Hello! These Azure AI Search videos have been helping me a lot. Quick question, do you have a video about how to split the content of the file (PDF, PPT, etc) into chunks and storing at an Index? I'm struggling a little to bit to understand how to set the fields for this case. Thanks!
Semantic summarization and ranking are applied to just the top 50 results, as scored by the default scoring algorithm. Using those results as the document corpus, semantic ranking re-scores those results based on the semantic strength of the match. Therefore any scoring profiles used using the default scoring algorithm with simple or full query Types are overridden and not applicable when using Semantic as the re Ranker score's are used through the Semantic search models. Microsoft PG team are currently working on composing scoring profiles for Semantic search queries.
Hello! These Azure AI Search videos have been helping me a lot. Quick question, do you have a video about how to split the content of the file (PDF, PPT, etc) into chunks and storing at an Index? I'm struggling a little to bit to understand how to set the fields for this case. Thanks!
Great playlist. I loved all the videos in the playlist. In this video you rushed 😞 through Semantic ranking configuration and search.
Sorry about that. I will go deeper in semantic Search and Vector Search
How to implement scoring profile in semantic search?
Semantic summarization and ranking are applied to just the top 50 results, as scored by the default scoring algorithm. Using those results as the document corpus, semantic ranking re-scores those results based on the semantic strength of the match. Therefore any scoring profiles used using the default scoring algorithm with simple or full query Types are overridden and not applicable when using Semantic as the re Ranker score's are used through the Semantic search models. Microsoft PG team are currently working on composing scoring profiles for Semantic search queries.
Pls create a playlist for this
Sure