### Motivation and Context
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extend mypy coverage for prompt_templates, template_engine and some
other smaller folders.
still todo: connectors, memory and planner folders.
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### 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
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- Added static code checking to pre-commit (check-ast and
nbqa-check-ast)
- Added Bandit security checking to pre-commit
- Added pre-commit step to python-lint workflow, if it works, can delete
seperate mypy, ruff and black tests
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Working through improving test coverage
Small changes introduced:
- ai_service_selector: select_ai_service method, now has extra optional
variable for type_, when not set the behavior is the same as before,
otherwise allows any type of AI service present to be selected,
including embeddings (which was not possible)
### Description
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---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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Now that we only support 310 and up, we could unify all files, to use
the new style typing and annotations.
### Description
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- [x] I didn't break anyone 😄
### Motivation and Context
There may be classes or functions inside of SK Python that should be
deemed as experimental. Currently in Python there is no out-of-the-box
way to get the decorator.
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### Description
As there is a name clash with using `experimental` as the decorator
name, we are introducing two decorators: one `experimental_class` and
one `experimental_function`. The note that "This {function | class} is
experimental and may change in the future" is included in the docstring
and a bool `is_experimental` on the class is added.
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- [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.
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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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- [ ] 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.
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### 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
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- [X] I didn't break anyone 😄
### Motivation and Context
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### Description
1. Why is this change required?
- Allow developers to use Pinecone as a memory store
2. What problem does it solve?
- Currently using the Pinecone memory store class throws the deprecation
error message show below:
`AttributeError: init is no longer a top-level attribute of the pinecone
package.`
This PR fixes this issue by updating `pinecone-client` to a new major
version and using the new initialization.
3. If it fixes an open issue, please link to the issue here.
#4914
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- [x] I didn't break anyone 😄
---------
Co-authored-by: Lazaro Hurtado <[email protected]>
### Motivation and Context
We're working towards creating a core set of syntax examples (there are
four total). The core examples will be available in all 3 SK languages.
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### Description
This PR introduces two (of the four) new kernel syntax examples, which
will align with the new kernel examples coming soon for both dotnet and
Java. #5784
- Introduce a custom weather plugin that in conjunction with the core
TimePlugin, make use of auto function calling.
- Introduce a kernel syntax example that shows how to integrate with the
Microsoft Graph API to create a "restaurant booking." Note: this doesn't
actually place a real reservation, but shows how to interact with
msgraph.
- Also fixes an issue where the streaming tool call argument formation
was broken. Closes#6106
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- [X] I didn't break anyone 😄
### Motivation and Context
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### 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
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- [ ] I didn't break anyone 😄
---------
Co-authored-by: Abby Hartman <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
Python had the ability to handle operations for OpenAPI plugins, but at
a basic level. It did not have the ability to handle OpenAI plugins, nor
did it allow the user to configure an authentication callback. This PR
brings in that functionality.
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### Description
This PR:
- Allows a user to handle OpenAI plugins with/without auth. Closes#5355
- Improves the functionality around handling OpenAPI specs.
- Introduces an auth callback mechanism to allow for REST API calls to
use auth
- Introduces OpenAI/OpenAPI function execution settings.
- Provides two new kernel examples that show how to handle a simple
OpenAI plugin (Klarna) as well as a more complex plugin (Azure Key
Vault) that requires auth.
- Updates unit tests.
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- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄
### Motivation and Context
Auto tool calling hasn't been implemented in Python for OpenAI models.
This is a much needed feature, which helps open the possibilities and
extends being able to use the OpenAI models for just pure back and forth
chats.
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### Description
This PR:
- introduces auto tool calls for OpenAI models. If the prompt function
is of an OpenAI chat completion type, then we pass the kernel as an
argument to the OpenAI Chat Completion Base class. The kernel is used to
auto-invoke tools/functions seamlessly as part of the chat completion
request. Closes#4553
- splits the current chat completion/chat completion stream methods into
smaller, more readable/functional methods in the OpenAI Chat Completion
Base class.
- Adds an integration test to cover auto invoke tool calls
- Updates a kernel example to be able to work for a
streaming/non-streaming tool call. Other Kernel Examples that don't use
auto invoke tools still work as they did before.
- Removes old code in the utils file that was related to the manual form
of invoking function calls.
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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
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The existing Exceptions structure was very much inspired by dotnet, this
is now replaced with a pythonic implementations.
This means all Exceptions derive from KernelException and then
specialise for different purposes.
Closes#2194
### Description
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Added folder for all exception types, all can be imported through `from
semantic_kernel.exceptions import ...` no need for a user to know which
file the relevant one is in, but keeps things tidy for developers.
Removed old KernelException, added back subtypes for the errorcodes.
Went through everything to make sure the `raise ... from exc` pattern is
used as much as possible as that returns a better stacktrace.
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- [x] I didn't break anyone 😄
### Motivation and Context
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Major work to replace the context with Kernel Arguments on the front-end
and Function Results on the back.
Updated the function decorator to a new approach, in line with dotnet.
Revamps the way function are called, allowing native functions to be
completely ignorant of SK, other then the decorator.
This also moves things into the new folder structure in sync with
dotnet.
### Description
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Adds:
- KernelArguments, a dict-like class that replaces the variables in
KernelContext. Closes#4565
- FunctionResult, a class to hold the results of function executions,
includes the function, the value and metadata, as well as two convenient
function to get the value out of it (str and get_inner_content) the
first is generic, the second specifically for KernelContent responses
(from AI services).
- AI Service Selector class, has a default, which is based on the order
in the arguments followed by the order in the functions, can be
overridden to implement your own strategy. Closes#4631
- Introduces ChatHistory and refactors the PromptTemplateConfig. Closes
#4856, #4630
- Improves parsing of templates, will now all validate during creation
and throw an error then, instead of some that do not check for validaty
until used.
- Introduces named_args block and thereby the ability to have multiple
arguments for a function call in a template. Closes#5003
Updates:
- kernel_function decorators, the parameter decorator was removed and
instead we now use Annotated to add a description of a field and we get
the type and required from the function definition.
- core plugins, use the new approach to kernel_function decorators.
- planners, template engines have all been updated to use the kernel and
kernelarguments instead of Context.
- Events have been updated, now use kernelarguments and function_result
- Tokenizers support for named_args and improvements on parsing and
checking.
- Kernel examples and notebooks to use the latest code.
- All unit and integration tests. There is more code coverage now than
before.
Removed:
- kernelContext
- kernel_function_parameter_decorator
- delegate handling code for native functions
- file_io_plugin and tests
- SemanticFunctionConfig
- ChatPromptTemplate
### Contribution Checklist
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- [ ] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
This PR solves two works items: #4560 and #4667. The Python SDK had
different ways of handling plugins and collections. The work in the PR
helps align the SDK with dotnet nomenclature and structure.
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### Description
A `KernelPluginCollection` is introduced with an underlying
`DefaultKernelPlugin` and a `KernelPlugin` base class. Other code is
refactored to use this new `KernelPluginCollection` model which should
simplify how plugins are handled. Unit tests are added to test the new
functionality. Existing unit tests and integration tests, along with
kernel examples and notebooks were updated based on these changes.
Unlike before, we're requiring plugins to be defined with a name, which
removes the GLOBAL_PLUGIN constant name of "__GLOBAL_PLUGIN__". In some
senses, while creating a plugin or a function, a name may not be
provided and so a random name in the form of a plugin
(p_<random_ascii_chars>) or function (f_<random_ascii_chars>) is used.
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### Contribution Checklist
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- [X] The code builds clean without any errors or warnings
- [X] The PR follows the [SK Contribution
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raises no violations
- [X] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
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
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- [ ] The code builds clean without any errors or warnings
- [ ] The PR follows the [SK Contribution
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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
To better align with SK dotnet v1, Python skills have been renamed to
plugins. This removes the root samples skills directory as dotnet
supports the plugins and their configs. This the beginning of more work
to consolidate other files/names, per the backlog. I've run several
greps for files or file names containing "skill" (case insensitive), and
no results appear. Do alert if you find anything else that includes
"skill."
Fixes#3319
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### Description
This is a large, breaking change PR as the replacement of "skills" to
"plugins" affects many files. It was difficult to break this up into
smaller chunks without leaving the SK Python repo in a bad state for
some time. Other updates include:
- Prompt template has been updated to use `execution_settings`, which
aligns with dotnet, from the previous `completion` that Python used.
- AzureOpenAI/OpenAI function calling has been updated to match the
current versions of tools/tool_choice. Parsing messages in the open_ai
utils respects this as it now looks for tool_calls and it configures the
function_call with the id (new), name, and argument).
- File renames, where the filename used to contain "skill" will look
like new code, but it's the same code, just with "plugin" in the name.
- Kernel examples and notebooks have been tested and are working. Unit
tests and integration tests are passing.
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### Contribution Checklist
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- [X] The code builds clean without any errors or warnings
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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
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As we are tightening the formatting and linting setups, this was
missing, now the line-length for black and ruff are the same, thereby no
longer running into black issues as often.
### Description
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### Contribution Checklist
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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 replaces passing Logger instances as arguments around with the
preferred Python way of having each module define a module-wide `logger`
instance at the top of the module. Fixes#3711
### Description
Removes any `log` argument and argument documentation. Replaces usage of
those `log` arguments with the module-wide `logger` instance.
### 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
Fixes#4280. Currently, the str representation of SK functions look
verbose:
'uppercase': SKFunction(FUNCTION_PARAM_NAME_REGEX='^[0-9A-Za-z_]*$',
FUNCTION_NAME_REGEX='^[0-9A-Za-z_]*$',
SKILL_NAME_REGEX='^[0-9A-Za-z_]*$')
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### Description
This PR removes unnecessary class attributes from SKFunctionBase and
exposes some constants in utils/validation.
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### Contribution Checklist
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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
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This change adds a python connector for Azure OpenAI chat completions
with data, which was already supported in the .NET SDK.
### Description
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Add connector for Azure OpenAI chat completions with data and associated
sample code for how to use it. Some minor changes were made to the SK
function to handle the "tool" message that contains the citations and
other information returned from the chat completions extensions API.
### Contribution Checklist
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- [ ] The code builds clean without any errors or warnings
- [ ] The PR follows the [SK Contribution
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raises no violations
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Sarah Widder <[email protected]>
### Motivation and Context
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Sample notebook 06-memory-and-embeddings demonstrated how semantic
kernel lets users add memory to their kernels and also work around
ever-growing contexts that eventually exceed the maximum token limit. It
did so by using the in RAM VolatileMemoryStore and teased the
possibility of offloading this to an external vector store. With this
pull request, we want to provide guidance and repeatable steps so that
users can actually perform semantic search by leveraging an external
vector store, Azure AI Search (formerly known as Azure Cognitive Search
### Description
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The changes consist in new cells added at the tail end of the existing
notebook. We register a new memory store, external this time and show
case how the rest of the code can be reused as-is. We add the capability
of providing access credentials to Azure AI Search in environment
variables as well.
### Contribution Checklist
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- [ ] 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
- [x] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[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!
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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
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design.
These notes will help you understand how your code works. Thanks! -->
### Contribution Checklist
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- [ ] 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
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### Description
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* 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
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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
- [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
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1. #2889
2. Allows Contributors and Devs to seamlessly use `NullLogger` or
generalized `Logger` class without having to do attribute checks
3. See details in #2889
4. #2889
### Description
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* I added in a `_NullerMeta` metaclass to the `NullLogger` to iterate
over all the function signatures in the `Logger` class and have them
issue no returns.
* This is backward compatible and breaks no existing implementations.
### 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
- [x] I didn't break anyone 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
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Noticed that another commonly edited environment setting for api_version
was missing from the settings util. And want to pass the env to the
constructor.
### Description
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These notes will help understanding how your code works. Thanks! -->
Added api_version to the settings util so that that can be controlled
through .env as well.
Added convenience function that outputs a dict that can be passed
directly into Completion constructors.
Updated sample to use that.
### Contribution Checklist
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- [x] The code builds clean without any errors or warnings
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Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
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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
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Lee Miller <[email protected]>
### Motivation and Context
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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
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Implemented a memory connector for Redis and made unit tests for it.
Also added .env configuration for a connection string.
### 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#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
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implement stepwise planner #2221
### Description
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### Contribution Checklist
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- [ ] The code builds clean without any errors or warnings
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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: Lee Miller <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
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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
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When loading config from .env, the assertions like `assert api_key is
not None, "Azure OpenAI API key not found in .env file"` don't catch
anything. While `dotenv_values(".env")` does put None values for keys
with missing values, that's only if there's no equals sign after the
key. For example, this file:
```
OPENAI_API_KEY=""
OPENAI_ORG_ID="org_id"
AZURE_OPENAI_DEPLOYMENT_NAME=
AZURE_OPENAI_ENDPOINT
```
Would be parsed like this:
```
OrderedDict([('OPENAI_API_KEY', ''), ('OPENAI_ORG_ID', 'org_id'), ('AZURE_OPENAI_DEPLOYMENT_NAME', ''), ('AZURE_OPENAI_ENDPOINT', None)])
```
As you can see, OPENAI_API_KEY and AZURE_OPENAI_DEPLOYMENT_NAME are
loaded with empty strings, not None.
### Description
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These notes will help understanding 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: Shawn Callegari <[email protected]>
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
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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]>
### Motivation and Context
Replacing flake8 and isort with ruff in the pre-commit hook.
Adding "I" to ruff rules so that it checks for import errors.
Co-authored-by: Abby Harrison <[email protected]>
### Motivation and Context
This changes our parsing of the .env file to use
[`python-dotenv`](https://pypi.org/project/python-dotenv/) which will be
more flexible.
Tested that the notebooks would still run correctly.
### 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)
This PR provides a path to using the ChatGPT API in the Python Preview
of Semantic Kernel. In addition, this provides `Azure*` versions of many
existing models (so Python users can leverage Azure OpenAI).
I think that, in general, it may be worth considering how best to work
with models that have different modalities: now we have text
completions, embeddings, chat completions (and I'd imagine images/etc.
may be nice to support someday too).
Regardless, this PR provides a fast path to using the exciting new Chat
APIs from OpenAI with SK!
See the new `python/tests/chat_gpt_api.py` for a usage example.
