### Motivation and Context
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Overhaul of the VectorStores to get it matching with dotnet and the
latest development there.
Closes:
- #11938
- #11598
- #11597
- #11517
- #10561
- #10391
- #9911
- #9892
- #10867
### Description
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Many changes, some highlights:
- Moved from different vector fields types, to a single with a enum
first VectorStoreField()
- Renamed several items
- Made embeddinggenerrator a part of the vector field spec and do
automatic vectorization from there or from the same param on a
collection
- Adds hybrid search using the same setup
- Removed the intermediate TextSeachVectorSearch object need, by
allowing `create_search_function` directly on a collection
Will write out full changes in a migration guide in the docs.
### Contribution Checklist
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- [ ] I didn't break anyone 😄
### Motivation and Context
Added BRAVE SEARCH CAPABILITIES in Semantic Kernel Python based on PR
#11308 .
### Description
- Introduce BraveConnector & BraveSearch for Brave Search API
integration,
- BraveSettings and response models were added, respectively.
- Add unit Tests
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- [x] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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Removed some of the old search samples.
### Description
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### Motivation and Context
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This adds a new version of the Google Search implementation based on the
new Text Search interfaces.
Easily create a plugin from the different search functions to perform
RAG with online docs!
Closes#6834
### Description
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Creates a new folder under connectors.search for Google.
Adds GoogleSearch
Adds classes to capture the response.
Adds constants for the query parameters
Adds a sample showing how to use it with a filter.
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- [x] I didn't break anyone 😄
### 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
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- [ ] I didn't break anyone 😄
---------
Co-authored-by: Tao Chen <[email protected]>
### Motivation and Context
Fix sample plugin path for Windows. Re-enable samples CI tests.
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### Description
Fix sample plugin path for Windows. Re-enable samples CI tests.
- Tests passing locally on both MacOS and Windows.
- Adds logic to each notebook to properly set up the correct path to
find imports. Notebooks should now run out of the box to close#6588
- Added retry logic for the samples tests to retry 3 times in the event
of a failure.
- Install notebook pip package at kernel level instead of shell level
- Updated all occurrences of `gpt-3.5-turbo-1106` to `gpt-3.5-turbo`
which is `gpt-3.5-turbo-0125`.
The GH Action code quality check was failing for this same code in
#6675, so this is superseding it. Now that the GH actions are fixed,
this also supersedes #6681.
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- [X] I didn't break anyone 😄
### Motivation and Context
We had many broken concept examples. These samples are also not covered
by tests so it's hard to know when things are out of sync.
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### Description
Fix the Python concept samples, and also cover them with tests so we
aren't checking in code that is going to break them.
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- [X] I didn't break anyone 😄
### Motivation and Context
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Added a section on documentation to the DEV_SETUP
Added a check for the copyright row to pre-commit
Closes#5629
### Description
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### Motivation and Context
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Refactoring some of the code following [this
advice](https://github.com/microsoft/semantic-kernel/pull/6371#discussion_r1611138252)
from the maintainers.
### Description
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Look up kernel functions and plugins with the `get_function` and
`get_plugin` method. Removed the unused `DEFAULT_CHAT_SYSTEM_PROMPT`
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---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
SK Python is tightly coupled to the use of a `.env` file to read all
secrets, keys, endpoints, and more. This doesn't scale well for users
who wish to be able to use environment variables with their SK
Applications. By introducing Pydantic Settings, it is possible to use
both environment variables as well as have a fall-back to a `.env` file
(via a `env_file_path` parameter), if desired.
By introducing Pydantic Settings, we are removing the requirement to
have to create Text/Embedding/Chat completion objects with an `api_key`
or other previously required information (in the case of
AzureChatCompletion that means an `endpoint`, an `api_key`, a
`deployment_name`, and an `api_version`). When the AI connector is
created, the Pydantic settings are loaded either via env vars or the
fall-back `.env` file, and that means the user can create a chat
completion object like:
```python
chat_completion = OpenAIChatCompletion(service_id="test")
```
or, to optionally override the `ai_model_id` env var:
```python
chat_completion = OpenAIChatCompletion(service_id="test", ai_model_id="gpt-4-1106")
```
Note: we have left the ability to specific an `api_key`/`org_id` for
`OpenAIChatCompletion` or a `deployment_name`, `endpoint`, `base_url`,
and `api_version` for `AzureChatCompletion` as before, but if your
settings are configured to use env vars/.env file then there is no need
to pass this information.
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### Description
The PR introduces the use of Pydantic settings and removes the use of
the python-dotenv library.
- Closes#1779
- Updates notebooks, samples, code and tests to remove the explicit
config of api_key or other previous .env files values.
- Adds new unit test config using monkeypatch to simulate env variables
for testing
- All unit and integration tests passing
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### Motivation and Context
All previous samples were either in kernel syntax or a separate
notebooks folder. The goal is to make the samples easier to understand
and have a better structure.
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### Description
The PR restructures the kernel syntax examples into new folders:
concepts (with subfolders for previous syntax examples), demos,
getting_started, and learn_resources.
- Closes#6119
- Adds a new concept/function example for understanding kernel
arguments.
- Updates the bookings plugin
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Eduard van Valkenburg