### Motivation and Context
Chat completion models are requested and can be significantly less
expensive.
### Description
- Update KernelHttpServer to use chat completion models
- Added notes to sample READMEs
### Motivation and Context
To match changes in #450
### Description
This pull request renames the SemanticTextPartitioner class to
TextChunker, and updates all references to this class in the code and
documentation. The TextChunker class is responsible for splitting text
into smaller chunks based on tokens and paragraphs, which is useful for
processing large texts or code files. The new name TextChunker reflects
the functionality of the class more clearly and avoids confusion with
the SemanticPartitioner class, which is a different concept that splits
text into semantic segments based on embeddings.
Details:
- Rename SemanticTextPartitioner.cs to TextChunker.cs and update the
class name and namespace accordingly. The TextChunker class is now in
the Text namespace, which is more consistent with its purpose and the
other classes in the namespace.
- Update all references to SemanticTextPartitioner in the code and
documentation to use TextChunker instead. This includes the
ConversationSummarySkill and the DocumentImportController classes, which
use the TextChunker class to split text into sentences and paragraphs
for summarization and import.
- Update the README.md file for the GitHub Repo Q&A Bot sample to
explain how the TextChunker class works with memory and embeddings.
- Make some minor formatting changes to the TextChunker class, such as
adding line breaks and spaces for readability, and removing an unused
using directive.
It is not obvious to users that Sample 4 currently only processes
markdown files.
1. Add a paragraph in the sample 4 readme that points out the app only
processes markdown files.
2. Add a tooltip in the repo selection page that informs users only the
files of the specified type will be processed.
### Motivation and Context
This sample continues in the spirit of prior samples in the samples/apps
folder with a goal of teaching developers how the concepts of memory and
embeddings can be integrated.
### Description
This sample includes a frontend app that allows the developer to enter
the address of a publicly available Github repository, which is
downloaded to the temp directory of the machine hosting the
KernelHttpService sample application (can be the same machine).
Once downloaded the developer is able to have a conversation with the
repository and see the impact of the relevance slider on the responses.
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Co-authored-by: Craig Presti <[email protected]>
Co-authored-by: teresaqhoang <[email protected]>
Co-authored-by: markwallace-microsoft <[email protected]>
Co-authored-by: Tao Chen <[email protected]>
Co-authored-by: Mark Wallace <[email protected]>
Co-authored-by: Devis Lucato <[email protected]>
Co-authored-by: Devis Lucato <[email protected]>
Co-authored-by: Adrian Bonar <[email protected]>
Adrian Bonar