### Motivation and Context
To take advantage of APIs offered by most LLMs and to be in sync with
.NET SK, this PR introduces the ability to generate multiple text
completions or chat completions from a single prompt.
### Description
- Return type hint for `complete_async` and `complete_chat_async`
changed from `str -> Union[str, List[str]]`. The use of `Union` is the
proper way to indicate multiple return types prior to Python 3.10+. 3.10
supports the use of the `|` symbol, but since the Python SK is supported
on 3.8 and 3.9, I did not adopt the newer standard.
- `complete_async`, `complete_stream_async`, `complete_chat_async`, and
`complete_chat_stream_async` now support settings field
`number_of_responses` greater than 1. Previously only a value of 1 was
supported.
- **Note: hf_text_completion does not support streaming multiple
responses due to a limitation of TextIteratorStreamer. This feature
requires the ability to parse distinct responses from
TextIteratorStreamer.**
- Fixed a bug where `complete_async` was streaming single responses as
1D arrays, content now is simply a string.
### Example Usage
#### Setup
```
kernel = sk.Kernel()
# Configure OpenAI service
api_key, org_id = sk.openai_settings_from_dot_env()
oai_text_service = OpenAITextCompletion("text-davinci-003", api_key, org_id)
oai_chat_service = OpenAIChatCompletion("gpt-3.5-turbo", api_key, org_id)
# Configure Hugging Face service
hf_text_service = HuggingFaceTextCompletion("gpt2", task="text-generation")
# Configure Prompt
prompt = "what is the purpose of a rubber duck?"
# Configure Request Settings
text_request_settings_multi = CompleteRequestSettings(
max_tokens=100,
temperature=0.7,
top_p=1,
frequency_penalty=0.5,
presence_penalty=0.8,
number_of_responses=4
)
chat_request_settings_multi = ChatRequestSettings(
max_tokens=100,
temperature=0.7,
top_p=1,
frequency_penalty=0.5,
presence_penalty=0.8,
number_of_responses=4
)
```
#### Text Completion (Standard)
```
texts = await oai_text_service.complete_async(prompt, text_request_settings_multi)
i = 0
for text in texts:
print("Option " + str(i) + ": " + text)
i += 1
```
#### Streaming Text Completion
```
multi_stream = oai_text_service.complete_stream_async(prompt, text_request_settings_multi)
texts = [''] * text_request_settings_multi.number_of_responses
async for text in multi_stream:
i = 0
os.system('cls' if os.name == 'nt' else 'clear') # clear the screen for a better experience
print("PROMPT: " + prompt)
for option in text:
texts[i] = texts[i] + option
print("{0}: {1}".format(i, texts[i]))
i += 1
```
#### Chat Completion (Standard)
```
texts = await oai_chat_service.complete_chat_async([("user",prompt)], chat_request_settings_multi)
i = 0
for text in texts:
print("Option " + str(i) + ": " + text)
i += 1
```
#### Streaming Chat Completion
```
multi_stream = oai_chat_service.complete_chat_stream_async([("user",prompt)], chat_request_settings_multi)
texts = [''] * chat_request_settings_multi.number_of_responses
async for text in multi_stream:
i = 0
os.system('cls' if os.name == 'nt' else 'clear') # clear the screen for a better experience
print("PROMPT: " + prompt)
for option in text:
texts[i] = texts[i] + option
print("{0}: {1}".format(i, texts[i]))
i += 1
```
#### HuggingFace Standard Completion
```
texts = await hf_text_service.complete_async(prompt, request_settings_multi)
i = 0
for text in texts:
print("-----------------------------------")
print("Option " + str(i) + ": " + text)
i += 1
```
### 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)
* Add python getting started notebook
* Fix notebooks usage of Azure text completion
* Fix bug with skills definition
* Update READMEs
* New release version 0.2.2
### Motivation and Context
This PR fixes#235 and adds a test for the functionality. This is ahead
of the planning skill.
### Description
1. Fix to fix the typo in the `from_dict()` method in
`prompt_template_config.py`
2. Add assignment back to skill config so results from JSON aren't
discarded in `import_semantic_skill_from_directory.py`
3. Add tests
### Motivation and Context
The C# Semantic Kernel has recently undergone an upgrade to its
`PromptTemplateEngine`. This brings Python back in line with the
semantics of Prompt Templates in C#.
### Description
Here, unlike the original port, I've tried to make things more
pythonic/idiomatic (instead of trying to directly mirror the C#
codebase). I've also brought over the corresponding unit tests (and
added some of my own as I was building/validating).
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.
Abby Harrison