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semantic-kernel/python/semantic_kernel/functions
Eduard van ValkenburgandTao Chen c8b4094835 Python: Introducing vector and text search (#9345)
### Motivation and Context

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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

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- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

---------

Co-authored-by: Tao Chen <[email protected]>
2024-11-06 13:22:50 +00:00
..