feat: Add Synthetic Data Gen and Evals for Agents using W&B Weave + Vertex

.github/actions/spelling/allow.txt

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 TOKENLIST
 TPU
 TPUs
+TRK
 TSLA
 TSMC
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+aggfunc
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.github/actions/spelling/line_forbidden.patterns

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 # Should be Colab
 \s(?!Colab)Co[Ll][Ll]?abs?\b
 
-# Should be Kaggle
-\skaggle\b
-
 # Should be TPU or TPUs
 \btpus?\b
 

gemini/evaluation/synthetic-data-evals/.gitignore

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+data
+evaluation_results
+*.png

gemini/evaluation/synthetic-data-evals/README.md

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+# Agent Evaluation Framework
+
+This repository contains a framework for generating, evaluating, and analyzing the performance of LLM-powered agents in customer support scenarios.
+
+## Overview
+
+The framework consists of three main components:
+
+1. **Customer Support Agent** - An LLM-powered agent with tools for handling e-commerce customer queries
+2. **Dataset Generator** - A system for creating synthetic evaluation datasets with realistic customer queries
+3. **Agent Evaluator** - A comprehensive evaluation system for measuring agent performance
+
+## Customer Support Agent
+
+The customer support agent is built using the `smolagents` framework and provides several tools for handling e-commerce queries:
+
+- `ProductSearchTool` - Search product catalog by name, category, or description
+- `OrderStatusTool` - Check order status by order ID
+- `CategoryBrowseTool` - Browse products by category
+- `PriceCheckTool` - Check product price by product ID
+- `CustomerOrderHistoryTool` - Get order history for a customer
+
+The agent can be configured with different LLM models, including Gemini 1.5 Pro, and supports planning capabilities to handle complex multi-step queries.
+
+```python
+agent = create_customer_support_agent(
+    model_id="google/gemini-1.5-pro",
+    use_weave=True,
+    temperature=0.2,
+    planning_interval=1,
+    max_steps=3
+)
+```
+
+## Dataset Generator
+
+The dataset generator creates realistic evaluation examples by:
+
+1. Generating diverse e-commerce customer queries
+2. Running the agent on these queries and recording its trajectory
+3. Evaluating each step and the final response using a judge model
+4. Filtering examples based on quality thresholds
+5. Saving high-quality examples to a dataset for evaluation
+
+```python
+generator = DatasetGenerator(
+    agent=agent,
+    judge_model="gemini/gemini-1.5-pro",
+    thresholds={
+        "final_response": 0.7,
+        "single_step": 0.7,
+        "trajectory": 0.7
+    },
+    debug=True
+)
+
+examples = create_customer_support_agent_evaluation_dataset(generator, agent, num_prompts=10)
+```
+
+## Agent Evaluator
+
+The evaluator provides comprehensive metrics for agent performance:
+
+- **Response Correctness** - Accuracy and completeness of the agent's final response
+- **Tool Selection** - Appropriate use of available tools
+- **Trajectory Analysis** - Efficiency and effectiveness of the agent's path to solution
+- **Reasoning Quality** - Quality of the agent's reasoning process
+- **Coherence** - Consistency and clarity of the agent's communication
+
+The evaluator generates detailed reports, visualizations, and metrics to analyze agent performance.
+
+```python
+evaluator = AgentEvaluator(
+    model_name="gemini-1.5-pro",
+    temperature=0.1,
+    verbosity=2,
+    use_weave=True
+)
+
+results = evaluator.run_evaluation(agent, eval_dataset)
+```
+
+## Getting Started
+
+1. Install dependencies:
+   ```
+   uv sync
+   ```
+
+2. Set up environment variables (some of these will auto populate if you run `setup.py`):
+   ```
+   # Create a .env file with your API keys or colab secrets
+    GEMINI_API_KEY
+    HUGGING_FACE_HUB_TOKEN
+    VERTEX_PROJECT_ID
+    VERTEX_LOCATION
+    VERTEX_MODEL_ID
+    VERTEX_ENDPOINT_ID
+    DEEPSEEK_ENDPOINT_ID
+   ```
+
+3. Generate evaluation dataset:
+   ```python
+   from dataset_generator import DatasetGenerator, create_customer_support_agent_evaluation_dataset
+   from customer_support_agent import create_customer_support_agent
+
+   agent = create_customer_support_agent()
+   generator = DatasetGenerator(agent=agent)
+   examples = create_customer_support_agent_evaluation_dataset(generator, agent)
+   generator.save_dataset(examples, "evaluation_dataset.json")
+   ```
+
+4. Run evaluation:
+   ```python
+   from evaluator import AgentEvaluator, load_dataset
+
+   eval_dataset = load_dataset("evaluation_dataset.json")
+   evaluator = AgentEvaluator()
+   results = evaluator.run_evaluation(agent, eval_dataset)
+   ```
+
+## Features
+
+- **Realistic Data Generation**: Creates synthetic but realistic customer queries based on e-commerce data
+- **Comprehensive Evaluation**: Measures multiple aspects of agent performance
+- **Visualization**: Generates plots and tables for analysis
+- **Weave Integration**: Tracks experiments and results with Weave
+  - Logs agent trajectories and evaluation metrics
+  - Enables experiment comparison across different agent configurations
+  - Provides interactive dashboards for analyzing agent performance
+  - Supports versioning of evaluation datasets and results
+  - Facilitates collaboration through shareable experiment links
+- **Configurable Thresholds**: Adjustable quality thresholds for dataset generation
+
+## Weave Integration
+
+The framework leverages Weave for experiment tracking and visualization:
+
+1. **Experiment Tracking**: Each agent run is logged as a Weave experiment with detailed metrics
+2. **Trajectory Visualization**: Agent trajectories are visualized step-by-step for analysis
+3. **Comparative Analysis**: Compare performance across different agent configurations and models
+4. **Custom Dashboards**: Create custom dashboards to monitor specific metrics
+5. **Artifact Management**: Store and version datasets, agent configurations, and evaluation results
+
+```python
+# Enable Weave logging in agent creation
+agent = create_customer_support_agent(
+    model_id="google/gemini-1.5-pro",
+    use_weave=True,  # Enable Weave logging
+    temperature=0.2
+)
+
+# Enable Weave in evaluator
+evaluator = AgentEvaluator(
+    model_name="gemini-1.5-pro",
+    use_weave=True,  # Enable Weave logging
+    verbosity=2
+)
+```
+
+## Requirements
+
+- Python 3.8+
+- Vertex AI API access
+- [Weights & Biases account](https://wandb.ai)
+- Required Python packages (see pyproject.toml)
+
+## Contributors
+
+- [Anish Shah](https://github.com/ash0ts)

gemini/evaluation/synthetic-data-evals/clean.sh

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+#!/bin/bash
+
+# Find and clean all Python files in main folder, excluding .venv and other hidden directories
+echo "Cleaning Python files..."
+find . -name "*.py" -type f -not -path "*/\.*" -not -path "*/venv/*" -not -path "*/.venv/*" | xargs black
+find . -name "*.py" -type f -not -path "*/\.*" -not -path "*/venv/*" -not -path "*/.venv/*" | xargs isort
+
+# Find and clean all Jupyter notebook files in main folder, excluding hidden directories
+echo "Cleaning Jupyter notebook files..."
+find . -name "*.ipynb" -type f -not -path "*/\.*" -not -path "*/venv/*" -not -path "*/.venv/*" | xargs nbqa black
+find . -name "*.ipynb" -type f -not -path "*/\.*" -not -path "*/venv/*" -not -path "*/.venv/*" | xargs nbqa isort
+
+# Run nox format session for any remaining files
+echo "Running final format check..."
+nox -s format

gemini/evaluation/synthetic-data-evals/config.py

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+WEAVE_PROJECT_NAME = "agent_evaluation_workshop"