agent/agent-framework
1
0
Fork 0
mirror of https://github.com/microsoft/agent-framework.git synced 2026-06-16 21:04:09 +08:00
Code Issues Packages Projects Releases Wiki Activity

Python: latency improvements (#3014)

Browse Source 
* latency improvements

* fixed mypy, added coding standards and instructions

* slight logic improvement
This commit is contained in:
Eduard van Valkenburg
2025-12-23 17:04:34 +01:00
committed by GitHub
Unverified
parent 8b743af217
commit a32702cf38
9 changed files with 496 additions and 411 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/DEV_SETUP.md
+45 -375
View File
@@ -4,6 +4,8 @@ This document describes how to setup your environment with Python and uv,
if you're working on new features or a bug fix for Agent Framework, or simply
want to run the tests included.
For coding standards and conventions, see [CODING_STANDARD.md](CODING_STANDARD.md).
## System setup
We are using a tool called [poethepoet](https://github.com/nat-n/poethepoet) for task management and [uv](https://github.com/astral-sh/uv) for dependency management. At the [end of this document](#available-poe-tasks), you will find the available Poe tasks.
@@ -117,51 +119,6 @@ from agent_framework.openai import OpenAIChatClient
chat_client = OpenAIChatClient(env_file_path="openai.env")
```
## Coding Standards
### Code Style and Formatting
We use [ruff](https://github.com/astral-sh/ruff) for both linting and formatting with the following configuration:
- **Line length**: 120 characters
- **Target Python version**: 3.10+
- **Google-style docstrings**: All public functions, classes, and modules should have docstrings following Google conventions
### Function Parameter Guidelines
To make the code easier to use and maintain:
- **Positional parameters**: Only use for up to 3 fully expected parameters
- **Keyword parameters**: Use for all other parameters, especially when there are multiple required parameters without obvious ordering
- **Avoid additional imports**: Do not require the user to import additional modules to use the function, so provide string based overrides when applicable, for instance:
```python
def create_agent(name: str, tool_mode: ChatToolMode) -> Agent:
# Implementation here
```
Should be:
```python
def create_agent(name: str, tool_mode: Literal['auto', 'required', 'none'] | ChatToolMode) -> Agent:
# Implementation here
if isinstance(tool_mode, str):
tool_mode = ChatToolMode(tool_mode)
```
- **Document kwargs**: Always document how `kwargs` are used, either by referencing external documentation or explaining their purpose
- **Separate kwargs**: When combining kwargs for multiple purposes, use specific parameters like `client_kwargs: dict[str, Any]` instead of mixing everything in `**kwargs`
Example:
```python
chat_completion = OpenAIChatClient(env_file_path="openai.env")
```
# Method naming inside connectors
When naming methods inside connectors, we have a loose preference for using the following conventions:
- Use `_prepare_<object>_for_<purpose>` as a prefix for methods that prepare data for sending to the external service.
- Use `_parse_<object>_from_<source>` as a prefix for methods that process data received from the external service.
This is not a strict rule, but a guideline to help maintain consistency across the codebase.
## Tests
All the tests are located in the `tests` folder of each package. There are tests that are marked with a `@skip_if_..._integration_tests_disabled` decorator, these are integration tests that require an external service to be running, like OpenAI or Azure OpenAI.
@@ -179,264 +136,6 @@ uv run poe --directory packages/core test
These commands also output the coverage report.
## Implementation Decisions
### Asynchronous programming
It's important to note that most of this library is written with asynchronous in mind. The
developer should always assume everything is asynchronous. One can use the function signature
with either `async def` or `def` to understand if something is asynchronous or not.
### Documentation
Each file should have a single first line containing: # Copyright (c) Microsoft. All rights reserved.
We follow the [Google Docstring](https://github.com/google/styleguide/blob/gh-pages/pyguide.md#383-functions-and-methods) style guide for functions and methods.
They are currently not checked for private functions (functions starting with '_').
They should contain:
- Single line explaining what the function does, ending with a period.
- If necessary to further explain the logic a newline follows the first line and then the explanation is given.
- The following three sections are optional, and if used should be separated by a single empty line.
- Arguments are then specified after a header called `Args:`, with each argument being specified in the following format:
- `arg_name`: Explanation of the argument.
- if a longer explanation is needed for a argument, it should be placed on the next line, indented by 4 spaces.
- Type and default values do not have to be specified, they will be pulled from the definition.
- Returns are specified after a header called `Returns:` or `Yields:`, with the return type and explanation of the return value.
- Finally, a header for exceptions can be added, called `Raises:`, with each exception being specified in the following format:
- `ExceptionType`: Explanation of the exception.
- if a longer explanation is needed for a exception, it should be placed on the next line, indented by 4 spaces.
Putting them all together, gives you at minimum this:
```python
def equal(arg1: str, arg2: str) -> bool:
"""Compares two strings and returns True if they are the same."""
...
```
Or a complete version of this:
```python
def equal(arg1: str, arg2: str) -> bool:
"""Compares two strings and returns True if they are the same.
Here is extra explanation of the logic involved.
Args:
arg1: The first string to compare.
arg2: The second string to compare.
Returns:
True if the strings are the same, False otherwise.
"""
```
### Attributes vs Inheritance
Prefer attributes over inheritance when parameters are mostly the same:
```python
# ✅ Preferred - using attributes
from agent_framework import ChatMessage
user_msg = ChatMessage(role="user", content="Hello, world!")
asst_msg = ChatMessage(role="assistant", content="Hello, world!")
# ❌ Not preferred - unnecessary inheritance
from agent_framework import UserMessage, AssistantMessage
user_msg = UserMessage(content="Hello, world!")
asst_msg = AssistantMessage(content="Hello, world!")
```
### Logging
Use the centralized logging system:
```python
from agent_framework import get_logger
# For main package
logger = get_logger()
# For subpackages
logger = get_logger('agent_framework.azure')
```
**Do not use** direct logging module imports:
```python
# ❌ Avoid this
import logging
logger = logging.getLogger(__name__)
```
### Import Structure
The package follows a flat import structure:
- **Core**: Import directly from `agent_framework`
```python
from agent_framework import ChatAgent, ai_function
```
- **Components**: Import from `agent_framework.<component>`
```python
from agent_framework.vector_data import VectorStoreModel
from agent_framework.guardrails import ContentFilter
```
- **Connectors**: Import from `agent_framework.<vendor/platform>`
```python
from agent_framework.openai import OpenAIChatClient
from agent_framework.azure import AzureOpenAIChatClient
```
## Testing
### Running Tests
```bash
# Run all tests with coverage
uv run poe test
# Run specific test file
uv run pytest tests/test_agents.py
# Run with verbose output
uv run pytest -v
```
### Test Coverage
- Target: Minimum 80% test coverage for all packages
- Coverage reports are generated automatically during test runs
- Tests should be in corresponding `test_*.py` files in the `tests/` directory
## Documentation
### Building Documentation
```bash
# Build documentation
uv run poe docs-build
# Serve documentation locally with auto-reload
uv run poe docs-serve
# Check documentation for warnings
uv run poe docs-check
```
### Docstring Style
Use Google-style docstrings for all public APIs:
```python
def create_agent(name: str, chat_client: ChatClientProtocol) -> Agent:
"""Create a new agent with the specified configuration.
Args:
name: The name of the agent.
chat_client: The chat client to use for communication.
Returns:
True if the strings are the same, False otherwise.
Raises:
ValueError: If one of the strings is empty.
"""
...
```
If in doubt, use the link above to read much more considerations of what to do and when, or use common sense.
## Coding standards
```plaintext
agent_framework/
├── __init__.py # Tier 0: Core components
├── _agents.py # Agent implementations
├── _tools.py # Tool definitions
├── _models.py # Type definitions
├── _logging.py # Logging utilities
├── context_providers.py # Tier 1: Context providers
├── guardrails.py # Tier 1: Guardrails and filters
├── vector_data.py # Tier 1: Vector stores
├── workflows.py # Tier 1: Multi-agent orchestration
└── azure/ # Tier 2: Azure connectors (lazy loaded)
└── __init__.py # Imports from agent-framework-azure
```
### Pydantic and Serialization
This section describes how one can enable serialization for their class using Pydantic.
For more info you can refer to the [Pydantic Documentation](https://docs.pydantic.dev/latest/).
#### Upgrading existing classes to use Pydantic
Let's take the following example:
```python
class A:
def __init__(self, a: int, b: float, c: List[float], d: dict[str, tuple[float, str]] = {}):
self.a = a
self.b = b
self.c = c
self.d = d
```
You would convert this to a Pydantic class by sub-classing from the `AFBaseModel` class.
```python
from pydantic import Field
from ._pydantic import AFBaseModel
class A(AFBaseModel):
# The notation for the fields is similar to dataclasses.
a: int
b: float
c: list[float]
# Only, instead of using dataclasses.field, you would use pydantic.Field
d: dict[str, tuple[float, str]] = Field(default_factory=dict)
```
#### Classes with data that need to be serialized, and some of them are Generic types
Let's take the following example:
```python
from typing import TypeVar
T1 = TypeVar("T1")
T2 = TypeVar("T2", bound=<some class>)
class A:
def __init__(a: int, b: T1, c: T2):
self.a = a
self.b = b
self.c = c
```
You can use the `AFBaseModel` to convert these to pydantic serializable classes.
```python
from typing import Generic, TypeVar
from ._pydantic import AFBaseModel
T1 = TypeVar("T1")
T2 = TypeVar("T2", bound=<some class>)
class A(AFBaseModel, Generic[T1, T2]):
# T1 and T2 must be specified in the Generic argument otherwise, pydantic will
# NOT be able to serialize this class
a: int
b: T1
c: T2
```
## Code quality checks
To run the same checks that run during a commit and the GitHub Action `Python Code Quality`, you can use this command, from the [python](../python) folder:
@@ -497,7 +196,7 @@ and then you can run the following tasks:
uv sync --all-extras --dev
```
After this initial setup, you can use the following tasks to manage your development environment, it is adviced to use the following setup command since that also installs the pre-commit hooks.
After this initial setup, you can use the following tasks to manage your development environment. It is advised to use the following setup command since that also installs the pre-commit hooks.
#### `setup`
Set up the development environment with a virtual environment, install dependencies and pre-commit hooks:
@@ -555,64 +254,6 @@ Run MyPy type checking:
uv run poe mypy
```
### Testing
#### `test`
Run unit tests with coverage:
```bash
uv run poe test
```
### Documentation
#### `docs-install`
Install including the documentation tools:
```bash
uv run poe docs-install
```
#### `docs-clean`
Remove the docs build directory:
```bash
uv run poe docs-clean
```
#### `docs-build`
Build the documentation:
```bash
uv run poe docs-build
```
#### `docs-full`
Build the packages, clean and build the documentation:
```bash
uv run poe docs-full
```
#### `docs-rebuild`
Clean and build the documentation:
```bash
uv run poe docs-rebuild
```
#### `docs-full-install`
Install the docs dependencies, build the packages, clean and build the documentation:
```bash
uv run poe docs-full-install
```
#### `docs-debug`
Build the documentation with debug information:
```bash
uv run poe docs-debug
```
#### `docs-rebuild-debug`
Clean and build the documentation with debug information:
```bash
uv run poe docs-rebuild-debug
```
### Code Validation
#### `markdown-code-lint`
@@ -621,37 +262,66 @@ Lint markdown code blocks:
uv run poe markdown-code-lint
```
#### `samples-code-check`
Run type checking on samples:
```bash
uv run poe samples-code-check
```
### Comprehensive Checks
#### `check`
Run all quality checks (format, lint, pyright, mypy, test, markdown lint, samples check):
Run all quality checks (format, lint, pyright, mypy, test, markdown lint):
```bash
uv run poe check
```
#### `pre-commit-check`
Run pre-commit specific checks (all of the above, excluding `mypy`):
### Testing
#### `test`
Run unit tests with coverage by invoking the `test` task in each package sequentially:
```bash
uv run poe pre-commit-check
uv run poe test
```
### Building
To run tests for a specific package only, use the `--directory` flag:
```bash
# Run tests for the core package
uv run --directory packages/core poe test
# Run tests for the azure-ai package
uv run --directory packages/azure-ai poe test
```
#### `all-tests`
Run all tests in a single pytest invocation across all packages in parallel (excluding lab and devui). This is faster than `test` as it uses pytest's parallel execution:
```bash
uv run poe all-tests
```
#### `all-tests-cov`
Same as `all-tests` but with coverage reporting enabled:
```bash
uv run poe all-tests-cov
```
### Building and Publishing
#### `build`
Build the package:
Build all packages:
```bash
uv run poe build
```
#### `clean-dist`
Clean the dist directories:
```bash
uv run poe clean-dist
```
#### `publish`
Publish packages to PyPI:
```bash
uv run poe publish
```
## Pre-commit Hooks
You can also run all checks using pre-commit directly:
Pre-commit hooks run automatically on commit and execute a subset of the checks on changed files only. You can also run all checks using pre-commit directly:
```bash
uv run pre-commit run -a