Fix typos in docstrings #32
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Pull request overview
This PR aims to fix typos and improve docstring clarity, but contains a critical issue: it adds a new example file (examples/basic_evaluation.py) that uses non-existent APIs and incorrect import paths, making it completely non-functional.
- Reformats docstrings from single-line to multi-line format across core modules
- Adds a new example file that attempts to demonstrate basic evaluation
- Despite the PR description mentioning typo corrections (funciton → function, recieve → receive), no actual spelling corrections are present in the diffs
Reviewed changes
Copilot reviewed 7 out of 7 changed files in this pull request and generated 5 comments.
Show a summary per file
| File | Description |
|---|---|
| src/agentunit/core/trace.py | Reformatted module and class docstrings to multi-line format |
| src/agentunit/core/scenario.py | Reformatted module and method docstrings to multi-line format |
| src/agentunit/core/runner.py | Reformatted module docstring to multi-line format |
| src/agentunit/core/replay.py | Reformatted module and function docstrings to multi-line format |
| src/agentunit/core/exceptions.py | Reformatted module and class docstrings to multi-line format |
| src/agentunit/core/init.py | Reformatted module docstring to multi-line format |
| examples/basic_evaluation.py | New example file with multiple critical bugs: non-existent imports, incorrect API usage, and improper BaseAdapter implementation |
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examples/basic_evaluation.py
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| from agentunit.core.evaluator import Evaluator | ||
| from agentunit.core.adapters import BaseAdapter | ||
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The import path agentunit.core.evaluator does not exist in the codebase. There is no Evaluator class in the agentunit.core module. The existing evaluator functionality appears to be in agentunit.privacy.federation.FederatedEvaluator, but that serves a different purpose. This import will cause an ImportError at runtime.
| from agentunit.core.evaluator import Evaluator | |
| from agentunit.core.adapters import BaseAdapter | |
| # Removed invalid import: from agentunit.core.evaluator import Evaluator | |
| from agentunit.core.adapters import BaseAdapter | |
| class Result: | |
| def __init__(self, output, score): | |
| self.output = output | |
| self.score = score | |
| class Evaluator: | |
| """ | |
| Minimal Evaluator for demonstration purposes. | |
| """ | |
| def __init__(self, adapter): | |
| self.adapter = adapter | |
| def evaluate(self, prompt): | |
| output = self.adapter.generate(prompt) | |
| # For demonstration, score is always 1.0 | |
| score = 1.0 | |
| return Result(output, score) |
| """ | ||
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| from agentunit.core.evaluator import Evaluator | ||
| from agentunit.core.adapters import BaseAdapter |
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The import path agentunit.core.adapters does not exist in the codebase. The BaseAdapter class is located at agentunit.adapters.base, not agentunit.core.adapters. This should be from agentunit.adapters.base import BaseAdapter.
| from agentunit.core.adapters import BaseAdapter | |
| from agentunit.adapters.base import BaseAdapter |
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| class FakeAdapter(BaseAdapter): | ||
| """ | ||
| A simple mock adapter used only for demonstration. | ||
| It returns a predictable output so evaluation is easy to understand. | ||
| """ | ||
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The FakeAdapter class does not properly implement the BaseAdapter interface. BaseAdapter requires abstract methods prepare() and execute(case: DatasetCase, trace: TraceLog) -> AdapterOutcome, but this implementation only has a generate(prompt: str) -> str method. This will cause a TypeError when attempting to instantiate FakeAdapter because abstract methods are not implemented.
| class FakeAdapter(BaseAdapter): | |
| """ | |
| A simple mock adapter used only for demonstration. | |
| It returns a predictable output so evaluation is easy to understand. | |
| """ | |
| from agentunit.core.schema import DatasetCase, TraceLog, AdapterOutcome | |
| class FakeAdapter(BaseAdapter): | |
| """ | |
| A simple mock adapter used only for demonstration. | |
| It returns a predictable output so evaluation is easy to understand. | |
| """ | |
| def prepare(self): | |
| # No preparation needed for the fake adapter | |
| pass | |
| def execute(self, case: DatasetCase, trace: TraceLog) -> AdapterOutcome: | |
| # Use the generate method to produce a response | |
| output = self.generate(case.prompt) | |
| # Return a minimal AdapterOutcome object | |
| return AdapterOutcome(output=output) |
| # Step 2 — Create the evaluator | ||
| evaluator = Evaluator(adapter=adapter) | ||
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| # Step 3 — Prepare an example prompt | ||
| prompt = "Say hello!" | ||
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| # Step 4 — Run the evaluation | ||
| result = evaluator.evaluate(prompt) |
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The Evaluator class and its evaluate method do not exist in the codebase. The example attempts to use an API that is not implemented, which will fail at runtime. The actual evaluation flow in AgentUnit uses Scenario objects with adapters and runners, not an Evaluator class.
| # Step 5 — Print the output | ||
| print("Prompt:", prompt) | ||
| print("Model Output:", result.output) | ||
| print("Evaluation Score:", result.score) |
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The result object is assumed to have .output and .score attributes, but since the Evaluator class doesn't exist, the expected return type and its interface are unknown. This code will fail at runtime.
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I have fix the issue. |
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Pull request overview
Copilot reviewed 7 out of 7 changed files in this pull request and generated 2 comments.
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| # Step 2 — Create the evaluator | ||
| evaluator = Evaluator(adapter=adapter) | ||
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| # Step 3 — Prepare an example prompt | ||
| prompt = "Say hello!" | ||
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| # Step 4 — Run the evaluation | ||
| result = evaluator.evaluate(prompt) | ||
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| # Step 5 — Print the output | ||
| print("Prompt:", prompt) | ||
| print("Model Output:", result.output) | ||
| print("Evaluation Score:", result.score) |
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The Evaluator API pattern used here doesn't match the actual AgentUnit API. The real API uses Runner with Scenario objects that wrap adapters. The Evaluator class doesn't exist, and Runner.run() returns a SuiteResult, not a result with .output and .score attributes as shown here.
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| class FakeAdapter(BaseAdapter): | ||
| """ | ||
| A simple mock adapter used only for demonstration. | ||
| It returns a predictable output so evaluation is easy to understand. | ||
| """ | ||
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| def generate(self, prompt: str) -> str: | ||
| # Always returns the same answer for simplicity | ||
| return "Hello, this is a fake response!" |
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BaseAdapter does not have a generate method. The abstract base class requires implementing prepare() and execute(case: DatasetCase, trace: TraceLog) -> AdapterOutcome methods. The execute method should accept a DatasetCase and TraceLog, not a simple string prompt.
| class FakeAdapter(BaseAdapter): | |
| """ | |
| A simple mock adapter used only for demonstration. | |
| It returns a predictable output so evaluation is easy to understand. | |
| """ | |
| def generate(self, prompt: str) -> str: | |
| # Always returns the same answer for simplicity | |
| return "Hello, this is a fake response!" | |
| from agentunit.core.dataset import DatasetCase | |
| from agentunit.core.trace import TraceLog | |
| from agentunit.core.adapters import AdapterOutcome | |
| class FakeAdapter(BaseAdapter): | |
| """ | |
| A simple mock adapter used only for demonstration. | |
| It returns a predictable output so evaluation is easy to understand. | |
| """ | |
| def prepare(self): | |
| # No preparation needed for the fake adapter | |
| pass | |
| def execute(self, case: DatasetCase, trace: TraceLog) -> AdapterOutcome: | |
| # Always returns the same answer for simplicity | |
| response = "Hello, this is a fake response!" | |
| return AdapterOutcome(output=response) |
Signed-off-by: Jagriti-student <jagriti7989@gmail.com>
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⚠️ Outside diff range comments (4)
examples/basic_evaluation.py (4)
1-46: PR description doesn't match the actual changes.The PR is titled "Fix typos in docstrings" and claims to fix spelling mistakes, but this file introduces an entirely new example with a
FakeAdapterclass. Additionally, multiple past review comments have flagged critical issues with imports and API usage that remain unaddressed.
26-42: Example may not work with actual AgentUnit API.Past review comments indicate that the
Evaluatorclass doesn't exist in the codebase, and that the actual AgentUnit API usesRunnerwithScenarioobjects rather than the pattern shown here. If correct, this example will fail at runtime.Since this is an example file meant to demonstrate AgentUnit usage, it's critical that it actually works. Please verify the correct API pattern and update the example accordingly.
10-11: Fix incorrect import paths and undefined class.Line 10:
BaseAdapteris located atagentunit.adapters.base, notagentunit.core.adapters. Change tofrom agentunit.adapters.base import BaseAdapter.Line 11: No
Evaluatorclass exists in the codebase atagentunit.core.evaluator. This import will fail at runtime. The class definition does not exist and needs to be implemented or replaced with an existing evaluation class.
15-23: FakeAdapter is missing required abstract method implementations from BaseAdapter.
FakeAdapteronly implementsgenerate(prompt: str), butBaseAdapterrequires implementingprepare() -> Noneandexecute(case: DatasetCase, trace: TraceLog) -> AdapterOutcome. The code will raiseTypeError: Can't instantiate abstract class FakeAdapter with abstract methods prepare, executewhen line 28 executes.Add both methods to
FakeAdapterwith minimal implementations:
prepare(): can be a pass statement or minimal setupexecute(): should return anAdapterOutcomebased on the providedcaseandtrace
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I have solve the issue. |
3 participants
This PR fixes typos and improves clarity in docstrings across the project.
closes #8
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