diff --git a/notebooks/code_samples/agents/langgraph_agent_simple_banking_demo.ipynb b/notebooks/code_samples/agents/langgraph_agent_simple_banking_demo.ipynb index 2b5f863a8..937049d34 100644 --- a/notebooks/code_samples/agents/langgraph_agent_simple_banking_demo.ipynb +++ b/notebooks/code_samples/agents/langgraph_agent_simple_banking_demo.ipynb @@ -231,7 +231,7 @@ " api_key=\"...\",\n", " api_secret=\"...\",\n", " model=\"...\",\n", - ")" + ")\n" ] }, { @@ -275,6 +275,11 @@ "from banking_tools import AVAILABLE_TOOLS\n", "from validmind.tests import run_test\n", "\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_colwidth', None)\n", + "pd.set_option('display.width', None)\n", + "pd.set_option('display.max_rows', None)\n", + "\n", "# Load environment variables if using .env file\n", "try:\n", " from dotenv import load_dotenv\n", @@ -722,15 +727,7 @@ "\n", "print(\"Banking Test Dataset Initialized in ValidMind!\")\n", "print(f\"Dataset ID: {vm_test_dataset.input_id}\")\n", - "print(f\"Dataset columns: {vm_test_dataset._df.columns}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ + "print(f\"Dataset columns: {vm_test_dataset._df.columns}\")\n", "vm_test_dataset._df.head(1)" ] }, @@ -754,7 +751,8 @@ "vm_test_dataset.assign_predictions(vm_banking_model)\n", "\n", "print(\"Banking Agent Predictions Generated Successfully!\")\n", - "print(f\"Predictions assigned to {len(vm_test_dataset._df)} test cases\")" + "print(f\"Predictions assigned to {len(vm_test_dataset._df)} test cases\")\n", + "vm_test_dataset._df.head()" ] }, { @@ -772,11 +770,11 @@ "metadata": {}, "outputs": [], "source": [ - "pd.set_option('display.max_colwidth', 40)\n", - "pd.set_option('display.width', 120)\n", - "pd.set_option('display.max_colwidth', None)\n", - "print(\"Banking Test Dataset with Predictions:\")\n", - "vm_test_dataset._df.head()" + "# pd.set_option('display.max_colwidth', 40)\n", + "# pd.set_option('display.width', 120)\n", + "# pd.set_option('display.max_colwidth', None)\n", + "# print(\"Banking Test Dataset with Predictions:\")\n", + "# vm_test_dataset._df.head()" ] }, { diff --git a/notebooks/code_sharing/geval_deepeval_integration_demo.ipynb b/notebooks/code_sharing/geval_deepeval_integration_demo.ipynb new file mode 100644 index 000000000..6f73fe2d5 --- /dev/null +++ b/notebooks/code_sharing/geval_deepeval_integration_demo.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "# G-Eval Integration for DeepEval within ValidMind\n", + "\n", + "Let's learn how to integrate [DeepEval](https://github.com/confident-ai/deepeval) with the ValidMind Library to evaluate Large Language Models (LLMs) and AI agents. \n", + "Large Language Model (LLM) evaluation requires robust metrics to assess model outputs. G-Eval, a key feature of DeepEval, uses LLMs themselves to evaluate model responses across dimensions like factual accuracy, coherence, and relevance, etc. This notebook demonstrates how to leverage G-Eval metrics within ValidMind's testing infrastructure to create comprehensive, automated evaluations of LLM outputs.\n", + "\n", + "To integrate DeepEval with ValidMind, we'll:\n", + " 1. Set up both frameworks and install required dependencies\n", + " 2. Create a dataset with source texts and generated summaries\n", + " 3. Analyze the evaluation results using G-eval custom metrics\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "## Contents \n", + "- [Introduction](#toc1_) \n", + " - [Before you begin](#toc2_1_) \n", + " - [Key concepts](#toc2_2_) \n", + "- [Setting up](#toc3_) \n", + " - [Install required packages](#toc3_1_) \n", + " - [Initialize ValidMind](#toc3_2_) \n", + "- [Custom Metrics with G-Eval](#toc4_) \n", + " - [Technical accuracy](#toc4_1_) \n", + " - [Clarity and Comprehensiveness](#toc4_2_) \n", + " - [Business Context Appropriateness](#toc4_3_) \n", + " - [Tool Usage Appropriateness](#toc4_4_) \n", + " - [Coherence Evaluation](#toc4_5_) \n", + "- [In summary](#toc5_) \n", + "- [Next steps](#toc6_) \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "\n", + "## Introduction\n", + "**G-Eval** is a framework that uses large language models (LLMs) as evaluators—essentially treating an LLM as a “judge” to assess the quality of other LLM outputs. Instead of relying on traditional metrics like BLEU or ROUGE, G-Eval enables natural-language evaluation criteria (e.g., “rate how factual this summary is”). The framework guides the judge model through structured reasoning steps, producing more consistent, transparent, and interpretable scoring results. It is particularly effective for subjective or open-ended tasks such as summarization, dialogue generation, and content evaluation.\n", + "\n", + "Key advantages of G-Eval include:\n", + "\n", + "* **Structured reasoning:** Uses a step-by-step approach to improve reliability and reduce bias.\n", + "* **Custom evaluation criteria:** Supports diverse factors like accuracy, tone, safety, or style.\n", + "* **Enhanced consistency:** Provides more repeatable judgments than earlier LLM-as-a-judge methods.\n", + "* **Production scalability:** Integrates easily with CI/CD pipelines via tools like *DeepEval*.\n", + "* **Broader applicability:** Works across multiple domains and task types, from creative writing to factual QA." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "\n", + "### Before you begin\n", + "\n", + "This notebook assumes you have basic familiarity with Python and Large Language Models. You'll need:\n", + "\n", + "- Python 3.8 or higher\n", + "- Access to OpenAI API (for DeepEval metrics evaluation)\n", + "- ValidMind account and model registration\n", + "\n", + "If you encounter errors due to missing modules, install them with `pip install` and re-run the notebook.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "\n", + "### Key concepts\n", + "\n", + "**LLMTestCase**: A DeepEval object that represents a single test case with input, expected output, actual output, and optional context.\n", + "\n", + "**G-Eval**: Generative evaluation using LLMs to assess response quality based on custom criteria.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "\n", + "## Setting up\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "\n", + "### Install required packages\n", + "\n", + "First, let's install the required packages and set up our environment.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -q validmind" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "\n", + "### Initialize ValidMind\n", + "\n", + "ValidMind generates a unique _code snippet_ for each registered model to connect with your developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests are uploaded to the correct model when you run the notebook.\n", + "\n", + "
For access to all features available in this notebook, you'll need access to a ValidMind account.\n", + "

\n", + "Register with ValidMind
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load your model identifier credentials from an `.env` file\n", + "%load_ext dotenv\n", + "%dotenv .env\n", + "\n", + "# # Or replace with your code snippet\n", + "import validmind as vm\n", + "\n", + "vm.init(\n", + " api_host=\"...\",\n", + " api_key=\"...\",\n", + " api_secret=\"...\",\n", + " model=\"...\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Core imports\n", + "import warnings\n", + "from deepeval.test_case import LLMTestCase\n", + "from deepeval.metrics.g_eval.utils import Rubric\n", + "from deepeval.test_case import LLMTestCaseParams\n", + "from validmind.datasets.llm import LLMAgentDataset\n", + "import pandas as pd\n", + "\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "pd.set_option('display.max_columns', None)\n", + "pd.set_option('display.max_colwidth', None)\n", + "pd.set_option('display.width', None)\n", + "pd.set_option('display.max_rows', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Create test cases\n", + "\n", + "Let's create test cases to demonstrate the G-Eval custom metrics functionality." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a test dataset for evaluating the custom metrics\n", + "test_cases = [\n", + " LLMTestCase(\n", + " input=\"What is machine learning?\",\n", + " actual_output=\"Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It uses statistical techniques to allow computers to find patterns in data.\",\n", + " context=[\"Machine learning is a branch of AI that focuses on building applications that learn from data and improve their accuracy over time without being programmed to do so.\"],\n", + " expected_output=\"Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.\"\n", + " ), \n", + " LLMTestCase(\n", + " input=\"How do I implement a neural network?\",\n", + " actual_output=\"To implement a neural network, you need to: 1) Define the network architecture (layers, neurons), 2) Initialize weights and biases, 3) Implement forward propagation, 4) Calculate loss, 5) Perform backpropagation, and 6) Update weights using gradient descent.\",\n", + " context=[\"Neural networks are computing systems inspired by biological neural networks. They consist of layers of interconnected nodes that process and transmit signals.\"],\n", + " expected_output=\"Neural network implementation involves defining network architecture, initializing parameters, implementing forward and backward propagation, and using optimization algorithms for training.\"\n", + " )\n", + "]\n", + "\n", + "# Create Agent dataset\n", + "geval_dataset = LLMAgentDataset.from_test_cases(\n", + " test_cases=test_cases,\n", + " input_id=\"geval_dataset\"\n", + ")\n", + "geval_dataset._df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scorers in ValidMind\n", + "\n", + "Scorers are evaluation metrics that analyze model outputs and store their results in the dataset. When using `assign_scores()`:\n", + "\n", + "- For Geval scorer adds new columns (score, reason and criteria) to the dataset with format: `GEval_{metric_name}_score`, `GEval_{metric_name}_reason` and `GEval_{metric_name}_criteria`\n", + "- The column contains the numeric score (typically 0-1) for each example\n", + "- Multiple scorers can be run on the same dataset, each adding their own columns\n", + "- Scores are persisted in the dataset for later analysis and visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "## Custom Metrics with G-Eval\n", + "One of DeepEval's most powerful features is the ability to create custom evaluation metrics using G-Eval (Generative Evaluation). This enables domain-specific evaluation criteria tailored to your use case.\n", + "\n", + "\n", + "\n", + "\n", + "### Technical accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name=\"Technical Accuracy\"\n", + "criteria=\"\"\"Evaluate whether the response is technically accurate and uses appropriate \n", + "terminology for the domain. Consider if the explanations are scientifically sound \n", + "and if technical concepts are explained correctly.\"\"\"\n", + "threshold=0.8\n", + "geval_dataset.assign_scores(\n", + " metrics = \"validmind.scorer.llm.deepeval.GEval\",\n", + " metric_name=name, \n", + " criteria = criteria,\n", + " threshold=threshold,\n", + " evaluation_params={\n", + " LLMTestCaseParams.INPUT: \"input\",\n", + " LLMTestCaseParams.ACTUAL_OUTPUT: \"actual_output\",\n", + " }\n", + ")\n", + "geval_dataset._df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Clarity and Comprehensiveness\n", + "This evaluation assesses the clarity and comprehensiveness of responses, focusing on how well-structured and understandable they are. The criteria examines whether responses are logically organized, address all aspects of questions thoroughly, and maintain an appropriate level of detail without being overly verbose.\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name=\"Clarity and Comprehensiveness\"\n", + "criteria=\"\"\"Evaluate the clarity, structure, and comprehensiveness of the actual output \n", + "in relation to the expected output. The response should be clear, well-organized, and \n", + "comparable in coverage to the expected output, addressing all relevant aspects without \n", + "being overly verbose. Deduct points if important points or details present in the expected \n", + "output are missing or inaccurately conveyed in the actual output.\"\"\"\n", + "threshold=0.75\n", + "\n", + "geval_dataset.assign_scores(\n", + " metrics = \"validmind.scorer.llm.deepeval.GEval\",\n", + " metric_name=name, \n", + " criteria = criteria,\n", + " threshold=threshold,\n", + " evaluation_params={\n", + " LLMTestCaseParams.INPUT: \"input\",\n", + " LLMTestCaseParams.ACTUAL_OUTPUT: \"actual_output\",\n", + " LLMTestCaseParams.EXPECTED_OUTPUT: \"expected_output\",\n", + " }\n", + ")\n", + "geval_dataset._df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Business Context Appropriateness\n", + "\n", + "This evaluation assesses whether responses are appropriate for a business context, considering factors like professional tone, business relevance, and actionable insights. The criteria focuses on ensuring content would be valuable and applicable for business users.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "name=\"Business Context Appropriateness\"\n", + "criteria=\"\"\"Evaluate whether the response is appropriate for a business context. \n", + "Consider if the tone is professional, if the content is relevant to business needs, \n", + "and if it provides actionable information that would be valuable to a business user.\"\"\"\n", + "threshold=0.7\n", + "geval_dataset.assign_scores(\n", + " metrics = \"validmind.scorer.llm.deepeval.GEval\",\n", + " metric_name=name, \n", + " criteria = criteria,\n", + " threshold=threshold,\n", + " evaluation_params={\n", + " LLMTestCaseParams.INPUT: \"input\",\n", + " LLMTestCaseParams.ACTUAL_OUTPUT: \"actual_output\",\n", + " LLMTestCaseParams.EXPECTED_OUTPUT: \"expected_output\",\n", + " }\n", + ")\n", + "geval_dataset._df.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "### Conciseness Evaluation\n", + "This evaluation assesses how well the responses flow and connect logically. It examines whether the content builds naturally from sentence to sentence to form a coherent narrative, rather than just being a collection of related but disconnected information. The evaluation considers factors like fluency, logical progression, and overall readability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "criteria = \"\"\"\n", + " Evaluate the conciseness of the generation on a continuous scale from 0 to 1.\n", + " A generation can be considered concise (Score: 1) if it directly and succinctly\n", + " answers the question posed, focusing specifically on the information requested\n", + " without including unnecessary, irrelevant, or excessive details.\"\"\"\n", + "\n", + "evaluation_steps=[\n", + " \"Read the input and identify which pieces of information need to be conveyed.\"\n", + " \"Read the actual_output and check if it includes all the required information.\",\n", + " \"Check if the actual_output excludes irrelevant details or redundancies.\",\n", + " \"Check if the wording is as brief as possible while still being clear and complete.\",\n", + " \"Assign a score (e.g., 0-10) based on how well the actual_output meets the above.\"\n", + " ]\n", + "\n", + "rubric=[\n", + " Rubric(score_range=(0, 1), expected_outcome=\"Very poor Conciseness\"),\n", + " Rubric(score_range=(2, 3), expected_outcome=\"Poor Conciseness\"),\n", + " Rubric(score_range=(4, 5), expected_outcome=\"Fair Conciseness\"),\n", + " Rubric(score_range=(6, 7), expected_outcome=\"Good Conciseness\"),\n", + " Rubric(score_range=(8, 10), expected_outcome=\"Excellent Conciseness\"),\n", + " ]\n", + "\n", + "geval_dataset.assign_scores(\n", + " metrics = \"validmind.scorer.llm.deepeval.GEval\",\n", + " metric_name=\"Conciseness\", \n", + " criteria = criteria,\n", + " rubric=rubric,\n", + " evaluation_steps=evaluation_steps,\n", + " evaluation_params={\n", + " LLMTestCaseParams.INPUT: \"input\",\n", + " LLMTestCaseParams.ACTUAL_OUTPUT: \"actual_output\",\n", + " }\n", + ")\n", + "geval_dataset._df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot all of these metrics together in a Boxplot Test" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vm.tests.run_test(\n", + " \"validmind.plots.BoxPlot\",\n", + " inputs={\"dataset\": geval_dataset},\n", + " params={\n", + " \"columns\": [\n", + " \"GEval_Technical_Accuracy_score\",\n", + " \"GEval_Clarity_and_Comprehensiveness_score\",\n", + " \"GEval_Business_Context_Appropriateness_score\",\n", + " \"GEval_Conciseness_score\"\n", + " ],\n", + " \"title\": \"Distribution of G-Eval Scores\",\n", + " \"ylabel\": \"Score\",\n", + " }\n", + ").log()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + "\n", + "## Next steps\n", + "\n", + "**Explore Advanced Features:**\n", + "- **Continuous Evaluation**: Set up automated LLM evaluation pipelines\n", + "- **Metrics Customization**: Create domain-specific evaluation criteria\n", + "- **Integration Patterns**: Embed evaluation into your LLM development workflow\n", + "\n", + "**Additional Resources:**\n", + "- [ValidMind Library Documentation](https://docs.validmind.ai/developer/validmind-library.html) - Complete API reference and tutorials\n", + "\n", + "**Try These Examples:**\n", + "- Implement custom business-specific evaluation metrics\n", + "- Create automated evaluation pipelines for model deployment\n", + "- Integrate with your existing ML infrastructure and workflows\n", + "- Explore multi-modal evaluation scenarios (text, code, images)\n", + "\n", + "Start building comprehensive LLM evaluation workflows that combine the power of DeepEval's specialized metrics with ValidMind's structured testing and documentation framework.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ValidMind Library", + "language": "python", + "name": "validmind" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + 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">=1.2.0,<1.3.0" catalogue = ">=2.0.4,<2.1.0" confection = ">=0.0.1,<1.0.0" cymem = ">=2.0.2,<2.1.0" murmurhash = ">=1.0.2,<1.1.0" -numpy = ">=2.0.0,<3.0.0" +numpy = {version = ">=1.19.0,<3.0.0", markers = "python_version >= \"3.9\""} packaging = ">=20.0" preshed = ">=3.0.2,<3.1.0" -pydantic = ">=2.0.0,<3.0.0" +pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0" setuptools = "*" srsly = ">=2.4.0,<3.0.0" wasabi = ">=0.8.1,<1.2.0" @@ -10724,7 +10541,7 @@ datasets = ["datasets"] explainability = ["shap"] huggingface = ["sentencepiece", "transformers"] llm = ["deepeval", "langchain-openai", "pycocoevalcap", "ragas", "sentencepiece", "torch", "transformers"] -nlp = ["bert-score", "evaluate", "langdetect", "nltk", "rouge", "textblob"] +nlp = ["bert-score", "evaluate", "langdetect", "nltk", "pyarrow", "rouge", "textblob"] pii-detection = ["presidio-analyzer", "presidio-structured"] pytorch = ["torch"] stats = ["arch", "scipy", "statsmodels"] @@ -10733,4 +10550,4 @@ xgboost = ["xgboost"] [metadata] lock-version = "2.1" python-versions = ">=3.9,<3.13" -content-hash = "a71d5d3474039cdc4a5e81fed3a212aac66e5cd69e9ffe9e73a3403cd287865b" +content-hash = "babd1bcff7e4e48f226c0fda60cd2cdee0803de89bcd3f3e64046fae246e9a42" diff --git a/pyproject.toml b/pyproject.toml index ff8a7f5bc..ef55251d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "validmind" -version = "2.10.1" +version = "2.10.2" description = "ValidMind Library" readme = "README.pypi.md" requires-python = ">=3.9,<3.13" @@ -19,6 +19,7 @@ dependencies = [ "matplotlib", "mistune (>=3.0.2,<4.0.0)", "nest-asyncio (>=1.6.0,<2.0.0)", + "numpy (>=1.23,<2.0.0)", "openai (>=1)", "pandas (>=2.0.3,<3.0.0)", "plotly (>=5.0.0)", @@ -74,6 +75,7 @@ nlp = [ "evaluate", "rouge (>=1)", "bert-score (>=0.3.13)", + "pyarrow (<16)", ] pytorch = ["torch (>=2.0.0)"] stats = ["scipy", "statsmodels", "arch"] diff --git a/validmind/__version__.py b/validmind/__version__.py index 565443f86..6c96c9755 100644 --- a/validmind/__version__.py +++ b/validmind/__version__.py @@ -1 +1 @@ -__version__ = "2.10.1" +__version__ = "2.10.2" diff --git a/validmind/datasets/llm/agent_dataset.py b/validmind/datasets/llm/agent_dataset.py index abb4335af..ace2b5066 100644 --- a/validmind/datasets/llm/agent_dataset.py +++ b/validmind/datasets/llm/agent_dataset.py @@ -127,111 +127,62 @@ def _convert_to_dataframe(self) -> pd.DataFrame: pandas.DataFrame: Tabular representation of test cases and goldens. """ data = [] + data.extend(self._process_test_cases()) + data.extend(self._process_goldens()) - # Process test cases + if not data: + data = [self._get_empty_row()] + + return pd.DataFrame(data) + + def _process_test_cases(self) -> List[Dict[str, Any]]: + """Process test cases into DataFrame rows.""" + data = [] for i, test_case in enumerate(self.test_cases): row = { "id": f"test_case_{i}", "input": test_case.input, "actual_output": test_case.actual_output, - "expected_output": getattr(test_case, "expected_output", None), - "context": self._serialize_list_field( - getattr(test_case, "context", None) - ), - "retrieval_context": self._serialize_list_field( - getattr(test_case, "retrieval_context", None) - ), - "tools_called": getattr(test_case, "tools_called", None), - "expected_tools": self._serialize_tools_field( - getattr(test_case, "expected_tools", None) - ), - "type": "test_case", } + self._add_optional_fields(row, test_case) data.append(row) + return data - # Process goldens + def _process_goldens(self) -> List[Dict[str, Any]]: + """Process goldens into DataFrame rows.""" + data = [] for i, golden in enumerate(self.goldens): - row = { - "id": f"golden_{i}", - "input": golden.input, - "actual_output": getattr(golden, "actual_output", None), - "expected_output": getattr(golden, "expected_output", None), - "context": self._serialize_list_field(getattr(golden, "context", None)), - "retrieval_context": self._serialize_list_field( - getattr(golden, "retrieval_context", None) - ), - "tools_called": self._serialize_tools_field( - getattr(golden, "tools_called", None) - ), - "expected_tools": self._serialize_tools_field( - getattr(golden, "expected_tools", None) - ), - "type": "golden", - } + row = {"id": f"golden_{i}", "input": golden.input} + self._add_optional_fields(row, golden) data.append(row) - - if not data: - # Create empty DataFrame with expected columns - data = [ - { - "id": "", - "input": "", - "actual_output": "", - "expected_output": "", - "context": "", - "retrieval_context": "", - "tools_called": "", - "expected_tools": "", - "type": "", - } - ] - - return pd.DataFrame(data) - - def _serialize_list_field(self, field: Optional[List[str]]) -> str: - """Serialize list field to string for DataFrame storage. - - Args: - field (Optional[List[str]]): List of strings to serialize. - - Returns: - str: Pipe-delimited string. - """ - if field is None: - return "" - return "|".join(str(item) for item in field) - - def _serialize_tools_field(self, tools: Optional[List]) -> str: - """Serialize tools list to string for DataFrame storage. - - Args: - tools (Optional[List]): List of tool objects or names. - - Returns: - str: Pipe-delimited string of tool names. - """ - if tools is None: - return "" - tool_strs = [] - for tool in tools: - if hasattr(tool, "name"): - tool_strs.append(tool.name) - else: - tool_strs.append(str(tool)) - return "|".join(tool_strs) - - def _deserialize_list_field(self, field_str: str) -> List[str]: - """Deserialize string back to list. - - Args: - field_str (str): Pipe-delimited string. - - Returns: - List[str]: List of string tokens. - """ - if not field_str: - return [] - return field_str.split("|") + return data + + def _add_optional_fields(self, row: Dict[str, Any], obj: Any) -> None: + """Add optional fields to a row from an object.""" + optional_fields = [ + "expected_output", + "context", + "retrieval_context", + "tools_called", + "expected_tools", + ] + for field in optional_fields: + value = getattr(obj, field, None) + if value is not None: + row[field] = value + + def _get_empty_row(self) -> Dict[str, str]: + """Get an empty row with all expected columns.""" + return { + "id": "", + "input": "", + "actual_output": "", + "expected_output": "", + "context": "", + "retrieval_context": "", + "tools_called": "", + "expected_tools": "", + } @classmethod def from_test_cases( @@ -460,12 +411,8 @@ def to_deepeval_test_cases(self) -> List[Any]: if pd.notna(row["actual_output"]) else "", expected_output=expected_output_val, - context=self._deserialize_list_field(context_val) - if context_val - else None, - retrieval_context=self._deserialize_list_field( - retrieval_context_val - ) + context=context_val if context_val else None, + retrieval_context=retrieval_context_val if retrieval_context_val else None, # Note: tools_called deserialization would need more complex logic diff --git a/validmind/scorer/llm/deepeval/GEval.py b/validmind/scorer/llm/deepeval/GEval.py new file mode 100644 index 000000000..de8d7e0ba --- /dev/null +++ b/validmind/scorer/llm/deepeval/GEval.py @@ -0,0 +1,145 @@ +# Copyright © 2023-2024 ValidMind Inc. All rights reserved. +# See the LICENSE file in the root of this repository for details. +# SPDX-License-Identifier: AGPL-3.0 AND ValidMind Commercial + +from typing import Any, Dict, List + +from validmind import tags, tasks +from validmind.ai.utils import get_client_and_model +from validmind.errors import MissingDependencyError +from validmind.tests.decorator import scorer +from validmind.vm_models.dataset import VMDataset + +try: + from deepeval.metrics import GEval as geval + from deepeval.metrics.g_eval.utils import Rubric + from deepeval.test_case import LLMTestCase, LLMTestCaseParams +except ImportError as e: + if "deepeval" in str(e): + raise MissingDependencyError( + "Missing required package `deepeval` for GEval. " + "Please run `pip install validmind[llm]` to use LLM tests", + required_dependencies=["deepeval"], + extra="llm", + ) from e + + raise e + + +# Create custom ValidMind tests for DeepEval metrics +@scorer() +@tags("llm", "GEval", "deepeval") +@tasks("llm") +def GEval( + dataset: VMDataset, + metric_name: str, + criteria: str, + evaluation_params: Dict[LLMTestCaseParams, str], + evaluation_steps: List[str] = [], + rubric: List[Rubric] = None, + strict_mode: bool = True, + threshold: float = 0.5, +) -> List[Dict[str, Any]]: + """Detects evaluation criteria in LLM outputs using deepeval's GEval metric. + + This scorer evaluates whether an LLM's output contains the specified evaluation criteria. It uses the GEval framework + (https://arxiv.org/pdf/2303.16634.pdf) to assess outputs against defined criteria and rubrics. The scorer processes each row + in the dataset and returns evaluation scores and explanations. + + The GEval metric requires the dataset to contain 'input', 'actual_output', and 'expected_output' columns. The 'input' column + should contain the prompts given to the LLM, 'actual_output' should contain the LLM's responses, and 'expected_output' should + contain the expected/reference responses. + + Args: + dataset (VMDataset): Dataset containing input prompts and LLM outputs to evaluate. Must have columns: + - input: Prompts given to the LLM + - actual_output: LLM's responses to evaluate + - expected_output: Expected/reference responses + metric_name (str): Name of the GEval metric to use for evaluation (e.g., "response_quality", "factual_accuracy") + criteria (str): Evaluation criteria to assess the outputs against. Should clearly specify what aspects to evaluate. + evaluation_steps (List[str], optional): Step-by-step instructions for evaluation. Each step should be a clear directive. + Defaults to empty list. + rubric (List[Rubric], optional): List of Rubric objects defining evaluation criteria. Each rubric should specify + scoring criteria and descriptions. Defaults to None. + strict_mode (bool, optional): If True, enforces binary scoring (0 or 1). If False, allows fractional scores. + Defaults to True. + threshold (float, optional): Minimum score threshold for considering an evaluation successful. Range 0.0-1.0. + Defaults to 0.5. + + Returns: + List[Dict[str, Any]]: List of evaluation results per dataset row. Each dictionary contains: + - score (float): Evaluation score between 0.0 and 1.0 (or 0/1 if strict_mode=True) + - reason (str): Detailed explanation of the evaluation and score assignment + - metric_name (str): Name of the metric used for evaluation + - criteria (str): Evaluation criteria used + - threshold (float): Score threshold used + + Raises: + ValueError: If required columns ('input', 'actual_output', 'expected_output') are missing from dataset + MissingDependencyError: If the required deepeval package is not installed + + Example: + >>> results = GEval( + ... dataset=my_dataset, + ... metric_name="response_quality", + ... criteria="Response should be clear, accurate and well-structured", + ... rubric=[ + ... Rubric(score=1, description="Perfect response meeting all criteria"), + ... Rubric(score=0, description="Response fails to meet criteria") + ... ], + ... strict_mode=True, + ... threshold=0.7 + ... ) + """ + _, model = get_client_and_model() + + results: List[Dict[str, Any]] = [] + evaluation_params_dict = { + value: key.value for key, value in evaluation_params.items() + } + df = dataset._df.copy(deep=True) + # Check if all evaluation parameter columns exist in dataframe + missing_cols = [ + col for col in evaluation_params_dict.keys() if col not in df.columns + ] + if missing_cols: + raise ValueError(f"Required columns missing from dataset: {missing_cols}") + df = df.rename(columns=evaluation_params_dict) + columns = df.columns.tolist() + + for _, row in df.iterrows(): + test_case_dict = { + key: row[key.value] + for key in evaluation_params.keys() + if key.value in columns and row[key.value] is not None + } + test_case = LLMTestCase( + **{key.value: row[key.value] for key in test_case_dict.keys()} + ) + + # evaluation_params = [] + # for param in test_case_dict.keys(): + # evaluation_params.append(getattr(LLMTestCaseParams, param.upper())) + + metric = geval( + name=metric_name, + criteria=criteria, + evaluation_params=list(test_case_dict.keys()), + model=model, + evaluation_steps=evaluation_steps if evaluation_steps else None, + rubric=rubric if rubric else None, + strict_mode=strict_mode, + verbose_mode=False, + threshold=threshold, + ) + metric.measure(test_case) + metric_name = metric_name.replace(" ", "_") + results.append( + { + f"{metric_name}_score": metric.score, + f"{metric_name}_reason": metric.reason, + f"{metric_name}_criteria": criteria, + } + ) + + return results diff --git a/validmind/tests/plots/BoxPlot.py b/validmind/tests/plots/BoxPlot.py index cd0b1b4a1..8dac44dd4 100644 --- a/validmind/tests/plots/BoxPlot.py +++ b/validmind/tests/plots/BoxPlot.py @@ -93,7 +93,7 @@ def _create_single_boxplot( dataset, column, colors, show_outliers, title_prefix, width, height ): """Create single column box plot.""" - data = dataset.df[column].dropna() + data = dataset._df[column].dropna() if len(data) == 0: raise SkipTestError(f"No data available for column {column}") @@ -124,7 +124,7 @@ def _create_multiple_boxplots( dataset, columns, colors, show_outliers, title_prefix, width, height ): """Create multiple column box plots in subplot layout.""" - n_cols = min(3, len(columns)) + n_cols = min(2, len(columns)) n_rows = (len(columns) + n_cols - 1) // n_cols subplot_titles = [f"{title_prefix} {col}" for col in columns] @@ -132,14 +132,14 @@ def _create_multiple_boxplots( rows=n_rows, cols=n_cols, subplot_titles=subplot_titles, - vertical_spacing=0.1, - horizontal_spacing=0.1, + vertical_spacing=0.2, # Increased vertical spacing between plots + horizontal_spacing=0.15, # Increased horizontal spacing between plots ) for idx, column in enumerate(columns): row = (idx // n_cols) + 1 col = (idx % n_cols) + 1 - data = dataset.df[column].dropna() + data = dataset._df[column].dropna() if len(data) > 0: color = colors[idx % len(colors)] @@ -185,8 +185,8 @@ def BoxPlot( dataset: VMDataset, columns: Optional[List[str]] = None, group_by: Optional[str] = None, - width: int = 1200, - height: int = 600, + width: int = 1800, + height: int = 1200, colors: Optional[List[str]] = None, show_outliers: bool = True, title_prefix: str = "Box Plot of",