Contribute advanced Hybrid search example in OpenAI Cookbook (python)

[tylerhutcherson]

The existing cookbook just touches the surface:
https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/redis/getting-started-with-redis-and-openai.ipynb

Contribute a Python notebook that demonstrates complex Hybrid queries with Redis VSS and other search features (an ecommerce dataset might work nicely) including

• Numeric range filters
• Tag filters
• Full text search "filters"
• Client-side hybrid scoring combing both BM25 lexical AND semantic search. This could be done in a pipeline to send 1 redis call to fetch both search results (top K) and then merge the sets. Show performance improvement with this technique over pure lexical or pure semantic?

[michaelskyuan]

Submitted PR

[tylerhutcherson]

Initial review submitted from our end >>> openai/openai-cookbook#417

@michaelskyuan at some point we will also want to make an update to this notebook that covers bullet point 4 above. This is a bit "green field" in the sense that we have not yet explicitly tried this. But it's theoretically possible to do true weighted hybrid search using a redis pipeline command and "merging" results from the two scoring algorithms (BM25 + KNN/CosineD). I sense the lift will be a bit more on this, and since not immediately pressing, I will spin it off into a separate issue that we can re-prioritize when the time is right, probably in the next month.

[michaelskyuan]

I agree @tylerhutcherson. And I believe this topic deserves it's own separate notebook with a denser text dataset.
Let's leave OOTB Redis Hybrid search functionality on the current notebook and have a specific notebook that will address normalization of lexical and semantic scoring using a more appropriate dataset instead of an ecommerce dataset.

[Spartee]

@michaelskyuan This App/notebook was recently contributed by OpenAI. We could use some of this.