Getting Started With Embeddings

Summary

Embeddings represent unstructured information such as text and images as numerical vectors in a shared semantic space. To demonstrate their utility, a simple semantic search engine is built over US Social Security Medicare frequently asked questions. The system generates 384-dimensional vector representations for thirteen FAQ entries by dispatching POST requests to the Hugging Face Inference API using the sentence-transformers/all-MiniLM-L6-v2 model. Incoming user queries are converted into matching vector representations and evaluated against stored dataset vectors using the util.semantic_search function from the Sentence Transformers library. By calculating cosine similarity scores, the system retrieves and ranks the five most semantically relevant questions without requiring custom keyword rules or massive labeled training sets.

Takeaways

1. 1

   The sentence-transformers/all-MiniLM-L6-v2 model encodes text inputs into 384-dimensional vector representations that capture semantic meaning.

2. 2

   The initial call to the Hugging Face feature extraction endpoint can take approximately 20 seconds while the model downloads and installs on the server, after which subsequent inference requests execute much faster.

3. 3

   Sentence Transformers util.semantic_search relies on cosine similarity by default to compare query embeddings against a dataset corpus, though distance metrics such as the dot product can also be used.