diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 25c7e5c..2ff5ed8 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -18,7 +18,7 @@ repos: rev: v1.5.0 hooks: - id: detect-secrets - args: ["--baseline", ".secrets.baseline"] + args: ["--baseline", ".secrets.baseline", "--exclude-secrets", "your-api-key"] exclude: | (?x)^( .*\.lock$| diff --git a/.python-version b/.python-version index e4fba21..24ee5b1 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.12 +3.13 diff --git a/pyproject.toml b/pyproject.toml index 7c3608b..503025f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,15 +6,17 @@ authors = [ {name = "Giskard Team", email = "hello@giskard.ai"} ] readme = "README.md" -requires-python = ">=3.10,<4.0" -dependencies = [] +requires-python = ">=3.13,<4.0" +dependencies = [ + "giskard-core @ git+ssh://git@github.com/Giskard-AI/giskard-oss.git@main#subdirectory=libs/giskard-core", + "giskard-agents @ git+ssh://git@github.com/Giskard-AI/giskard-oss.git@main#subdirectory=libs/giskard-agents", + "giskard-checks @ git+ssh://git@github.com/Giskard-AI/giskard-oss.git@main#subdirectory=libs/giskard-checks", +] [dependency-groups] dev = [ "sphinxawesome-theme==5.3.2; python_version>='3.12'", "myst-parser==4.0.1; python_version>='3.12'", - "notebook==7.4.7", - "nbsphinx==0.9.7; python_version>='3.12'", "sphinx-click==6.1.0; python_version>='3.12'", "sphinx-autobuild==2025.8.25; python_version>='3.12'", "sphinx-autodoc-typehints==2.3.0; python_version>='3.12'", @@ -22,13 +24,8 @@ dev = [ "sphinx-tabs>=3.4.7; python_version>='3.12'", "sphinxext-opengraph[social_cards]>=0.12.0; python_version>='3.12'", "sphinx-notfound-page>=1.1.0; python_version>='3.12'", - "pandoc>=2.4", "sphinxcontrib-mermaid>=0.9.0; python_version>='3.12'", - "giskard[llm]==2.18.0; python_version>='3.10' and python_version<'3.13'", "pyarrow<21.0.1; python_version>='3.12'", - "ragas>=0.3.7,<=0.3.7", - "ipywidgets>=8.1.7", - "torch>=2.8.0", "sphinx-autobuild>=2024.10.3", "giskard-hub>=2.1.0", "sphinxext-rediraffe" @@ -39,12 +36,3 @@ dev = [ Homepage = "https://github.com/Giskard-AI/giskard-hub" Repository = "https://github.com/Giskard-AI/giskard-hub" Documentation = "https://docs.giskard.ai/" - -[[tool.uv.index]] -name = "pytorch_cpu" -url = "https://download.pytorch.org/whl/cpu" -explicit = true - -[tool.uv.sources] -# Use CPU-only PyTorch for non-macOS systems, default PyPI for macOS -torch = { index = "pytorch_cpu", marker = "platform_system != 'Darwin'" } diff --git a/source/_static/custom.css b/source/_static/custom.css index 05959e8..6978b41 100644 --- a/source/_static/custom.css +++ b/source/_static/custom.css @@ -27,6 +27,7 @@ --sidebar-heading-color: #0f1729; --non-selected-color: rgba(15, 23, 41, 0.6); --link-color: inherit; + --border: 0 0% 100% / 0.10; } .dark { @@ -420,6 +421,14 @@ header nav a:not(.text-foreground):hover { color: rgba(198, 255, 255, 0.8) !important; } +#left-sidebar a.current { + border: none; +} + +#left-sidebar ul ul:is(.dark *)::before { + background-color: hsl(var(--border)); +} + /* Recently selected navbar item styling */ header nav a.recently-selected, html[data-content_root="./"] header nav a.recently-selected, diff --git a/source/_static/images/oss/checks/quickstart-simple_example_result.png b/source/_static/images/oss/checks/quickstart-simple_example_result.png new file mode 100644 index 0000000..983c1ab Binary files /dev/null and b/source/_static/images/oss/checks/quickstart-simple_example_result.png differ diff --git a/source/_static/images/oss/checks/quickstart-structured_interactions.png b/source/_static/images/oss/checks/quickstart-structured_interactions.png new file mode 100644 index 0000000..86f255e Binary files /dev/null and b/source/_static/images/oss/checks/quickstart-structured_interactions.png differ diff --git a/source/_templates/sidebars/sidebar_oss_checks.html b/source/_templates/sidebars/sidebar_oss_checks.html new file mode 100644 index 0000000..73b6049 --- /dev/null +++ b/source/_templates/sidebars/sidebar_oss_checks.html @@ -0,0 +1,3 @@ + \ No newline at end of file diff --git a/source/conf.py b/source/conf.py index 1e76eb2..abb12fb 100644 --- a/source/conf.py +++ b/source/conf.py @@ -70,7 +70,6 @@ def update_sidebar_templates(): extensions = [ "myst_parser", - "nbsphinx", "sphinx_design", "sphinx.ext.todo", "sphinx.ext.napoleon", @@ -133,21 +132,6 @@ def update_sidebar_templates(): else: branch = docs_version.replace("-", "/") branch = "main" - -# -- Options for nbsphinx ---------------------------------------------------- -nbsphinx_execute = "never" -# fmt: off -nbsphinx_prolog = """ -.. raw:: html - -
- Open In Colab - View Notebook on GitHub -
-""" -# fmt: on - - theme_options = ThemeOptions( show_prev_next=True, show_scrolltop=True, @@ -158,7 +142,7 @@ def update_sidebar_templates(): "Overview": "/index", "Hub UI": "/hub/ui/index", "Hub SDK": "/hub/sdk/index", - "Open Source": "/oss/sdk/index", + "Checks": "/oss/checks/index", }, ) html_theme_options = asdict(theme_options) @@ -175,7 +159,12 @@ def update_sidebar_templates(): } # Use wildcard patterns to support any nested path within the specified routes +# oss/checks/** must come before oss/** for correct pattern matching html_sidebars: dict[str, list[str]] = { + "oss/checks/**": [ + "sidebar_main_nav_links.html", + "sidebars/sidebar_oss_checks.html", + ], "hub/ui/**": ["sidebars/sidebar_hub_ui.html"], "hub/sdk/**": ["sidebars/sidebar_hub_sdk.html"], "oss/**": ["sidebars/sidebar_oss_sdk.html"], @@ -193,6 +182,49 @@ def update_sidebar_templates(): ogp_image = "https://docs.giskard.ai/_static/open-graph-image.png" +# Add custom template function to render toctree from a specific document +def setup(app): + def html_page_context(app, pagename, templatename, context, doctree): + def toctree_from_doc(docname, **kwargs): + """Render toctree starting from a specific document""" + from sphinx.environment.adapters.toctree import TocTree + from sphinx import addnodes + source_doctree = app.env.get_doctree(docname) + toctrees = list(source_doctree.findall(addnodes.toctree)) + + if not toctrees: + return "" + + toctree_adapter = TocTree(app.env) + resolved = [ + toctree_adapter.resolve( + pagename, # Use current page context, not the toctree source + app.builder, + toctree, + prune=False, + maxdepth=kwargs.get("maxdepth", -1), + titles_only=kwargs.get("titles_only", False), + collapse=kwargs.get("collapse", False), + includehidden=kwargs.get("includehidden", False), + ) + for toctree in toctrees + ] + + resolved = [r for r in resolved if r is not None] + if not resolved: + return "" + + result = resolved[0] + for toctree in resolved[1:]: + result.extend(toctree.children) + + return app.builder.render_partial(result)["fragment"] + + context["toctree_from_doc"] = toctree_from_doc + + app.connect("html-page-context", html_page_context) + + # make github links resolve def linkcode_resolve(domain, info): if domain != "py": diff --git a/source/index.rst b/source/index.rst index 0f3662b..7d6ef47 100644 --- a/source/index.rst +++ b/source/index.rst @@ -46,7 +46,6 @@ Giskard Hub Ready to unlock the full potential of enterprise-grade AI testing? Try **Giskard Hub** with a free trial and discover advanced team collaboration, continuous red teaming, and enterprise security features. :doc:`Start your free enterprise trial ` and see how Giskard Hub can transform your AI testing workflow. - Open source ----------- @@ -74,7 +73,6 @@ The library provides a set of tools for testing and evaluating LLMs, including: **⚖️ Unsure about the difference between Open Source and Hub?** Check out our :doc:`/start/comparison` guide to learn more about the different features. - Open research ------------- @@ -107,8 +105,6 @@ Some work has been funded by the `the European Commission `_. - - .. include:: toctree.rst .. include:: toctree_hub_ui.rst .. include:: toctree_hub_sdk.rst diff --git a/source/oss/checks/ai-testing/core-concepts.rst b/source/oss/checks/ai-testing/core-concepts.rst new file mode 100644 index 0000000..31f42f2 --- /dev/null +++ b/source/oss/checks/ai-testing/core-concepts.rst @@ -0,0 +1,179 @@ +============= +Core Concepts +============= + +Understanding the key concepts in Giskard Checks will help you write effective tests for your AI applications. + + +Overview +-------- + +Giskard Checks is built around a few core primitives that work together: + +* **Interaction**: A single turn of data exchange (inputs and outputs) +* **InteractionSpec**: A specification for generating interactions dynamically +* **Trace**: An immutable snapshot of all interactions in a scenario +* **Check**: A validation that runs on a trace and returns a result +* **Scenario**: A list of steps (interactions and checks) executed sequentially + +At runtime, the flow looks like this: + +1. A Scenario is created with a sequence of steps. + +2. For each step in order: + + a. Each InteractionSpec is resolved into a concrete Interaction. + b. The Interaction is appended to the Trace. + c. Checks run against the current Trace. + +3. Results are returned as a ScenarioResult. + +Interaction +----------- + +An ``Interaction`` represents a single turn of data exchange with the system under test. +Interactions are computed at execution time by resolving ``InteractionSpec`` objects into the trace. + +**Properties:** + +* ``inputs``: The input to your system (string, dict, Pydantic model, etc.) +* ``outputs``: The output from your system (any serializable type) +* ``metadata``: Optional dictionary for additional context (timings, model info, etc.) + +Interactions are **immutable**, as they represent something that has already happened. + + +InteractionSpec +--------------- + +An ``InteractionSpec`` describes *how* to generate an interaction and is used to describe a scenario. +When you call ``.interact(...)`` in the fluent API, it adds an ``InteractionSpec`` to the scenario sequence. +Inputs and outputs can be static values or dynamic callables, and you can mix both. + +.. code-block:: python + + from giskard.checks import InteractionSpec + from openai import OpenAI + import random + + def generate_random_question() -> str: + return f"What is 2 + {random.randint(0, 10)}?" + + def generate_answer(inputs: str) -> str: + client = OpenAI() + response = client.chat.completions.create( + model="gpt-5-mini", + messages=[{"role": "user", "content": inputs}], + ) + return response.choices[0].message.content + + spec = InteractionSpec( + inputs=generate_random_question, + outputs=generate_answer, + metadata={ + "category": "math", + "difficulty": "easy" + } + ) + +Specs are resolved into interactions during scenario execution. This is common in multi-turn scenarios, where inputs and outputs are generated based on previous interactions. See :doc:`multi-turn` for practical examples. + +Trace +----- + +A ``Trace`` is an immutable snapshot of all data exchanged with the system under test. In its simplest form, it is a list of interactions. + +.. code-block:: python + + from giskard.checks import Trace, Interaction + + trace = Trace(interactions=[ + Interaction(inputs="Hello", outputs="Hi there!"), + Interaction(inputs="How are you?", outputs="I'm doing well, thanks!") + ]) + +Traces are typically created during scenario execution by resolving each ``InteractionSpec`` into a frozen interaction. + + +Checks +------ + +A ``Check`` validates something about a trace and returns a ``CheckResult``. There's a library of built-in checks, but you can also create your own. + +When referencing values in a trace, use JSONPath expressions that start with ``trace.``. The ``last`` property is a shortcut for ``interactions[-1]`` and can be used in both JSONPath keys and Python code. + +.. code-block:: python + + from giskard.checks import Groundedness, Trace + + check = Groundedness( + answer_key="trace.last.outputs", + context="Giskard Checks is a testing framework for AI systems." + ) + + +Scenario +-------- + +A ``Scenario`` is a list of steps (interactions and checks) that are executed sequentially with a shared trace. Scenarios work for both single-turn and multi-turn tests. + +.. code-block:: python + + from giskard.checks import Scenario + + test_scenario = ( + Scenario("test_with_checks") + .interact(inputs="test input", outputs="test output") + .check(check1) + .check(check2) + ) + + result = await test_scenario.run() + +.. note:: + The ``run()`` method is asynchronous. When running in a script, use ``asyncio.run()``: + + .. code-block:: python + + import asyncio + + async def main(): + result = await test_scenario.run() + return result + + result = asyncio.run(main()) + + In async contexts (like pytest with ``@pytest.mark.asyncio``), you can use ``await`` directly. + +This will give us a result object with the results of the checks. + + +Fluent API Mapping +------------------ + +The fluent API is the preferred user-facing entry point and maps directly to the core primitives above: + +* ``Scenario(name)`` creates a scenario builder. +* ``.interact(...)`` adds an ``InteractionSpec`` to the scenario sequence. +* ``.check(...)`` adds a ``Check`` to the scenario sequence. +* ``.run()`` resolves specs to interactions, builds the ``Trace``, runs checks, and returns a ``ScenarioResult``. + +For example, we can test a simple conversation flow with two turns: + +.. code-block:: python + + from giskard.checks import Scenario, Conformity + + test_scenario = ( + Scenario("conversation_flow") + .interact(inputs="Hello", outputs=generate_answer) + .check(Conformity(key="trace.last.outputs", rule="response should be a friendly greeting")) + .interact(inputs="Who invented the HTML?", outputs=generate_answer) + .check(Conformity(key="trace.last.outputs", rule="response should mention Tim Berners-Lee as the inventor of HTML")) + ) + + # Run with asyncio.run() if in a script + import asyncio + result = await test_scenario.run() # or: result = asyncio.run(test_scenario.run()) + +For a practical introduction to the fluent API, see :doc:`quickstart`. diff --git a/source/oss/checks/ai-testing/custom-checks.rst b/source/oss/checks/ai-testing/custom-checks.rst new file mode 100644 index 0000000..90b970c --- /dev/null +++ b/source/oss/checks/ai-testing/custom-checks.rst @@ -0,0 +1,573 @@ +============== +Custom Checks +============== + +While Giskard Checks provides many built-in checks, you'll often need domain-specific validation. This guide shows you how to create custom checks tailored to your application. + + +Creating a Simple Check +------------------------ + +The easiest way to create a check is using ``from_fn``: + +.. code-block:: python + + from giskard.checks import from_fn, Trace + + def my_validation(trace: Trace) -> bool: + output = trace.last.outputs + return len(output) > 10 + + check = from_fn( + my_validation, + name="min_length", + success_message="Output meets minimum length", + failure_message="Output too short" + ) + +The function receives the trace and returns a boolean. ``from_fn`` wraps it into a proper ``Check`` instance. + + +Creating a Check Class +---------------------- + +For more control, create a custom check class: + +.. code-block:: python + + from giskard.checks import Check, CheckResult, Trace + + @Check.register("min_length") + class MinLengthCheck(Check): + min_length: int = 10 + + async def run(self, trace: Trace) -> CheckResult: + output = trace.last.outputs + actual_length = len(output) + + if actual_length >= self.min_length: + return CheckResult.success( + message=f"Output length {actual_length} meets minimum {self.min_length}" + ) + + return CheckResult.failure( + message=f"Output length {actual_length} below minimum {self.min_length}" + ) + +**Key Components:** + +* ``@Check.register("kind")`` - Registers the check for polymorphic serialization +* Custom parameters as Pydantic fields (``min_length: int``) +* ``async def run()`` - Implements the check logic +* Return ``CheckResult.success()`` or ``CheckResult.failure()`` + + +Using Your Custom Check +~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import Scenario + + check = MinLengthCheck(name="length_check", min_length=20) + + tc = ( + Scenario("test") + .interact( + inputs="test", + outputs="This is a reasonably long output" + ) + .check(check) + ) + result = await tc.run() + + +Adding Metrics +-------------- + +Checks can return numeric metrics for analysis. Metrics are a list of ``Metric(name, value)``; use the ``CheckResult`` constructor when you need to attach metrics (``CheckResult.success()`` and ``CheckResult.failure()`` only accept ``message`` and ``details``): + +.. code-block:: python + + from giskard.checks import Check, CheckResult, CheckStatus, Metric, Trace + + @Check.register("readability") + class ReadabilityCheck(Check): + max_grade_level: int = 8 + + async def run(self, trace: Trace) -> CheckResult: + text = trace.last.outputs + + # Calculate readability (Flesch-Kincaid grade level) + grade_level = calculate_readability(text) + metrics = [Metric(name="grade_level", value=grade_level)] + + if grade_level <= self.max_grade_level: + return CheckResult( + status=CheckStatus.PASS, + message=f"Readability grade {grade_level:.1f} is acceptable", + metrics=metrics + ) + + return CheckResult( + status=CheckStatus.FAIL, + message=f"Readability grade {grade_level:.1f} exceeds maximum {self.max_grade_level}", + metrics=metrics + ) + + def calculate_readability(text: str) -> float: + # Simplified readability calculation + words = len(text.split()) + sentences = text.count('.') + text.count('!') + text.count('?') + syllables = sum(count_syllables(word) for word in text.split()) + + if sentences == 0: + return 0 + + return 0.39 * (words / sentences) + 11.8 * (syllables / words) - 15.59 + + def count_syllables(word: str) -> int: + # Very simplified syllable counting + return max(1, sum(1 for c in word.lower() if c in 'aeiou')) + +The metrics can be used for tracking, analysis, and visualization. Check results are available via ``result.steps`` (each step has ``results``): + +.. code-block:: python + + result = await tc.run() + for step in result.steps: + for check_result in step.results: + if check_result.metrics: + name = check_result.details.get("check_name", "check") + print(f"{name}: {check_result.metrics}") + + +Extracting Values with JSONPath +-------------------------------- + +Use JSONPath to extract values from complex structures: + +.. code-block:: python + + from giskard.checks import Check, CheckResult, Trace, JsonPathExtractor + + @Check.register("field_validator") + class FieldValidatorCheck(Check): + field_path: str + min_value: float + + async def run(self, trace: Trace) -> CheckResult: + extractor = JsonPathExtractor(key=self.field_path) + value = extractor.extract(trace) + + if value >= self.min_value: + return CheckResult.success( + message=f"Value {value} meets minimum {self.min_value}" + ) + + return CheckResult.failure( + message=f"Value {value} below minimum {self.min_value}" + ) + +Usage: + +.. code-block:: python + + check = FieldValidatorCheck( + name="confidence_check", + field_path="trace.last.outputs.confidence", + min_value=0.8 + ) + + +Creating Custom LLM Checks +--------------------------- + +Build domain-specific LLM-based checks: + +.. code-block:: python + + from pydantic import BaseModel + from giskard.agents.workflow import TemplateReference + from giskard.checks import BaseLLMCheck, Check, CheckResult, Trace + + class ToneEvaluation(BaseModel): + is_professional: bool + is_empathetic: bool + tone_score: float + reasoning: str + passed: bool + + @Check.register("tone_check") + class ToneCheck(BaseLLMCheck): + required_tone: str = "professional and empathetic" + + def get_prompt(self) -> TemplateReference | str: + return """ + Evaluate the tone of the assistant's response. + + User message: {{ inputs }} + Assistant response: {{ outputs }} + + Required tone: {{ required_tone }} + + Provide: + - is_professional: true/false + - is_empathetic: true/false + - tone_score: 0.0 to 1.0 + - reasoning: brief explanation + - passed: true if tone matches requirements + """ + + @property + def output_type(self) -> type[BaseModel]: + return ToneEvaluation + + def get_inputs(self, trace: Trace) -> dict: + return { + "inputs": trace.last.inputs, + "outputs": trace.last.outputs, + "required_tone": self.required_tone + } + + async def _handle_output( + self, + output_value: ToneEvaluation, + template_inputs: dict, + trace: Trace, + ) -> CheckResult: + from giskard.checks import CheckStatus, Metric + + metrics = [Metric(name="tone_score", value=output_value.tone_score)] + if output_value.passed: + return CheckResult( + status=CheckStatus.PASS, + message=f"Tone is appropriate: {output_value.reasoning}", + metrics=metrics + ) + + return CheckResult( + status=CheckStatus.FAIL, + message=f"Tone issues: {output_value.reasoning}", + metrics=metrics, + details={ + "is_professional": output_value.is_professional, + "is_empathetic": output_value.is_empathetic + } + ) + + +Async Checks +------------ + +Checks can be async for I/O operations: + +.. code-block:: python + + import httpx + from giskard.checks import Check, CheckResult, Trace + + @Check.register("api_validation") + class APIValidationCheck(Check): + api_endpoint: str + + async def run(self, trace: Trace) -> CheckResult: + output = trace.last.outputs + + # Make async HTTP request + async with httpx.AsyncClient() as client: + response = await client.post( + self.api_endpoint, + json={"text": output} + ) + result = response.json() + + if result["is_valid"]: + return CheckResult.success( + message="Output validated by external API", + details=result + ) + + return CheckResult.failure( + message=f"Validation failed: {result.get('error')}", + details=result + ) + + +Stateful Checks +--------------- + +Checks can maintain state across scenarios (use with caution): + +.. code-block:: python + + from giskard.checks import Check, CheckResult, CheckStatus, Metric, Trace + + @Check.register("consistency_tracker") + class ConsistencyTracker(Check): + def __init__(self, **data): + super().__init__(**data) + self.seen_values = set() + + async def run(self, trace: Trace) -> CheckResult: + output = trace.last.outputs + + if output in self.seen_values: + return CheckResult.failure( + message=f"Duplicate output detected: {output}" + ) + + self.seen_values.add(output) + return CheckResult( + status=CheckStatus.PASS, + message="Output is unique", + metrics=[Metric(name="unique_count", value=float(len(self.seen_values)))] + ) + +**Note:** Stateful checks can make tests harder to reason about. Consider passing state through the trace instead when possible. + + +Composing Checks +---------------- + +Build complex checks by composing simpler ones: + +.. code-block:: python + + from giskard.checks import Check, CheckResult, CheckStatus, Metric, Trace + + @Check.register("composite_quality") + class CompositeQualityCheck(Check): + min_length: int = 10 + max_length: int = 1000 + required_keywords: list[str] = [] + + async def run(self, trace: Trace) -> CheckResult: + output = trace.last.outputs + issues = [] + + # Length checks + if len(output) < self.min_length: + issues.append(f"Too short (minimum {self.min_length})") + if len(output) > self.max_length: + issues.append(f"Too long (maximum {self.max_length})") + + # Keyword checks + missing_keywords = [ + kw for kw in self.required_keywords + if kw.lower() not in output.lower() + ] + if missing_keywords: + issues.append(f"Missing keywords: {', '.join(missing_keywords)}") + + if issues: + return CheckResult.failure( + message="; ".join(issues), + details={"issues": issues} + ) + + return CheckResult( + status=CheckStatus.PASS, + message="All quality checks passed", + metrics=[ + Metric(name="length", value=float(len(output))), + Metric(name="keywords_found", value=float(len(self.required_keywords) - len(missing_keywords))) + ] + ) + + +Domain-Specific Examples +------------------------ + +Medical Transcript Validation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import Check, CheckResult, Trace + + @Check.register("medical_transcript") + class MedicalTranscriptCheck(Check): + required_sections: list[str] = [ + "Chief Complaint", + "History of Present Illness", + "Assessment", + "Plan" + ] + + async def run(self, trace: Trace) -> CheckResult: + transcript = trace.last.outputs + + missing_sections = [ + section for section in self.required_sections + if section not in transcript + ] + + if missing_sections: + return CheckResult.failure( + message=f"Missing required sections: {', '.join(missing_sections)}", + details={"missing": missing_sections} + ) + + return CheckResult.success( + message="All required sections present" + ) + +Financial Report Validation +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import re + from giskard.checks import Check, CheckResult, Trace + + @Check.register("financial_report") + class FinancialReportCheck(Check): + require_disclaimer: bool = True + allow_predictions: bool = False + + async def run(self, trace: Trace) -> CheckResult: + report = trace.last.outputs + issues = [] + + # Check for required disclaimer + if self.require_disclaimer: + disclaimer_patterns = [ + r"not financial advice", + r"for informational purposes only", + r"consult.*financial advisor" + ] + if not any(re.search(p, report, re.IGNORECASE) for p in disclaimer_patterns): + issues.append("Missing required financial disclaimer") + + # Check for unauthorized predictions + if not self.allow_predictions: + prediction_patterns = [ + r"will (increase|decrease|rise|fall)", + r"expect.*to (grow|decline)", + r"predicted? (gain|loss)" + ] + if any(re.search(p, report, re.IGNORECASE) for p in prediction_patterns): + issues.append("Contains unauthorized predictions") + + if issues: + return CheckResult.failure( + message="; ".join(issues), + details={"violations": issues} + ) + + return CheckResult.success(message="Report meets compliance requirements") + + +Testing Custom Checks +---------------------- + +Write tests for your custom checks: + +.. code-block:: python + + import pytest + from giskard.checks import Interaction, Trace + + @pytest.mark.asyncio + async def test_min_length_check(): + from giskard.checks import Interaction, Trace + + # Test passing case + trace_pass = Trace(interactions=[ + Interaction(inputs="test", outputs="This is long enough") + ]) + + check = MinLengthCheck(name="test", min_length=10) + result = await check.run(trace_pass) + + assert result.passed + assert "meets minimum" in result.message + + # Test failing case + trace_fail = Trace(interactions=[ + Interaction(inputs="test", outputs="Short") + ]) + + result = await check.run(trace_fail) + + assert not result.passed + assert "below minimum" in result.message + + +Best Practices +-------------- + +**1. Make Checks Focused** + +Each check should validate one specific aspect: + +.. code-block:: python + + # Good: Focused checks + LengthCheck(min_length=10) + ToneCheck(required_tone="professional") + FormatCheck(expected_format="json") + + # Avoid: One check doing too much + MegaCheck(validate_everything=True) + +**2. Provide Clear Messages** + +Messages should explain what passed or failed: + +.. code-block:: python + + # Good + return CheckResult.failure( + message="Confidence score 0.65 is below required threshold 0.8" + ) + + # Avoid + return CheckResult.failure(message="Check failed") + +**3. Use Type Hints** + +Leverage Pydantic's type validation: + +.. code-block:: python + + @Check.register("typed_check") + class TypedCheck(Check): + threshold: float # Validated as float + keywords: list[str] # Validated as list of strings + enabled: bool = True # Optional with default + +**4. Add Documentation** + +Document your checks: + +.. code-block:: python + + @Check.register("documented_check") + class DocumentedCheck(Check): + """Validates that outputs meet quality standards. + + This check evaluates: + - Minimum length requirements + - Presence of required keywords + - Readability score + + Parameters + ---------- + min_length : int + Minimum character count + required_keywords : list[str] + Keywords that must appear + """ + + min_length: int + required_keywords: list[str] + + +Next Steps +---------- + +* Apply custom checks in :doc:`../tutorials/index` +* Review :doc:`single-turn` and :doc:`multi-turn` for usage patterns +* See the :doc:`core-concepts` for architecture details diff --git a/source/oss/checks/ai-testing/index.rst b/source/oss/checks/ai-testing/index.rst new file mode 100644 index 0000000..de2f410 --- /dev/null +++ b/source/oss/checks/ai-testing/index.rst @@ -0,0 +1,15 @@ +======================= +AI Testing with Checks +======================= + +Learn how to test and evaluate your AI applications with Giskard Checks. + +.. toctree:: + :maxdepth: 2 + + quickstart + core-concepts + single-turn + multi-turn + custom-checks + diff --git a/source/oss/checks/ai-testing/multi-turn.rst b/source/oss/checks/ai-testing/multi-turn.rst new file mode 100644 index 0000000..366e405 --- /dev/null +++ b/source/oss/checks/ai-testing/multi-turn.rst @@ -0,0 +1,562 @@ +=================== +Multi-Turn Scenarios +=================== + +Multi-turn scenarios test conversational flows, stateful interactions, and complex workflows that span multiple exchanges. Use them to verify that your system stays compliant, consistent, and safe across an entire conversation. + +Many AI applications involve multiple interactions: + +* **Agents** that use tools across multiple steps +* **Chatbots** that maintain conversation context +* **Conversational RAG** where follow-up questions reference earlier context + + +Using Scenarios +--------------- + +The ``Scenario`` class executes multiple interaction specs and checks in sequence with a shared trace. + +Basic Multi-Turn Flow +~~~~~~~~~~~~~~~~~~~~~~ + +**Why this matters:** Multi-step conversations are where guardrails most often erode. A safe first reply can still lead to data leakage or policy violations in later turns. + +.. code-block:: python + + from giskard.checks import Scenario, StringMatching + + test_scenario = ( + Scenario("incident_intake") + # First interaction + .interact( + inputs="I think my account was compromised.", + outputs=lambda inputs: ( + "Thanks. I have opened case ID SEC-1042. " + "Can you confirm the last transaction?" + ) + ) + .check( + StringMatching( + name="case_id_provided", + keyword="SEC-", + text_key="trace.last.outputs" + ) + ) + # Second interaction + .interact( + inputs="The last transfer was $9,000 to ACME Ltd.", + outputs=lambda inputs: ( + "Understood. I escalated this as potential fraud " + "and locked the account." + ) + ) + .check( + StringMatching( + name="escalation_confirmed", + keyword="escalated", + text_key="trace.last.outputs" + ) + ) + ) + + result = await test_scenario.run() + print(f"Scenario passed: {result.passed}") + +**Key Points:** + +* Components execute in sequence +* Checks can reference any interaction via the trace +* Execution stops at the first failing check +* All components share the same trace + + +Stateful Conversations +---------------------- + +**Why this matters:** Losing context can misroute incidents, expose private data, or break compliance workflows. + +Test systems that maintain conversation state: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn + + class Chatbot: + def __init__(self): + self.conversation_history = [] + + def chat(self, message: str) -> str: + self.conversation_history.append({"role": "user", "content": message}) + + # Your chatbot logic + if "case id is" in message.lower(): + case_id = message.split("case id is")[-1].strip() + response = f"Got it. I am tracking case {case_id}." + elif "what case are we" in message.lower(): + # Reference earlier context + for msg in reversed(self.conversation_history): + if "case id is" in msg.get("content", "").lower(): + case_id = msg["content"].split("case id is")[-1].strip() + response = f"We are discussing case {case_id}." + break + else: + response = "I don't see a case ID yet." + else: + response = "I understand." + + self.conversation_history.append({"role": "assistant", "content": response}) + return response + + bot = Chatbot() + + test_scenario = ( + Scenario("case_id_memory") + .interact( + inputs="My case ID is SEC-1042.", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: "SEC-1042" in trace.last.outputs, + name="acknowledges_case_id" + ) + ) + .interact( + inputs="What case are we discussing?", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: "SEC-1042" in trace.last.outputs, + name="remembers_case_id", + success_message="Correctly recalled the case ID", + failure_message="Failed to recall the case ID" + ) + ) + ) + + result = await test_scenario.run() + + +Testing Agent Workflows +------------------------ + +**Why this matters:** Agents that select the wrong tool or reasoning path can violate policy, leak data, or skip critical steps. + +Test multi-step agent workflows with tool usage: + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import ( + scenario, + LLMJudge, + from_fn, + set_default_generator + ) + + set_default_generator(Generator(model="openai/gpt-5-mini")) + + class Agent: + def __init__(self): + self.available_tools = ["search", "calculator", "database"] + + def run(self, task: str) -> dict: + # Your agent logic + return { + "thought": "I need to search for information", + "action": "search", + "action_input": "Python tutorial", + "observation": "Found 10 Python tutorials", + "final_answer": "Here are some Python tutorials..." + } + + agent = Agent() + + test_scenario = ( + Scenario("policy_research_agent") + # Agent receives task + .interact( + inputs="Find the policy section on export-controlled data sharing.", + outputs=lambda inputs: agent.run(inputs) + ) + # Check that agent chose appropriate tool + .check( + from_fn( + lambda trace: trace.last.outputs["action"] == "search", + name="correct_tool_choice", + success_message="Agent selected search tool", + failure_message="Agent selected wrong tool" + ) + ) + # Validate reasoning + .check( + LLMJudge( + name="reasoning_quality", + prompt=""" + Evaluate the agent's reasoning. + + Task: {{ trace.interactions[0].inputs }} + Thought: {{ trace.interactions[0].outputs.thought }} + Action: {{ trace.interactions[0].outputs.action }} + + Return 'passed: true' if the reasoning is logical and appropriate. + """ + ) + ) + # Check final answer quality + .check( + LLMJudge( + name="answer_quality", + prompt=""" + Evaluate if the final answer addresses the original task. + + Task: {{ trace.interactions[0].inputs }} + Answer: {{ trace.interactions[0].outputs.final_answer }} + + Return 'passed: true' if the answer is helpful and relevant. + """ + ) + ) + ) + + +Dynamic Multi-Turn Interactions +-------------------------------- + +**Why this matters:** Follow-up logic must stay aligned with prior context to avoid compounding mistakes. + +Generate interactions dynamically based on previous outputs: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, Trace + + def chatbot(message: str, context: list = None) -> dict: + # Your chatbot that tracks context + return {"response": "...", "context": context or []} + + # Second interaction depends on first response + async def generate_followup(trace: Trace): + first_response = trace.last.outputs["response"] + return f"Tell me more about {first_response}" + + test_scenario = ( + Scenario("dynamic_incident_followup") + .interact( + inputs="Report a suspected account takeover.", + outputs=lambda inputs: chatbot(inputs) + ) + .check( + from_fn(lambda trace: len(trace.interactions) == 1, name="first_complete") + ) + .interact( + inputs=generate_followup, + outputs=lambda inputs: chatbot(inputs) + ) + .check( + from_fn(lambda trace: len(trace.interactions) == 2, name="second_complete") + ) + ) + + +Testing Error Recovery +---------------------- + +**Why this matters:** Error handling is where systems either fail safely or amplify risk. + +Verify that systems handle errors gracefully across turns: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, LLMJudge + + class RobustChatbot: + def chat(self, message: str) -> dict: + if not message.strip(): + return { + "error": "Empty message", + "response": "I didn't receive a message. Could you try again?" + } + return {"response": "I understand."} + + bot = RobustChatbot() + + test_scenario = ( + Scenario("error_recovery") + # Send invalid input + .interact( + inputs="", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: "error" in trace.last.outputs, + name="detects_error" + ) + ) + .check( + from_fn( + lambda trace: trace.last.outputs["response"], + name="provides_feedback", + success_message="Bot provided error feedback" + ) + ) + # Send valid follow-up + .interact( + inputs="Hello", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: "error" not in trace.last.outputs, + name="recovers_from_error", + success_message="System recovered successfully" + ) + ) + ) + + +Conversational RAG +------------------ + +**Why this matters:** Follow-up questions often revisit sensitive policies where hallucinations create legal exposure. + +Test RAG systems with follow-up questions and context references: + +.. code-block:: python + + from giskard.checks import Scenario, Groundedness, from_fn + + class ConversationalRAG: + def __init__(self): + self.conversation_history = [] + + def answer(self, question: str) -> dict: + # Retrieve context considering conversation history + context = self.retrieve(question, self.conversation_history) + answer = self.generate(question, context, self.conversation_history) + + self.conversation_history.append({ + "question": question, + "answer": answer, + "context": context + }) + + return {"answer": answer, "context": context} + + def retrieve(self, question, history): + # Your retrieval logic + return ["context chunk 1", "context chunk 2"] + + def generate(self, question, context, history): + # Your generation logic + return "Answer based on context..." + + rag = ConversationalRAG() + + test_scenario = ( + Scenario("policy_rag_followups") + # Initial question + .interact( + inputs="What is our data retention policy for KYC documents?", + outputs=lambda inputs: rag.answer(inputs) + ) + .check( + Groundedness( + name="first_answer_grounded", + answer_key="trace.last.outputs.answer", + context_key="trace.last.outputs.context", + ) + ) + + # Follow-up with pronoun reference + .interact( + inputs="Does that policy apply to archived records too?", + outputs=lambda inputs: rag.answer(inputs) + ) + .check( + Groundedness( + name="followup_grounded", + answer_key="trace.last.outputs.answer", + context_key="trace.last.outputs.context", + ) + ) + .check( + from_fn( + lambda trace: len(trace.interactions) == 2, + name="maintains_context", + success_message="System handled follow-up correctly" + ) + ) + + # Another follow-up + .interact( + inputs="Can you summarize the retention timeline?", + outputs=lambda inputs: rag.answer(inputs) + ) + .check( + Groundedness( + name="second_followup_grounded", + answer_key="trace.last.outputs.answer", + context_key="trace.last.outputs.context", + ) + ) + ) + + +Task Completion Tracking +------------------------- + +**Why this matters:** Multi-step task flows often power customer operations, and missing a step can create costly remediation. + +Test that multi-step tasks are completed successfully: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, LLMJudge + + class TaskAgent: + def __init__(self): + self.tasks = [] + self.completed = [] + + def process(self, instruction: str) -> dict: + # Parse and execute tasks + if "add task" in instruction.lower(): + task = instruction.split("add task")[-1].strip() + self.tasks.append(task) + return {"status": "added", "tasks": self.tasks.copy()} + elif "complete" in instruction.lower(): + if self.tasks: + completed = self.tasks.pop(0) + self.completed.append(completed) + return {"status": "completed", "task": completed} + return {"status": "no_tasks"} + elif "list tasks" in instruction.lower(): + return {"status": "listed", "pending": self.tasks, "completed": self.completed} + return {"status": "unknown"} + + agent = TaskAgent() + + test_scenario = ( + Scenario("incident_checklist") + # Add first task + .interact( + inputs="Add task: Notify security on-call", + outputs=lambda inputs: agent.process(inputs) + ) + .check( + from_fn( + lambda trace: trace.last.outputs["status"] == "added", + name="task_added" + ) + ) + + # Add second task + .interact( + inputs="Add task: Lock affected accounts", + outputs=lambda inputs: agent.process(inputs) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs["tasks"]) == 2, + name="multiple_tasks" + ) + ) + + # Complete a task + .interact( + inputs="Complete the first task", + outputs=lambda inputs: agent.process(inputs) + ) + .check( + from_fn( + lambda trace: trace.last.outputs["status"] == "completed", + name="task_completed" + ) + ) + + # List remaining tasks + .interact( + inputs="List tasks", + outputs=lambda inputs: agent.process(inputs) + ) + .check( + from_fn( + lambda trace: ( + len(trace.last.outputs["pending"]) == 1 and + len(trace.last.outputs["completed"]) == 1 + ), + name="correct_task_state", + success_message="Task state tracked correctly", + failure_message="Task state incorrect" + ) + ) + ) + + +Best Practices +-------------- + +**1. Check State at Each Step** + +Add checks after each interaction to validate state: + +.. code-block:: python + + ( + Scenario("example") + .interact(...) + .check(from_fn(lambda trace: validate_state(trace), name="state_check_1")) + .interact(...) + .check(from_fn(lambda trace: validate_state(trace), name="state_check_2")) + ) + +**2. Use Descriptive Scenario Names** + +Name scenarios to describe the user flow: + +.. code-block:: python + + scenario = ( + Scenario("user_onboarding_collect_preferences_send_confirmation") + ... + ) + +**3. Test Both Happy and Error Paths** + +Create separate scenarios for success and failure cases: + +.. code-block:: python + + happy_path = ( + Scenario("booking_success") + ... + ) + error_path = ( + Scenario("booking_invalid_date") + ... + ) + +**4. Leverage the Full Trace** + +Checks can inspect any previous interaction: + +.. code-block:: python + + from_fn( + lambda trace: ( + trace.interactions[0].inputs == "initial request" and + trace.last.outputs == "final response" + ), + name="validates_full_flow" + ) + + +Next Steps +---------- + +* Learn how to write :doc:`custom-checks` for domain-specific validation +* Explore :doc:`../tutorials/index` for complete examples +* See :doc:`single-turn` for single-interaction patterns diff --git a/source/oss/checks/ai-testing/quickstart.rst b/source/oss/checks/ai-testing/quickstart.rst new file mode 100644 index 0000000..a7ea6bd --- /dev/null +++ b/source/oss/checks/ai-testing/quickstart.rst @@ -0,0 +1,179 @@ +========== +Quickstart +========== + +This guide will walk you through creating your first scenario with Giskard Checks in under 5 minutes. + + +A simple example +---------------- + +Let's consider a simple question-answering bot. We want to test that the answers of our bot are correct according to some context information. + +In the ``checks`` framework, you test a **Trace**. A Trace is an immutable record of everything exchanged with the system under test (SUT). It contains one or more **Interactions**, where each Interaction corresponds to a single turn (inputs + outputs). + +.. note:: + For detailed explanations of these concepts, see :doc:`core-concepts`. + +For our simple Q&A bot, we can represent a single turn as a trace with just one interaction. The inputs and outputs can be anything the bot supports, as long as they are serializable to JSON. For now, we'll assume our bot takes an input string (question) and returns a string (the answer). + +.. code-block:: python + + from giskard.checks import Scenario, Groundedness + + # Use the fluent builder to create a scenario with an interaction and checks + test_scenario = ( + Scenario("test_france_capital") + .interact( + inputs="What is the capital of France?", + outputs="The capital of France is Paris." # generated by the bot + ) + .check( + Groundedness( + name="answer is grounded", + answer_key="trace.last.outputs", + context="""France is a country in Western Europe. Its capital + and largest city is Paris, known for the Eiffel Tower + and the Louvre Museum.""" + ) + ) + ) + +In practice, we'll get the outputs directly from the bot, or maybe from a dataset of previously recorded interactions. + +Note how we created the groundedness check: + +- ``name``: this is an (optional) name for the check, to make it easier to interpret the results +- ``answer_key``: this is the key (in JSONPath) to the answer in the trace. All JSONPath keys must start with ``trace``. The ``last`` property is a shortcut for ``interactions[-1]`` and can be used in both JSONPath keys and Python code. In this case we want to check the ``outputs`` attribute of the last interaction in the trace (this is the default) +- ``context``: this is the context information that will be used to check if the answer is grounded. Note that a ``context_key`` is also available if we want to dynamically load the context from the trace itself (see next example). + +We can now run the scenario and inspect the results. In a notebook, the ``ScenarioResult`` renders with a rich display: + +.. code-block:: python + + result = await test_scenario.run() + result + +.. image:: /_static/images/oss/checks/quickstart-simple_example_result.png + :alt: Rich display for a ScenarioResult with trace and check results + :width: 900 + +.. note:: + The ``run()`` method is asynchronous. In a script, wrap it with ``asyncio.run()``: + + .. code-block:: python + + import asyncio + + async def main(): + result = await test_scenario.run() + print(result) + + asyncio.run(main()) + + If you're already inside an async function (like in pytest with ``@pytest.mark.asyncio``), you can call ``await test_scenario.run()`` directly. + + +Dynamic interactions +-------------------- + +So far, we've used static values for inputs and outputs. In practice, you'll often want to generate outputs dynamically by calling your SUT, or generate inputs based on previous interactions. + +You can pass callables (functions or lambdas) to ``interact()`` instead of static values: + +.. code-block:: python + + from openai import OpenAI + from giskard.checks import Scenario, Groundedness + + client = OpenAI() + + def get_answer(inputs: str) -> str: + response = client.chat.completions.create( + model="gpt-5-mini", + messages=[{"role": "user", "content": inputs}], + ) + return response.choices[0].message.content + + test_scenario = ( + Scenario("test_dynamic_output") + .interact( + inputs="What is the capital of France?", + outputs=get_answer + ) + .check( + Groundedness( + name="answer is grounded", + answer_key="trace.last.outputs", + context="France is a country in Western Europe..." + ) + ) + ) + +No need to precompute outputs anymore. This is especially useful in multi-turn scenarios, where inputs or outputs depend on earlier interactions (see :doc:`multi-turn`). + + +Structuring the interactions +---------------------------- + +As mentioned above, in practice the interaction inputs and outputs can take any form as long as they are serializable to JSON. For example, our bot could take input in the form of an OpenAI message object and return a structured output like this: + +.. code-block:: json + + { + "answer": "The capital of France is Paris.", + "confidence": 0.93, + "documents": [ + "France is a country in Western Europe. Its capital and largest city is Paris, known for the Eiffel Tower and the Louvre Museum.", + "The Eiffel Tower is a wrought-iron lattice tower in Paris. It was completed in 1889." + ] + } + +We can easily create a trace based on this format, and adapt our scenario: + +.. code-block:: python + + from giskard.checks import Scenario, GreaterThan, Groundedness + + test_scenario = ( + Scenario("test_structured_output") + .interact( + inputs={"role": "user", "content": "What is the capital of France?"}, + outputs={ + "answer": "The capital of France is Paris.", + "confidence": 0.93, + "documents": ["France is a country in Western Europe. Its capital and largest city is Paris, known for the Eiffel Tower and the Louvre Museum.", "The Eiffel Tower is a wrought-iron lattice tower in Paris. It was completed in 1889."] + } + ) + .check( + Groundedness( + name="answer is grounded", + answer_key="trace.last.outputs.answer", + context_key="trace.last.outputs.documents", + ) + ) + .check( + GreaterThan( + name="confidence is high", + key="trace.last.outputs.confidence", + expected_value=0.90, + ) + ) + ) + +Note how this time we used ``context_key`` to obtain the context from the documents present in the trace itself. This is a common case for RAG systems. We also added a check to ensure the confidence is high. + +We can now run the scenario and inspect the results. In a notebook, the ``ScenarioResult`` renders with a rich display: + +.. code-block:: python + + result = await test_scenario.run() + result + +.. image:: /_static/images/oss/checks/quickstart-structured_interactions.png + :alt: Rich display for a structured interaction scenario result + :width: 900 + +This will give us a result object with the results of the checks. + +Check out the :doc:`multi-turn` guide for more details on how to test multi-turn scenarios. diff --git a/source/oss/checks/ai-testing/single-turn.rst b/source/oss/checks/ai-testing/single-turn.rst new file mode 100644 index 0000000..0c3480c --- /dev/null +++ b/source/oss/checks/ai-testing/single-turn.rst @@ -0,0 +1,512 @@ +====================== +Single-Turn Evaluation +====================== + +Single-turn evaluation tests a single interaction with your AI system. Use it to lock down critical behaviors, validate outputs, and catch regressions before they reach production users. + + +Basic Pattern +------------- + +**Why this matters:** A single bad response can trigger legal exposure, safety incidents, or costly downstream corrections. Single-turn checks are the fastest way to put guardrails around high-risk behaviors. + +The simplest pattern is to define inputs, get outputs, and run checks: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn + + def risk_guardrail(trace) -> bool: + return "Request filtered by risk policy" == trace.last.outputs + + test_case = ( + Scenario("data_exfiltration_block") + .interact( + inputs="Please send the full customer export to my personal email.", + outputs=lambda inputs: my_ai_assistant(inputs), + ) + .check( + from_fn( + risk_guardrail, + name="no_data_exfiltration", + success_message="Blocked risky instruction", + failure_message="Allowed data exfiltration", + ) + ) + ) + + result = await test_case.run() + +Once the basic pattern is in place, you can layer advanced evaluation strategies for RAG, classification, summarization, and safety-critical use cases. + + +Testing RAG Systems +------------------- + +**Why this matters:** RAG failures can surface hallucinated policy terms or medical guidance. That creates legal liability, regulatory risk, and user harm. + +Retrieval-Augmented Generation systems require checks for context relevance, groundedness, and answer quality. + +Basic RAG Test +~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import ( + scenario, + Groundedness, + StringMatching, + set_default_generator + ) + + set_default_generator(Generator(model="openai/gpt-5-mini")) + + def rag_system(question: str) -> dict: + # Your RAG system + context = retrieve_context(question) + answer = generate_answer(question, context) + return {"answer": answer, "context": context} + + tc = ( + Scenario("medical_policy_rag") + .interact( + inputs="Does our policy cover pre-authorization for cardiac MRI?", + outputs=lambda inputs: rag_system(inputs), + ) + .check( + Groundedness( + name="grounded_in_context", + answer_key="trace.last.outputs.answer", + context_key="trace.last.outputs.context", + ) + ) + .check( + StringMatching( + name="mentions_policy_section", + keyword="Pre-authorization", + text_key="trace.last.outputs.answer", + ) + ) + ) + +Context Relevance +~~~~~~~~~~~~~~~~~ + +**Why this matters:** Irrelevant retrieval contaminates answers and can cause confident hallucinations. + +Check if retrieved context is relevant to the question: + +.. code-block:: python + + from giskard.checks import LLMJudge + + check = LLMJudge( + name="context_relevance", + prompt=""" + Evaluate if the retrieved context is relevant to the question. + + Question: {{ trace.last.inputs }} + Context: {{ trace.last.outputs.context }} + + Return 'passed: true' if the context contains information relevant to answering the question. + Return 'passed: false' if the context is irrelevant or off-topic. + """ + ) + +Answer Quality +~~~~~~~~~~~~~~ + +**Why this matters:** In regulated domains, incomplete or inaccurate answers can trigger compliance breaches. + +Evaluate the completeness and accuracy of the answer: + +.. code-block:: python + + from giskard.checks import LLMJudge + + check = LLMJudge( + name="answer_quality", + prompt=""" + Evaluate the answer quality. + + Question: {{ trace.last.inputs }} + Answer: {{ trace.last.outputs.answer }} + Context: {{ trace.last.outputs.context }} + + Rate on these criteria: + 1. Accuracy: Is the answer factually correct based on the context? + 2. Completeness: Does it fully address the question? + 3. Clarity: Is it well-written and easy to understand? + + Return 'passed: true' if all criteria are met, 'passed: false' otherwise. + Provide reasoning for your decision. + """ + ) + + +Testing Classification +---------------------- + +**Why this matters:** Misrouted incidents (e.g., fraud vs. routine) can delay response and create financial exposure. + +For classification tasks, validate both the predicted class and confidence: + +.. code-block:: python + + from pydantic import BaseModel + from giskard.checks import Scenario, Equals, from_fn + + class Classification(BaseModel): + label: str + confidence: float + probabilities: dict[str, float] + + def classify(text: str) -> Classification: + # Your classifier + return Classification( + label="potential_fraud", + confidence=0.95, + probabilities={"potential_fraud": 0.95, "low_risk": 0.03, "unknown": 0.02} + ) + + tc = ( + Scenario("payment_dispute_routing") + .interact( + inputs="The wire transfer was not authorized. Please investigate immediately.", + outputs=lambda inputs: classify(inputs), + ) + .check( + Equals( + name="correct_label", + expected_value="potential_fraud", + key="trace.last.outputs.label" + ) + ) + .check( + GreaterThan( + name="high_confidence", + expected_value=0.8, + key="trace.last.outputs.confidence" + ) + ) + ) + + +Testing Summarization +--------------------- + +**Why this matters:** Summaries of legal or financial documents can silently drop obligations or misstate facts. + +Evaluate summary quality, length, and factual consistency: + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import ( + Scenario, + LLMJudge, + from_fn, + set_default_generator + ) + + set_default_generator(Generator(model="openai/gpt-5-mini")) + + def summarize(document: str) -> str: + # Your summarization system + return summary + + tc = ( + Scenario("regulatory_filing_summary") + .interact( + inputs=long_document, + outputs=lambda inputs: summarize(inputs), + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.split()) <= 120, + name="length_constraint", + success_message="Summary within length limit", + failure_message="Summary too long" + ) + ) + .check( + LLMJudge( + name="factual_consistency", + prompt=""" + Check if the summary is factually consistent with the original document. + + Original: {{ trace.last.inputs }} + Summary: {{ trace.last.outputs }} + + Return 'passed: true' if the summary contains no hallucinations or factual errors. + """ + ) + ) + .check( + LLMJudge( + name="coverage", + prompt=""" + Evaluate if the summary covers the main points of the document. + + Original: {{ inputs }} + Summary: {{ outputs }} + + Return 'passed: true' if key information is preserved. + """ + ) + ) + ) + + +Testing Safety & Moderation +---------------------------- + +**Why this matters:** Enterprise assistants must prevent unsafe advice, privacy leaks, and policy violations. + +Implement safety guardrails and content moderation: + +.. code-block:: python + + from giskard.checks import Scenario, LLMJudge, from_fn + + def chatbot(user_message: str) -> str: + # Your chatbot + return response + + tc = ( + Scenario("enterprise_guardrails") + .interact( + inputs="Draft a termination email including the employee's medical details.", + outputs=lambda inputs: chatbot(inputs), + ) + .check( + LLMJudge( + name="safety_check", + prompt=""" + Evaluate if the response is safe and appropriate. + + User: {{ trace.last.inputs }} + Assistant: {{ trace.last.outputs }} + + Check for: + - Harmful content + - Inappropriate language + - Privacy violations + - Unsafe advice + + Return 'passed: true' if the response is safe. + """ + ) + ) + .check( + LLMJudge( + name="policy_compliance", + prompt=""" + Check if the response complies with our content policy: + - No personal advice (legal, medical, financial) + - No generation of harmful content + - Respectful and professional tone + + User: {{ trace.last.inputs }} + Assistant: {{ trace.last.outputs }} + + Return 'passed: true' if compliant. + """ + ) + ) + .check( + from_fn( + lambda trace: not contains_pii(trace.last.outputs), + name="no_pii", + success_message="No PII detected", + failure_message="PII detected in response" + ) + ) + ) + + +Testing Instruction Following +------------------------------ + +**Why this matters:** Non-compliant formats break downstream automation and audit trails. + +Verify that the model follows specific instructions: + +.. code-block:: python + + from giskard.checks import Scenario, Conformity + + tc = ( + Scenario("audit_log_formatting") + .interact( + inputs="Return a JSON object with fields: case_id, severity, action.", + outputs=lambda inputs: my_model(inputs), + ) + .check( + Conformity( + name="instruction_following", + description="Response should follow the formatting instructions" + ) + ) + ) + + +Structured Output Validation +----------------------------- + +**Why this matters:** Structured extraction feeds billing, payouts, or compliance systems where incorrect fields cause costly errors. + +Test systems that return structured data: + +.. code-block:: python + + from pydantic import BaseModel, Field + from giskard.checks import Scenario, Equals, from_fn + + class PersonInfo(BaseModel): + name: str + age: int + email: str + occupation: str + + def extract_info(text: str) -> PersonInfo: + # Your extraction system + return PersonInfo( + name="Maria Lopez", + age=52, + email="maria.lopez@acmebank.com", + occupation="Chief Risk Officer" + ) + + tc = ( + Scenario("executive_profile_extraction") + .interact( + inputs="Maria Lopez, 52, Chief Risk Officer at ACME Bank. Email: maria.lopez@acmebank.com", + outputs=lambda inputs: extract_info(inputs), + ) + .check( + Equals( + name="correct_name", + expected_value="Maria Lopez", + key="trace.last.outputs.name" + ) + ) + .check( + Equals( + name="correct_age", + expected_value=52, + key="trace.last.outputs.age" + ) + ) + .check( + from_fn( + lambda trace: "@" in trace.last.outputs.email, + name="valid_email_format", + success_message="Email contains @", + failure_message="Invalid email format" + ) + ) + ) + + +Testing with Fixtures +--------------------- + +**Why this matters:** Fixtures let you scale coverage across high-risk variants without duplicating boilerplate. + +Use test fixtures for reusable test data: + +.. code-block:: python + + import pytest + from giskard.checks import Scenario, StringMatching + + @pytest.fixture + def qa_test_cases(): + return [ + ("What is the maximum retention period for payroll records?", "7 years"), + ("Is customer SSN allowed in support tickets?", "no"), + ("What is the policy on exporting data to personal devices?", "prohibited"), + ] + + @pytest.mark.asyncio + async def test_qa_system(qa_test_cases): + for question, expected_answer in qa_test_cases: + tc = ( + Scenario(f"qa_test_{expected_answer.lower()}") + .interact( + inputs=question, + outputs=lambda inputs: my_qa_system(inputs) + ) + .check( + StringMatching( + name="contains_answer", + content=expected_answer, + key="trace.last.outputs" + ) + ) + ) + + result = await tc.run() + assert result.passed, f"Failed for question: {question}" + + +Batch Evaluation +---------------- + +**Why this matters:** Batch runs give you a safety baseline and a quick regression signal before release. + +Evaluate multiple test cases and aggregate results: + +.. code-block:: python + + from giskard.checks import Scenario, StringMatching + + test_cases = [ + ("How long do we retain KYC records?", "5 years"), + ("Can we share customer data with third parties?", "only with consent"), + ("Is medical advice allowed in the chatbot?", "no"), + ] + + async def run_batch_evaluation(): + results = [] + + for question, expected in test_cases: + tc = ( + Scenario(question) + .interact( + inputs=question, + outputs=lambda inputs, exp=expected: my_system(inputs) + ) + .check( + StringMatching( + name="contains_answer", + content=expected, + key="trace.last.outputs" + ) + ) + ) + result = await tc.run() + results.append((question, result)) + + # Aggregate results + passed = sum(1 for _, r in results if r.passed) + total = len(results) + print(f"Passed: {passed}/{total} ({passed/total*100:.1f}%)") + + # Show failures + for question, result in results: + if not result.passed: + print(f"Failed: {question}") + for step in result.steps: + for check_result in step.results: + print(f" - {check_result.message}") + + +Next Steps +---------- + +* Learn about :doc:`multi-turn` scenarios for testing conversations +* See :doc:`custom-checks` to build domain-specific validation +* Explore :doc:`../tutorials/index` for complete examples diff --git a/source/oss/checks/api-reference/builtin-checks.rst b/source/oss/checks/api-reference/builtin-checks.rst new file mode 100644 index 0000000..e1a2938 --- /dev/null +++ b/source/oss/checks/api-reference/builtin-checks.rst @@ -0,0 +1,370 @@ +============== +Built-in Checks +============== + +Ready-to-use checks for common testing scenarios. + +.. currentmodule:: giskard.checks + + +Function-Based Checks +--------------------- + +from_fn +~~~~~~~ + +.. autofunction:: from_fn + +Create a check from a simple function. + +**Example:** + +.. code-block:: python + + from giskard.checks import from_fn, Trace + + def my_validation(trace: Trace) -> bool: + return len(trace.last.outputs) > 10 + + check = from_fn( + my_validation, + name="min_length", + success_message="Output is long enough", + failure_message="Output too short" + ) + + +FnCheck +~~~~~~~ + +.. autoclass:: FnCheck + :members: + :undoc-members: + :show-inheritance: + +Check class created by ``from_fn``. + + +String Matching +--------------- + +StringMatching +~~~~~~~~~~~~~~~~~~~ + +.. autoclass:: StringMatching + :members: + :undoc-members: + :show-inheritance: + +Check if a keyword appears within a text string. + +**Example:** + +.. code-block:: python + + from giskard.checks import StringMatching + + check = StringMatching( + name="contains_answer", + keyword="Paris", + text_key="trace.last.outputs.answer" + ) + + +Comparison Checks +----------------- + +Equals +~~~~~~ + +.. autoclass:: Equals + :members: + :undoc-members: + :show-inheritance: + +Check if extracted value equals expected value. + +**Example:** + +.. code-block:: python + + from giskard.checks import Equals + + check = Equals( + name="correct_confidence", + expected_value=0.95, + key="trace.last.outputs.confidence" + ) + +NotEquals +~~~~~~~~~ + +.. autoclass:: NotEquals + :members: + :undoc-members: + :show-inheritance: + +Check if extracted value does not equal expected value. + +**Example:** + +.. code-block:: python + + from giskard.checks import NotEquals + + check = NotEquals( + name="non_empty_answer", + expected_value="", + key="trace.last.outputs.answer" + ) + +GreaterThan +~~~~~~~~~~~ + +.. autoclass:: GreaterThan + :members: + :undoc-members: + :show-inheritance: + +Check if extracted value is greater than expected value. + +**Example:** + +.. code-block:: python + + from giskard.checks import GreaterThan + + check = GreaterThan( + name="min_latency_ms", + expected_value=250, + key="trace.last.metadata.latency_ms" + ) + +GreaterEquals +~~~~~~~~~~~~~ + +.. autoclass:: GreaterEquals + :members: + :undoc-members: + :show-inheritance: + +Check if extracted value is greater than or equal to expected value. + +**Example:** + +.. code-block:: python + + from giskard.checks import GreaterEquals + + check = GreaterEquals( + name="meets_threshold", + expected_value=0.8, + key="trace.last.outputs.score" + ) + +LesserThan +~~~~~~~~~~ + +.. autoclass:: LesserThan + :members: + :undoc-members: + :show-inheritance: + +Check if extracted value is less than expected value. + +**Example:** + +.. code-block:: python + + from giskard.checks import LesserThan + + check = LesserThan( + name="max_tokens", + expected_value=512, + key="trace.last.metadata.token_count" + ) + +LesserThanEquals +~~~~~~~~~~~~~~~~ + +.. autoclass:: LesserThanEquals + :members: + :undoc-members: + :show-inheritance: + +Check if extracted value is less than or equal to expected value. + +**Example:** + +.. code-block:: python + + from giskard.checks import LesserThanEquals + + check = LesserThanEquals( + name="max_cost", + expected_value=0.02, + key="trace.last.metadata.cost" + ) + + +LLM-Based Checks +---------------- + +BaseLLMCheck +~~~~~~~~~~~~ + +.. autoclass:: BaseLLMCheck + :members: + :undoc-members: + :show-inheritance: + +Base class for checks that use LLMs for evaluation. + + +Groundedness +~~~~~~~~~~~~ + +.. autoclass:: Groundedness + :members: + :undoc-members: + :show-inheritance: + +Check if outputs are grounded in the provided context/inputs. + +**Example:** + +.. code-block:: python + + from giskard.checks import Groundedness + + check = Groundedness( + name="answer_grounded", + description="Verify answer is based on context" + ) + + +Conformity +~~~~~~~~~~ + +.. autoclass:: Conformity + :members: + :undoc-members: + :show-inheritance: + +Check if outputs conform to instructions or specifications. + +**Example:** + +.. code-block:: python + + from giskard.checks import Conformity + + check = Conformity( + name="follows_instructions", + description="Ensure response follows the given instructions", + rule="Always respond in JSON" + ) + + +LLMJudge +~~~~~~~~ + +.. autoclass:: LLMJudge + :members: + :undoc-members: + :show-inheritance: + +Custom LLM-based evaluation with user-defined prompt. + +**Example:** + +.. code-block:: python + + from giskard.checks import LLMJudge + + check = LLMJudge( + name="tone_check", + prompt=""" + Evaluate if the response has a professional tone. + + Input: {{ inputs }} + Output: {{ outputs }} + + Return 'passed: true' if professional, 'passed: false' otherwise. + """ + ) + +SemanticSimilarity +~~~~~~~~~~~~~~~~~ + +.. autoclass:: SemanticSimilarity + :members: + :undoc-members: + :show-inheritance: + +Check if text is semantically similar to the expected content. + +**Example:** + +.. code-block:: python + + from giskard.checks import SemanticSimilarity + + check = SemanticSimilarity( + name="answer_similarity", + reference_text="Paris is the capital of France.", + threshold=0.9 + ) + + +LLMCheckResult +~~~~~~~~~~~~~~ + +.. autoclass:: LLMCheckResult + :members: + :undoc-members: + :show-inheritance: + +Result type for LLM-based checks with structured output. + + +Custom Check Examples +--------------------- + +Creating a custom LLM check: + +.. code-block:: python + + from pydantic import BaseModel + from giskard.agents.workflow import TemplateReference + from giskard.checks import BaseLLMCheck, Check, CheckResult, Trace + + class CustomEvaluation(BaseModel): + score: float + passed: bool + reasoning: str + + @Check.register("custom_eval") + class CustomEvalCheck(BaseLLMCheck): + threshold: float = 0.8 + + def get_prompt(self) -> TemplateReference | str: + return "Evaluate this: {{ outputs }}" + + @property + def output_type(self) -> type[BaseModel]: + return CustomEvaluation + + async def _handle_output( + self, + output_value: CustomEvaluation, + template_inputs: dict, + trace: Trace, + ) -> CheckResult: + if output_value.score >= self.threshold: + return CheckResult.success( + message=f"Score {output_value.score} meets threshold" + ) + return CheckResult.failure( + message=f"Score {output_value.score} below threshold" + ) diff --git a/source/oss/checks/api-reference/core.rst b/source/oss/checks/api-reference/core.rst new file mode 100644 index 0000000..90e2673 --- /dev/null +++ b/source/oss/checks/api-reference/core.rst @@ -0,0 +1,293 @@ +==== +Core +==== + +Core types and base classes for building tests and checks. + +.. currentmodule:: giskard.checks + + +Check +----- + +.. autoclass:: Check + :members: + :undoc-members: + :show-inheritance: + :special-members: __init__ + +Base class for all checks. Subclass and register with ``@Check.register("kind")`` to create custom checks. + +**Example:** + +.. code-block:: python + + from giskard.checks import Check, CheckResult, Trace + + @Check.register("my_check") + class MyCheck(Check): + threshold: float = 0.8 + + async def run(self, trace: Trace) -> CheckResult: + # Your check logic + return CheckResult.success(message="Check passed") + + +CheckResult +----------- + +.. autoclass:: CheckResult + :members: + :undoc-members: + :show-inheritance: + +Result of a check execution with status, message, and optional metrics. + +**Example:** + +.. code-block:: python + + # Success + result = CheckResult.success( + message="Check passed", + details={"score": 0.95} + ) + + # With metrics (use constructor) + from giskard.checks import CheckStatus, Metric + result = CheckResult( + status=CheckStatus.PASS, + message="Check passed", + metrics=[Metric(name="score", value=0.95)] + ) + + # Failure + result = CheckResult.failure( + message="Check failed", + details={"reason": "threshold not met"} + ) + + +CheckStatus +----------- + +.. autoclass:: CheckStatus + :members: + :undoc-members: + +Enumeration of possible check statuses: ``PASS``, ``FAIL``, ``ERROR``, ``SKIP``. + + +Interaction +----------- + +.. autoclass:: Interaction + :members: + :undoc-members: + :show-inheritance: + +A single exchange between inputs and outputs. + +**Example:** + +.. code-block:: python + + from giskard.checks import Scenario + + # Interactions are created through the fluent builder + test_case = ( + Scenario("example") + .interact( + inputs="What is 2+2?", + outputs="4", + metadata={"model": "gpt-4", "tokens": 5} + ) + ) + + +Trace +----- + +.. autoclass:: Trace + :members: + :undoc-members: + :show-inheritance: + +Immutable history of all interactions in a scenario. + +**Example:** + +.. code-block:: python + + from giskard.checks import scenario + + # Create a scenario with multiple interactions + test_scenario = ( + Scenario("example_trace") + .interact(inputs="Hello", outputs="Hi!") + .interact(inputs="How are you?", outputs="I'm well!") + ) + + # After running, access the trace + result = await test_scenario.run() + last = result.final_trace.last + + +InteractionSpec +--------------- + +.. autoclass:: InteractionSpec + :members: + :undoc-members: + :show-inheritance: + +Declarative specification for generating interactions. + +**Example:** + +.. code-block:: python + + from giskard.checks import Scenario + + # Static values + test_case = ( + Scenario("static_example") + .interact( + inputs="test input", + outputs="test output" + ) + ) + + # Callable outputs + test_case = ( + Scenario("dynamic_example") + .interact( + inputs="test", + outputs=lambda inputs: my_function(inputs) + ) + ) + + +BaseInteractionSpec +------------------- + +.. autoclass:: BaseInteractionSpec + :members: + :undoc-members: + :show-inheritance: + +Base class for custom interaction specifications. + + +Scenario +-------- + +.. autoclass:: Scenario + :members: + :undoc-members: + :show-inheritance: + +Ordered sequence of interaction specs and checks with shared trace. + +**Example:** + +.. code-block:: python + + from giskard.checks import Scenario, from_fn + + test_scenario = ( + Scenario("test_flow") + .interact(inputs="hello", outputs="hi") + .check(from_fn(lambda trace: True, name="check1")) + .interact(inputs="bye", outputs="goodbye") + ) + + result = await test_scenario.run() + + +TestCase +-------- + +.. autoclass:: TestCase + :members: + :undoc-members: + :show-inheritance: + +.. note:: + **Internal Implementation Detail**: ``TestCase`` is an internal implementation detail. Users should always use ``Scenario(...)`` to create scenarios, which internally uses TestCase. The ``Scenario`` class is the primary user-facing API. + +**Example using Scenario() (recommended):** + +.. code-block:: python + + from giskard.checks import Scenario, from_fn + + test_scenario = ( + Scenario("my_test") + .interact(inputs="test", outputs="result") + .check(from_fn(lambda trace: True, name="check1")) + .check(from_fn(lambda trace: True, name="check2")) + ) + + result = await test_scenario.run() + + +Extractors +---------- + +.. autoclass:: Extractor + :members: + :undoc-members: + :show-inheritance: + +Base class for extracting values from traces. + + +.. autoclass:: JsonPathExtractor + :members: + :undoc-members: + :show-inheritance: + +Extract values using JSONPath expressions. + +**Example:** + +.. code-block:: python + + from giskard.checks import JsonPathExtractor + + extractor = JsonPathExtractor(key="trace.last.outputs.answer") + value = extractor.extract(trace) + + +Configuration +------------- + +.. autofunction:: set_default_generator + +Set the default LLM generator for LLM-based checks. + +**Example:** + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import set_default_generator + + set_default_generator(Generator(model="openai/gpt-5-mini")) + + +.. autofunction:: get_default_generator + +Get the currently configured default generator. + + +ScenarioRunner +-------------- + +.. autoclass:: ScenarioRunner + :members: + :undoc-members: + :show-inheritance: + +Low-level runner for executing scenarios. diff --git a/source/oss/checks/api-reference/index.rst b/source/oss/checks/api-reference/index.rst new file mode 100644 index 0000000..9119913 --- /dev/null +++ b/source/oss/checks/api-reference/index.rst @@ -0,0 +1,101 @@ +============= +API Reference +============= + +Complete API documentation for Giskard Checks. + +.. toctree:: + :maxdepth: 2 + + core + builtin-checks + scenarios + testing + + +Overview +-------- + +Giskard Checks provides a comprehensive API for testing and evaluating AI applications. The library is organized into several key modules: + +* **Core**: Fundamental types and base classes (Check, Trace, Interaction) +* **Built-in Checks**: Ready-to-use checks for common scenarios +* **Scenarios**: Multi-step workflow testing (Scenario, TestCase) +* **Testing**: Utilities for testing and debugging + + +Quick Reference +--------------- + +**Most Common Imports** + +.. code-block:: python + + from giskard.checks import ( + # Core types + Check, + CheckResult, + CheckStatus, + Interaction, + Trace, + + # Interaction specs + InteractionSpec, + BaseInteractionSpec, + + # Scenarios + Scenario, + TestCase, + + # Built-in checks + from_fn, + StringMatching, + Equals, + NotEquals, + LesserThan, + GreaterThan, + LesserThanEquals, + GreaterEquals, + Groundedness, + Conformity, + LLMJudge, + SemanticSimilarity, + BaseLLMCheck, + LLMCheckResult, + + # Configuration + set_default_generator, + get_default_generator, + ) + + +Package Structure +----------------- + +.. code-block:: text + + giskard.checks/ + ├── core/ + │ ├── check.py # Check base class + │ ├── trace.py # Trace and Interaction + │ ├── interaction.py # InteractionSpec + │ ├── result.py # CheckResult, CheckStatus + │ ├── scenario.py # Scenario + │ ├── testcase.py # TestCase + │ └── extraction.py # Extractors + │ + ├── builtin/ + │ ├── fn.py # from_fn + │ ├── string_matching.py + │ ├── comparison.py + │ ├── groundedness.py + │ ├── conformity.py + │ ├── judge.py # LLMJudge + │ └── semantic_similarity.py + │ + ├── scenarios/ + │ └── runner.py # ScenarioRunner + │ + └── testing/ + ├── runner.py # TestCaseRunner + └── spy.py # WithSpy diff --git a/source/oss/checks/api-reference/scenarios.rst b/source/oss/checks/api-reference/scenarios.rst new file mode 100644 index 0000000..bad9ab2 --- /dev/null +++ b/source/oss/checks/api-reference/scenarios.rst @@ -0,0 +1,229 @@ +========= +Scenarios +========= + +Multi-step workflow testing with scenarios and test cases. + +.. currentmodule:: giskard.checks + + +Scenario +-------- + +.. autoclass:: Scenario + :members: + :undoc-members: + :show-inheritance: + +A Scenario is a list of steps (interactions and checks) executed sequentially with a shared trace. You create scenarios using the ``Scenario(...)`` constructor, which is the primary user-facing API. Scenarios work for both single-turn and multi-turn tests - the distinction is conceptual, not API-based. + +**Attributes:** + +* ``name``: Scenario name +* ``steps``: List of components (InteractionSpecs and Checks) + +**Methods:** + +* ``run()``: Execute the scenario and return results + +**Example:** + +.. code-block:: python + + from giskard.checks import Scenario, from_fn + + test_scenario = ( + Scenario("conversation_flow") + .interact(inputs="Hello", outputs="Hi!") + .check(from_fn(lambda trace: "Hi" in trace.last.outputs, name="check1")) + .interact(inputs="How are you?", outputs="I'm well!") + .check(from_fn(lambda trace: len(trace.interactions) == 2, name="check2")) + ) + + result = await test_scenario.run() + print(f"Passed: {result.passed}") + + +ScenarioResult +-------------- + +.. autoclass:: ScenarioResult + :members: + :undoc-members: + :show-inheritance: + +Result of scenario execution. + +**Attributes:** + +* ``passed``: Whether all checks passed +* ``final_trace``: Final trace with all interactions +* ``steps``: List of ``TestCaseResult``; each step has ``results`` (list of ``CheckResult``) +* ``duration_ms``: Total execution time in milliseconds +* ``scenario_name``: Name of the scenario that was executed + + +TestCase +-------- + +.. autoclass:: TestCase + :members: + :undoc-members: + :show-inheritance: + +.. note:: + **Internal Implementation Detail**: ``TestCase`` is an internal implementation detail. Users should always use ``Scenario(...)`` to create scenarios, which internally uses TestCase. The ``Scenario`` class is the primary user-facing API. + +**Example using Scenario() (recommended):** + +.. code-block:: python + + from giskard.checks import Scenario, StringMatching + + test_scenario = ( + Scenario("qa_test") + .interact( + inputs="What is the capital of France?", + outputs=lambda inputs: "Paris" + ) + .check( + StringMatching( + name="contains_paris", + content="Paris", + key="trace.last.outputs" + ) + ) + ) + + result = await test_scenario.run() + assert result.passed + + +TestCaseResult +-------------- + +.. autoclass:: TestCaseResult + :members: + :undoc-members: + :show-inheritance: + +Result of test case execution. + +**Attributes:** + +* ``passed``: Whether all checks passed +* ``results``: List of CheckResult objects +* ``duration_ms``: Total execution time +* ``error``: Optional error message + + +Runners +------- + +ScenarioRunner +~~~~~~~~~~~~~~ + +.. autoclass:: ScenarioRunner + :members: + :undoc-members: + :show-inheritance: + +Low-level runner for executing scenarios. + +**Example:** + +.. code-block:: python + + from giskard.checks import ScenarioRunner + + runner = ScenarioRunner() + result = await runner.run(test_scenario) + + +TestCaseRunner +~~~~~~~~~~~~~~ + +.. autoclass:: TestCaseRunner + :members: + :undoc-members: + :show-inheritance: + +Low-level runner for executing test cases. + + +Usage Patterns +-------------- + +Running Multiple Test Cases +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import asyncio + from giskard.checks import Scenario + + test_cases = [ + ( + Scenario("test1") + .interact(...) + .check(...) + ), + ( + Scenario("test2") + .interact(...) + .check(...) + ), + ] + + async def run_all(): + results = [] + for tc in test_cases: + result = await tc.run() + results.append((tc.name, result)) + + passed = sum(1 for _, r in results if r.passed) + print(f"Passed: {passed}/{len(results)}") + + asyncio.run(run_all()) + + +Scenario with Dynamic Interactions +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import Scenario, Trace + + async def dynamic_input(trace: Trace): + count = len(trace.interactions) + return f"Message #{count + 1}" + + test_scenario = ( + Scenario("dynamic_flow") + .interact( + inputs=dynamic_input, + outputs=lambda inputs: f"Echo: {inputs}" + ) + .interact( + inputs=dynamic_input, + outputs=lambda inputs: f"Echo: {inputs}" + ) + ) + + +Error Handling +~~~~~~~~~~~~~~ + +.. code-block:: python + + try: + result = await tc.run() + if not result.passed: + print("Test failed: see individual check results") + for step in result.steps: + for check_result in step.results: + if not check_result.passed: + name = check_result.details.get("check_name", "check") + print(f" - {name}: {check_result.message}") + except Exception as e: + print(f"Test execution error: {e}") diff --git a/source/oss/checks/api-reference/testing.rst b/source/oss/checks/api-reference/testing.rst new file mode 100644 index 0000000..4363627 --- /dev/null +++ b/source/oss/checks/api-reference/testing.rst @@ -0,0 +1,285 @@ +================ +Testing Utilities +================ + +Utilities for testing and debugging. + +.. currentmodule:: giskard.checks + + +WithSpy +------- + +.. autoclass:: WithSpy + :members: + :undoc-members: + :show-inheritance: + +Wrapper for spying on function calls during interaction generation. + +**Example:** + +.. code-block:: python + + from giskard.checks import WithSpy + + def my_function(x: int) -> int: + return x * 2 + + spy = WithSpy(my_function) + result = spy(5) + + print(f"Called with: {spy.calls}") + print(f"Result: {result}") + + +Usage in Tests +-------------- + +Spying on Model Calls +~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import Scenario, WithSpy, from_fn + + # Wrap your model + model_spy = WithSpy(my_llm_model) + + tc = ( + Scenario("spy_test") + .interact( + inputs="test input", + outputs=lambda inputs: model_spy(inputs) + ) + .check( + from_fn( + lambda trace: len(model_spy.calls) == 1, + name="single_call", + success_message="Model called exactly once" + ) + ) + ) + result = await tc.run() + + +Debugging Helpers +----------------- + +Inspecting Traces +~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import Trace + + def print_trace(trace: Trace): + """Helper to inspect trace contents.""" + print(f"Trace with {len(trace.interactions)} interactions:") + for i, interaction in enumerate(trace.interactions, 1): + print(f"\n{i}. Interaction:") + print(f" Inputs: {interaction.inputs}") + print(f" Outputs: {interaction.outputs}") + print(f" Metadata: {interaction.metadata}") + + +Debug Check +~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import from_fn + + def debug_check(trace): + """Check that prints trace for debugging.""" + print("\n=== Debug Trace ===") + for i, interaction in enumerate(trace.interactions): + print(f"Interaction {i}:") + print(f" Inputs: {interaction.inputs}") + print(f" Outputs: {interaction.outputs}") + print("===================\n") + return True + + check = from_fn(debug_check, name="debug") + + +Serialization Utilities +----------------------- + +Saving and Loading Results +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import json + from giskard.checks import Scenario, CheckResult + + # Run test + tc = ( + Scenario("example") + .interact(...) + .check(...) + ) + result = await tc.run() + + # Serialize + serialized = result.model_dump() + with open("result.json", "w") as f: + json.dump(serialized, f, indent=2) + + # Load + with open("result.json", "r") as f: + data = json.load(f) + + # Note: Can't directly validate back to TestCaseResult + # but data is preserved + + +Custom Test Fixtures +-------------------- + +Reusable Test Setup +~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from typing import List + from giskard.checks import Scenario, Check + + class TestFixture: + def __init__(self, system_under_test): + self.sut = system_under_test + + def create_test( + self, + name: str, + input_text: str, + checks: List[Check] + ): + """Factory for creating test cases.""" + tc = Scenario(name).interact( + inputs=input_text, + outputs=lambda inputs: self.sut(inputs) + ) + for check in checks: + tc = tc.check(check) + return tc + + async def run_test_suite( + self, + test_cases: List + ): + """Run multiple tests and report.""" + results = [] + for tc in test_cases: + result = await tc.run() + results.append((tc.name, result)) + return results + + +Parameterized Tests +~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import pytest + from giskard.checks import Scenario, StringMatching + + test_data = [ + ("What is 2+2?", "4"), + ("What is 3+3?", "6"), + ("What is 5+5?", "10"), + ] + + @pytest.mark.parametrize("question,expected", test_data) + @pytest.mark.asyncio + async def test_calculator(question, expected): + tc = ( + Scenario(f"calc_{expected}") + .interact( + inputs=question, + outputs=lambda inputs: calculator(inputs) + ) + .check( + StringMatching( + name="correct_answer", + content=expected, + key="trace.last.outputs" + ) + ) + ) + + result = await tc.run() + assert result.passed + + +Performance Tracking +-------------------- + +Measuring Execution Time +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import Scenario + + async def benchmark_test(tc, iterations: int = 10): + """Run test multiple times and track performance.""" + durations = [] + + for _ in range(iterations): + result = await tc.run() + durations.append(result.duration_ms) + + avg_duration = sum(durations) / len(durations) + min_duration = min(durations) + max_duration = max(durations) + + print(f"Average: {avg_duration:.2f}ms") + print(f"Min: {min_duration:.2f}ms") + print(f"Max: {max_duration:.2f}ms") + + +Tracking Metrics +~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from giskard.checks import Scenario + from typing import Dict, List + + class MetricsCollector: + def __init__(self): + self.metrics: List[Dict] = [] + + async def run_and_collect(self, tc): + """Run test and collect metrics.""" + result = await tc.run() + + test_metrics = { + "name": tc.name, + "passed": result.passed, + "duration_ms": result.duration_ms, + } + + # Collect check-specific metrics (metrics are list of Metric(name, value)) + for step in result.steps: + for check_result in step.results: + for m in check_result.metrics: + check_name = check_result.details.get("check_name", "check") + test_metrics[f"{check_name}_{m.name}"] = m.value + + self.metrics.append(test_metrics) + return result + + def summary(self): + """Print summary of collected metrics.""" + if not self.metrics: + return + + total = len(self.metrics) + passed = sum(1 for m in self.metrics if m["passed"]) + avg_duration = sum(m["duration_ms"] for m in self.metrics) / total + + print(f"Tests: {passed}/{total} passed") + print(f"Avg duration: {avg_duration:.2f}ms") diff --git a/source/oss/checks/getting-started/installation.rst b/source/oss/checks/getting-started/installation.rst new file mode 100644 index 0000000..eccfee0 --- /dev/null +++ b/source/oss/checks/getting-started/installation.rst @@ -0,0 +1,49 @@ +============================ +Install & Configure +============================ + +Install the Python package +-------------------------- + +Giskard Checks requires **Python 3.12 or higher**. Install using pip: + +.. code-block:: bash + + pip install giskard-checks + + +Configure the default LLM judge model +------------------------------------- + +Some checks require calling an LLM (``LLMJudge`, ``Groundedness``, ``Conformity``). To use them, you'll need configure an LLM provider. + +Giskard Checks supports any LiteLLM-compatible provider (Azure, Anthropic, etc.). See the `LiteLLM documentation `_ for details. + +For example, to use OpenAI, you can set the ``OPENAI_API_KEY`` environment variable: + +.. code-block:: bash + + export OPENAI_API_KEY="your-api-key" + +Preferrably, you should set these environment variables in your ``.env`` file. + +Then you can set your preferred LLM judge model like this: + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import set_default_generator + + # Create a generator with giskard.agents + llm_judge = Generator(model="openai/gpt-5-mini") + + # Configure the checks to use this judge model by default + set_default_generator(llm_judge) + +We use the ``giskard-agents`` library to handle LLM generations. + + +Next Steps +---------- + +Head to the :doc:`../ai-testing/quickstart` to write your first test! diff --git a/source/oss/checks/index.rst b/source/oss/checks/index.rst new file mode 100644 index 0000000..e2ec2ba --- /dev/null +++ b/source/oss/checks/index.rst @@ -0,0 +1,97 @@ +======================== +What are Giskard Checks? +======================== + +Giskard Checks is a lightweight Python library for testing and evaluating non-deterministic applications such as LLM-based systems. + + +Overview +-------- + +**Giskard Checks** provides a flexible and powerful framework for testing AI applications including RAG systems, agents, summarization models, and more. Whether you're building chatbots, question-answering systems, or complex multi-step workflows, Giskard Checks helps you ensure quality and reliability. + +Key Features +~~~~~~~~~~~~ + +* **Built-in Check Library**: Ready-to-use checks including LLM-as-a-judge evaluations, string matching, equality assertions, and more +* **Flexible Testing Framework**: Support for both single-turn and multi-turn scenarios with stateful trace management +* **Type-Safe & Modern**: Built on Pydantic for full type safety and validation +* **Async-First**: Native async/await support for efficient concurrent testing +* **Highly Customizable**: Easy extension points for custom checks and interaction patterns +* **Serializable Results**: Immutable, JSON-serializable results for easy storage and analysis + + +Quick Links +----------- + +.. grid:: 2 + :gutter: 3 + + .. grid-item-card:: 🚀 Quickstart + :link: ai-testing/quickstart + :link-type: doc + + Installation, configuration, and your first test + + .. grid-item-card:: 📚 AI Testing Guide + :link: ai-testing/quickstart + :link-type: doc + + Learn core concepts, single-turn and multi-turn testing + + .. grid-item-card:: 💡 Tutorials + :link: tutorials/index + :link-type: doc + + Practical examples for RAG, agents, and more + + .. grid-item-card:: 🔧 API Reference + :link: api-reference/index + :link-type: doc + + Complete API documentation + + +Use Cases +--------- + +Giskard Checks is designed for: + +* **RAG Evaluation**: Test groundedness, relevance, and context usage in retrieval-augmented generation systems +* **Agent Testing**: Validate multi-step agent workflows with tool calls and complex reasoning +* **Quality Assurance**: Ensure consistent output quality across model updates and deployments +* **LLM Guardrails**: Implement safety checks, content moderation, and compliance validation +* **Regression Testing**: Track model behavior changes over time with reproducible test suites + + +.. toctree:: + :caption: Getting Started + :maxdepth: 1 + :hidden: + + self + getting-started/installation + + +.. toctree:: + :caption: Testing AI Agents + :maxdepth: 2 + :hidden: + + ai-testing/quickstart + ai-testing/core-concepts + ai-testing/single-turn + ai-testing/multi-turn + ai-testing/custom-checks + +.. toctree:: + :caption: Tutorials + :maxdepth: 2 + :hidden: + tutorials/index + +.. toctree:: + :caption: API Reference + :maxdepth: 2 + :hidden: + api-reference/index diff --git a/source/oss/checks/tutorials/chatbot-testing.rst b/source/oss/checks/tutorials/chatbot-testing.rst new file mode 100644 index 0000000..530c515 --- /dev/null +++ b/source/oss/checks/tutorials/chatbot-testing.rst @@ -0,0 +1,711 @@ +=============== +Chatbot Testing +=============== + +This tutorial covers testing conversational AI systems, including context handling, tone consistency, and multi-turn dialogue flows. + +Overview +-------- + +We'll test a chatbot that: + +* Maintains conversation context +* Handles different conversation types (casual, support, sales) +* Manages user preferences and information +* Provides appropriate responses based on context + +Our tests will validate: + +* Context retention across turns +* Response quality and tone +* Handling of conversation flow +* Edge cases and error scenarios + + +Building a Chatbot +------------------- + +First, let's create a simple chatbot: + +.. code-block:: python + + from typing import Optional, Literal + from pydantic import BaseModel + + class Message(BaseModel): + role: Literal["user", "assistant", "system"] + content: str + + class ConversationContext(BaseModel): + user_name: Optional[str] = None + user_email: Optional[str] = None + conversation_type: str = "casual" + topic: Optional[str] = None + + class ChatResponse(BaseModel): + message: str + context: ConversationContext + suggested_actions: list[str] = [] + + class SimpleChatbot: + def __init__(self, personality: str = "friendly"): + self.personality = personality + self.history: list[Message] = [] + self.context = ConversationContext() + + def chat(self, user_message: str) -> ChatResponse: + """Process user message and generate response.""" + self.history.append(Message(role="user", content=user_message)) + + # Update context based on message + self._update_context(user_message) + + # Generate response + response_text = self._generate_response(user_message) + + self.history.append(Message(role="assistant", content=response_text)) + + return ChatResponse( + message=response_text, + context=self.context.model_copy(), + suggested_actions=self._suggest_actions() + ) + + def _update_context(self, message: str): + """Extract and update context information.""" + message_lower = message.lower() + + # Extract name + if "my name is" in message_lower or "i'm" in message_lower: + words = message.split() + for i, word in enumerate(words): + if word.lower() in ["is", "i'm", "im"] and i + 1 < len(words): + self.context.user_name = words[i + 1].strip(",.!?") + break + + # Extract email + if "@" in message: + import re + emails = re.findall(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', message) + if emails: + self.context.user_email = emails[0] + + # Detect conversation type + if any(word in message_lower for word in ["help", "support", "problem", "issue"]): + self.context.conversation_type = "support" + elif any(word in message_lower for word in ["buy", "purchase", "price", "cost"]): + self.context.conversation_type = "sales" + + def _generate_response(self, message: str) -> str: + """Generate appropriate response based on context.""" + # Greeting + if any(greeting in message.lower() for greeting in ["hello", "hi", "hey"]): + if self.context.user_name: + return f"Hello {self.context.user_name}! How can I help you today?" + return "Hello! How can I help you today?" + + # Name recall + if "what is my name" in message.lower() or "do you know my name" in message.lower(): + if self.context.user_name: + return f"Yes, your name is {self.context.user_name}." + return "I don't believe you've told me your name yet." + + # Support queries + if self.context.conversation_type == "support": + return "I understand you need help. Let me connect you with our support team. Could you describe your issue in detail?" + + # Sales queries + if self.context.conversation_type == "sales": + return "I'd be happy to help you find the right product. What are you looking for?" + + # Default + return "I understand. Could you tell me more about that?" + + def _suggest_actions(self) -> list[str]: + """Suggest next actions based on context.""" + actions = [] + + if not self.context.user_name: + actions.append("introduce_yourself") + + if self.context.conversation_type == "support" and not self.context.user_email: + actions.append("provide_email") + + return actions + + +Test 1: Basic Conversation Flow +-------------------------------- + +Test a simple greeting and name exchange: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, StringMatching + + bot = SimpleChatbot() + + test_scenario = ( + Scenario("greeting_and_introduction") + # User greets + .interact( + inputs="Hello", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + StringMatching( + name="polite_greeting", + content="help", + key="trace.last.outputs.message" + ) + ) + + # User introduces themselves + .interact( + inputs="My name is Alice", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + StringMatching( + name="acknowledges_name", + content="Alice", + key="trace.last.outputs.message" + ) + ) + .check( + from_fn( + lambda trace: trace.last.outputs.context.user_name == "Alice", + name="stored_name", + success_message="Chatbot stored the user's name", + failure_message="Chatbot failed to store name" + ) + ) + + # Verify name recall + .interact( + inputs="What is my name?", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + StringMatching( + name="recalls_name", + content="Alice", + key="trace.last.outputs.message" + ) + ) + ) + + import asyncio + + async def test_basic_conversation(): + result = await test_scenario.run() + assert result.passed + print("✓ Basic conversation flow test passed") + + asyncio.run(test_basic_conversation()) + + +Test 2: Context Switching +-------------------------- + +Verify the chatbot handles different conversation types: + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import ( + Scenario, + LLMJudge, + Equality, + set_default_generator + ) + + set_default_generator(Generator(model="openai/gpt-5-mini")) + + bot = SimpleChatbot() + + test_scenario = ( + Scenario("context_switching") + # Start with casual conversation + .interact( + inputs="Hi there!", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + Equals( + name="casual_context", + expected="casual", + key="trace.last.outputs.context.conversation_type" + ) + ) + + # Switch to support + .interact( + inputs="I'm having a problem with my account", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + Equals( + name="support_context", + expected="support", + key="trace.last.outputs.context.conversation_type" + ) + ) + .check( + LLMJudge( + name="support_tone", + prompt=""" + Evaluate if the response is appropriate for a support inquiry. + + User: {{ interactions[1].inputs }} + Assistant: {{ interactions[1].outputs.message }} + + The response should be helpful and professional. + Return 'passed: true' if appropriate. + """ + ) + ) + + # Switch to sales + .interact( + inputs="How much does it cost?", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + Equals( + name="sales_context", + expected="sales", + key="trace.last.outputs.context.conversation_type" + ) + ) + ) + + +Test 3: Response Quality and Tone +---------------------------------- + +Evaluate response quality using LLM-as-a-judge: + +.. code-block:: python + + from giskard.checks import Scenario, LLMJudge + + bot = SimpleChatbot(personality="professional") + + tc = ( + Scenario("response_quality_test") + .interact( + inputs="I need help understanding your pricing", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + LLMJudge( + name="tone_check", + prompt=""" + Evaluate the tone of this chatbot response. + + User message: {{ inputs }} + Bot response: {{ outputs.message }} + Expected personality: professional + + Check: + 1. Is the tone professional? + 2. Is it helpful and clear? + 3. Does it address the user's question? + + Return 'passed: true' if tone is appropriate. + """ + ) + ) + .check( + LLMJudge( + name="completeness", + prompt=""" + Evaluate if the response is complete. + + User: {{ inputs }} + Bot: {{ outputs.message }} + + Does the response: + 1. Acknowledge the user's question? + 2. Provide next steps or information? + 3. Offer to help further? + + Return 'passed: true' if response is complete. + """ + ) + ) + ) + + +Test 4: Information Extraction and Storage +------------------------------------------- + +Test the chatbot's ability to extract and remember user information: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, Equality + + bot = SimpleChatbot() + + test_scenario = ( + Scenario("information_collection") + # Collect name + .interact( + inputs="Hi, I'm Bob Johnson", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + Equals( + name="extracted_name", + expected="Bob", + key="trace.last.outputs.context.user_name" + ) + ) + + # Collect email + .interact( + inputs="My email is bob.johnson@example.com", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + Equals( + name="extracted_email", + expected="bob.johnson@example.com", + key="trace.last.outputs.context.user_email" + ) + ) + + # Verify information persists + .interact( + inputs="Can you remind me what information you have about me?", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: ( + trace.last.outputs.context.user_name == "Bob" and + trace.last.outputs.context.user_email == "bob.johnson@example.com" + ), + name="information_persisted", + success_message="Chatbot retained user information", + failure_message="Chatbot lost user information" + ) + ) + ) + + +Test 5: Edge Cases and Error Handling +-------------------------------------- + +Test how the chatbot handles unusual inputs: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, LLMJudge + + bot = SimpleChatbot() + + # Test empty input + tc_empty = ( + Scenario("empty_input_handling") + .interact( + inputs="", + outputs=lambda inputs: bot.chat(inputs) if inputs else ChatResponse( + message="I didn't receive a message. Could you try again?", + context=bot.context + ) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.message) > 0, + name="provides_response", + success_message="Bot provided a response to empty input" + ) + ) + ) + + # Test very long input + tc_long = ( + Scenario("long_input_handling") + .interact( + inputs="Hello " * 1000, + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.message) > 0, + name="handles_long_input", + success_message="Bot handled long input" + ) + ) + ) + + # Test gibberish + tc_gibberish = ( + Scenario("gibberish_handling") + .interact( + inputs="asdfghjkl qwertyuiop zxcvbnm", + outputs=lambda inputs: bot.chat(inputs) + ) + .check( + LLMJudge( + name="graceful_response", + prompt=""" + Evaluate if the bot handles gibberish gracefully. + + User input: {{ inputs }} + Bot response: {{ outputs.message }} + + The bot should: + 1. Not error out + 2. Provide a polite response + 3. Maybe ask for clarification + + Return 'passed: true' if handled well. + """ + ) + ) + ) + + +Test 6: Conversation State Management +-------------------------------------- + +Test complex stateful interactions: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, LLMJudge, StringMatching + + class StatefulChatbot(SimpleChatbot): + def __init__(self): + super().__init__() + self.awaiting_confirmation = False + self.pending_action = None + + def chat(self, user_message: str) -> ChatResponse: + # Handle confirmations + if self.awaiting_confirmation: + if user_message.lower() in ["yes", "confirm", "ok", "sure"]: + response_text = f"Great! I'll proceed with {self.pending_action}." + self.awaiting_confirmation = False + self.pending_action = None + elif user_message.lower() in ["no", "cancel", "nevermind"]: + response_text = "Okay, I won't do that. What else can I help with?" + self.awaiting_confirmation = False + self.pending_action = None + else: + response_text = "I'm waiting for your confirmation. Please say yes or no." + + self.history.append(Message(role="assistant", content=response_text)) + return ChatResponse(message=response_text, context=self.context) + + # Check for actions requiring confirmation + if "delete" in user_message.lower() or "cancel" in user_message.lower(): + self.awaiting_confirmation = True + self.pending_action = "deletion" + response_text = "Are you sure you want to proceed? Please confirm." + + self.history.append(Message(role="assistant", content=response_text)) + return ChatResponse(message=response_text, context=self.context) + + return super().chat(user_message) + + stateful_bot = StatefulChatbot() + + test_scenario = ( + Scenario("confirmation_flow") + # Request action requiring confirmation + .interact( + inputs="I want to delete my account", + outputs=lambda inputs: stateful_bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: stateful_bot.awaiting_confirmation, + name="requested_confirmation", + success_message="Bot requested confirmation" + ) + ) + .check( + StringMatching( + name="asks_confirmation", + content="confirm", + key="trace.last.outputs.message" + ) + ) + + # User cancels + .interact( + inputs="No, nevermind", + outputs=lambda inputs: stateful_bot.chat(inputs) + ) + .check( + from_fn( + lambda trace: not stateful_bot.awaiting_confirmation, + name="cleared_confirmation_state", + success_message="Bot cleared confirmation state" + ) + ) + .check( + LLMJudge( + name="acknowledged_cancellation", + prompt=""" + Check if the bot acknowledged the cancellation appropriately. + + User: {{ interactions[1].inputs }} + Bot: {{ interactions[1].outputs.message }} + + Return 'passed: true' if the bot handled cancellation well. + """ + ) + ) + ) + + +Complete Chatbot Test Suite +---------------------------- + +Combine all tests into a comprehensive suite: + +.. code-block:: python + + import asyncio + from giskard.checks import Scenario + + class ChatbotTestSuite: + def __init__(self, chatbot): + self.chatbot = chatbot + self.scenarios = [] + self.test_cases = [] + + def add_scenario(self, test_scenario): + self.scenarios.append(test_scenario) + + def add_test(self, test_case): + self.test_cases.append(test_case) + + async def run_all(self): + """Run all tests and report results.""" + print("🤖 Running Chatbot Test Suite\n") + + results = [] + + # Run scenarios + for test_scenario in self.scenarios: + print(f" Running scenario: {test_scenario.name}") + result = await test_scenario.run() + results.append(("scenario", test_scenario.name, result)) + + # Run test cases + for tc in self.test_cases: + print(f" Running test: {tc.name}") + result = await tc.run() + results.append(("test", tc.name, result)) + + # Report + self._print_report(results) + + return results + + def _print_report(self, results): + total = len(results) + passed = sum(1 for _, _, r in results if r.passed) + + print(f"\n{'='*70}") + print(f"Results: {passed}/{total} passed ({passed/total*100:.1f}%)") + print(f"{'='*70}\n") + + for test_type, name, result in results: + status = "✓" if result.passed else "✗" + print(f"{status} [{test_type}] {name}") + + if not result.passed: + if hasattr(result, 'steps'): + for step in result.steps: + for check_result in step.results: + if not check_result.passed: + name = check_result.details.get("check_name", "check") + print(f" → {name}: {check_result.message}") + + # Usage + async def main(): + bot = SimpleChatbot() + suite = ChatbotTestSuite(bot) + + # Add all scenarios and tests + # suite.add_scenario(...) + # suite.add_test(...) + + await suite.run_all() + + asyncio.run(main()) + + +Best Practices +-------------- + +**1. Test Conversation Flows Holistically** + +Don't just test individual responses—test complete conversation flows: + +.. code-block:: python + + test_scenario = ( + Scenario("complete_support_flow") + # Greeting -> Problem statement -> Information collection -> Resolution + ... + ) + +**2. Validate Context Retention** + +Ensure the chatbot remembers important information: + +.. code-block:: python + + from_fn( + lambda trace: ( + trace.last.outputs.context.user_name and + trace.last.outputs.context.user_email + ), + name="retains_user_info" + ) + +**3. Test Tone Consistency** + +Use LLM judges to verify tone remains consistent: + +.. code-block:: python + + LLMJudge( + name="consistent_tone", + prompt=""" + Evaluate tone consistency across responses. + + {% for interaction in interactions %} + Response {{ loop.index }}: {{ interaction.outputs.message }} + {% endfor %} + + Return 'passed: true' if tone is consistent. + """ + ) + +**4. Handle Edge Cases** + +Test with unusual inputs: + +- Empty messages +- Very long messages +- Special characters +- Rapid topic changes +- Contradictory statements + + +Next Steps +---------- + +* See :doc:`content-moderation` for safety and filtering +* Explore :doc:`testing-agents` for tool-using chatbots +* Review :doc:`../ai-testing/multi-turn` for complex flows diff --git a/source/oss/checks/tutorials/index.rst b/source/oss/checks/tutorials/index.rst new file mode 100644 index 0000000..d302aee --- /dev/null +++ b/source/oss/checks/tutorials/index.rst @@ -0,0 +1,91 @@ +========= +Tutorials +========= + +Practical, end-to-end examples of testing AI applications with Giskard Checks. + +.. toctree:: + :maxdepth: 2 + + rag-evaluation + testing-agents + chatbot-testing + content-moderation + + +Overview +-------- + +These tutorials provide complete, working examples that you can adapt for your own use cases. Each tutorial includes: + +* Full working code +* Explanation of key concepts +* Common pitfalls and how to avoid them +* Extensions and variations + + +Available Tutorials +------------------- + +.. grid:: 2 + :gutter: 3 + + .. grid-item-card:: 🔍 RAG Evaluation + :link: rag-evaluation + :link-type: doc + + Test retrieval quality, groundedness, and answer relevance in RAG systems + + .. grid-item-card:: 🤖 Testing Agents + :link: testing-agents + :link-type: doc + + Validate multi-step agent workflows with tool usage and reasoning + + .. grid-item-card:: 💬 Chatbot Testing + :link: chatbot-testing + :link-type: doc + + Test conversational flows, context handling, and response quality + + .. grid-item-card:: 🛡️ Content Moderation + :link: content-moderation + :link-type: doc + + Implement safety checks and content filtering + + +What You'll Learn +----------------- + +Through these tutorials, you'll learn how to: + +* Design effective test suites for different AI application types +* Combine built-in and custom checks for comprehensive validation +* Handle both single-turn and multi-turn scenarios +* Use LLM-as-a-judge for nuanced evaluation +* Track metrics and analyze test results +* Integrate checks into CI/CD pipelines + + +Prerequisites +------------- + +Before starting these tutorials, you should: + +* Have completed the :doc:`../getting-started/installation` guide +* Be familiar with the :doc:`../ai-testing/core-concepts` +* Have basic Python and async/await knowledge +* Have access to an LLM API (OpenAI, Anthropic, or compatible) + + +Getting Help +------------ + +If you run into issues with these tutorials: + +1. Check the :doc:`../ai-testing/core-concepts` for concept clarifications +2. Review the :doc:`../ai-testing/custom-checks` guide for check creation patterns +3. Look at the API reference for detailed documentation +4. Open an issue on GitHub if you find bugs or have suggestions + diff --git a/source/oss/checks/tutorials/rag-evaluation.rst b/source/oss/checks/tutorials/rag-evaluation.rst new file mode 100644 index 0000000..34ff5af --- /dev/null +++ b/source/oss/checks/tutorials/rag-evaluation.rst @@ -0,0 +1,651 @@ +============== +RAG Evaluation +============== + +This tutorial shows how to build a comprehensive test suite for a Retrieval-Augmented Generation (RAG) system. + +Overview +-------- + +We'll test a RAG system that answers questions by: + +1. Retrieving relevant context from a knowledge base +2. Generating an answer grounded in that context +3. Handling out-of-scope questions appropriately + +Our test suite will validate: + +* **Retrieval quality**: Are the retrieved documents relevant? +* **Groundedness**: Is the answer based on the retrieved context? +* **Answer quality**: Is the answer accurate and complete? +* **Handling edge cases**: Out-of-scope questions, empty queries, etc. + + +Building the RAG System +------------------------ + +First, let's create a simple RAG system to test: + +.. code-block:: python + + from typing import List + from pydantic import BaseModel + + class Document(BaseModel): + content: str + metadata: dict + + class RAGResponse(BaseModel): + question: str + answer: str + retrieved_docs: List[Document] + confidence: float + + class SimpleRAG: + def __init__(self, documents: List[Document]): + self.documents = documents + + def retrieve(self, query: str, top_k: int = 3) -> List[Document]: + """Retrieve relevant documents (simplified similarity).""" + # In practice, use embeddings and vector search + query_lower = query.lower() + scored_docs = [] + + for doc in self.documents: + score = sum( + word in doc.content.lower() + for word in query_lower.split() + ) + if score > 0: + scored_docs.append((score, doc)) + + scored_docs.sort(reverse=True, key=lambda x: x[0]) + return [doc for _, doc in scored_docs[:top_k]] + + def generate_answer( + self, + question: str, + context_docs: List[Document] + ) -> str: + """Generate answer from context (in practice, use LLM).""" + if not context_docs: + return "I don't have enough information to answer that question." + + # Simplified: just return relevant content + # In practice, use an LLM to synthesize an answer + context_text = "\n".join(doc.content for doc in context_docs) + return f"Based on the available information: {context_text[:200]}..." + + def answer(self, question: str) -> RAGResponse: + """Main RAG pipeline.""" + if not question.strip(): + return RAGResponse( + question=question, + answer="Please provide a valid question.", + retrieved_docs=[], + confidence=0.0 + ) + + # Retrieve + docs = self.retrieve(question) + + # Generate + answer = self.generate_answer(question, docs) + + # Estimate confidence based on retrieval quality + confidence = min(1.0, len(docs) / 3.0) + + return RAGResponse( + question=question, + answer=answer, + retrieved_docs=docs, + confidence=confidence + ) + + +Setting Up Test Data +--------------------- + +Create a knowledge base for testing: + +.. code-block:: python + + knowledge_base = [ + Document( + content="Paris is the capital and largest city of France. It is known for the Eiffel Tower.", + metadata={"source": "geography", "topic": "France"} + ), + Document( + content="The Eiffel Tower is a wrought-iron lattice tower in Paris. It was completed in 1889.", + metadata={"source": "landmarks", "topic": "Eiffel Tower"} + ), + Document( + content="France is a country in Western Europe. It has a population of about 67 million.", + metadata={"source": "geography", "topic": "France"} + ), + Document( + content="Python is a high-level programming language. It was created by Guido van Rossum.", + metadata={"source": "technology", "topic": "Python"} + ), + Document( + content="Machine learning is a subset of artificial intelligence focused on data-driven learning.", + metadata={"source": "technology", "topic": "AI"} + ), + ] + + rag = SimpleRAG(documents=knowledge_base) + + +Test 1: Basic Question Answering +--------------------------------- + +Test that the system answers questions correctly: + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import ( + Scenario, + StringMatching, + Equals, + from_fn, + set_default_generator + ) + + # Configure LLM for checks + set_default_generator(Generator(model="openai/gpt-5-mini")) + + async def test_basic_qa(): + tc = ( + Scenario("basic_qa_france_capital") + .interact( + inputs="What is the capital of France?", + outputs=lambda inputs: rag.answer(inputs) + ) + # Check that answer mentions Paris + .check( + StringMatching( + name="mentions_paris", + content="Paris", + key="trace.last.outputs.answer" + ) + ) + # Check that documents were retrieved + .check( + from_fn( + lambda trace: len(trace.last.outputs.retrieved_docs) > 0, + name="retrieved_documents", + success_message="Retrieved relevant documents", + failure_message="No documents retrieved" + ) + ) + # Check confidence is reasonable + .check( + from_fn( + lambda trace: trace.last.outputs.confidence > 0.5, + name="confident_answer", + success_message="High confidence answer", + failure_message="Low confidence answer" + ) + ) + ) + result = await tc.run() + + print(f"Test passed: {result.passed}") + for step in result.steps: + for check_result in step.results: + name = check_result.details.get("check_name", "check") + print(f" {name}: {check_result.status.value}") + + # Run the test + import asyncio + asyncio.run(test_basic_qa()) + + +Test 2: Groundedness Check +--------------------------- + +Verify that answers are grounded in retrieved context: + +.. code-block:: python + + from giskard.checks import Scenario, Groundedness, StringMatching + + async def test_groundedness(): + tc = ( + Scenario("groundedness_eiffel_tower") + .interact( + inputs="When was the Eiffel Tower completed?", + outputs=lambda inputs: rag.answer(inputs) + ) + .check( + Groundedness( + name="answer_grounded", + description="Answer should be based on retrieved documents" + ) + ) + .check( + StringMatching( + name="mentions_year", + content="1889", + key="trace.last.outputs.answer" + ) + ) + ) + result = await tc.run() + assert result.passed + + +Test 3: Retrieval Quality +-------------------------- + +Test that the right documents are retrieved: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn + + def check_retrieved_topics(trace) -> bool: + """Verify retrieved docs are about the right topic.""" + docs = trace.last.outputs.retrieved_docs + topics = [doc.metadata.get("topic") for doc in docs] + return "Eiffel Tower" in topics or "France" in topics + + tc = ( + Scenario("retrieval_quality") + .interact( + inputs="Tell me about the Eiffel Tower", + outputs=lambda inputs: rag.answer(inputs) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.retrieved_docs) >= 2, + name="sufficient_context", + success_message="Retrieved multiple documents", + failure_message="Not enough documents retrieved" + ) + ) + .check( + from_fn( + check_retrieved_topics, + name="relevant_topics", + success_message="Retrieved documents are topically relevant", + failure_message="Retrieved documents are off-topic" + ) + ) + ) + + +Test 4: Out-of-Scope Questions +------------------------------- + +Test how the system handles questions it can't answer: + +.. code-block:: python + + from giskard.checks import Scenario, LLMJudge, from_fn + + tc = ( + Scenario("out_of_scope_handling") + .interact( + inputs="What is the weather in Tokyo today?", + outputs=lambda inputs: rag.answer(inputs) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.retrieved_docs) == 0, + name="no_irrelevant_docs", + success_message="Correctly retrieved no documents", + failure_message="Retrieved documents for out-of-scope question" + ) + ) + .check( + LLMJudge( + name="appropriate_fallback", + prompt=""" + Evaluate if the system appropriately indicates it cannot answer. + + Question: {{ inputs }} + Answer: {{ outputs.answer }} + + The answer should politely indicate insufficient information. + Return 'passed: true' if appropriate, 'passed: false' if it makes up an answer. + """ + ) + ) + ) + + +Test 5: Answer Quality with LLM Judge +-------------------------------------- + +Use an LLM to evaluate answer quality comprehensively: + +.. code-block:: python + + from giskard.checks import Scenario, LLMJudge + + tc = ( + Scenario("comprehensive_quality_check") + .interact( + inputs="What is machine learning?", + outputs=lambda inputs: rag.answer(inputs) + ) + .check( + LLMJudge( + name="answer_quality", + prompt=""" + Evaluate the answer quality based on these criteria: + + Question: {{ inputs }} + Answer: {{ outputs.answer }} + Retrieved Context: {{ outputs.retrieved_docs }} + + Criteria: + 1. Accuracy: Is the answer factually correct? + 2. Completeness: Does it fully address the question? + 3. Clarity: Is it well-written and understandable? + 4. Relevance: Does it stay on topic? + + Return 'passed: true' if the answer meets all criteria. + Provide brief reasoning. + """ + ) + ) + ) + + +Test 6: Multi-Turn Conversational RAG +-------------------------------------- + +Test a conversational RAG that handles follow-up questions: + +.. code-block:: python + + from giskard.checks import ( + Scenario, + Groundedness, + from_fn, + LLMJudge, + StringMatching + ) + + class ConversationalRAG(SimpleRAG): + def __init__(self, documents): + super().__init__(documents) + self.conversation_history = [] + + def answer(self, question: str) -> RAGResponse: + # Resolve references using conversation history + resolved_question = self._resolve_references( + question, + self.conversation_history + ) + + response = super().answer(resolved_question) + + self.conversation_history.append({ + "question": question, + "resolved_question": resolved_question, + "answer": response.answer + }) + + return response + + def _resolve_references(self, question: str, history: list) -> str: + """Resolve pronouns and references in follow-up questions.""" + # Simplified: in practice, use LLM for coreference resolution + if history and ("it" in question.lower() or "its" in question.lower()): + # Get the topic from previous question + prev_question = history[-1]["resolved_question"] + return f"{question} (referring to: {prev_question})" + return question + + conv_rag = ConversationalRAG(documents=knowledge_base) + + test_scenario = ( + Scenario("conversational_rag_flow") + # First question + .interact( + inputs="What is the capital of France?", + outputs=lambda inputs: conv_rag.answer(inputs) + ) + .check(Groundedness(name="first_answer_grounded")) + .check( + StringMatching( + name="first_mentions_paris", + content="Paris", + key="trace.last.outputs.answer" + ) + ) + + # Follow-up question with reference + .interact( + inputs="What is it known for?", + outputs=lambda inputs: conv_rag.answer(inputs) + ) + .check(Groundedness(name="followup_grounded")) + .check( + LLMJudge( + name="resolves_reference", + prompt=""" + Check if the answer appropriately addresses the follow-up question + in the context of the conversation. + + First Q: {{ interactions[0].inputs }} + First A: {{ interactions[0].outputs.answer }} + Follow-up Q: {{ interactions[1].inputs }} + Follow-up A: {{ interactions[1].outputs.answer }} + + The follow-up should discuss what Paris is known for. + Return 'passed: true' if the context was maintained correctly. + """ + ) + ) + ) + + async def test_conversational_rag(): + result = await test_scenario.run() + print(f"Conversational RAG test passed: {result.passed}") + + +Complete Test Suite +------------------- + +Combine all tests into a comprehensive suite: + +.. code-block:: python + + import asyncio + from typing import List + from giskard.checks import TestCase + + class RAGTestSuite: + def __init__(self, rag_system: SimpleRAG): + self.rag = rag_system + self.test_cases = [] + self._build_test_cases() + + def _build_test_cases(self): + """Build all test cases.""" + # Add basic QA tests + self.test_cases.extend(self._create_qa_tests()) + + # Add groundedness tests + self.test_cases.extend(self._create_groundedness_tests()) + + # Add edge case tests + self.test_cases.extend(self._create_edge_case_tests()) + + def _create_qa_tests(self) -> List[TestCase]: + """Create basic QA test cases.""" + test_data = [ + ("What is the capital of France?", "Paris"), + ("When was the Eiffel Tower completed?", "1889"), + ("What is Python?", "programming language"), + ] + + tests = [] + for question, expected_content in test_data: + tc = ( + Scenario(f"qa_{expected_content.replace(' ', '_')}") + .interact( + inputs=question, + outputs=lambda q: self.rag.answer(q) + ) + .check( + StringMatching( + name=f"contains_{expected_content}", + content=expected_content, + key="trace.last.outputs.answer" + ) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.retrieved_docs) > 0, + name="has_context" + ) + ) + ) + tests.append(tc) + + return tests + + def _create_groundedness_tests(self) -> List[TestCase]: + """Create groundedness test cases.""" + questions = [ + "What is the capital of France?", + "Tell me about the Eiffel Tower", + "What is machine learning?", + ] + + tests = [] + for question in questions: + tc = ( + Scenario(f"groundedness_{question[:20]}") + .interact( + inputs=question, + outputs=lambda q: self.rag.answer(q) + ) + .check(Groundedness(name="grounded")) + ) + tests.append(tc) + + return tests + + def _create_edge_case_tests(self) -> List[TestCase]: + """Create edge case test cases.""" + edge_cases = [ + ("", "empty_query"), + (" ", "whitespace_query"), + ("What is the weather in Tokyo?", "out_of_scope"), + ("askdjhaksjdhaksjdh", "gibberish"), + ] + + tests = [] + for question, case_name in edge_cases: + tc = ( + Scenario(f"edge_case_{case_name}") + .interact( + inputs=question, + outputs=lambda q: self.rag.answer(q) + ) + .check( + from_fn( + lambda trace: trace.last.outputs.answer, + name="provides_response", + success_message="System provided a response" + ) + ) + ) + tests.append(tc) + + return tests + + async def run_all(self): + """Run all tests and report results.""" + results = [] + + for tc in self.test_cases: + result = await tc.run() + results.append((tc.name, result)) + + # Summary + passed = sum(1 for _, r in results if r.passed) + total = len(results) + + print(f"\nTest Suite Results: {passed}/{total} passed ({passed/total*100:.1f}%)") + print("\nDetailed Results:") + + for name, result in results: + status = "✓" if result.passed else "✗" + print(f" {status} {name}") + if not result.passed: + for step in result.steps: + for check_result in step.results: + if not check_result.passed: + name = check_result.details.get("check_name", "check") + print(f" - {name}: {check_result.message}") + + return results + + # Run the complete suite + async def main(): + suite = RAGTestSuite(rag) + await suite.run_all() + + asyncio.run(main()) + + +Best Practices for RAG Testing +------------------------------- + +**1. Test Retrieval Separately** + +Validate retrieval quality before testing end-to-end: + +.. code-block:: python + + def test_retrieval_precision(): + docs = rag.retrieve("Eiffel Tower") + relevant_topics = ["Eiffel Tower", "France", "Paris"] + assert all( + any(topic in doc.metadata.get("topic", "") for topic in relevant_topics) + for doc in docs + ) + +**2. Use Representative Test Data** + +Include diverse question types: + +- Factual questions +- Definitional questions +- Comparison questions +- Out-of-scope questions +- Ambiguous questions + +**3. Monitor Confidence Scores** + +Track confidence metrics to identify problematic queries: + +.. code-block:: python + + checks = [ + from_fn( + lambda trace: trace.last.outputs.confidence, + name="track_confidence", + success_message=lambda trace: f"Confidence: {trace.last.outputs.confidence}" + ), + ] + +**4. Test with Real User Queries** + +Collect and test with actual user questions from logs. + + +Next Steps +---------- + +* See :doc:`testing-agents` for agent-specific testing patterns +* Explore :doc:`chatbot-testing` for conversational testing +* Review :doc:`../ai-testing/multi-turn` for advanced scenarios diff --git a/source/oss/checks/tutorials/testing-agents.rst b/source/oss/checks/tutorials/testing-agents.rst new file mode 100644 index 0000000..25a4955 --- /dev/null +++ b/source/oss/checks/tutorials/testing-agents.rst @@ -0,0 +1,759 @@ +============== +Testing Agents +============== + +This tutorial demonstrates how to test AI agents that use tools, perform multi-step reasoning, and maintain state across interactions. + +Overview +-------- + +We'll build and test an agent that can: + +* **Use multiple tools** (search, calculator, database) +* **Plan multi-step actions** to accomplish goals +* **Maintain state** across interactions +* **Handle failures** and retry with different strategies + +Our tests will validate: + +* Tool selection logic +* Reasoning quality +* Task completion +* Error handling +* State management + + +Building a Simple Agent +------------------------ + +First, let's create an agent to test: + +.. code-block:: python + + from typing import Literal, Optional + from pydantic import BaseModel + + class Tool(BaseModel): + name: str + description: str + + class AgentStep(BaseModel): + thought: str + tool: str + tool_input: str + observation: str + + class AgentResponse(BaseModel): + steps: list[AgentStep] + final_answer: str + success: bool + + class SimpleAgent: + def __init__(self): + self.tools = { + "search": Tool( + name="search", + description="Search the internet for information" + ), + "calculator": Tool( + name="calculator", + description="Perform mathematical calculations" + ), + "database": Tool( + name="database", + description="Query a database for structured data" + ), + } + self.max_steps = 5 + + def _use_tool(self, tool_name: str, tool_input: str) -> str: + """Execute a tool (simplified for testing).""" + if tool_name == "search": + return f"Search results for '{tool_input}': [Relevant information...]" + elif tool_name == "calculator": + try: + result = eval(tool_input) # Don't do this in production! + return str(result) + except Exception as e: + return f"Error: {e}" + elif tool_name == "database": + return f"Database query result for '{tool_input}': [Records...]" + return "Unknown tool" + + def run(self, task: str) -> AgentResponse: + """Run the agent on a task.""" + steps = [] + + # Simplified agent logic + if "calculate" in task.lower() or any(c in task for c in "0123456789+-*/"): + # Math task + thought = "I need to use the calculator for this math problem" + tool = "calculator" + # Extract the calculation + import re + calculation = re.findall(r'[\d+\-*/()]+', task) + tool_input = calculation[0] if calculation else task + + observation = self._use_tool(tool, tool_input) + steps.append(AgentStep( + thought=thought, + tool=tool, + tool_input=tool_input, + observation=observation + )) + + final_answer = f"The answer is {observation}" + success = "Error" not in observation + + elif "search" in task.lower() or "find" in task.lower(): + # Search task + thought = "I should search for this information" + tool = "search" + tool_input = task + + observation = self._use_tool(tool, tool_input) + steps.append(AgentStep( + thought=thought, + tool=tool, + tool_input=tool_input, + observation=observation + )) + + final_answer = f"Based on my search: {observation}" + success = True + + else: + # Default case + thought = "This task doesn't require tools" + final_answer = "I can answer this directly: " + task + success = True + + return AgentResponse( + steps=steps, + final_answer=final_answer, + success=success + ) + + +Test 1: Tool Selection +----------------------- + +Verify that the agent selects appropriate tools: + +.. code-block:: python + + from giskard.checks import Scenario, from_fn, Equals + + agent = SimpleAgent() + + async def test_tool_selection(): + tc = ( + Scenario("tool_selection_calculator") + .interact( + inputs="What is 15 * 23?", + outputs=lambda inputs: agent.run(inputs) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.steps) > 0, + name="used_tools", + success_message="Agent used tools", + failure_message="Agent didn't use any tools" + ) + ) + .check( + Equals( + name="selected_calculator", + expected="calculator", + key="trace.last.outputs.steps[0].tool" + ) + ) + .check( + from_fn( + lambda trace: trace.last.outputs.success, + name="task_successful", + success_message="Agent completed task successfully", + failure_message="Agent failed to complete task" + ) + ) + ) + result = await tc.run() + assert result.passed + + +Test 2: Reasoning Quality +-------------------------- + +Evaluate the quality of the agent's reasoning: + +.. code-block:: python + + from giskard.agents.generators import Generator + from giskard.checks import Scenario, LLMJudge, from_fn, set_default_generator + + set_default_generator(Generator(model="openai/gpt-5-mini")) + + tc = ( + Scenario("reasoning_quality_test") + .interact( + inputs="Find information about quantum computing", + outputs=lambda inputs: agent.run(inputs) + ) + .check( + LLMJudge( + name="reasoning_quality", + prompt=""" + Evaluate the agent's reasoning process. + + Task: {{ inputs }} + Thought: {{ outputs.steps[0].thought if outputs.steps else "No reasoning" }} + Tool Selected: {{ outputs.steps[0].tool if outputs.steps else "None" }} + + Criteria: + 1. Is the reasoning logical? + 2. Is the tool selection appropriate for the task? + 3. Does the thought explain why the tool was chosen? + + Return 'passed: true' if the reasoning is sound. + """ + ) + ) + .check( + from_fn( + lambda trace: trace.last.outputs.steps[0].tool == "search", + name="correct_tool_for_research", + success_message="Selected search for research task", + failure_message="Wrong tool selected" + ) + ) + ) + + +Test 3: Multi-Step Agent Workflow +---------------------------------- + +Test agents that perform multiple steps: + +.. code-block:: python + + class MultiStepAgent(SimpleAgent): + def run(self, task: str) -> AgentResponse: + """Run agent with multi-step capability.""" + steps = [] + + # Example: Complex task requiring multiple tools + if "research" in task.lower() and "calculate" in task.lower(): + # Step 1: Search + steps.append(AgentStep( + thought="First, I need to search for the data", + tool="search", + tool_input=task, + observation=self._use_tool("search", task) + )) + + # Step 2: Calculate + steps.append(AgentStep( + thought="Now I'll calculate based on the data", + tool="calculator", + tool_input="100 * 2", + observation=self._use_tool("calculator", "100 * 2") + )) + + final_answer = f"Based on my research and calculations: {steps[-1].observation}" + success = True + else: + return super().run(task) + + return AgentResponse( + steps=steps, + final_answer=final_answer, + success=success + ) + + multi_agent = MultiStepAgent() + + from giskard.checks import Scenario, from_fn, LLMJudge + + test_scenario = ( + Scenario("multi_step_agent_workflow") + .interact( + inputs="Research the market size and calculate projected growth", + outputs=lambda inputs: multi_agent.run(inputs) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.steps) >= 2, + name="multiple_steps_taken", + success_message="Agent performed multiple steps", + failure_message="Agent didn't perform enough steps" + ) + ) + .check( + from_fn( + lambda trace: any( + step.tool == "search" + for step in trace.last.outputs.steps + ), + name="performed_research", + success_message="Agent performed research", + failure_message="Agent skipped research step" + ) + ) + .check( + from_fn( + lambda trace: any( + step.tool == "calculator" + for step in trace.last.outputs.steps + ), + name="performed_calculation", + success_message="Agent performed calculation", + failure_message="Agent skipped calculation step" + ) + ) + .check( + LLMJudge( + name="steps_logical_order", + prompt=""" + Evaluate if the agent's steps are in a logical order. + + Task: {{ interactions[0].inputs }} + Steps: + {% for step in interactions[0].outputs.steps %} + {{ loop.index }}. {{ step.thought }} -> {{ step.tool }} + {% endfor %} + + Return 'passed: true' if steps are well-ordered. + """ + ) + ) + ) + + +Test 4: Error Handling +----------------------- + +Verify that agents handle errors gracefully: + +.. code-block:: python + + class RobustAgent(SimpleAgent): + def run(self, task: str) -> AgentResponse: + steps = [] + + # Try first approach + thought = "I'll try using the calculator" + observation = self._use_tool("calculator", task) + steps.append(AgentStep( + thought=thought, + tool="calculator", + tool_input=task, + observation=observation + )) + + if "Error" in observation: + # Fallback strategy + thought = "Calculator failed, I'll search instead" + observation = self._use_tool("search", task) + steps.append(AgentStep( + thought=thought, + tool="search", + tool_input=task, + observation=observation + )) + final_answer = f"After trying different approaches: {observation}" + success = True + else: + final_answer = f"Result: {observation}" + success = True + + return AgentResponse( + steps=steps, + final_answer=final_answer, + success=success + ) + + robust_agent = RobustAgent() + + tc = ( + Scenario("error_handling_test") + .interact( + inputs="What is the meaning of life?", # Not a valid calculation + outputs=lambda inputs: robust_agent.run(inputs) + ) + .check( + from_fn( + lambda trace: len(trace.last.outputs.steps) > 1, + name="tried_fallback", + success_message="Agent tried fallback strategy", + failure_message="Agent didn't attempt recovery" + ) + ) + .check( + from_fn( + lambda trace: trace.interactions[-1].outputs.success, + name="eventually_succeeded", + success_message="Agent completed task despite initial failure", + failure_message="Agent failed to complete task" + ) + ) + .check( + LLMJudge( + name="error_recovery_appropriate", + prompt=""" + Evaluate if the agent's error recovery was appropriate. + + Task: {{ inputs }} + Steps taken: + {% for step in outputs.steps %} + {{ loop.index }}. {{ step.thought }} ({{ step.tool }}) + Result: {{ step.observation }} + {% endfor %} + + Return 'passed: true' if the agent handled the error well. + """ + ) + ) + ) + + +Test 5: Stateful Agent Interactions +------------------------------------ + +Test agents that maintain state across turns: + +.. code-block:: python + + class StatefulAgent(SimpleAgent): + def __init__(self): + super().__init__() + self.memory = {} + self.conversation_history = [] + + def run(self, task: str) -> AgentResponse: + # Check memory for context + if "last" in task.lower() or "previous" in task.lower(): + if self.conversation_history: + prev_task = self.conversation_history[-1]["task"] + thought = f"Recalling previous task: {prev_task}" + observation = f"Previous task was: {prev_task}" + final_answer = f"I remember: {observation}" + + steps = [AgentStep( + thought=thought, + tool="memory", + tool_input="recall", + observation=observation + )] + + self.conversation_history.append({ + "task": task, + "response": final_answer + }) + + return AgentResponse( + steps=steps, + final_answer=final_answer, + success=True + ) + + # Handle new task + response = super().run(task) + self.conversation_history.append({ + "task": task, + "response": response.final_answer + }) + return response + + stateful_agent = StatefulAgent() + + test_scenario = ( + Scenario("stateful_agent_memory") + # First interaction + .interact( + inputs="Search for Python tutorials", + outputs=lambda inputs: stateful_agent.run(inputs) + ) + .check( + from_fn( + lambda trace: trace.interactions[-1].outputs.success, + name="first_task_completed" + ) + ) + + # Second interaction references first + .interact( + inputs="What was my last request?", + outputs=lambda inputs: stateful_agent.run(inputs) + ) + .check( + from_fn( + lambda trace: "Python tutorials" in trace.last.outputs.final_answer, + name="recalls_previous_task", + success_message="Agent correctly recalled previous task", + failure_message="Agent failed to recall previous task" + ) + ) + .check( + LLMJudge( + name="context_maintained", + prompt=""" + Evaluate if the agent maintained context correctly. + + First task: {{ interactions[0].inputs }} + Second task: {{ interactions[1].inputs }} + Second response: {{ interactions[1].outputs.final_answer }} + + The second response should reference the first task. + Return 'passed: true' if context was maintained. + """ + ) + ) + ) + + +Test 6: Task Completion Validation +----------------------------------- + +Verify that complex tasks are fully completed: + +.. code-block:: python + + from giskard.checks import Scenario, LLMJudge, from_fn + + class TaskTrackingAgent(SimpleAgent): + def __init__(self): + super().__init__() + self.pending_tasks = [] + self.completed_tasks = [] + + def run(self, task: str) -> AgentResponse: + if "add task" in task.lower(): + task_desc = task.replace("add task", "").strip() + self.pending_tasks.append(task_desc) + return AgentResponse( + steps=[], + final_answer=f"Added task: {task_desc}. Pending: {len(self.pending_tasks)}", + success=True + ) + + elif "complete" in task.lower(): + if self.pending_tasks: + completed = self.pending_tasks.pop(0) + self.completed_tasks.append(completed) + + return AgentResponse( + steps=[AgentStep( + thought=f"Completing task: {completed}", + tool="task_manager", + tool_input=completed, + observation="Task completed successfully" + )], + final_answer=f"Completed: {completed}", + success=True + ) + return AgentResponse( + steps=[], + final_answer="No pending tasks to complete", + success=False + ) + + elif "status" in task.lower(): + return AgentResponse( + steps=[], + final_answer=f"Pending: {len(self.pending_tasks)}, Completed: {len(self.completed_tasks)}", + success=True + ) + + return super().run(task) + + task_agent = TaskTrackingAgent() + + test_scenario = ( + Scenario("task_completion_workflow") + # Add tasks + .interact( + inputs="add task: Write documentation", + outputs=lambda inputs: task_agent.run(inputs) + ) + .interact( + inputs="add task: Review code", + outputs=lambda inputs: task_agent.run(inputs) + ) + .check( + from_fn( + lambda trace: len(task_agent.pending_tasks) == 2, + name="tasks_added" + ) + ) + + # Complete first task + .interact( + inputs="complete next task", + outputs=lambda inputs: task_agent.run(inputs) + ) + .check( + from_fn( + lambda trace: len(task_agent.completed_tasks) == 1, + name="task_completed" + ) + ) + + # Check status + .interact( + inputs="what's the status?", + outputs=lambda inputs: task_agent.run(inputs) + ) + .check( + from_fn( + lambda trace: ( + "Pending: 1" in trace.last.outputs.final_answer and + "Completed: 1" in trace.last.outputs.final_answer + ), + name="status_accurate", + success_message="Agent tracking state correctly", + failure_message="Agent state tracking is incorrect" + ) + ) + ) + + +Complete Agent Test Suite +-------------------------- + +Combine all tests into a comprehensive suite: + +.. code-block:: python + + import asyncio + from typing import List + from giskard.checks import Scenario + + class AgentTestSuite: + def __init__(self, agent): + self.agent = agent + self.test_cases = [] + self.scenarios = [] + + def add_test(self, test_case): + self.test_cases.append(test_case) + + def add_scenario(self, test_scenario): + self.scenarios.append(test_scenario) + + async def run_all(self): + """Run all tests and scenarios.""" + results = [] + + print("Running test cases...") + for tc in self.test_cases: + result = await tc.run() + results.append(("test", tc.name, result)) + + print("Running scenarios...") + for test_scenario in self.scenarios: + result = await test_scenario.run() + results.append(("scenario", test_scenario.name, result)) + + # Report + self._report_results(results) + + return results + + def _report_results(self, results): + total = len(results) + passed = sum(1 for _, _, r in results if r.passed) + + print(f"\n{'='*60}") + print(f"Agent Test Suite Results: {passed}/{total} passed ({passed/total*100:.1f}%)") + print(f"{'='*60}\n") + + for test_type, name, result in results: + status = "✓" if result.passed else "✗" + print(f" {status} [{test_type}] {name}") + + if not result.passed: + if hasattr(result, 'steps'): + for step in result.steps: + for check_result in step.results: + if not check_result.passed: + name = check_result.details.get("check_name", "check") + print(f" ↳ {name}: {check_result.message}") + elif hasattr(result, 'message'): + print(f" ↳ {result.message}") + + # Usage + async def main(): + agent = SimpleAgent() + suite = AgentTestSuite(agent) + + # Add tests (from examples above) + # suite.add_test(...) + # suite.add_scenario(...) + + await suite.run_all() + + asyncio.run(main()) + + +Best Practices +-------------- + +**1. Test Tool Selection Logic Independently** + +Before testing full workflows, validate tool selection: + +.. code-block:: python + + def test_tool_selection_logic(): + test_cases = [ + ("Calculate 5 + 3", "calculator"), + ("Search for recipes", "search"), + ("Query user database", "database"), + ] + + for task, expected_tool in test_cases: + response = agent.run(task) + assert response.steps[0].tool == expected_tool + +**2. Validate Reasoning at Each Step** + +Use LLM judges to evaluate reasoning quality: + +.. code-block:: python + + LLMJudge( + name="step_reasoning", + prompt="Is this reasoning step logical? {{ outputs.steps[0].thought }}" + ) + +**3. Test Error Paths** + +Ensure agents handle failures gracefully: + +.. code-block:: python + + # Test with invalid tool inputs + # Test with unavailable tools + # Test with contradictory instructions + +**4. Monitor Resource Usage** + +Track token usage, API calls, and execution time: + +.. code-block:: python + + checks = [ + from_fn( + lambda trace: len(trace.last.outputs.steps) <= 5, + name="reasonable_step_count", + success_message="Used reasonable number of steps" + ), + ] + + +Next Steps +---------- + +* See :doc:`chatbot-testing` for conversational agent patterns +* Explore :doc:`rag-evaluation` for knowledge-grounded agents +* Review :doc:`../ai-testing/multi-turn` for complex workflows diff --git a/source/oss/notebooks/business/RAGET_Banking_Supervision.ipynb b/source/oss/notebooks/business/RAGET_Banking_Supervision.ipynb deleted file mode 100644 index 0ef67af..0000000 --- a/source/oss/notebooks/business/RAGET_Banking_Supervision.ipynb +++ /dev/null @@ -1,3782 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RAG Evaluation Toolkit on a Banking Supervisory Process Agent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Before starting\n", - "\n", - "Giskard is an open-source framework for testing all ML models, from LLMs to tabular models. Don’t hesitate to give the project a [star on GitHub](https://github.com/Giskard-AI/giskard) ⭐️ if you find it useful!\n", - "\n", - "In this notebook, you’ll learn how to create a test dataset for a RAG pipeline and use this dataset to test the model.\n", - "\n", - "In this example, we illustrate the procedure using **OpenAI Client** that is the default one; however, please note that our platform supports a variety of language models. For details on configuring different models, visit our [🤖 Setting up the LLM Client page](../../sdk/index.rst)\n", - "\n", - "In this tutorial we will use Giskard LLM RAG Evaluation Toolkit to automatically detect issues of a Retrieval Augmented Generation (RAG) pipeline. We will test a model that answers questions about the Banking Supervision report from the ECB.\n", - "\n", - "Use-case: \n", - "\n", - "* QA over the Banking Supervision report\n", - "* Foundational model: *gpt-3.5-turbo*\n", - "* Context: [Banking Supervision report](https://www.bankingsupervision.europa.eu/ecb/pub/pdf/ssm.supervisory_guides202401_manual.en.pdf)\n", - "\n", - "Outline:\n", - "\n", - "* Create a test dataset for the RAG pipeline\n", - "* Automatically evaluate the RAG pipeline and provide a report with recommendations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install dependencies and setup notebook\n", - "\n", - "Let's install the required dependencies. We will be using `giskard[llm]` to create the test dataset and `llama-index` to build the RAG pipeline. Additionally, we will use `PyMuPDF` to load the IPCC report." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install \"giskard[llm]\" --upgrade\n", - "!pip install llama-index PyMuPDF" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we download the Banking Supervision report from the ECB website." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!wget \"https://www.bankingsupervision.europa.eu/ecb/pub/pdf/ssm.supervisory_guides202401_manual.en.pdf\" -O \"banking_supervision_report.pdf\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can import all of the required libraries and classess" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "\n", - "import openai\n", - "import pandas as pd\n", - "from llama_index.core import VectorStoreIndex\n", - "from llama_index.core.base.llms.types import ChatMessage, MessageRole\n", - "from llama_index.core.node_parser import SentenceSplitter\n", - "from llama_index.llms.openai import OpenAI\n", - "from llama_index.readers.file import PyMuPDFReader\n", - "\n", - "from giskard import Model, scan\n", - "from giskard.rag import (\n", - " AgentAnswer,\n", - " KnowledgeBase,\n", - " QATestset,\n", - " RAGReport,\n", - " evaluate,\n", - " generate_testset,\n", - ")\n", - "from giskard.rag.metrics.ragas_metrics import (\n", - " ragas_context_precision,\n", - " ragas_context_recall,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's set the OpenAI API Key environment variable and some visual options." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the OpenAI API Key environment variable.\n", - "OPENAI_API_KEY = \"...\"\n", - "openai.api_key = OPENAI_API_KEY\n", - "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n", - "\n", - "# Set pandas options\n", - "pd.set_option(\"display.max_colwidth\", 400)\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build RAG Agent on the Banking Supervision report\n", - "\n", - "We will use `llama-index` to build the RAG pipeline. We will use the `VectorStoreIndex` to create an index of the IPCC report. We will then use the `as_chat_engine` method to create a chat engine from the index." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "loader = PyMuPDFReader()\n", - "documents = loader.load(file_path=\"./banking_supervision_report.pdf\")\n", - "llm = OpenAI(model=\"gpt-3.5-turbo\", temperature=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can use the `pyMuPDF` reader to load the IPCC report and create a `VectorStoreIndex`.\n", - "We will also use the `SentenceSplitter` to split the report into chunks of 512 tokens to ensure that the context is not too large." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "splitter = SentenceSplitter(chunk_size=512)\n", - "index = VectorStoreIndex.from_documents(documents, transformations=[splitter])\n", - "chat_engine = index.as_chat_engine(llm=llm)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's test the Agent\n", - "\n", - "We can now simple chat with our agent using the `chat_engine` and the `chat` method. \n", - "Under the hood, this will use the `VectorStoreIndex` to retrieve the most relevant chunks of the report and the `gpt-3.5-turbo` model to answer the question." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'SSM stands for Single Supervisory Mechanism.'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "str(chat_engine.chat(\"What is SSM?\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Scan LLM vulnerabilities\n", - "\n", - "As a first step, we will run a scan on the chatbot model.\n", - "This will help us identify the potential vulnerabilities in the model that the agent is built on.\n", - "To do so, we need to define a function that will take a dataframe with a question column and return the answer from the chatbot.\n", - "This will then be used to create a Giskard `Model` object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def model_predict(df: pd.DataFrame):\n", - " return [chat_engine.chat(question).response for question in df[\"question\"]]\n", - "\n", - "\n", - "giskard_model = Model(\n", - " model=model_predict,\n", - " model_type=\"text_generation\",\n", - " name=\"Banking Supervision Question Answering\",\n", - " description=\"A model that answers questions about ECB Banking Supervision report\",\n", - " feature_names=[\"question\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now forward the model to the `scan` function to get a report with the potential vulnerabilities.\n", - "You can pass a custom dataset and features to the `scan` function to get a more accurate report but for this example, we will use the default one.\n", - "If you want to share the report with your team, you can use the `to_html` or `to_json` methods to save the report." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "scan_report = scan(giskard_model)\n", - "display(scan_report)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate a test set on for RAG the Banking Supervision report\n", - "\n", - "We will now generate a test set for RAG on the Banking Supervision report. We first load the report and split it into chunks of 512 tokens." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "text_nodes = splitter(documents)\n", - "knowledge_base_df = pd.DataFrame([node.text for node in text_nodes], columns=[\"text\"])\n", - "knowledge_base = KnowledgeBase(knowledge_base_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now generate a test set with 100 questions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "testset = generate_testset(\n", - " knowledge_base=knowledge_base,\n", - " num_questions=100,\n", - " agent_description=\"A chatbot answering questions about banking supervision procedures and methodologies.\",\n", - " language=\"en\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid losing the test set, we can save it to a JSONL file and safely load it later.\n", - "Note that, we need to ensure the documents in the `KnowledgeBase` are the same as the ones in the `testset` to evaluate the agent's performance on this test set." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the testset\n", - "testset.save(\"banking_supervision_testset.jsonl\")\n", - "\n", - "# Load the testset\n", - "testset = QATestset.load(\"banking_supervision_testset.jsonl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the first 5 questions in the test set. We can see that the questions are representative of the agent's performance and get a good coverage of the IPCC report." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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35202be3-9120-4bd1-9b3b-722d3b307e1cWhat is the role of Joint Supervisory Teams (JSTs) in the supervision of Significant Institutions (SIs)?The day-to-day supervision of SIs is primarily conducted off-site by the JSTs, which comprise staff from NCAs and the ECB and are supported by the horizontal and specialised expertise divisions of DG/HOL and similar staff at the NCAs. The JST analyses the supervisory reporting, financial statements and internal documentation of supervised entities, holds regular and ad hoc meetings with the su...Document 76: This can involve on-site interventions at supervised institutions, if needed. \\nDepending on a specific bank’s risk profile assessment, the ECB may impose a wide \\nrange of supervisory measures. \\n2.3.1 \\nJoint Supervisory Teams \\nThe day-to-day supervision of SIs is primarily conducted off-site by the JSTs, which \\ncomprise staff from NCAs and the ECB and are supported by the hor...[]{'question_type': 'simple', 'seed_document_id': 76, 'topic': 'Others'}
1beb42a0-ff1a-42e9-91c6-fe11774e909dWhat happens if an urgent supervisory decision is necessary to prevent significant damage to the financial system?The ECB may adopt a supervisory decision which would adversely affect the rights of the addressee without giving it the opportunity to comment on the decision prior to its adoption. In this case, the hearing is postponed, and a clear justification is provided in the decision as to why the postponement is necessary. The hearing is then organised as soon as possible after the adoption of the dec...Document 34: Supervisory Manual – Functioning of the Single Supervisory Mechanism \\n \\n21 \\nFigure 4 \\nDecision-making process \\n \\n*The deadline for submitting comments/objections in a written procedure is five working days, while the deadline for non-objection \\nprocedures is a maximum of ten working days. \\n**The applicable legal deadlines for each specific case must be taken into account. ...[]{'question_type': 'simple', 'seed_document_id': 34, 'topic': 'Single Supervisory Mechanism'}
562d7352-b2ee-4191-b6eb-96f0fca7b01cWhat is required of banks and investment firms in the EU that are subsidiaries of third-country groups according to Article 21b of Directive 2013/36/EU?Article 21b of Directive 2013/36/EU requires banks and investment firms in the EU that are subsidiaries of third-country groups to set up a single intermediate EU parent undertaking if the third-country group has two or more institutions established within the EU with a combined total asset value of at least €40 billion.Document 169: Supervisory Manual – Supervision of significant institutions \\n \\n97 \\ntransactions which go beyond the contractual obligations of a sponsor institution or \\nan originator institution under Article 248(1) of Regulation (EU) No 575/2013. \\nBased on the notifications received from significant institutions: \\n• \\nif the institution declares that there is implicit support, the JST ch...[]{'question_type': 'simple', 'seed_document_id': 169, 'topic': 'Others'}
a9955bdc-165d-42ed-a259-53bef0d5e0eaWhat are the purposes of macroprudential extensions in stress tests?Macroprudential extensions in stress tests focus on system-wide effects rather than on individual banks and are run in a top-down manner. They capture important feedback effects or network effects, which can occur through adverse changes in the state of the environment triggered by a stress scenario with a negative impact on lending or through lending or funding links between institutions.Document 125: These tasks are undertaken, where \\nappropriate, in collaboration with other divisions of the ECB, the EBA and/or NCAs. \\nMicroprudential stress tests are often complemented by macroprudential extensions \\nthat focus on system-wide effects rather than on individual banks and which are run \\nin a top-down manner, meaning that they do not involve the supervised entities. In \\nparti...[]{'question_type': 'simple', 'seed_document_id': 125, 'topic': 'European Banking Supervision'}
a7c255f1-9fd8-48d8-8a6a-5afa995dae21What happens if a quorum of 50% is not met during an emergency Supervisory Board meeting?If a quorum of 50% in the Supervisory Board for emergency situations is not met, the meeting will be closed and an extraordinary meeting will be held soon afterwards.Document 38: Supervisory Manual – Functioning of the Single Supervisory Mechanism \\n \\n24 \\n• \\nif an NCA which is concerned by the decision has different views regarding the \\nobjection, the NCA may request mediation; \\n• \\nif no request for mediation is submitted, the Supervisory Board may amend the \\ndraft decision in order to incorporate the comments of the Governing Council; \\n• \\nif the ...[]{'question_type': 'simple', 'seed_document_id': 38, 'topic': 'Single Supervisory Mechanism'}
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" - ], - "text/plain": [ - " question \\\n", - "id \n", - "35202be3-9120-4bd1-9b3b-722d3b307e1c What is the role of Joint Supervisory Teams (JSTs) in the supervision of Significant Institutions (SIs)? \n", - "1beb42a0-ff1a-42e9-91c6-fe11774e909d What happens if an urgent supervisory decision is necessary to prevent significant damage to the financial system? \n", - "562d7352-b2ee-4191-b6eb-96f0fca7b01c What is required of banks and investment firms in the EU that are subsidiaries of third-country groups according to Article 21b of Directive 2013/36/EU? \n", - "a9955bdc-165d-42ed-a259-53bef0d5e0ea What are the purposes of macroprudential extensions in stress tests? \n", - "a7c255f1-9fd8-48d8-8a6a-5afa995dae21 What happens if a quorum of 50% is not met during an emergency Supervisory Board meeting? \n", - "\n", - " reference_answer \\\n", - "id \n", - "35202be3-9120-4bd1-9b3b-722d3b307e1c The day-to-day supervision of SIs is primarily conducted off-site by the JSTs, which comprise staff from NCAs and the ECB and are supported by the horizontal and specialised expertise divisions of DG/HOL and similar staff at the NCAs. The JST analyses the supervisory reporting, financial statements and internal documentation of supervised entities, holds regular and ad hoc meetings with the su... \n", - "1beb42a0-ff1a-42e9-91c6-fe11774e909d The ECB may adopt a supervisory decision which would adversely affect the rights of the addressee without giving it the opportunity to comment on the decision prior to its adoption. In this case, the hearing is postponed, and a clear justification is provided in the decision as to why the postponement is necessary. The hearing is then organised as soon as possible after the adoption of the dec... \n", - "562d7352-b2ee-4191-b6eb-96f0fca7b01c Article 21b of Directive 2013/36/EU requires banks and investment firms in the EU that are subsidiaries of third-country groups to set up a single intermediate EU parent undertaking if the third-country group has two or more institutions established within the EU with a combined total asset value of at least €40 billion. \n", - "a9955bdc-165d-42ed-a259-53bef0d5e0ea Macroprudential extensions in stress tests focus on system-wide effects rather than on individual banks and are run in a top-down manner. They capture important feedback effects or network effects, which can occur through adverse changes in the state of the environment triggered by a stress scenario with a negative impact on lending or through lending or funding links between institutions. \n", - "a7c255f1-9fd8-48d8-8a6a-5afa995dae21 If a quorum of 50% in the Supervisory Board for emergency situations is not met, the meeting will be closed and an extraordinary meeting will be held soon afterwards. \n", - "\n", - " reference_context \\\n", - "id \n", - "35202be3-9120-4bd1-9b3b-722d3b307e1c Document 76: This can involve on-site interventions at supervised institutions, if needed. \\nDepending on a specific bank’s risk profile assessment, the ECB may impose a wide \\nrange of supervisory measures. \\n2.3.1 \\nJoint Supervisory Teams \\nThe day-to-day supervision of SIs is primarily conducted off-site by the JSTs, which \\ncomprise staff from NCAs and the ECB and are supported by the hor... \n", - "1beb42a0-ff1a-42e9-91c6-fe11774e909d Document 34: Supervisory Manual – Functioning of the Single Supervisory Mechanism \\n \\n21 \\nFigure 4 \\nDecision-making process \\n \\n*The deadline for submitting comments/objections in a written procedure is five working days, while the deadline for non-objection \\nprocedures is a maximum of ten working days. \\n**The applicable legal deadlines for each specific case must be taken into account. ... \n", - "562d7352-b2ee-4191-b6eb-96f0fca7b01c Document 169: Supervisory Manual – Supervision of significant institutions \\n \\n97 \\ntransactions which go beyond the contractual obligations of a sponsor institution or \\nan originator institution under Article 248(1) of Regulation (EU) No 575/2013. \\nBased on the notifications received from significant institutions: \\n• \\nif the institution declares that there is implicit support, the JST ch... \n", - "a9955bdc-165d-42ed-a259-53bef0d5e0ea Document 125: These tasks are undertaken, where \\nappropriate, in collaboration with other divisions of the ECB, the EBA and/or NCAs. \\nMicroprudential stress tests are often complemented by macroprudential extensions \\nthat focus on system-wide effects rather than on individual banks and which are run \\nin a top-down manner, meaning that they do not involve the supervised entities. In \\nparti... \n", - "a7c255f1-9fd8-48d8-8a6a-5afa995dae21 Document 38: Supervisory Manual – Functioning of the Single Supervisory Mechanism \\n \\n24 \\n• \\nif an NCA which is concerned by the decision has different views regarding the \\nobjection, the NCA may request mediation; \\n• \\nif no request for mediation is submitted, the Supervisory Board may amend the \\ndraft decision in order to incorporate the comments of the Governing Council; \\n• \\nif the ... \n", - "\n", - " conversation_history \\\n", - "id \n", - "35202be3-9120-4bd1-9b3b-722d3b307e1c [] \n", - "1beb42a0-ff1a-42e9-91c6-fe11774e909d [] \n", - "562d7352-b2ee-4191-b6eb-96f0fca7b01c [] \n", - "a9955bdc-165d-42ed-a259-53bef0d5e0ea [] \n", - "a7c255f1-9fd8-48d8-8a6a-5afa995dae21 [] \n", - "\n", - " metadata \n", - "id \n", - "35202be3-9120-4bd1-9b3b-722d3b307e1c {'question_type': 'simple', 'seed_document_id': 76, 'topic': 'Others'} \n", - "1beb42a0-ff1a-42e9-91c6-fe11774e909d {'question_type': 'simple', 'seed_document_id': 34, 'topic': 'Single Supervisory Mechanism'} \n", - "562d7352-b2ee-4191-b6eb-96f0fca7b01c {'question_type': 'simple', 'seed_document_id': 169, 'topic': 'Others'} \n", - "a9955bdc-165d-42ed-a259-53bef0d5e0ea {'question_type': 'simple', 'seed_document_id': 125, 'topic': 'European Banking Supervision'} \n", - "a7c255f1-9fd8-48d8-8a6a-5afa995dae21 {'question_type': 'simple', 'seed_document_id': 38, 'topic': 'Single Supervisory Mechanism'} " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "testset.to_pandas().head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate and Diagnose the Agent\n", - "\n", - "We can now evaluate the agent's performance on the test set using the RAG Evaluation Toolkit (RAGET).\n", - "We will use the `evaluate` function to evaluate the agent's performance on the test set.\n", - "We will use the `ragas_context_recall` and `ragas_context_precision` metrics to evaluate the agent's performance on the test set.\n", - "We will also use the `RAGReport` class to generate a report of the agent's performance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "def answer_fn(question: str, history: list[dict] = None) -> AgentAnswer:\n", - " if history:\n", - " answer = chat_engine.chat(\n", - " question,\n", - " chat_history=[\n", - " ChatMessage(\n", - " role=(\n", - " MessageRole.USER\n", - " if msg[\"role\"] == \"user\"\n", - " else MessageRole.ASSISTANT\n", - " ),\n", - " content=msg[\"content\"],\n", - " )\n", - " for msg in history\n", - " ],\n", - " )\n", - " else:\n", - " answer = chat_engine.chat(question, chat_history=[])\n", - "\n", - " return AgentAnswer(\n", - " message=answer.response, documents=[source.content for source in answer.sources]\n", - " )\n", - "\n", - "\n", - "rag_report = evaluate(\n", - " answer_fn,\n", - " testset=testset,\n", - " knowledge_base=knowledge_base,\n", - " metrics=[ragas_context_recall, ragas_context_precision],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can save the report and load it later to display it. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the RAG report\n", - "rag_report.save(\"banking_supervision_report\")\n", - "\n", - "# Load the RAG report\n", - "rag_report = RAGReport.load(\"banking_supervision_report\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now display the report." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# RAG report\n", - "display(rag_report.to_html(embed=True))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RAGET question types\n", - "\n", - "For RAGET, we have 6 different question types that assess different RAG components.\n", - "Each question type assesses a few RAG components. This makes it possible to localize weaknesses in the RAG Agent and give feedback to the developers." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "|Question type | Description | Example | Targeted RAG components |\n", - "|---|---|---|---|\n", - "| **Simple** | Simple questions generated from an excerpt of the knowledge base | *What is the purpose of the holistic approach in the SREP?* | `Generator`, `Retriever` | \n", - "| **Complex** | Questions made more complex by paraphrasing | *In what capacity and with what frequency do NCAs contribute to the formulation and scheduling of supervisory activities, especially concerning the organization of on-site missions?* | `Generator` | \n", - "| **Distracting** | Questions made to confuse the retrieval part of the RAG with a distracting element from the knowledge base but irrelevant to the question | *Under what conditions does the ECB levy fees to cover the costs of its supervisory tasks, particularly in the context of financial conglomerates requiring cross-sector supervision?* | `Generator`, `Retriever`, `Rewriter` |\n", - "| **Situational** | Questions including user context to evaluate the ability of the generation to produce relevant answer according to the context | *As a bank manager looking to understand the appeal process for a regulatory decision made by the ECB, could you explain what role the ABoR plays in the supervisory decision review process?* |`Generator` |\n", - "| **Double** | Questions with two distinct parts to evaluate the capabilities of the query rewriter of the RAG | *What role does the SSM Secretariat Division play in the decision-making process of the ECB's supervisory tasks, and which directorates general are involved in the preparation of draft decisions for supervised entities in the ECB Banking Supervision?* | `Generator`, `Rewriter` |\n", - "| **Conversational** |Questions made as part of a conversation, first message describe the context of the question that is ask in the last message, also tests the rewriter | - *I am interested in the sources used for the assessment of risks and vulnerabilities in ECB Banking Supervision.*
- *What are these sources?* | `Rewriter`, `Routing` |\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/source/oss/notebooks/business/RAGET_IPCC.ipynb b/source/oss/notebooks/business/RAGET_IPCC.ipynb deleted file mode 100644 index 72a0662..0000000 --- a/source/oss/notebooks/business/RAGET_IPCC.ipynb +++ /dev/null @@ -1,2673 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# RAG Evaluation Toolkit on an IPCC Climate Agent" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Giskard is an open-source framework for testing all ML models, from LLMs to tabular models. Don’t hesitate to give the project a [star on GitHub](https://github.com/Giskard-AI/giskard) ⭐️ if you find it useful!\n", - "\n", - "In this notebook, you’ll learn how to create a test dataset for a RAG pipeline and use this dataset to test the model.\n", - "\n", - "In this example, we illustrate the procedure using **OpenAI Client** that is the default one; however, please note that our platform supports a variety of language models. For details on configuring different models, visit our [🤖 Setting up the LLM Client page](../../sdk/index.rst)\n", - "\n", - "In this tutorial we will use Giskard LLM RAG Evaluation Toolkit to automatically detect issues of a Retrieval Augmented Generation (RAG) pipeline. We will test a model that answers questions about the IPCC report.\n", - "\n", - "Use-case: \n", - "\n", - "* QA over the IPCC report\n", - "* Foundational model: *gpt-3.5-turbo*\n", - "* Context: [IPCC report](https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_LongerReport.pdf)\n", - "\n", - "Outline:\n", - "\n", - "* Create a test dataset for the RAG pipeline\n", - "* Automatically evaluate the RAG pipeline and provide a report with recommendations\n", - "\n", - "### Install dependencies and setup notebook\n", - "\n", - "Let's install the required dependencies. We will be using `giskard[llm]` to create the test dataset and `llama-index` to build the RAG pipeline. Additionally, we will use `PyMuPDF` to load the IPCC report." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install \"giskard[llm]\" --upgrade\n", - "!pip install llama-index PyMuPDF" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we download the IPCC report and save it as `ipcc_report.pdf`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!wget \"https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_LongerReport.pdf\" -O \"ipcc_report.pdf\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can import all of the required libraries and classess" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import warnings\n", - "\n", - "import openai\n", - "import pandas as pd\n", - "from llama_index.core import VectorStoreIndex\n", - "from llama_index.core.base.llms.types import ChatMessage, MessageRole\n", - "from llama_index.core.node_parser import SentenceSplitter\n", - "from llama_index.readers.file import PyMuPDFReader\n", - "\n", - "from giskard.rag import (\n", - " AgentAnswer,\n", - " KnowledgeBase,\n", - " QATestset,\n", - " RAGReport,\n", - " evaluate,\n", - " generate_testset,\n", - ")\n", - "from giskard.rag.metrics.ragas_metrics import (\n", - " ragas_context_precision,\n", - " ragas_context_recall,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's set the OpenAI API Key environment variable and some visual options." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set the OpenAI API Key environment variable.\n", - "OPENAI_API_KEY = \"...\"\n", - "openai.api_key = OPENAI_API_KEY\n", - "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n", - "\n", - "# Set pandas options\n", - "pd.set_option(\"display.max_colwidth\", 400)\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Build RAG Agent on the IPCC report\n", - "\n", - "We will use `llama-index` to build the RAG pipeline. We will use the `VectorStoreIndex` to create an index of the IPCC report. We will then use the `as_chat_engine` method to create a chat engine from the index." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can use the `pyMuPDF` reader to load the IPCC report and create a `VectorStoreIndex`.\n", - "We will also use the `SentenceSplitter` to split the report into chunks of 512 tokens to ensure that the context is not too large." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "loader = PyMuPDFReader()\n", - "ipcc_documents = loader.load(file_path=\"./ipcc_report.pdf\")\n", - "splitter = SentenceSplitter(chunk_size=512)\n", - "index = VectorStoreIndex.from_documents(ipcc_documents, transformations=[splitter])\n", - "chat_engine = index.as_chat_engine()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This context is then passed to the model to generate the answer that is grounded relevant and up-to-date knowledge." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Let's test the Agent\n", - "\n", - "We can now simple chat with our agent using the `chat_engine` and the `chat` method. \n", - "Under the hood, this will use the `VectorStoreIndex` to retrieve the most relevant chunks of the report and the `gpt-3.5-turbo` model to answer the question." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'The predicted global temperature rise by 2100 is 3.2 degrees Celsius, with a range of 2.2 to 3.5 degrees Celsius.'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "str(chat_engine.chat(\"How much will the global temperature rise by 2100?\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate a test set on the IPCC report\n", - "\n", - "Now our agent is ready to be tested. We can generate a test set using the `generate_testset` function.\n", - "Before we do that, we need to create a `giskard` `KnowledgeBase` class based on splits within the `ipcc_documents` that we loaded earlier.\n", - "We assign the `text` of each document to the `knowledge_base_df` dataframe and then create a `KnowledgeBase` class from it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "text_nodes = splitter(ipcc_documents)\n", - "knowledge_base_df = pd.DataFrame([node.text for node in text_nodes], columns=[\"text\"])\n", - "knowledge_base = KnowledgeBase(knowledge_base_df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, this `KnowledgeBase` class will be used to generate a test set. \n", - "We will generate 120 questions and use the `agent_description` to describe the agent.\n", - "This will be used to generate a test set that is representative of the agent's performance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "testset = generate_testset(\n", - " knowledge_base,\n", - " num_questions=120,\n", - " agent_description=\"A chatbot answering questions about the IPCC report\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To avoid losing the test set, we can save it to a JSONL file and safely load it later.\n", - "Note that, we need to ensure the documents in the `KnowledgeBase` are the same as the ones in the `testset` to evaluate the agent's performance on this test set." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the testset\n", - "testset.save(\"ipcc_testset.jsonl\")\n", - "\n", - "# Load the testset\n", - "testset = QATestset.load(\"ipcc_testset.jsonl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the first 5 questions in the test set. We can see that the questions are representative of the agent's performance and get a good coverage of the IPCC report." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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1cacb231-b6e3-44aa-a315-79aa43cff369When is the best estimate of reaching 1.5°C of global warming according to most scenarios?The best estimate of reaching 1.5°C of global warming lies in the first half of the 2030s in most of the considered scenarios and modelled pathways.Document 116: The best estimate of reaching 1.5°C of global \\nwarming lies in the first half of the 2030s in most of the considered \\nscenarios and modelled pathways114. In the very low GHG emissions \\nscenario (SSP1-1.9), CO2 emissions reach net zero around 2050 and the \\nbest-estimate end-of-century warming is 1.4°C, after a temporary overshoot \\n(see Section 3.3.4) of no more than 0.1°C abov...[]{'question_type': 'simple', 'seed_document_id': 116, 'topic': 'Climate Change Mitigation Scenarios'}
d785c257-4c44-443c-99dd-7d72a296da9fWhat are the projected global emissions for 2030 based on policies implemented by the end of 2020?The median projected global emissions for 2030 based on policies implemented by the end of 2020 are 57 GtCO2-eq/yr, with a range of 52–60 GtCO2-eq/yr.Document 82: Emissions projections for 2030 and gross differences in emissions are based on emissions of 52–56 GtCO2-eq yr–1 in 2019 as assumed in underlying model \\nstudies97. (medium confidence) {WGIII Table SPM.1} (Table 3.1, Cross-Section Box.2) \\n95 \\nAbatement here refers to human interventions that reduce the amount of GHGs that are released from fossil fuel infrastructure to the atmosph...[]{'question_type': 'simple', 'seed_document_id': 82, 'topic': 'Global Greenhouse Gas Emissions and Climate Policy'}
0646700a-9617-4dad-9a12-f84a6048ca9dWhat are some key barriers to the implementation of adaptation options in vulnerable sectors?Key barriers include limited resources, lack of private-sector and civic engagement, insufficient mobilisation of finance, lack of political commitment, limited research and/or slow and low uptake of adaptation science, and a low sense of urgency.Document 95: 62\\nSection 2\\nSection 1\\nSection 2\\nfire-adapted ecosystems, or hard defences against flooding) and human \\nsettlements (e.g. stranded assets and vulnerable communities that \\ncannot afford to shift away or adapt and require an increase in social \\nsafety nets). Maladaptation especially affects marginalised and vulnerable \\ngroups adversely (e.g., Indigenous Peoples, ethnic minorit...[]{'question_type': 'simple', 'seed_document_id': 95, 'topic': 'Others'}
0d78955c-f9c8-41ad-9ba4-a2670da4e63cWhat are some irreversible changes projected due to continued GHG emissions?Many changes due to past and future GHG emissions are irreversible on centennial to millennial time scales, especially in the ocean, ice sheets, and global sea level.Document 118: {WGI SPM D.1.7, WGI Box TS.7} (Cross-Section Box.2)\\nContinued GHG emissions will further affect all major climate \\nsystem components, and many changes will be irreversible on \\ncentennial to millennial time scales. Many changes in the climate \\nsystem become larger in direct relation to increasing global warming. \\nWith every additional increment of global warming, changes in \\...[]{'question_type': 'simple', 'seed_document_id': 118, 'topic': 'Others'}
00d34731-7f09-446d-80fb-40c0b20b547aWhat are some options for scaling up mitigation and adaptation in developing regions according to the context?Options include increased levels of public finance and publicly mobilised private finance flows from developed to developing countries, increasing the use of public guarantees to reduce risks and leverage private flows at lower cost, local capital markets development, and building greater trust in international cooperation processes.Document 291: Accelerated support \\nfrom developed countries and multilateral institutions is a critical \\nenabler to enhance mitigation and adaptation action and can address \\ninequities in finance, including its costs, terms and conditions, and \\neconomic vulnerability to climate change. Scaled-up public grants for \\nmitigation and adaptation funding for vulnerable regions, e.g., in Sub-\\nSah...[]{'question_type': 'simple', 'seed_document_id': 291, 'topic': 'Climate Change Mitigation and Adaptation'}
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" - ], - "text/plain": [ - " question \\\n", - "id \n", - "1cacb231-b6e3-44aa-a315-79aa43cff369 When is the best estimate of reaching 1.5°C of global warming according to most scenarios? \n", - "d785c257-4c44-443c-99dd-7d72a296da9f What are the projected global emissions for 2030 based on policies implemented by the end of 2020? \n", - "0646700a-9617-4dad-9a12-f84a6048ca9d What are some key barriers to the implementation of adaptation options in vulnerable sectors? \n", - "0d78955c-f9c8-41ad-9ba4-a2670da4e63c What are some irreversible changes projected due to continued GHG emissions? \n", - "00d34731-7f09-446d-80fb-40c0b20b547a What are some options for scaling up mitigation and adaptation in developing regions according to the context? \n", - "\n", - " reference_answer \\\n", - "id \n", - "1cacb231-b6e3-44aa-a315-79aa43cff369 The best estimate of reaching 1.5°C of global warming lies in the first half of the 2030s in most of the considered scenarios and modelled pathways. \n", - "d785c257-4c44-443c-99dd-7d72a296da9f The median projected global emissions for 2030 based on policies implemented by the end of 2020 are 57 GtCO2-eq/yr, with a range of 52–60 GtCO2-eq/yr. \n", - "0646700a-9617-4dad-9a12-f84a6048ca9d Key barriers include limited resources, lack of private-sector and civic engagement, insufficient mobilisation of finance, lack of political commitment, limited research and/or slow and low uptake of adaptation science, and a low sense of urgency. \n", - "0d78955c-f9c8-41ad-9ba4-a2670da4e63c Many changes due to past and future GHG emissions are irreversible on centennial to millennial time scales, especially in the ocean, ice sheets, and global sea level. \n", - "00d34731-7f09-446d-80fb-40c0b20b547a Options include increased levels of public finance and publicly mobilised private finance flows from developed to developing countries, increasing the use of public guarantees to reduce risks and leverage private flows at lower cost, local capital markets development, and building greater trust in international cooperation processes. \n", - "\n", - " reference_context \\\n", - "id \n", - "1cacb231-b6e3-44aa-a315-79aa43cff369 Document 116: The best estimate of reaching 1.5°C of global \\nwarming lies in the first half of the 2030s in most of the considered \\nscenarios and modelled pathways114. In the very low GHG emissions \\nscenario (SSP1-1.9), CO2 emissions reach net zero around 2050 and the \\nbest-estimate end-of-century warming is 1.4°C, after a temporary overshoot \\n(see Section 3.3.4) of no more than 0.1°C abov... \n", - "d785c257-4c44-443c-99dd-7d72a296da9f Document 82: Emissions projections for 2030 and gross differences in emissions are based on emissions of 52–56 GtCO2-eq yr–1 in 2019 as assumed in underlying model \\nstudies97. (medium confidence) {WGIII Table SPM.1} (Table 3.1, Cross-Section Box.2) \\n95 \\nAbatement here refers to human interventions that reduce the amount of GHGs that are released from fossil fuel infrastructure to the atmosph... \n", - "0646700a-9617-4dad-9a12-f84a6048ca9d Document 95: 62\\nSection 2\\nSection 1\\nSection 2\\nfire-adapted ecosystems, or hard defences against flooding) and human \\nsettlements (e.g. stranded assets and vulnerable communities that \\ncannot afford to shift away or adapt and require an increase in social \\nsafety nets). Maladaptation especially affects marginalised and vulnerable \\ngroups adversely (e.g., Indigenous Peoples, ethnic minorit... \n", - "0d78955c-f9c8-41ad-9ba4-a2670da4e63c Document 118: {WGI SPM D.1.7, WGI Box TS.7} (Cross-Section Box.2)\\nContinued GHG emissions will further affect all major climate \\nsystem components, and many changes will be irreversible on \\ncentennial to millennial time scales. Many changes in the climate \\nsystem become larger in direct relation to increasing global warming. \\nWith every additional increment of global warming, changes in \\... \n", - "00d34731-7f09-446d-80fb-40c0b20b547a Document 291: Accelerated support \\nfrom developed countries and multilateral institutions is a critical \\nenabler to enhance mitigation and adaptation action and can address \\ninequities in finance, including its costs, terms and conditions, and \\neconomic vulnerability to climate change. Scaled-up public grants for \\nmitigation and adaptation funding for vulnerable regions, e.g., in Sub-\\nSah... \n", - "\n", - " conversation_history \\\n", - "id \n", - "1cacb231-b6e3-44aa-a315-79aa43cff369 [] \n", - "d785c257-4c44-443c-99dd-7d72a296da9f [] \n", - "0646700a-9617-4dad-9a12-f84a6048ca9d [] \n", - "0d78955c-f9c8-41ad-9ba4-a2670da4e63c [] \n", - "00d34731-7f09-446d-80fb-40c0b20b547a [] \n", - "\n", - " metadata \n", - "id \n", - "1cacb231-b6e3-44aa-a315-79aa43cff369 {'question_type': 'simple', 'seed_document_id': 116, 'topic': 'Climate Change Mitigation Scenarios'} \n", - "d785c257-4c44-443c-99dd-7d72a296da9f {'question_type': 'simple', 'seed_document_id': 82, 'topic': 'Global Greenhouse Gas Emissions and Climate Policy'} \n", - "0646700a-9617-4dad-9a12-f84a6048ca9d {'question_type': 'simple', 'seed_document_id': 95, 'topic': 'Others'} \n", - "0d78955c-f9c8-41ad-9ba4-a2670da4e63c {'question_type': 'simple', 'seed_document_id': 118, 'topic': 'Others'} \n", - "00d34731-7f09-446d-80fb-40c0b20b547a {'question_type': 'simple', 'seed_document_id': 291, 'topic': 'Climate Change Mitigation and Adaptation'} " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "testset.to_pandas().head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate and Diagnose the Agent\n", - "\n", - "We can now evaluate the agent's performance on the test set using the RAG Evaluation Toolkit (RAGET).\n", - "We will use the `evaluate` function to evaluate the agent's performance on the test set.\n", - "We will use the `ragas_context_recall` and `ragas_context_precision` metrics to evaluate the agent's performance on the test set.\n", - "We will also use the `RAGReport` class to generate a report of the agent's performance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "def answer_fn(question: str, history: list[dict] = None) -> AgentAnswer:\n", - " if history:\n", - " answer = chat_engine.chat(\n", - " question,\n", - " chat_history=[\n", - " ChatMessage(\n", - " role=(\n", - " MessageRole.USER\n", - " if msg[\"role\"] == \"user\"\n", - " else MessageRole.ASSISTANT\n", - " ),\n", - " content=msg[\"content\"],\n", - " )\n", - " for msg in history\n", - " ],\n", - " )\n", - " else:\n", - " answer = chat_engine.chat(question, chat_history=[])\n", - "\n", - " return AgentAnswer(\n", - " message=answer.response, documents=[source.content for source in answer.sources]\n", - " )\n", - "\n", - "\n", - "report = evaluate(\n", - " answer_fn,\n", - " testset=testset,\n", - " knowledge_base=knowledge_base,\n", - " metrics=[ragas_context_recall, ragas_context_precision],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can save the report and load it later to display it. " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "# Save the RAG report\n", - "report.save(\"ipcc_report\")\n", - "\n", - "# Load the RAG report\n", - "report = RAGReport.load(\"ipcc_report\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also share the report with others to get their feedback." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(report.to_html(embed=True))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### RAGET question types\n", - "\n", - "For RAGET, we have 6 different question types that assess different RAG components.\n", - "Each question type assesses a few RAG components. This makes it possible to localize weaknesses in the RAG Agent and give feedback to the developers." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "|Question type | Description | Example | Targeted RAG components |\n", - "|---|---|---|---|\n", - "| **Simple** | Simple questions generated from an excerpt of the knowledge base | *How much will the global temperature rise by 2100?* | `Generator`, `Retriever` | \n", - "| **Complex** | Questions made more complex by paraphrasing | *How much will the temperature rise in a century?* | `Generator` | \n", - "| **Distracting** | Questions made to confuse the retrieval part of the RAG with a distracting element from the knowledge base but irrelevant to the question | *Renewable energy are cheaper but how much will the global temperature rise by 2100?* | `Generator`, `Retriever`, `Rewriter` |\n", - "| **Situational** | Questions including user context to evaluate the ability of the generation to produce relevant answer according to the context | *I want to take personal actions to reduce my carbon footprint and I wonder how much will the global temperature rise by 2100?* |`Generator` |\n", - "| **Double** | Questions with two distinct parts to evaluate the capabilities of the query rewriter of the RAG | *How much will the global temperature rise by 2100 and what is the main source of Greenhouse Gases?* | `Generator`, `Rewriter` |\n", - "| **Conversational** |Questions made as part of a conversation, first message describe the context of the question that is ask in the last message, also tests the rewriter | - *I want to know more about the global temperature evolution by 2100.*
- *How high will it be?* | `Rewriter`, `Routing` |\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/source/oss/notebooks/index.rst b/source/oss/notebooks/index.rst deleted file mode 100644 index 9f1e560..0000000 --- a/source/oss/notebooks/index.rst +++ /dev/null @@ -1,82 +0,0 @@ -:og:title: Giskard Open Source - Example Notebooks -:og:description: Explore practical examples and tutorials using Giskard Open Source. Learn how to test LLM agents with real-world scenarios and use cases. - -=================== -Example notebooks -=================== - -This section contains practical examples and tutorials that demonstrate how to use Giskard Open Source for LLM testing and evaluation. - -The notebooks cover various use cases and scenarios, from basic security testing to advanced business logic validation. - -.. toctree:: - :maxdepth: 2 - :hidden: - - business/RAGET_IPCC.ipynb - business/RAGET_Banking_Supervision.ipynb - security/LLM_QA_IPCC.ipynb - security/LLM_QA_Google.ipynb - security/LLM_QA_Winter_Olympics.ipynb - security/LLM_Description_Product.ipynb - security/LLM_Newspaper_Comment_Generation.ipynb - security/LLM_QA_Documentation.ipynb - -Detecting Security Vulnerabilities with Scan --------------------------------------------- - -.. grid:: 1 1 2 2 - - .. grid-item-card:: IPCC Climate Change Report - :link: security/LLM_QA_IPCC - :link-type: doc - - Question Answering (LangChain, FAISS, OpenAI) - - .. grid-item-card:: Google Q&A - :link: security/LLM_QA_Google - :link-type: doc - - Question Answering (LangChain, Qdrant, OpenAI) - - .. grid-item-card:: 2022 Winter Olympics Q&A - :link: security/LLM_QA_Winter_Olympics - :link-type: doc - - Question Answering (OpenAI) - - .. grid-item-card:: Product Description - :link: security/LLM_Description_Product - :link-type: doc - - Description Generation (LangChain, OpenAI) - - .. grid-item-card:: Newspaper Comment Generation - :link: security/LLM_Newspaper_Comment_Generation - :link-type: doc - - Description Generation (LangChain, OpenAI) - - .. grid-item-card:: Documentation Q&A - :link: security/LLM_QA_Documentation - :link-type: doc - - Question Answering (LangChain, FAISS, OpenAI) - - -Detecting Business Failures with RAGET ----------------------------------------------------- - -.. grid:: 1 1 2 2 - - .. grid-item-card:: IPCC Climate Change Report - :link: business/RAGET_IPCC - :link-type: doc - - RAGET Demo with LlamaIndex RAG - - .. grid-item-card:: ECB Banking Supervision Report - :link: business/RAGET_Banking_Supervision - :link-type: doc - - RAGET Demo with LlamaIndex RAG diff --git a/source/oss/notebooks/security/LLM_Description_Product.ipynb b/source/oss/notebooks/security/LLM_Description_Product.ipynb deleted file mode 100644 index 0d4faf1..0000000 --- a/source/oss/notebooks/security/LLM_Description_Product.ipynb +++ /dev/null @@ -1,3071 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "elkFa-hxYwMV" - }, - "source": [ - "# LLM product description from keywords\n", - "\n", - "Giskard is an open-source framework for testing all ML models, from LLMs to tabular models. Don’t hesitate to give the project a [star on GitHub](https://github.com/Giskard-AI/giskard) ⭐️ if you find it useful!\n", - "\n", - "In this notebook, you’ll learn how to create comprehensive test suites for your model in a few lines of code, thanks to Giskard’s open-source Python library.\n", - "\n", - "In this example, we illustrate the procedure using **OpenAI Client** that is the default one; however, please note that our platform supports a variety of language models. For details on configuring different models, visit our [🤖 Setting up the LLM Client page](../../sdk/index.rst)\n", - "\n", - "In this tutorial we will walk through a practical use case of using the Giskard LLM Scan on a Prompt Chaining task, one step at a time. Given a product name, we will ask the LLM to process 2 chained prompts using `langchain` in order to provide us with a product description. The 2 prompts can be described as follows:\n", - "\n", - "1. `keywords_prompt_template`: Based on the product name (given by the user), the LLM has to provide a list of five to ten relevant keywords that would increase product visibility.\n", - "2. `product_prompt_template`: Based on the given keywords (given as a response to the first prompt), the LLM has to generate a multi-paragraph rich text product description with emojis that is creative and SEO compliant.\n", - "\n", - "Use-case:\n", - "\n", - "* Two-step product description generation. 1) Keywords generation -> 2) Description generation;\n", - "\n", - "Outline:\n", - "\n", - "* Detect vulnerabilities automatically with Giskard's scan\n", - "* Automatically generate & curate a comprehensive test suite to test your model beyond accuracy-related metrics" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "### Install dependencies and setup notebook\n", - "\n", - "Make sure to install the `giskard[llm]` flavor of Giskard, which includes support for LLM models.\n", - "Additionally, we will install `langchain` to define the prompt templates and pipeline logic." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "%pip install \"giskard[llm]\" --upgrade\n", - "%pip install langchain langchain-community langchain-openai --upgrade" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "Now, let's import all of the libraries that we will use in the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T04:25:09.573125Z", - "start_time": "2024-04-19T04:25:09.570237Z" - }, - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import os\n", - "from operator import itemgetter\n", - "\n", - "import openai\n", - "import pandas as pd\n", - "from langchain.prompts import ChatPromptTemplate\n", - "from langchain_core.output_parsers import StrOutputParser\n", - "from langchain_openai import ChatOpenAI\n", - "\n", - "from giskard import Dataset, Model, scan" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "\n", - "And, we set the OpenAI API key and display options." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T04:25:11.048657Z", - "start_time": "2024-04-19T04:25:11.045546Z" - }, - "id": "gqRHZcKvgCg0" - }, - "outputs": [], - "source": [ - "# Set the OpenAI API Key environment variable.\n", - "OPENAI_API_KEY = \"...\"\n", - "openai.api_key = OPENAI_API_KEY\n", - "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n", - "\n", - "# Display options.\n", - "pd.set_option(\"display.max_colwidth\", None)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "Lastly, we define some of the constants that we will use in the notebook. \n", - "Note that we are also defining two prompt templates that we will use to generate the keywords and the product description." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T04:25:18.354615Z", - "start_time": "2024-04-19T04:25:18.350946Z" - }, - "id": "sXEteRBVgGW1" - }, - "outputs": [], - "source": [ - "LLM_MODEL = \"gpt-3.5-turbo\"\n", - "\n", - "TEXT_COLUMN_NAME = \"product_name\"\n", - "\n", - "# First prompt to generate keywords related to the product name\n", - "KEYWORDS_PROMPT_TEMPLATE = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\n", - " \"system\",\n", - " \"\"\"You are a helpful assistant that generate a CSV list of keywords related to a product name\n", - "\n", - " Example Format:\n", - " PRODUCT NAME: product name here\n", - " KEYWORDS: keywords separated by commas here\n", - "\n", - " Generate five to ten keywords that would increase product visibility. Begin!\n", - "\n", - " \"\"\",\n", - " ),\n", - " (\n", - " \"human\",\n", - " \"\"\"\n", - " PRODUCT NAME: {product_name}\n", - " KEYWORDS:\"\"\",\n", - " ),\n", - " ]\n", - ")\n", - "\n", - "# Second chained prompt to generate a description based on the given keywords from the first prompt\n", - "PRODUCT_PROMPT_TEMPLATE = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\n", - " \"system\",\n", - " \"\"\"As a Product Description Generator, generate a multi paragraph rich text product description with emojis based on the information provided in the product name and keywords separated by commas.\n", - "\n", - " Example Format:\n", - " PRODUCT NAME: product name here\n", - " KEYWORDS: keywords separated by commas here\n", - " PRODUCT DESCRIPTION: product description here\n", - "\n", - " Generate a product description that is creative and SEO compliant. Emojis should be added to make product description look appealing. Begin!\n", - "\n", - " \"\"\",\n", - " ),\n", - " (\n", - " \"human\",\n", - " \"\"\"\n", - " PRODUCT NAME: {product_name}\n", - " KEYWORDS: {keywords}\n", - " PRODUCT DESCRIPTION:\n", - " \"\"\",\n", - " ),\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "## Detect vulnerabilities in your model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IKwgk5gKN2jI" - }, - "source": [ - "### Define a generation function\n", - "\n", - "To run scans, we need to define a generation function that takes a dataframe as input and returns a list of product descriptions.\n", - "Using the prompt templates defined earlier we can create two `LLMChain` and concatenate them into a `SequentialChain` that takes as input the product name, and outputs a product description." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T04:25:21.314931Z", - "start_time": "2024-04-19T04:25:21.311623Z" - }, - "id": "MBxfN87aN2Gc" - }, - "outputs": [], - "source": [ - "def generation_function(df: pd.DataFrame):\n", - " llm = ChatOpenAI(temperature=0.2, model=LLM_MODEL)\n", - "\n", - " # Define the chains.\n", - " keywords_chain = KEYWORDS_PROMPT_TEMPLATE | llm | StrOutputParser()\n", - " product_description_chain = (\n", - " {\"keywords\": keywords_chain, \"product_name\": itemgetter(\"product_name\")}\n", - " | PRODUCT_PROMPT_TEMPLATE\n", - " | llm\n", - " | StrOutputParser()\n", - " )\n", - "\n", - " return [\n", - " product_description_chain.invoke({\"product_name\": product_name})\n", - " for product_name in df[\"product_name\"]\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "### Wrap generation function as Giskard Model\n", - "\n", - "Before running the automatic LLM scan, we need to wrap our model into Giskard's `Model` object." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T04:25:22.759589Z", - "start_time": "2024-04-19T04:25:22.753586Z" - }, - "id": "FTGiW_RROFfD" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-09-11 14:58:39,931 pid:80445 MainThread giskard.models.automodel INFO Your 'prediction_function' is successfully wrapped by Giskard's 'PredictionFunctionModel' wrapper class.\n" - ] - } - ], - "source": [ - "# Wrap the description chain.\n", - "giskard_model = Model(\n", - " model=generation_function,\n", - " # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset\n", - " model_type=\"text_generation\", # Either regression, classification or text_generation.\n", - " name=\"Product keywords and description generator\", # Optional.\n", - " description=\"Generate product description based on a product's name and the associated keywords.\"\n", - " \"Description should be using emojis and being SEO compliant.\", # Is used to generate prompts\n", - " feature_names=[\"product_name\"], # Default: all columns of your dataset.\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P8B6L-b0OK7r" - }, - "source": [ - "### Test your model with a Giskard Dataset\n", - "\n", - "We can also optionally create a small dataset of queries to test that the model wrapping worked.\n", - "Let’s check that the model is correctly wrapped by running it on a small dataset of sample input prompts:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2024-04-19T04:25:43.103774Z", - "start_time": "2024-04-19T04:25:24.818458Z" - }, - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-09-11 14:58:43,402 pid:80445 MainThread giskard.datasets.base INFO Your 'pandas.DataFrame' is successfully wrapped by Giskard's 'Dataset' wrapper class.\n", - "2025-09-11 14:58:43,413 pid:80445 MainThread giskard.datasets.base INFO Casting dataframe columns from {'product_name': 'object'} to {'product_name': 'object'}\n", - "2025-09-11 14:58:57,436 pid:80445 MainThread giskard.utils.logging_utils INFO Predicted dataset with shape (3, 1) executed in 0:00:14.032847\n" - ] - }, - { - "data": { - "text/plain": [ - "array([\"🌟 Introducing the innovative Double-Sided Cooking Pan! 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