs for the test and expanding and using a general model to generate vectors.
In addition, we made the arbitrary choice to do everything in Python here, which gives you the ability to re-execute the same examples in a Google Colab notebook for educational purposes.
All the code from this chapter is available in the chapter 7
folder of this book’s GitHub repository: https://github.com/PacktPublishing/Vector-Search-for-Practitioners-with-Elastic/tree/main/chapter7.
Note that instead of applying the first approach and trying to generate vectors directly from the logs, we will adopt the strategy of expanding them to a human-readable description first, allowing us to avoid the intensive process of model training.
We are now going to learn how to generate synthetic logs.
With synthetic logs, we enable...