Python: Introducing vector and text search - #9345

Motivation and Context

This PR does the following things:

• Introduces TextSearch abstractions, including implementation for Bing
  • This consists of the TextSearch class, which implements three public search methods, and handles the internals, the search methods are: 'search' returns a string, 'get_text_search_results' returns a TextSearchResult object and 'get_search_results' returns a object native to the search service (i.e. BingWebPages for Bing)
  • This also has a method called "create_{search_method}' which returns a KernelFunction based on the search method. This function can be adapted by setting the parameters and has several adaptability options and allows you to create a RAG pipeline easily with custom names and descriptions of both the functions and the parameters!
• Introduces VectorSearch abstractions, including implementation for Azure AI Search
  • This consists of a VectorStoreBase class which handles all the internal and three public interfaces, vectorized_search (supply a vector), vectorizable_text_search (supply a string that get's vectorized downstream), vector_text_search (supply a string), each vector store record collection can pick and choose which ones they need to support by importing one or more next to the VectorSearchBase class.
• Introduces VectorStoreTextSearch as a way to leverage text search against vector stores
  • Since this builds on TextSearch this is now the best way to create a super powerfull RAG setup with your own data model!
• Adds all the related classes, samples and tests for the above.
• Also reorders the data folder, which might cause some slight breaking changes for the few stores that have the new vector store model.
• Adds additional IndexKinds and DistanceFunctions to stay in sync with dotnet.
• Renames VolatileStore and VolatileCollection to InMemoryVectorStore and InMemoryVectorCollection.

Closes #6832 #6833

Contribution Checklist

• The code builds clean without any errors or warnings
• The PR follows the SK Contribution Guidelines and the pre-submission formatting script raises no violations
• All unit tests pass, and I have added new tests where possible
• I didn't break anyone 😄