### Motivation and Context
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Added comments about each of the rules to pyproject.toml
Removed D101 from ignore list and fixed that, now docstrings at the
class level are required.
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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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Now that we only support 310 and up, we could unify all files, to use
the new style typing and annotations.
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### Motivation and Context
The Python code base could handle some primitive types for the schema
for tool call objects and kernel parameter metadata. However, it
couldn't properly handle the more complex JSON schemas for tool call
objects.
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### Description
This PR introduces:
- JSON schema handling for KernelParameterMetadata and tool call
objects.
- Updates to the tool call utils method to properly recurse on the
KernelParameterMetadata's stucture.
- Adds unit tests for this code.
- Add experimental_class/experimental_functions to various parts of the
code base like Memory Connectors and OpenAPI plugin.
- Fixes#6310
- Fixes#6280
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Added kwargs handling throughout embeddings connectors and as a
additional field for the TextMemoryPlugin.
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When the root init has to load a lot of stuff, loading the whole thing
becomes slower, only one concept there Kernel, all other things must now
be loaded from a sub package.
The following is not the guidance:
- imports within SK use the full path
- init files are created as much as possible on the root+1 level (like
semantic_kernel.functions) with the pieces that common developers need,
except in connectors and utils, there a developer needs to go deeper
- within the connectors folder this is further detailed first to ai,
memory, search_engine, within ai it is further spread into the different
connectors, for instance everything for openai and azure openai can be
imported using `from semantic_kernel.connectors.ai.open_ai import ...`,
the same within memory
- imports in samples use the abbreviated path as much as possible.
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I noticed that when running a piece without any execution settings, it
errored out, while it should use 'default' service with the default
settings for that service, that is now fixed.
Small updates to the rag example.
Added TODO: to replace the embedding_generator service in
SemanticTextMemory with Kernel and the same service_selector pattern as
for KernelFunctions.
### Description
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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
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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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### Motivation and Context
As we move toward v1, it is necessary to remove the single kernel memory
attribute so that memory connectors can be handled via plugins. This
change also refactors the `import_plugin` method name to be similar with
dotnet as `import_plugin_from_object`, and introduces a simple
`kernel.invoke_prompt` method.
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### Description
Updates are:
- The memory connector is now passed to the `SemanticTextMemory` class,
along with the embedding generator. This allows the user to add memory
to multiple plugins/services as needed. Closes#4632
- Kernel Examples and Notebooks were updated to reflect these changes.
Now using Service Enum for all notebooks. Closes#4557
- There were planners using memory related to the kernel, to store the
functions, and many of these planners are non-existent in dotnet's v1
SDK. Python has pending work to update the planner/remove deprecated
planners before getting to v1.
- A Kernel method called `invoke_prompt` was introduced to invoke a
simple prompt, and return the `FunctionResult` when complete.
- All unit/integration tests are passing.
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- [ ] 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
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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
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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.
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### 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
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---------
Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
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Upgraded pydantic to 2.0+, much cleaner, less complexity in different
types of base models.
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### 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
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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.
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- [ ] 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]>
SKFunction has been excluded for now.
Handles issues arising due to many circular imports Update
DelegateInference to handle some cases where annotation is given as a
string to avoid circular imports like in FileIOSkill.
### Motivation and Context
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### Description
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raises no violations
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- [x] I didn't break anyone 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Mark Karle <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Lee Miller <[email protected]>
Co-authored-by: Devis Lucato <[email protected]>
### Motivation and Context
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fixes#1915
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(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
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Shawn Callegari <[email protected]>
### Description
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Added documentation for files in `python/semantic-kernel/memory` that
were missing them, worded mostly the exact as in .NET
Closes#2141
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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: Abby Harrison <[email protected]>
### Motivation and Context
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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
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- [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)
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(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
<!-- 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? To fix the SemanticTextMemory
functionality
2. What problem does it solve? #1815
3. What scenario does it contribute to? All scenarios that intend on
using RAG
4. If it fixes an open issue, please link to the issue here. #1815
-->
1. Why is this change required? To fix the SemanticTextMemory
functionality
2. What problem does it solve? #1815
3. What scenario does it contribute to? All scenarios that intend on
using RAG
4. If it fixes an open issue, please link to the issue here. #1815
### Description
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Simple decomposition of the list of embeddings returned via
`generate_embeddings_async`
### Contribution Checklist
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verified with `dotnet format`
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- [x] I didn't break anyone 😄
### Motivation and Context
Closes#1271 and addresses several TODOs in ChromaMemoryStore
### Description
- added field `additional_metadata` to `MemoryRecord` and all downstream
methods/classes (see `SemanticTextMemoryBase`, `SemanticTextMemory`,
`MemoryQueryResult`, and `ChromaMemoryStore`)
- addressed ChromaMemoryStore TODO 'what is key used for?' For chromadb,
key and id are effectively the same value, but the two fields are
differentiated for memory stores like Qdrant which require that key be a
uuid. I updated the logic so that this is more apparent. Note that this
results in no change in end-user functionality.
- Updated ChromaMemoryStore TODO 'get_async say embedding is optional
but Record constructor requires it.` MemoryRecord constructor no longer
requires an embedding. Logic is updated and a test added to show that
embedding retrieval is functionally optional.
Following the tutorial notebooks:
```
async def populate_memory(kernel: sk.Kernel) -> None:
# Add some documents to the semantic memory
await kernel.memory.save_information_async(
"aboutMe", id="info1", text="My name is Andrea"
)
await kernel.memory.save_information_async(
"aboutMe", id="info2", text="I currently work as a tour guide"
)
await kernel.memory.save_information_async(
"aboutMe", id="info3", text="I've been living in Seattle since 2005"
)
await kernel.memory.save_information_async(
"aboutMe", id="info4", text="I visited France and Italy five times since 2015"
)
await kernel.memory.save_information_async(
"aboutMe", id="info5", text="My family is from New York"
)
```
and calling:
```
await kernel.memory.get_async("aboutMe", "info1")
```
Results in the error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 await kernel.memory.get_async("aboutMe", "info1")
File [~/code/ncp/semantic-kernel/python/semantic_kernel/memory/semantic_text_memory.py:116](https://file+.vscode-resource.vscode-cdn.net/Users/dleen/code/ncp/semantic-kernel/samples/notebooks/python/~/code/ncp/semantic-kernel/python/semantic_kernel/memory/semantic_text_memory.py:116), in SemanticTextMemory.get_async(self, collection, key)
106 """Get information from the memory (calls the memory store's get method).
107
108 Arguments:
(...)
113 Optional[MemoryQueryResult] -- The MemoryQueryResult if found, None otherwise.
114 """
115 record = await self._storage.get_async(collection_name=collection, key=key)
--> 116 return MemoryQueryResult.from_memory_record(record) if record else None
TypeError: MemoryQueryResult.from_memory_record() missing 1 required positional argument: 'relevance'
```
Since this is an exact match using `get_async` and `None` if not found,
we can assign a relevance of `1.0` meaning a perfect match.
### Motivation and Context
Use of abstraction was not consistent in the python SK. This PR makes
the import and use of ABC consistent and removes unused file kernel.py.
### Description
- MemoryStoreBase derives from ABC - this properly sets its metaclass.
MemoryStoreBase itself should never be instantiated, only derived.
- RetryMechanism derives ABC, so should be named 'Base' like all other
derivatives of ABC in this repo.
- removed kernel_base.py, was made obsolete by
https://github.com/microsoft/semantic-kernel/pull/1015
calling the memory record's embedding property throws
`RecursionError: maximum recursion depth exceeded`
return the attribute instead of the property
Signed-off-by: hsm207 <[email protected]>
### Motivation and Context
New release version 0.2.6
Fix bug in 0.2.5.dev0 pip package
Update notebooks to the latest pip package (0.2.6)
### Description of Changes
- fixed a param name error in the creation of a reference memory_record
- renamed text_service -> text_completion_service (consistency with .NET)
- renamed text_embedding_service -> text_embedding_generation_service
(consistency with .NET)
- Updated notebooks accordingly
- bumped pip versions to 0.2.6.dev0
### Motivation and Context
This PR significantly reduces the complexity of implementing a new
memory store. Previously, memory stores needed to implement
EmbeddingIndexBase, DataStoreBase, and MemoryStoreBase. Now, memory
stores only need to implement MemoryStoreBase. This PR also brings
MemoryStoreBase to parity with the .NET IMemoryStore interface.
### Description
- Removed `data_entry.py`, `data_store_base.py`,
`volatile_data_store.py`, and `embedding_index_base.py`
- Added `with_embedding` parameter to all get/search methods. Passing
the embedding between the application and storage is not optional and
defaults to False
- `MemoryStoreBase` (Python SK) == `IMemoryStore` (.NET SK)
- Updated `volatile_memory_store` to implement `MemoryStoreBase`
- Removed `ABC` from `MemoryStoreBase`
- Added documentation to memory files
Closes https://github.com/microsoft/semantic-kernel/issues/670
### Motivation and Context
This PR is a consistency update between the Python and .NET Semantic
Kernels.
**This PR does not contain any feature enhancements or improvements!**
### Description
- Replace terminology 'backend' with 'service'
- Move semantic_kernel.ai (semantic_kernel/ai) to
semantic_kernel.connectors.ai (semantic_kernel/connectors/ai)
### 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)
### Motivation and Context
Please help reviewers and future users, providing the following
information:
1. Why is this change required?
`SemanticTextMemory.save_reference_async` do nothing for its storage.
2. What problem does it solve? `save_reference_async` will work
appropriately
3. What scenario does it contribute to? embeddings
4. If it fixes an open issue, please link to the issue here.
https://github.com/microsoft/semantic-kernel/issues/165
### Description
I add missing codes with references of
`SemanticTextMemory.save_information_async` and C# version of
`SemanticTextMemory.save_reference_async`
Eduard van Valkenburg