### 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
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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
- [x] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
Adding Crew.AI as a plugin to the Semantic Kernel will allow users to
leverage the capabilities of Crew.AI in app. This plugin works with
Crews that have been deployed to the Crew.AI Enterprise service.
### Description
Adding Crew.AI as a plugin to the Semantic Kernel will allow users to
leverage the capabilities of Crew.AI in app. This plugin works with
Crews that have been deployed to the Crew.AI Enterprise service.
The plugin functionality has been manually verified with the concept
samples. Other tests will come in the next update.
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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
- [x] I didn't break anyone 😄
---------
Co-authored-by: Ben Thomas <[email protected]>
Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
Updating the AI Search environment variables to follow convention and
match the code as it was throwing an error and unable to connect to the
service as it was. It also loads in the environment properly using the
dotenv package.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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
- [X] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
Switches from poetry to uv
much faster and easier lock management
easier maintenance of pyproject.toml
fixes: #6584fixes: #8326
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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 😄
### Description
When running the getting-started Python notebooks, the user is
instructed to create and populate their own .env file. The values for
the OpenAI specifications included an underscore between 'open' and
'ai'. This led to an error when initializing the chat/text/embedding
model service.
I have removed the underscore from the base .env files, README, and in
the evaluate_with_prompt_flow.py script when reading from the .env file.
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
Since Google Palm will be deprecated soon, removing Google Palm from SK.
Closes#6362
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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 😄
### 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.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
### 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.
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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
- [X] I didn't break anyone 😄
### 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.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
### 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
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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 😄
### Motivation and Context
Adding a new core plugin to Semantic Kernel Python that leverages the
Azure Container Apps Python Sessions Container. This container allows
one, with the proper resource, to run Python in a safe, managed
environment.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
### Description
This PR introduces:
- The Python Sessions (code interpreter) plugin to execute code, upload
a file to the container, list files, and download files.
- It includes a README.md with the steps to set up the ACA resource.
- New samples to show use as a plugin and auto function calling
- Unit tests
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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
- [X] I didn't break anyone 😄
### Motivation and Context
This PR enriches the HTTP requests to the Astra DB Data API (occurring
in the AstraClient) with a User-Agent header specifying the identity of
the caller (specifically, "semantic_kernel"). While not mandatory, this
is good practice with Astra DB and helps collecting aggregate usage data
on the API side.
### Description
Using the `importlib.metadata` library the package version is extracted
(in a fail-safe way) and used, if present, to enrich the caller
information (variable `ASTRA_CALLER_IDENTITY`, later set as the
user-agent header for all HTTP requests to the Data API).
### Contribution Checklist
- [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
- [X] I didn't break anyone 😄
---------
Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
### Description
* Updated models to support the new preview API version
2024-02-15-preview, with some backward compatibility with previous API
shape through the use of aliases
* Updated samples, unit tests and integration tests
* Added experimental settings management using pydantic-settings -- not
strictly required here, but I thought it was useful for testing to be
able to set all values through the .env file, and validation comes for
free using Pydantic. Looking forward to feedback on this. :)
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [ ] The code builds clean without any errors or warnings
- [ ] 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
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Abby Hartman <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
We are going to add
[Astra](https://docs.datastax.com/en/home/docs/index.html) as a memory
store.
DataStax Astra DB is a serverless vector database that’s perfect for
managing mission-critical AI workloads. It’s built on Apache Cassandra®,
making Astra DB a highly scalable, reliable database technology. It’s a
powerful all-in-one data storage solution, ideal for Generative AI
projects.
### Description
- Add astra into python/semantic_kernel/connectors/memory/astradb
- Add test into
python/tests/integration/connectors/memory/test_astradb.py
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [ ] The code builds clean without any errors or warnings
- [ ] 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
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
Co-authored-by: jsbilgi <[email protected]>
Co-authored-by: Obioma Anomnachi <[email protected]>
### Motivation and Context
I have added Azure CosmosDB MongoDB vCore as a data store. MongoDB vCore
now supports vector search on embeddings, and it could be used to
seamlessly integrate your AI-based applications with your data stored in
the Azure CosmosDB. More details about Mongo vCore can be found here:
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search.
Issue https://github.com/microsoft/semantic-kernel/issues/2375
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, by providing the following
information:
-->
1. Why is this change required? **MongoDB vCore now supports vector
search on embeddings, and it could be used to seamlessly integrate your
AI-based applications with your data stored in the Azure CosmosDB.**
2. What problem does it solve? This adds a new memory store MongoDB
vCore(Azure CosmosDB)
### Description
<!-- Describe your changes, the overall approach, and the underlying
design.
These notes will help you understand how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [ ] The code builds clean without any errors or warnings
- [ ] 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
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Mark Wallace <[email protected]>
### Motivation and Context
Resolves: #2591
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
* Added in the `MongoDBAtlasMemoryStore`. A memory store abstraction
that facilitates connections between a MongoDB Atlas cluster to conduct
[Atlas Vector
Search](https://www.mongodb.com/products/platform/atlas-vector-search)
on the Microsoft Semantic Kernel.
* Leverages our async python driver
[motor](https://motor.readthedocs.io/en/stable/) to keep with the
heuristic of asynchronous code
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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
- [x] I didn't break anyone 😄
### Callout
* When writing my test, I noticed there's a habit of other tests
conducting a `try` on imports. Grepping through those implementations, I
see some of them [encapsulate their respective libraries in function
calls rather than doing direct imports in the
module.](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/memory/chroma/chroma_memory_store.py#L55-L63)
Is this the general code pattern we should follow? Or is it fine to have
our imports at the top of the module fine?
---------
Co-authored-by: Steven Silvester <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
1. .NET supports Redis memory while Python does not
2. Match up Python features w/ .NET
3. Users can utilize a Redis backend for memories
4. Closes#1981
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
Implemented a memory connector for Redis and made unit tests for it.
Also added .env configuration for a connection string.
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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#dev-scripts)
raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄
---------
Co-authored-by: Dmytro Struk <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
Implementation of Google PaLM connector with text completion and an
example file to demonstrate its functionality.
Closes#1979
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
1. Implemented Google Palm connector with text completion
2. Added example file to ```python/samples/kernel-syntax-examples```
3. Added integration tests with different inputs to kernel.run_async
4. Added unit tests to ensure successful initialization of the class and
successful API calls
5. 3 optional arguments (top_k, safety_settings, client) for
google.generativeai.generate_text were not included. See more
information about the function and its arguments:
https://developers.generativeai.google/api/python/google/generativeai/generate_text
I also opened a PR for text embedding and chat completion #2258
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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#dev-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
Currently no warnings, there was 1 warning when first installing genai
with `poetry add google.generativeai==v0.1.0rc2` from within poetry
shell: "The locked version 0.1.0rc2 for google-generativeai is a yanked
version. Reason for being yanked: Release is marked as supporting Py3.8,
but in practice it requires 3.9". We would need to require later
versions of python to fix it.
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
Implementation of Google search connector for web search skill.
Fixes: #1862
Sample usage can be found in
`python/samples/kernel-syntax-examples/google_search_skill.py`
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [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#dev-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
The service was renamed to Azure Cognitive Search in 2019, so we should
follow the new convention. The only thing still named "Azure Search" is
the old Azure SDK, out of scope.
### Description
* Revert nuget package name to the previous name (we'll delete the new
package, which has been out only for few days)
* Rename C# and Python classes and namespaces
* .NET, Python: Add new Memory Storage based on Azure Cognitive Search
with custom embeddings
* .NET: Delete previous Azure Search semantic memory component based on old
Azure Semantic Search w/ Lucene
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
Brings parity to python implementation
2. What problem does it solve?
With the pgvector extension, Postgres is a viable option for the storing
of embeddings for memory in Semantic Kernel.
The C# implementation is already capable of this, but the feature has
yet to be added to python.
3. What scenario does it contribute to?
Additional options for memory storage
4. If it fixes an open issue, please link to the issue here.
-->
### Description
Implemention of the MemoryStore to use a Postgresql (with pgvector
extension) to resolve#1270
Taking a different approach from the .net implementation:
Collections as postgres schemas as opposed to collection being another
column in the table, willing to change to match but would love to have a
discussion comparing the options.
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [x] The code builds clean without any errors or warnings
- [x] The PR follows SK Contribution Guidelines
(https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [x] The code follows the .NET coding conventions
(https://learn.microsoft.com/dotnet/csharp/fundamentals/coding-style/coding-conventions)
verified with `dotnet format`
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄
---------
Co-authored-by: Dmytro Struk <[email protected]>
Co-authored-by: Shawn Callegari <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
Adding a Pinecone Memory store using the Pinecone vector database client
for SK python
Add Pinecone vector db memory to SK Python
Memory storage for SK and vectors
### Description
- Using the existing pinecone python module/client
- Reviewing with console exceptions responses that come back from
pinecone, will update if any were omitted
- Currently investigating and would like feedback if I should respond to
throttling errors in a different manner
- Currently testing each API response, slight delay may occur due to
throttling of my starter account in Pinecone.
### Contribution Checklist
<!-- Before submitting this PR, please make sure: -->
- [ ] The code builds clean without any errors or warnings
- [X] The PR follows SK Contribution Guidelines
(https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
- [ ] The code follows the .NET coding conventions
(https://learn.microsoft.com/dotnet/csharp/fundamentals/coding-style/coding-conventions)
verified with `dotnet format`
- [ ] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄
---------
Co-authored-by: Lee Miller <[email protected]>
Co-authored-by: Casey Schadewitz <[email protected]>
Co-authored-by: cschadewitz <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Devis Lucato <[email protected]>
Co-authored-by: Dmytro Struk <[email protected]>
Co-authored-by: Shawn Callegari <[email protected]>
AAD tokens offer greater authentication security and is used by several products.
Add support for Azure Active Directory auth for the `Azure*` backends.
### Motivation and Context
In an effort to make the Python port more idiomatic, let's remove the
`Verify` class and the `./diagnostics` sub-module. There are many more
follow-on tasks to do here, but this is a good start (and already a
large enough change).
### Description
This PR does the following:
1. Removes the `Verify` class, and re-writes all instances of `Verify.*`
2. Adds a `validation.py` in the `./utils` sub-module to hand some of
the more complex cases from `Verify` (checking that skills/functions/and
function params have valid names)
3. Removes the rest of the `./diagnostics` sub-module (w/ a longer-term
goal of removing all/most of our custom exception classes and, instead,
using appropriate built-in error classes)
### Motivation and Context
The `python-preview` branch is under active development and many
docs/tests/examples are getting a bit out of sync. There are also a few
small cleanup chores/refactors to make _usage_ easier (e.g., to make
examples cleaner). I've grouped these small fixes (plus a few bug fixes)
into this "cleanup" PR.
### Description
This PR does the following:
1. Updates the out-of-date example in `README.md`
2. Fixes all 5 of the notebooks we support right now (1,2,3,4, and 6;
notebook 5 is planner, I've blanked out most of the code there so we
don't confuse people, especially as the planner API is changing)
3. Fixes a bug w/ `stop_sequences` in `(Azure)OpenAITextCompletion`
4. Fixes a bug introduced in our upgrades to the embeddings cosine-sim
check (we should add more thorough tests here)
5. Cleans up tests
6. Ensures end-to-end tests are all runnable
7. Fixes up the `kernel_extensions` to work more naturally (so that end
users can use them directly on the `kernel` object and get good
type-hinting)
