This tutorial is very complex. Here's how to get free semantic search with much less complexity:

  1. Install sentence-transformers [1]
  2. Initialize the MiniLM model - `model = SentenceTransformer('all-MiniLM-L6-v2')`
  3. Embed your corpus [2]
  4. Embed your queries, then search the corpus

This runs on CPU (~750 sentences per second), and GPU (18k sentences per second). You can use paragraphs instead of sentences if you need more text. The embeddings are accurate [3] and only 384 dimensions, so they're space-efficient [4].

Here's how to handle persistence. I recommend starting with the simplest strategy, and only getting more complex if you need higher performance:

  - Just save the embedding tensors to disk, and load them if you need them later.
  - Use Faiss to store the embeddings (it will use an index to retrieve them faster) [5]
  - Use pgvector, an extension for postgres that stores embeddings
  - If you really need it, use something like qdrant/weaviate/pinecone, etc.

This setup is much simpler and cheaper than using a ton of cloud services to do embeddings. I don't know why people make semantic search so complex.

I've used it for https://www.endless.academy, and https://www.dataquest.io and it's worked well in production.

[1] https://www.sbert.net/

[2] https://www.sbert.net/examples/applications/semantic-search/...

[3] https://huggingface.co/blog/mteb

[4] https://medium.com/@nils_reimers/openai-gpt-3-text-embedding...

[5] https://github.com/facebookresearch/faiss