Question 1

How does w2vgrep compare to grep for semantic search?

Accepted Answer

w2vgrep uses word embeddings to find semantically similar terms, while grep only matches exact strings or regex patterns. This makes w2vgrep better for conceptual searches but slower due to model loading, and it requires additional setup with embedding models.

Question 2

How to install w2vgrep on Windows?

Accepted Answer

The README primarily covers Linux/OSX, but for Windows, you can download the binary release, manually obtain a model file, and configure it via command-line arguments or JSON. Cross-compilation from source might be needed for full compatibility.

Question 3

What languages does w2vgrep support best?

Accepted Answer

It supports over 157 languages via fasttext models, with pre-processed binaries for some in the models/ directory. For others, you need to use the provided fasttext-to-bin tool to convert .vec.gz files, as explained in the multi-language support section.

Question 4

How can I reduce the model size in w2vgrep?

Accepted Answer

Use the reduce-model-size tool in the model_processing_utils directory to reduce dimensionality (e.g., from 300 to 100 dimensions), which decreases file size and memory usage while maintaining similar accuracy, as demonstrated in the README.

Question 5

Does w2vgrep work with phrase queries or only single words?

Accepted Answer

Based on the README, it focuses on word-level semantic search using embeddings, so it likely handles single words best. Phrase support isn't explicitly mentioned, implying limitations in contextual phrase matching.

Question 6

What's the default similarity threshold and how to adjust it?

Accepted Answer

The default threshold is 0.7, but you can adjust it with the --threshold option. Lower values increase matches (more recall), while higher values make searches stricter, as shown in the example with -threshold=0.55.

semantic-grep

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