Framework to create applications and model adapters for AI DIAL.
Applications and model adapters implemented using this framework will be compatible with AI DIAL API that was designed based on Azure OpenAI API.
| Variable | Default | Description |
|---|---|---|
| DIAL_SDK_LOG | WARNING | DIAL SDK log level |
| DIAL_SDK_HEADERS_TO_PROXY | `` | A comma-separated list of headers that should be proxied from incoming requests to outgoing requests to the DIAL API. By default, no headers are proxied. |
| PYDANTIC_V2 | False | When True and Pydantic V2 is installed, DIAL SDK classes for requests/responses will be based on Pydantic V2 BaseModel. Otherwise, they will be based on Pydantic V1 BaseModel. |
Install the library using pip:
pip install aidial-sdkThe echo application example replies to the user by repeating their last message:
# Save this as app.py
import uvicorn
from aidial_sdk import DIALApp
from aidial_sdk.chat_completion import ChatCompletion, Request, Response
# ChatCompletion is an abstract class for applications and model adapters
class EchoApplication(ChatCompletion):
async def chat_completion(self, request: Request, response: Response) -> None:
# Get last message (the newest) from the history
last_user_message = request.messages[-1]
# Generate response with a single choice
with response.create_single_choice() as choice:
# Fill the content of the response with the last user's content
choice.append_content(last_user_message.text())
# DIALApp extends FastAPI to provide a user-friendly interface for routing requests to your applications
app = DIALApp()
app.add_chat_completion("echo", EchoApplication())
# Run built app
if __name__ == "__main__":
uvicorn.run(app, port=5000)python3 app.pySend the next request:
curl http://127.0.0.1:5000/openai/deployments/echo/chat/completions \
-H "Content-Type: application/json" \
-H "Api-Key: DIAL_API_KEY" \
-d '{
"messages": [{"role": "user", "content": "Repeat me!"}]
}'You will see the JSON response as:
{
"choices":[
{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": "Repeat me!"
}
}
],
"usage": null,
"id": "d08cfda2-d7c8-476f-8b95-424195fcdafe",
"created": 1695298034,
"object": "chat.completion"
}This project requires Python ≥3.11 and Poetry ≥2.1.1 for dependency management.
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Install Poetry. See the official installation guide.
-
(Optional) Specify custom Python or Poetry executables in
.env.dev. This is useful if multiple versions are installed. By default,pythonandpoetryare used.POETRY_PYTHON=path-to-python-exe POETRY=path-to-poetry-exe
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Create and activate the virtual environment:
make init_env source .venv/bin/activate -
Install project dependencies (including linting, formatting, and test tools):
make install
The recommended IDE is VSCode. Open the project in VSCode and install the recommended extensions. VS Code is configured to use the Ruff formatter.
Alternatively you can use PyCharm that has built-in Ruff support.
Run the linting before committing:
make lintTo auto-fix formatting issues run:
make formatRun unit tests locally for available python versions:
make testRun unit tests for the specific python version:
make test PYTHON=3.11You may optionally install Git hooks that will automatically run the linting step on Git push. You only need to do it once for the given repository.
make install_git_hooksImportant
This command doesn't work if you have already installed Git hooks locally or globally.
To remove the virtual environment and build artifacts run:
make cleanTo build the package run:
make buildTo publish the package to PyPI run:
make publish