From 5021ee8575fe2fb4c8038a56aee8950367e274a6 Mon Sep 17 00:00:00 2001 From: Andrei Rusu Date: Wed, 15 Oct 2025 15:05:04 +0300 Subject: [PATCH 1/7] feat(samples): add UiPath gym sample with custom runtime --- samples/gym-sample/.env.example | 4 + samples/gym-sample/README.md | 733 ++++ samples/gym-sample/calculator.mermaid | 24 + samples/gym-sample/eval.sh | 27 + samples/gym-sample/export_evals.sh | 11 + samples/gym-sample/langgraph.json | 10 + samples/gym-sample/loan.mermaid | 24 + samples/gym-sample/pyproject.toml | 30 + samples/gym-sample/run.sh | 13 + .../src/gym_sample/calculator/agent.py | 339 ++ .../src/gym_sample/calculator/evals.py | 84 + .../src/gym_sample/calculator/test_input.json | 3 + .../gym-sample/src/gym_sample/export_evals.py | 261 ++ samples/gym-sample/src/gym_sample/graph.py | 115 + .../gym-sample/src/gym_sample/loan/agent.py | 353 ++ .../gym-sample/src/gym_sample/loan/evals.py | 78 + .../src/gym_sample/loan/test_input.json | 3 + .../gym-sample/src/gym_sample/loan/tools.py | 118 + samples/gym-sample/src/gym_sample/run.py | 177 + samples/gym-sample/src/gym_sample/tools.py | 125 + .../gym-sample/src/gym_sample/trace_utils.py | 59 + .../src/gym_sample/uipath_gym_types.py | 262 ++ samples/gym-sample/uipath.json | 71 + samples/gym-sample/uv.lock | 3621 +++++++++++++++++ 24 files changed, 6545 insertions(+) create mode 100644 samples/gym-sample/.env.example create mode 100644 samples/gym-sample/README.md create mode 100644 samples/gym-sample/calculator.mermaid create mode 100755 samples/gym-sample/eval.sh create mode 100755 samples/gym-sample/export_evals.sh create mode 100644 samples/gym-sample/langgraph.json create mode 100644 samples/gym-sample/loan.mermaid create mode 100644 samples/gym-sample/pyproject.toml create mode 100755 samples/gym-sample/run.sh create mode 100644 samples/gym-sample/src/gym_sample/calculator/agent.py create mode 100644 samples/gym-sample/src/gym_sample/calculator/evals.py create mode 100644 samples/gym-sample/src/gym_sample/calculator/test_input.json create mode 100644 samples/gym-sample/src/gym_sample/export_evals.py create mode 100644 samples/gym-sample/src/gym_sample/graph.py create mode 100644 samples/gym-sample/src/gym_sample/loan/agent.py create mode 100644 samples/gym-sample/src/gym_sample/loan/evals.py create mode 100644 samples/gym-sample/src/gym_sample/loan/test_input.json create mode 100644 samples/gym-sample/src/gym_sample/loan/tools.py create mode 100644 samples/gym-sample/src/gym_sample/run.py create mode 100644 samples/gym-sample/src/gym_sample/tools.py create mode 100644 samples/gym-sample/src/gym_sample/trace_utils.py create mode 100644 samples/gym-sample/src/gym_sample/uipath_gym_types.py create mode 100644 samples/gym-sample/uipath.json create mode 100644 samples/gym-sample/uv.lock diff --git a/samples/gym-sample/.env.example b/samples/gym-sample/.env.example new file mode 100644 index 00000000..b7737ab3 --- /dev/null +++ b/samples/gym-sample/.env.example @@ -0,0 +1,4 @@ +UIPATH_URL= +UIPATH_TENANT_ID= +UIPATH_ORGANIZATION_ID= +UIPATH_ACCESS_TOKEN= diff --git a/samples/gym-sample/README.md b/samples/gym-sample/README.md new file mode 100644 index 00000000..fd5f6f28 --- /dev/null +++ b/samples/gym-sample/README.md @@ -0,0 +1,733 @@ +# UiPath LangGraph Gym Sample + +A comprehensive sample demonstrating how to build **multiple LangGraph agents** with the UiPath platform. This project showcases proper input/output typing, dual-mode operation (CLI and batch evaluation), and integration with the UiPath evaluation system. + +## Key Features + +- **Multiple Agents**: Includes two complete agents (calculator and loan) with distinct capabilities +- **Dual-Mode Operation**: Each agent supports both CLI execution with typed inputs and batch evaluation +- **Type Safety**: Proper Pydantic models for inputs and outputs with runtime validation +- **Evaluation System**: Built-in comprehensive evaluation framework with multiple evaluator types +- **Export to UiPath Eval**: Export datapoints and evaluators to UiPath eval format via `export_evals.py` +- **Interactive Development**: Live testing with `uipath dev` +- **Tool Integration**: Mathematical tools, error handling, and structured output validation + +## Agents Overview + +This sample includes two agents demonstrating different use cases: + +### 1. Calculator Agent +Performs mathematical calculations with proper order of operations. + +**Input Schema** (`CalculatorInput`): +```json +{ + "expression": "15.0 + 7.0 * 3.0" +} +``` + +**Output Schema** (`CalculatorOutput`): +```json +{ + "answer": 36.0 +} +``` + +**Tools**: `add`, `multiply`, `end_execution`, `raise_error` + +### 2. Loan Agent +Processes loan requests from email bodies and can create Action Center tasks. + +**Input Schema** (`LoanInput`): +```json +{ + "EmailBody": "I would like to request a loan for $50,000..." +} +``` + +**Output Schema** (`LoanOutput`): +```json +{ + "ExecutionDetails": "Processed loan request...", + "ActionCenterTaskCreated": true, + "ActionCenterTaskURL": "https://..." +} +``` + +**Tools**: Custom loan processing tools, Action Center integration + +## Architecture + +Both agents follow the ReAct pattern: + +```mermaid +graph TD; + START([START]) --> prepare_input + prepare_input --> chatbot + chatbot -.-> end_execution + chatbot -.-> raise_error + chatbot -.-> tools + tools --> chatbot + end_execution --> END([END]) + raise_error --> END +``` + +**Workflow:** +1. **prepare_input**: Converts typed input into state with system/user messages +2. **chatbot**: LLM interaction with tool calling capability (using UiPath LLM Gateway) +3. **conditional_routing**: Determines next action based on LLM response +4. **tools**: Executes agent-specific tools +5. **end_execution**: Returns properly typed output matching the output schema + +## Prerequisites + +- Python 3.10+ +- UiPath account with LLM Gateway access +- `uv` for dependency management (recommended) + +## Installation + +```bash +# Install Python and create virtual environment +uv venv -p 3.11 .venv + +# Activate the virtual environment (optional if using `uv run`) +# Linux/macOS: source .venv/bin/activate +# Windows: .venv\Scripts\activate + +# Install dependencies (this properly installs gym_sample as a package) +uv sync +``` + +**Important**: After `uv sync`, the `gym_sample` package is properly installed and **no PYTHONPATH configuration is needed**. You can run Python modules directly, debug with F5 in VS Code, or use the convenience shell scripts. + +## Authentication + +Before running any agents, authenticate with UiPath: + +```bash +uv run uipath auth +``` + +This opens a browser login and writes credentials to `.env`. + +## Usage + +### 1. CLI Mode - Single Execution with `uipath run` + +Execute a single agent invocation with properly typed inputs: + +```bash +# Direct command - Run calculator agent with inline JSON +uv run uipath run calculator '{"expression": "2 + 2"}' + +# Direct command - Run loan agent +uv run uipath run loan '{"EmailBody": "I need a $50,000 loan for equipment"}' + +# Using convenience script - Reads from test_input.json file +./run.sh calculator + +# Using convenience script - With custom JSON input +./run.sh calculator '{"expression": "5 * 8"}' +./run.sh loan '{"EmailBody": "Need equipment loan"}' +``` + +**How it works:** +- The CLI validates JSON input against the agent's `input_schema` (e.g., `CalculatorInput`) +- The typed input is passed to the graph at runtime +- The `prepare_input` node converts it to state with system/human messages +- The agent executes and returns output matching the `output_schema` + +**Using `run.sh` script:** +- Without a second argument: uses `src/gym_sample/{agent}/test_input.json` +- With a second argument: passes the JSON directly to the agent + +### 2. Interactive Development Mode with `uipath dev` + +For interactive development and testing: + +```bash +uv run uipath dev +``` + +This provides: +- Live logs and traces +- Interactive chat interface +- Real-time debugging +- Agent selection prompt + +### 3. Evaluation Modes + +This sample supports **two types of evaluation**: + +#### A. Python-Based Evaluation (Custom Evaluators) + +Run comprehensive evaluations using Python-defined evaluators across all datapoints: + +```bash +# Direct command - Run all calculator datapoints +python -m gym_sample.run --agent_name calculator --verbose + +# Direct command - Run all loan datapoints +python -m gym_sample.run --agent_name loan --verbose + +# Include LLM judge evaluators (if available) +python -m gym_sample.run --agent_name calculator --include_llm_judge +``` + +**How it works:** +- For each datapoint, a separate graph is built with input **pre-bound at build time** +- Each graph is invoked with an empty dict: `graph.ainvoke({})` +- Traces are collected using OpenTelemetry +- Custom Python evaluators run against the execution traces and outputs +- Results are aggregated and displayed + +#### B. UiPath Platform Evaluation (via `uipath eval`) + +Run evaluations using the UiPath platform's evaluation system: + +```bash +# First, export Python evaluators and datapoints to UiPath eval format +./export_evals.sh + +# Or with only supported evaluators +./export_evals.sh --only-supported + +# Then run evaluation using UiPath CLI +./eval.sh calculator + +# Evaluate loan agent +./eval.sh loan + +# Or use direct command +uipath eval calculator evals/eval-sets/evaluation-set-calculator.json --output-file results/eval-results-calculator.json +``` + +**How it works:** +- Python-defined datapoints and evaluators are exported to JSON format +- The `uipath eval` CLI executes the agents with the evaluation framework +- Results are saved to `results/` directory +- Compatible with UiPath platform features and CI/CD pipelines + +## Two Modes of Operation + +This sample demonstrates a powerful pattern for supporting both CLI execution and batch evaluation: + +### Mode 1: CLI Graph Builder + +**Method**: `build_cli_graph()` + +```python +graph = StateGraph( + StateWithInput, + input_schema=CalculatorInput, + output_schema=CalculatorOutput +) +graph.add_node("prepare_input", self.prepare_input) +``` + +- Accepts input at runtime via `graph.ainvoke(CalculatorInput(...))` +- The `prepare_input` node receives typed input and converts to state +- Used by `uipath run` and `uipath dev` commands + +### Mode 2: Evaluation Graph Builder + +**Method**: `build_evaluation_graph(agent_input)` + +```python +final_agent_input = self.scenario.input_schema.model_validate(agent_input) + +graph = StateGraph( + StateBaseClass, + input_schema=CalculatorInput, + output_schema=CalculatorOutput +) + +# Lambda captures the pre-bound input +graph.add_node("prepare_input", lambda state=None: self.prepare_input(final_agent_input)) +``` + +- Input is captured in a closure at graph build time +- Invoked with empty dict: `graph.ainvoke({})` +- Used by `run.py` for batch evaluation across multiple datapoints +- Each datapoint gets its own graph instance + +**Why two separate builders?** +- CLI mode needs to accept input at `graph.ainvoke(input)` time +- Evaluation mode needs input pre-bound to avoid passing it multiple times +- Using closures instead of `partial` avoids signature conflicts with LangGraph + +## Agent Evaluation System + +### Overview + +The evaluation system captures OpenTelemetry traces during agent execution and runs multiple evaluators to assess performance. + +### Evaluator Types + +Both agents include comprehensive evaluators: + +#### ContainsEvaluator +- Checks if the output contains expected strings or values +- Returns boolean result (pass/fail) +- Used for validating specific content in responses + +#### ExactMatchEvaluator +- Compares actual output with expected output +- Uses canonical JSON comparison with number normalization +- Returns boolean score + +#### ToolCallOrderEvaluator +- Evaluates the sequence of tool calls made by the agent +- Extracts tool names from `ReadableSpan.attributes.tool_name` +- Returns numerical score (0.0 to 1.0) using Longest Common Subsequence (LCS) +- Example: For expression `15 + 7 * 3`, expects tool order `['multiply', 'add']` + +#### LLM Judge Evaluators (optional) +- Use an LLM to evaluate response quality +- Can be enabled with `--include_llm_judge` flag + +### Running Evaluations + +**Python-based evaluations (custom evaluators):** + +```bash +# Basic evaluation +python -m gym_sample.run --agent_name calculator + +# Verbose output with detailed traces +python -m gym_sample.run --agent_name calculator --verbose + +# Include LLM judge evaluators +python -m gym_sample.run --agent_name calculator --include_llm_judge + +# Evaluate loan agent +python -m gym_sample.run --agent_name loan --verbose +``` + +**UiPath platform evaluations:** + +```bash +# Export evaluators first (if not done already) +./export_evals.sh + +# Run evaluation with UiPath CLI +./eval.sh calculator +./eval.sh loan +``` + +### Example Evaluation Output + +``` +Running evaluation for agent: calculator + +Running datapoint 1/3: {'expression': '15.0 + 7.0 * 3.0'} +Agent completed. Result: {'answer': 36.0} +Collected 16 trace spans + +--- Evaluating Datapoint 1/3 --- +Input: {'expression': '15.0 + 7.0 * 3.0'} +Output: {'answer': 36.0} + + Evaluating ExactMatchEvaluator with criteria: {'answer': 36.0} + Result: score=1.0 evaluation_time=0.00004 + + Evaluating ToolCallOrderEvaluator with criteria: ['multiply', 'add'] + Result: score=1.0 evaluation_time=0.00001 +``` + +### Key Evaluation Files + +- **`run.py`** - Main evaluation script and agent execution orchestrator +- **`trace_utils.py`** - OpenTelemetry trace collection utilities +- **`calculator/evals.py`** - Calculator-specific evaluators +- **`loan/evals.py`** - Loan-specific evaluators +- **`uipath_gym_types.py`** - Core types (`Datapoint`, `AgentBaseClass`, `BasicLoop`) + +## Export to UiPath Eval Format + +The `export_evals.py` script converts Python-defined datapoints and evaluators into the JSON format used by `uipath eval`: + +### What Gets Exported + +1. **Evaluator Specs** (version 1.0) → `evals/evaluators/` + - Each evaluator is converted to a JSON file with configuration + - Includes evaluator type ID and agent-specific settings + +2. **Eval Sets** → `evals/eval-sets/` + - Each agent's datapoints exported as evaluation sets + - References the evaluator specs + - Includes inputs, expected outputs, and evaluation criteria + +### Running the Export + +```bash +# Using convenience script - Export all evaluators and datapoints +./export_evals.sh + +# Using convenience script - Export only supported evaluators +./export_evals.sh --only-supported + +# Or use direct Python command +python -m gym_sample.export_evals +python -m gym_sample.export_evals --only-supported +``` + +### Output Structure + +``` +evals/ +├── evaluators/ +│ ├── evaluator-calculator_ContainsEvaluator.json +│ ├── evaluator-calculator_ExactMatchEvaluator.json +│ ├── evaluator-calculator_ToolCallOrderEvaluator.json +│ ├── evaluator-loan_ContainsEvaluator.json +│ └── ... +└── eval-sets/ + ├── evaluation-set-calculator.json + └── evaluation-set-loan.json +``` + +### Using with `uipath eval` + +Once exported, you can use the standard UiPath eval CLI: + +```bash +# Using convenience script (recommended) +./eval.sh calculator +./eval.sh loan + +# Or use direct command with full control +uipath eval calculator evals/eval-sets/evaluation-set-calculator.json --output-file results/eval-results-calculator.json + +# The eval.sh script automatically: +# - Creates results/ directory +# - Cleans up previous state database for fresh runs +# - Saves results to results/eval-results-{agent}.json +``` + +**Benefits:** +- Define datapoints in Python with type safety +- Keep evaluators and test data in version control +- Export to standard format for CI/CD integration +- Share evaluation sets across teams +- Consistent evaluation process across local and CI/CD environments + +## How It Works + +### Input Handling Flow + +**CLI Mode:** +1. User runs: `uipath run calculator '{"expression": "2 + 2"}'` +2. CLI validates JSON against `CalculatorInput` schema +3. Passes `CalculatorInput(expression="2 + 2")` to graph at runtime +4. `prepare_input` receives the `CalculatorInput` +5. Converts to `StateBaseClass` with system/human messages +6. Rest of graph executes normally + +**Evaluation Mode:** +1. `build_evaluation_graph({"expression": "15.0 + 7.0 * 3.0"})` called +2. Input validated and captured in closure +3. Graph returned with input pre-bound +4. Later: `graph.ainvoke({})` - empty dict because input already bound +5. `prepare_input` closure executes, using captured input +6. Returns `StateBaseClass` with messages +7. Rest of graph executes normally + +### Output Schema Handling + +The `output_schema` is returned by wrapping the `end_execution` tool in a node function: + +```python +def end_execution_node(state: StateBaseClass) -> BaseModel: + """Wrapper node that calls end_execution tool and returns output_schema.""" + self.scenario.end_execution_tool.run(state) + # Return the output_schema populated from state.result + return self.scenario.output_schema.model_validate(state.result) + +graph.add_node("end_execution", end_execution_node) +``` + +**Why this approach:** +- The `end_execution` tool validates the LLM's arguments against the schema +- Stores the validated data in `state.result` +- The wrapper node converts `state.result` to the proper `output_schema` type +- LangGraph recognizes this as the final output since it matches `output_schema=` + +**Result:** When the graph completes, you get properly typed output like `{"answer": 36.0}` instead of just `{}`. + +## Adding New Agents + +To add a new agent with proper typing: + +### 1. Define Input/Output Schemas + +```python +# In src/gym_sample/myagent/agent.py +from pydantic import BaseModel, Field + +class MyAgentInput(BaseModel): + query: str = Field(description="The user's query") + max_results: int = Field(default=5, description="Maximum number of results") + +class MyAgentOutput(BaseModel): + results: List[str] = Field(description="List of results") + count: int = Field(description="Number of results returned") +``` + +### 2. Create Agent Configuration + +```python +from langchain.tools import StructuredTool +from gym_sample.uipath_gym_types import AgentBaseClass, Datapoint +from gym_sample.tools import EndExecutionTool + +def get_my_agent() -> AgentBaseClass: + """Define the agent configuration.""" + return AgentBaseClass( + system_prompt="You are a helpful research assistant...", + user_prompt="Process this query: {query}\nMax results: {max_results}", + input_schema=MyAgentInput, + output_schema=MyAgentOutput, + tools=[ + # Add your custom tools here + ], + datapoints=[ + Datapoint( + name="basic_query", + input={"query": "What is AI?", "max_results": 3}, + evaluation_criteria={ + "ExactMatchEvaluator": {"count": 3}, + "ContainsEvaluator": {"results": ["artificial", "intelligence"]} + } + ) + ] + ) +``` + +### 3. Create Entry Point + +```python +# In src/gym_sample/graph.py +@asynccontextmanager +async def my_agent() -> StateGraph: + """Pre-configured entry point for my_agent.""" + agent_scenario = get_agents()["my_agent"] + loop = BasicLoop(scenario=agent_scenario, llm=get_model(), print_trace=True) + return loop.build_cli_graph() + +# Update get_agents() to include your agent +def get_agents() -> Dict[str, AgentBaseClass]: + return { + "calculator": get_calculator_agent(), + "loan": get_loan_agent(), + "my_agent": get_my_agent(), # Add here + } +``` + +### 4. Register in langgraph.json + +```json +{ + "dependencies": ["."], + "graphs": { + "calculator": "./src/gym_sample/graph.py:calculator_agent", + "loan": "./src/gym_sample/graph.py:loan_agent", + "my_agent": "./src/gym_sample/graph.py:my_agent" + }, + "env": ".env" +} +``` + +### 5. Use Your Agent + +```bash +# CLI execution (direct) +uv run uipath run my_agent '{"query": "test", "max_results": 10}' + +# CLI execution (with convenience script) +./run.sh my_agent '{"query": "test", "max_results": 10}' + +# Python-based batch evaluation +python -m gym_sample.run --agent_name my_agent --verbose + +# Export to UiPath eval format and run platform evaluation +./export_evals.sh +./eval.sh my_agent +``` + +## Convenience Shell Scripts + +The project includes convenience scripts for common tasks: + +### `run.sh` - Execute agents via CLI + +```bash +# Run with test_input.json file +./run.sh calculator +./run.sh loan + +# Run with custom JSON input +./run.sh calculator '{"expression": "2 + 2"}' +./run.sh loan '{"EmailBody": "Need a loan"}' +``` + +**What it does:** +- Without second argument: Uses `src/gym_sample/{agent}/test_input.json` +- With second argument: Passes JSON directly to `uipath run` + +### `export_evals.sh` - Export evaluators + +```bash +# Export all evaluators +./export_evals.sh + +# Export only supported evaluators +./export_evals.sh --only-supported +``` + +**What it does:** +- Runs `python -m gym_sample.export_evals` to convert Python evaluators to JSON +- Creates files in `evals/evaluators/` and `evals/eval-sets/` + +### `eval.sh` - Run UiPath platform evaluations + +```bash +# Evaluate calculator agent +./eval.sh calculator + +# Evaluate loan agent +./eval.sh loan + +# Use custom eval set name +./eval.sh calculator custom-eval-set +``` + +**What it does:** +- Creates `results/` directory +- Cleans up previous state database (`__uipath/state.db`) +- Runs `uipath eval` with the specified agent and eval set +- Saves results to `results/eval-results-{eval-set-name}.json` + +## Common Patterns + +### Programmatic Usage + +```python +# CLI mode - pass input at runtime +async with calculator_agent() as graph: + compiled = graph.compile() + result = await compiled.ainvoke(CalculatorInput(expression="2 + 2")) + print(result) + +# Evaluation mode - input pre-bound +async with agents_with_datapoints("calculator") as graphs: + for graph, datapoint in graphs: + compiled = graph.compile() + result = await compiled.ainvoke({}) # Empty dict! + print(f"Result for {datapoint.input}: {result}") +``` + +### Creating Custom Tools + +```python +from langchain.tools import StructuredTool +from pydantic import BaseModel, Field + +class DivideInput(BaseModel): + a: float = Field(description="Dividend") + b: float = Field(description="Divisor") + +def divide(a: float, b: float) -> float: + """Divide two numbers.""" + if b == 0: + raise ValueError("Cannot divide by zero") + return a / b + +divide_tool = StructuredTool.from_function( + func=divide, + name="divide", + description="Divide two numbers", + args_schema=DivideInput +) + +# Add to agent's tools list +agent = AgentBaseClass( + # ... + tools=[divide_tool], +) +``` + +## Project Structure + +``` +gym-sample/ +├── src/ +│ └── gym_sample/ +│ ├── calculator/ +│ │ ├── agent.py # Calculator agent definition +│ │ ├── evals.py # Calculator evaluators +│ │ └── test_input.json # Sample test input +│ ├── loan/ +│ │ ├── agent.py # Loan agent definition +│ │ ├── evals.py # Loan evaluators +│ │ ├── tools.py # Loan-specific tools +│ │ └── test_input.json # Sample test input +│ ├── graph.py # Entry points and agent registry +│ ├── run.py # Python-based evaluation runner +│ ├── export_evals.py # Export to UiPath eval format +│ ├── trace_utils.py # OpenTelemetry utilities +│ ├── tools.py # Shared tools (end_execution, raise_error) +│ └── uipath_gym_types.py # Core types (AgentBaseClass, BasicLoop, Datapoint) +├── evals/ # Exported evaluation files (created by export_evals.sh) +│ ├── evaluators/ # Evaluator specs (version 1.0) +│ └── eval-sets/ # Evaluation sets +├── results/ # Evaluation results (created by eval.sh) +├── run.sh # Convenience script for uipath run +├── eval.sh # Convenience script for uipath eval +├── export_evals.sh # Convenience script for export +├── langgraph.json # LangGraph configuration +├── uipath.json # UiPath project configuration +├── pyproject.toml # Python dependencies & package config +└── README.md # This file +``` + +## Troubleshooting + +**"ModuleNotFoundError: No module named 'gym_sample'"** +→ Run `uv sync` to properly install the package. After installation, no PYTHONPATH is needed. + +**"got multiple values for argument"** +→ Using `partial` instead of closure in evaluation mode. Use a lambda closure in `build_evaluation_graph()`. + +**"Field required" in CLI mode** +→ Check that `input_schema=` is set correctly in the StateGraph constructor. + +**401 Unauthorized** +→ Run `uv run uipath auth` to authenticate with UiPath. + +**Input not being used in evaluation** +→ In evaluation mode, make sure you're calling `ainvoke({})` with an empty dict, not passing input again. + +**Tool not found** +→ Ensure the tool is added to the agent's `tools` list and properly bound with `llm.bind_tools()`. + +**Export fails with missing evaluator** +→ Check that all evaluators referenced in `datapoint.evaluation_criteria` are returned by `get_evaluators()`. + +**Evaluation criteria mismatch** +→ Ensure evaluator names in `datapoint.evaluation_criteria` match the `name` attribute of your evaluator classes. + +**Shell scripts not executable** +→ Run `chmod +x run.sh eval.sh export_evals.sh` to make them executable. + +## Additional Resources + +- [UiPath Python SDK Documentation](https://uipath.github.io/uipath-python/) +- [UiPath LangGraph SDK Documentation](https://uipath.github.io/uipath-python/langchain/quick_start/) +- [LangGraph Documentation](https://langchain-ai.github.io/langgraph/) +- [AGENTS.md](./AGENTS.md) - Comprehensive guide for UiPath workflows & agentic solutions + +## License + +See the main repository for license information. diff --git a/samples/gym-sample/calculator.mermaid b/samples/gym-sample/calculator.mermaid new file mode 100644 index 00000000..1cdddf49 --- /dev/null +++ b/samples/gym-sample/calculator.mermaid @@ -0,0 +1,24 @@ +--- +config: + flowchart: + curve: linear +--- +graph TD; + __start__([

__start__

]):::first + prepare_input(prepare_input) + chatbot(chatbot) + tools(tools) + end_execution(end_execution) + raise_error(raise_error) + __end__([

__end__

]):::last + __start__ --> prepare_input; + chatbot -.-> end_execution; + chatbot -.-> raise_error; + chatbot -.-> tools; + prepare_input --> chatbot; + tools --> chatbot; + end_execution --> __end__; + raise_error --> __end__; + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc diff --git a/samples/gym-sample/eval.sh b/samples/gym-sample/eval.sh new file mode 100755 index 00000000..85dac3e4 --- /dev/null +++ b/samples/gym-sample/eval.sh @@ -0,0 +1,27 @@ +#!/bin/bash + +# Usage: +# ./eval.sh calculator # Runs calculator agent with evaluation-set-calculator.json +# ./eval.sh loan # Runs loan agent with evaluation-set-loan.json +# ./eval.sh calculator custom-eval-set # Runs calculator agent with evaluation-set-custom-eval-set.json + +# First argument: agent name (e.g., "calculator", "loan") +AGENT_NAME=$1 + +# Second argument: eval set name (defaults to agent name if not provided) +EVAL_SET_NAME=${2:-$1} + +NUM_WORKERS=${3:-1} + +# Create results directory if it doesn't exist +mkdir -p results + +# Clean up previous state database to ensure fresh evaluation run +rm -f __uipath/state.db + +# Run UiPath evaluation +# - Uses the agent specified by $AGENT_NAME +# - Loads eval set from evals/eval-sets/evaluation-set-$EVAL_SET_NAME.json +# - Skips sending report to UiPath platform +# - Outputs results to results/eval-results-$EVAL_SET_NAME.json +uipath eval $AGENT_NAME evals/eval-sets/evaluation-set-$EVAL_SET_NAME.json --no-report --output-file results/eval-results-$EVAL_SET_NAME.json --workers $NUM_WORKERS diff --git a/samples/gym-sample/export_evals.sh b/samples/gym-sample/export_evals.sh new file mode 100755 index 00000000..cf7740c5 --- /dev/null +++ b/samples/gym-sample/export_evals.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +# Usage: +# ./export_evals.sh # Export all evaluators +# ./export_evals.sh --include_not_supported # Include not supported evaluators +# ./export_evals.sh --exclude_llm_judge # Exclude LLM judge evaluators +# ./export_evals.sh --small_set_size 10 # Export a small set of 10 datapoints + +# Export evaluators and eval sets to UiPath eval format +uv run python -m gym_sample.export_evals "$@" + diff --git a/samples/gym-sample/langgraph.json b/samples/gym-sample/langgraph.json new file mode 100644 index 00000000..1a49360c --- /dev/null +++ b/samples/gym-sample/langgraph.json @@ -0,0 +1,10 @@ +{ + "dependencies": [ + "." + ], + "graphs": { + "calculator": "./src/gym_sample/graph.py:calculator_agent", + "loan": "./src/gym_sample/graph.py:loan_agent" + }, + "env": ".env" +} diff --git a/samples/gym-sample/loan.mermaid b/samples/gym-sample/loan.mermaid new file mode 100644 index 00000000..1cdddf49 --- /dev/null +++ b/samples/gym-sample/loan.mermaid @@ -0,0 +1,24 @@ +--- +config: + flowchart: + curve: linear +--- +graph TD; + __start__([

__start__

]):::first + prepare_input(prepare_input) + chatbot(chatbot) + tools(tools) + end_execution(end_execution) + raise_error(raise_error) + __end__([

__end__

]):::last + __start__ --> prepare_input; + chatbot -.-> end_execution; + chatbot -.-> raise_error; + chatbot -.-> tools; + prepare_input --> chatbot; + tools --> chatbot; + end_execution --> __end__; + raise_error --> __end__; + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc diff --git a/samples/gym-sample/pyproject.toml b/samples/gym-sample/pyproject.toml new file mode 100644 index 00000000..64b9a13b --- /dev/null +++ b/samples/gym-sample/pyproject.toml @@ -0,0 +1,30 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "gym-sample" +version = "0.0.1" +description = "Agent gym sample" +authors = [{ name = "Andrei Rusu", email = "andrei.rusu@uipath.com" }] +dependencies = [ + "langgraph>=0.2.70", + "langchain-core>=0.3.34", + "langgraph-checkpoint-sqlite>=2.0.3", + "python-dotenv>=1.0.1", + "uipath-langchain==0.0.138", + "uipath @ git+https://github.com/UiPath/uipath-python.git@release/revamped-evals", + "pydantic>=2.10.6", + "aiohttp>=3.11.12", + "typing-extensions>=4.12.2", + "ipython>=8.32.0", + "jsonschema>=4.25.1" +] +requires-python = ">=3.10" + +[project.scripts] +gym-eval = "gym_sample.run:main" +gym-export = "gym_sample.export_evals:main" + +[tool.setuptools.packages.find] +where = ["src"] diff --git a/samples/gym-sample/run.sh b/samples/gym-sample/run.sh new file mode 100755 index 00000000..20f4e287 --- /dev/null +++ b/samples/gym-sample/run.sh @@ -0,0 +1,13 @@ +#!/bin/bash + +# Usage: +# ./run.sh calculator # Uses test_input.json +# ./run.sh calculator '{"expression": "2 + 2"}' # Uses direct JSON input + +if [ -z "$2" ]; then + # No second argument - use test_input.json file + uipath run $1 -f src/gym_sample/$1/test_input.json +else + # Second argument provided - use it directly + uipath run $1 "$2" +fi diff --git a/samples/gym-sample/src/gym_sample/calculator/agent.py b/samples/gym-sample/src/gym_sample/calculator/agent.py new file mode 100644 index 00000000..08a12021 --- /dev/null +++ b/samples/gym-sample/src/gym_sample/calculator/agent.py @@ -0,0 +1,339 @@ +from typing import List +from langchain.tools import BaseTool, StructuredTool +from pydantic import BaseModel +from ..uipath_gym_types import AgentBaseClass, Datapoint +from ..tools import EscalationTool + + +class CalculatorInput(BaseModel): + expression: str + + +class CalculatorOutput(BaseModel): + answer: float + + +def get_datapoints() -> List[Datapoint]: + """Get datapoints.""" + return [ + Datapoint( + name="TestSimpleAddition", + input={"expression": "how much is 2 + 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 7.0}}, + "ContainsEvaluator": {"search_text": "7"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 7.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 0)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "add", "args": {"a": 2, "b": 5}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 7}}, + }, + ), + Datapoint( + name="TestSimpleMultiplication", + input={"expression": "how much is 2 * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 10.0}}, + "ContainsEvaluator": {"search_text": "10"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 10.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 0), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "multiply", "args": {"a": 2, "b": 5}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 10}}, + }, + ), + Datapoint( + name="TestSimpleSubtraction", + input={"expression": "how much is 5 - 2"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 3.0}}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 3.0}}, + "ContainsEvaluator": {"search_text": "3"}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 0)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "add", "args": {"a": 5, "b": -2}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 3}}, + }, + ), + Datapoint( + name="TestCompoundOperation1", + input={"expression": "how much is 2 + 2 * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 12.0}}, + "ContainsEvaluator": {"search_text": "12"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 12.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 2, "b": 5}}, + {"name": "add", "args": {"a": 2, "b": 10}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 12}}, + }, + ), + Datapoint( + name="TestCompoundOperation2", + input={"expression": "how much is 3 * 5 + 2 * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 3, "b": 5}}, + {"name": "multiply", "args": {"a": 2, "b": 5}}, + {"name": "add", "args": {"a": 15, "b": 10}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + }, + ), + Datapoint( + name="TestCompoundOperation3", + input={"expression": "how much is 3 * (5 + 5) * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 150.0}}, + "ContainsEvaluator": {"search_text": "150"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 150.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 5, "b": 5}}, + {"name": "multiply", "args": {"a": 3, "b": 10}}, + {"name": "multiply", "args": {"a": 30, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 150}}, + }, + ), + Datapoint( + name="TestCompoundOperation4", + input={"expression": "how much is 2 + 3 * 4"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 14.0}}, + "ContainsEvaluator": {"search_text": "14"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 14.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 3, "b": 4}}, + {"name": "add", "args": {"a": 2, "b": 12}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 14}}, + }, + ), + Datapoint( + name="TestCompoundOperation5", + input={"expression": "how much is (2 + 3) * (4 + 1)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 2, "b": 3}}, + {"name": "add", "args": {"a": 4, "b": 1}}, + {"name": "multiply", "args": {"a": 5, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + }, + ), + Datapoint( + name="TestCompoundOperation6", + input={"expression": "how much is 10 * 0.5 + 3"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 8.0}}, + "ContainsEvaluator": {"search_text": "8"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 8.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 10, "b": 0.5}}, + {"name": "add", "args": {"a": 5, "b": 3}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 8}}, + }, + ), + Datapoint( + name="TestCompoundOperation7", + input={"expression": "how much is 5 * 5 + (-10)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 15.0}}, + "ContainsEvaluator": {"search_text": "15"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 15.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 5, "b": 5}}, + {"name": "add", "args": {"a": 25, "b": -10}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 15}}, + }, + ), + Datapoint( + name="TestCompoundOperation8", + input={"expression": "how much is (8 + (-3)) * (2 + 2)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 20.0}}, + "ContainsEvaluator": {"search_text": "20"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 20.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 8, "b": -3}}, + {"name": "add", "args": {"a": 2, "b": 2}}, + {"name": "multiply", "args": {"a": 5, "b": 4}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 20}}, + }, + ), + Datapoint( + name="TestNestedParentheses", + input={"expression": "how much is (2 * (3 + 4)) * (1 + 1)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 28.0}}, + "ContainsEvaluator": {"search_text": "28"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 28.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 3, "b": 4}}, + {"name": "multiply", "args": {"a": 2, "b": 7}}, + {"name": "add", "args": {"a": 1, "b": 1}}, + {"name": "multiply", "args": {"a": 14, "b": 2}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 28}}, + }, + ), + Datapoint( + name="TestMultipleNegatives", + input={"expression": "how much is (-2) * (-3) + (-5)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 1.0}}, + "ContainsEvaluator": {"search_text": "1"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 1.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": -2, "b": -3}}, + {"name": "add", "args": {"a": 6, "b": -5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 1}}, + }, + ), + Datapoint( + name="TestDecimalParentheses", + input={"expression": "how much is (0.5 + 1.5) * 2.5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 5.0}}, + "ContainsEvaluator": {"search_text": "5"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 5.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 0.5, "b": 1.5}}, + {"name": "multiply", "args": {"a": 2, "b": 2.5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 5}}, + }, + ), + Datapoint( + name="TestMissingNumberSimple", + input={"expression": "how much is x + 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 22.0}}, + "ContainsEvaluator": {"search_text": "22"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 22.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 17, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 22}}, + }, + ), + Datapoint( + name="TestMissingNumberMedium", + input={"expression": "how much is x + y * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 102.0}}, + "ContainsEvaluator": {"search_text": "102"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 102.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 17, "b": 5}}, + {"name": "add", "args": {"a": 17, "b": 85}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 102}}, + }, + ), + Datapoint( + name="TestMissingNumberComplex", + input={"expression": "how much is x + y * z + 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 311.0}}, + "ContainsEvaluator": {"search_text": "311"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 311.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "multiply": ("=", 1), "add": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "multiply", "add", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 17, "b": 17}}, + {"name": "add", "args": {"a": 17, "b": 289}}, + {"name": "add", "args": {"a": 306, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 311}}, + }, + ), + ] + + +def get_tools() -> List[BaseTool]: + return [ + StructuredTool.from_function( + func=lambda a, b: a + b, + name="add", + description="Add two numbers together", + args_schema={ + "type": "object", + "properties": { + "a": {"type": "number", "description": "First number"}, + "b": {"type": "number", "description": "Second number"} + }, + "required": ["a", "b"] + }, + ), + StructuredTool.from_function( + func=lambda a, b: a * b, + name="multiply", + description="Multiply two numbers together", + args_schema={ + "type": "object", + "properties": { + "a": {"type": "number", "description": "First number"}, + "b": {"type": "number", "description": "Second number"} + }, + "required": ["a", "b"] + }, + ), + EscalationTool( + name="escalation", + description="Escalate the issue to a human agent if you are missing a number. If you are missing more numbers, call this for each missing number.", + assign_to="random@uipath.com", + return_message="The missing number is 17. Continue with the evaluation.", + ), + ] + + + +def get_calculator_agent() -> AgentBaseClass: + return ( + AgentBaseClass( + system_prompt="You are a calculator agent. You are given a mathematical expression and you need to evaluate it. If any number is missing, use the escalation tool to get the value for it.", + user_prompt="Calculate the result of: {expression}.", + input_schema=CalculatorInput, + output_schema=CalculatorOutput, + tools=get_tools(), + datapoints=get_datapoints(), + ) +) diff --git a/samples/gym-sample/src/gym_sample/calculator/evals.py b/samples/gym-sample/src/gym_sample/calculator/evals.py new file mode 100644 index 00000000..20f9dd7d --- /dev/null +++ b/samples/gym-sample/src/gym_sample/calculator/evals.py @@ -0,0 +1,84 @@ +from typing import List +from uipath.eval.evaluators import LegacyBaseEvaluator +from uipath.eval.coded_evaluators import ( + BaseEvaluator, + ExactMatchEvaluator, + ContainsEvaluator, + JsonSimilarityEvaluator, + ToolCallOrderEvaluator, + ToolCallCountEvaluator, + ToolCallArgsEvaluator, + ToolCallOutputEvaluator, + LLMJudgeOutputEvaluator, + LLMJudgeStrictJSONSimilarityOutputEvaluator, + LLMJudgeTrajectoryEvaluator, + LLMJudgeSimulationTrajectoryEvaluator, +) + +def get_evaluators(include_llm_judge: bool = False) -> List[LegacyBaseEvaluator | BaseEvaluator]: + """Create evaluators for calculator agent. + """ + evaluators: List[LegacyBaseEvaluator | BaseEvaluator] = [ + ExactMatchEvaluator.model_validate({"id": "ExactMatchEvaluator", "config": {"negated": False}}), + ContainsEvaluator.model_validate({"id": "ContainsEvaluator", "config": {"negated": False, "target_output_key": "answer"}}), + JsonSimilarityEvaluator.model_validate({"id": "JsonSimilarityEvaluator", "config": {}}), + ToolCallOrderEvaluator.model_validate({ + "id": "ToolCallOrderEvaluator", + "config": { + "strict": False, + }, + }), + ToolCallCountEvaluator.model_validate({ + "id": "ToolCallCountEvaluator", + "config": { + "strict": False, + }, + }), + ToolCallArgsEvaluator.model_validate({ + "id": "ToolCallArgsEvaluator", + "config": { + "strict": False, + "subset": False, + }, + }), + ToolCallOutputEvaluator.model_validate({ + "id": "ToolCallOutputEvaluator", + "config": { + "strict": False, + }, + }), + ] + + if include_llm_judge: + evaluators.extend([ + LLMJudgeOutputEvaluator.model_validate({ + "id": "LLMJudgeOutputEvaluator", + "config": { + "model": "gpt-4o-2024-11-20", + "temperature": 0.0, + }, + }), + LLMJudgeStrictJSONSimilarityOutputEvaluator.model_validate({ + "id": "LLMJudgeStrictJSONSimilarityOutputEvaluator", + "config": { + "model": "gpt-4o-2024-11-20", + "temperature": 0.0, + }, + }), + LLMJudgeTrajectoryEvaluator.model_validate({ + "id": "LLMJudgeTrajectoryEvaluator", + "config": { + "model": "gpt-4o-2024-11-20", + "temperature": 0.0, + }, + }), + LLMJudgeSimulationTrajectoryEvaluator.model_validate({ + "id": "LLMJudgeSimulationTrajectoryEvaluator", + "config": { + "model": "gpt-4o-2024-11-20", + "temperature": 0.0, + }, + }), + ]) + + return evaluators diff --git a/samples/gym-sample/src/gym_sample/calculator/test_input.json b/samples/gym-sample/src/gym_sample/calculator/test_input.json new file mode 100644 index 00000000..eb581f60 --- /dev/null +++ b/samples/gym-sample/src/gym_sample/calculator/test_input.json @@ -0,0 +1,3 @@ +{ + "expression": "15 + 7 * 3" +} diff --git a/samples/gym-sample/src/gym_sample/export_evals.py b/samples/gym-sample/src/gym_sample/export_evals.py new file mode 100644 index 00000000..859a351f --- /dev/null +++ b/samples/gym-sample/src/gym_sample/export_evals.py @@ -0,0 +1,261 @@ +"""Export AgentBaseClass evaluators and datapoints to UiPath eval format. + +This script exports: +1. Evaluator specs in the new schema format (version 1.0) to evals/evaluators/ +2. Eval sets that reference those evaluators to evals/eval-sets/ +""" + +import argparse +import json +import uuid +from pathlib import Path +from typing import Any, Dict, List +from dotenv import load_dotenv, find_dotenv + +from gym_sample.graph import get_agents, get_all_evaluators + + +def evaluator_to_spec(evaluator: Any, agent_name: str) -> Dict[str, Any]: + """Convert an evaluator instance to UiPath evaluator spec format. + + Args: + evaluator: The evaluator instance + agent_name: Name of the agent this evaluator belongs to + + Returns: + Dict containing the evaluator spec in UiPath format (version 1.0) + """ + # Get the evaluator ID from the get_evaluator_id class method + evaluator_type_id = evaluator.get_evaluator_id() + + # Use agent-specific evaluator ID to avoid conflicts between agents + # Each agent may need different config (e.g., targetOutputKey) + evaluator_id = f"{agent_name}_{evaluator.name}" + + # Use the evaluator_config (Pydantic model) and dump with aliases (camelCase) + evaluator_config = evaluator.evaluator_config.model_dump( + by_alias=True, + exclude_none=True, + exclude_unset=False # Include defaults like targetOutputKey + ) + + return { + "version": "1.0", + "id": evaluator_id, + "description": f"{evaluator.name} for {agent_name} agent", + "evaluatorTypeId": evaluator_type_id, + "evaluatorConfig": evaluator_config, + } + + +def datapoint_to_evaluation( + datapoint: Any, + eval_set_id: str, + evaluator_refs: List[str], + agent_name: str +) -> Dict[str, Any]: + """Convert a Datapoint to an evaluation item in eval_set format (version 1.0). + + Args: + datapoint: The Datapoint object to convert + eval_set_id: The ID of the eval set this belongs to + evaluator_refs: List of evaluator IDs referenced by this eval set + agent_name: Name of the agent + + Returns: + Dict containing the evaluation in UiPath format + """ + # Map evaluation criteria to evaluator IDs + # The criteria keys from datapoint are just evaluator names (e.g., "ContainsEvaluator") + # But evaluator_refs now have agent prefix (e.g., "calculator_ContainsEvaluator") + evaluation_criterias = {} + for evaluator_name, criteria in datapoint.evaluation_criteria.items(): + # Add agent prefix to match the evaluator ID format + agent_prefixed_id = f"{agent_name}_{evaluator_name}" + if agent_prefixed_id in evaluator_refs: + evaluation_criterias[agent_prefixed_id] = criteria + + return { + "id": str(uuid.uuid4()), + "name": datapoint.name, + "inputs": datapoint.input, + "expectedOutput": {}, # Can be populated if needed + "simulationInstructions": datapoint.simulation_instructions, + "expectedAgentBehavior": "", + "simulateInput": False, + "inputGenerationInstructions": "", + "simulateTools": False, + "toolsToSimulate": [], + "evalSetId": eval_set_id, + "createdAt": "2025-01-01T00:00:00.000Z", + "updatedAt": "2025-01-01T00:00:00.000Z", + "source": "manual", + # New schema uses evaluationCriterias (plural) with evaluator IDs as keys + "evaluationCriterias": evaluation_criterias, + } + + +def export_evaluators(agent_name: str, output_dir: Path, only_supported: bool = False, include_llm_judge: bool = False) -> List[str]: + """Export evaluator specs for an agent. + + Args: + agent_name: Name of the agent (e.g., "calculator", "loan") + output_dir: Directory to write evaluator specs to + only_supported: If True, only export evaluators supported by the current PR + + Returns: + List of evaluator IDs that were exported + """ + # Currently supported evaluators in PR #685 + SUPPORTED_EVALUATORS = { + "ContainsEvaluator", + "ExactMatchEvaluator", + "JsonSimilarityEvaluator", + "LLMJudgeOutputEvaluator", + "LLMJudgeStrictJSONSimilarityOutputEvaluator", + } + + evaluators_getter = get_all_evaluators()[agent_name] + evaluators = evaluators_getter(include_llm_judge) + + output_dir.mkdir(parents=True, exist_ok=True) + evaluator_ids = [] + + for evaluator in evaluators: + # Skip unsupported evaluators if only_supported flag is set + if only_supported and evaluator.name not in SUPPORTED_EVALUATORS: + print(f" ⊘ Skipped {evaluator.name} (not yet supported in PR)") + continue + spec = evaluator_to_spec(evaluator, agent_name) + evaluator_id = spec["id"] + evaluator_ids.append(evaluator_id) + + output_path = output_dir / f"evaluator-{evaluator_id}.json" + + with open(output_path, 'w') as f: + json.dump(spec, f) + + print(f" ✅ Exported evaluator: {evaluator_id}") + + return evaluator_ids + + +def export_eval_set( + agent_name: str, + evaluator_refs: List[str], + output_dir: Path, + small_set_size: int = 0 +) -> None: + """Export an agent's datapoints as a UiPath eval_set JSON file (version 1.0). + + Args: + agent_name: Name of the agent (e.g., "calculator", "loan") + evaluator_refs: List of evaluator IDs to reference + output_dir: Directory to write the eval_set file to + """ + agents = get_agents() + agent = agents[agent_name] + + eval_set_id = str(uuid.uuid4()) + eval_set = { + "version": "1.0", + "id": eval_set_id, + "name": f"{agent_name.title()} Eval Set", + "evaluatorRefs": evaluator_refs, + "evaluations": [ + datapoint_to_evaluation(dp, eval_set_id, evaluator_refs, agent_name) + for dp in agent.datapoints + ], + "modelSettings": [], + "createdAt": "2025-01-01T00:00:00.000Z", + "updatedAt": "2025-01-01T00:00:00.000Z", + } + + output_dir.mkdir(parents=True, exist_ok=True) + output_path = output_dir / f"evaluation-set-{agent_name}.json" + + with open(output_path, 'w') as f: + json.dump(eval_set, f) + + print(small_set_size) + if small_set_size > 0: + eval_set["evaluations"] = [ + datapoint_to_evaluation(dp, eval_set_id, evaluator_refs, agent_name) + for dp in agent.datapoints[:small_set_size] + ] + output_path = output_dir / f"evaluation-set-{agent_name}-small.json" + with open(output_path, 'w') as f: + json.dump(eval_set, f) + + print(f" ✅ Exported eval set with {len(agent.datapoints)} evaluations") + + +def export_agent(agent_name: str, base_dir: Path, only_supported: bool = False, include_llm_judge: bool = False, small_set_size: int = 0) -> None: + """Export all evaluators and eval sets for a single agent. + + Args: + agent_name: Name of the agent (e.g., "calculator", "loan") + base_dir: Base evals directory + only_supported: If True, only export evaluators supported by the current PR + """ + print(f"\n📦 Exporting {agent_name} agent:") + + # Export evaluators + evaluators_dir = base_dir / "evaluators" + evaluator_ids = export_evaluators(agent_name, evaluators_dir, only_supported, include_llm_judge) + + # Export eval set + eval_sets_dir = base_dir / "eval-sets" + export_eval_set(agent_name, evaluator_ids, eval_sets_dir, small_set_size) + + print(f"✨ Completed {agent_name} agent export\n") + + +def main() -> None: + """Export all agent evaluators and eval sets.""" + parser = argparse.ArgumentParser(description="Export evaluators and eval sets for agents") + parser.add_argument( + "--include_not_supported", + action="store_true", + help="Include evaluators not supported by the current PR (currently: ContainsEvaluator)" + ) + parser.add_argument( + "--exclude_llm_judge", + action="store_true", + help="Include LLM judge evaluators" + ) + parser.add_argument( + "--small_set_size", + type=int, + default=0, + help="Size of the small eval set to export" + ) + args, _ = parser.parse_known_args() + + load_dotenv(find_dotenv()) + + # Export to the standard location that uipath eval discovers + base_dir = Path(__file__).parent.parent.parent / "evals" + + print("🚀 Starting export of evaluators and eval sets...") + if args.include_not_supported: + print(" (Including not supported evaluators)") + + for agent_name in ["calculator", "loan"]: + export_agent(agent_name, base_dir, only_supported=not args.include_not_supported, include_llm_judge=not args.exclude_llm_judge, small_set_size=args.small_set_size) + + print("✅ All exports completed!") + print(f"\n📁 Files exported to: {base_dir.absolute()}") + print(" ├── evaluators/") + print(" │ ├── evaluator-calculator_*.json") + print(" │ └── evaluator-loan_*.json") + print(" └── eval-sets/") + print(" ├── evaluation-set-calculator.json") + print(" └── evaluation-set-loan.json") + if args.small_set_size > 0: + print(f" └── evaluation-set-calculator-small.json") + print(f" └── evaluation-set-loan-small.json") + + +if __name__ == "__main__": + main() diff --git a/samples/gym-sample/src/gym_sample/graph.py b/samples/gym-sample/src/gym_sample/graph.py new file mode 100644 index 00000000..f87159d3 --- /dev/null +++ b/samples/gym-sample/src/gym_sample/graph.py @@ -0,0 +1,115 @@ +from collections.abc import Callable +import os +from typing import AsyncGenerator, Dict, List +from contextlib import asynccontextmanager + +from langchain_core.language_models.chat_models import BaseChatModel +from langgraph.graph import StateGraph +from uipath.eval.evaluators import LegacyBaseEvaluator +from uipath.eval.coded_evaluators import BaseEvaluator +from gym_sample.uipath_gym_types import ( + AgentBaseClass, + BasicLoop, + Datapoint, +) +from uipath_langchain.chat import UiPathAzureChatOpenAI +from gym_sample.calculator.agent import get_calculator_agent +from gym_sample.loan.agent import get_loan_agent +from gym_sample.calculator.evals import get_evaluators as get_calculator_evaluators +from gym_sample.loan.evals import get_evaluators as get_loan_evaluators + + +def get_model() -> BaseChatModel: + """Get the LLM (created lazily to allow environment loading).""" + return UiPathAzureChatOpenAI(model="gpt-4o-2024-11-20") + + +def get_agents() -> Dict[str, AgentBaseClass]: + """Get the agents (created lazily to allow environment loading).""" + return { + "calculator": get_calculator_agent(), + "loan": get_loan_agent(), + } + + +def get_all_evaluators() -> Dict[str, Callable[[bool], List[LegacyBaseEvaluator | BaseEvaluator]]]: + """Get the evaluators (created lazily to allow environment loading).""" + return { + "calculator": lambda include_llm_judge: get_calculator_evaluators(include_llm_judge), + "loan": lambda include_llm_judge: get_loan_evaluators(include_llm_judge), + } + + +@asynccontextmanager +async def agents_with_datapoints(agent_name: str = "calculator") -> AsyncGenerator[List[tuple[StateGraph, Datapoint]], None]: + """Create and return all LangGraph agents for evaluation mode. + + Each graph pre-binds its datapoint input at build time. + Each graph gets a fresh BasicLoop instance to avoid LLM state accumulation. + + Returns: + A list of (StateGraph, Datapoint) tuples that can be executed. + """ + agent_scenario = get_agents()[agent_name] + + graphs = [] + for datapoint in agent_scenario.datapoints: + # Create a fresh BasicLoop for each datapoint to avoid LLM state accumulation + loop = BasicLoop( + scenario=agent_scenario, + llm=get_model(), + print_trace=True, + parallel_tool_calls=False, + ) + # Build evaluation graph with pre-bound input + graph = loop.build_evaluation_graph(datapoint.input) + graphs.append((graph, datapoint)) + + yield graphs + + +async def agent(agent_name: str = "calculator") -> StateGraph: + """Create and return a LangGraph agent for CLI mode. + + Args: + agent_name: The name of the agent to create. + + Returns: + A StateGraph that can be executed. + """ + agent_scenario = get_agents()[agent_name] + loop = BasicLoop( + scenario=agent_scenario, + llm=get_model(), + print_trace=True, + parallel_tool_calls=False, + debug=False, + ) + + # Build CLI graph that accepts input at runtime + return loop.build_cli_graph() + + +async def calculator_agent() -> StateGraph: + """Pre-configured calculator agent entry point for CLI usage. + + Following the ticket-classification pattern: + - Graph uses input=CalculatorInput, output=CalculatorOutput + - Accepts typed input at runtime via graph.ainvoke(CalculatorInput(...)) + - CLI calls with agent_input=None and passes input at graph invocation + + Example: uipath run calculator '{"expression": "2 + 2"}' + + Returns: + StateGraph configured for CLI mode (accepts runtime input). + """ + return await agent("calculator") + + +async def loan_agent() -> StateGraph: + """Pre-configured loan agent entry point for CLI usage. + + Returns: + StateGraph configured for CLI mode (accepts runtime input). + """ + return await agent("loan") diff --git a/samples/gym-sample/src/gym_sample/loan/agent.py b/samples/gym-sample/src/gym_sample/loan/agent.py new file mode 100644 index 00000000..4be34709 --- /dev/null +++ b/samples/gym-sample/src/gym_sample/loan/agent.py @@ -0,0 +1,353 @@ +from typing import List + +from langchain.tools import BaseTool, StructuredTool +from pydantic import BaseModel, Field + +from ..tools import EscalationTool +from ..uipath_gym_types import AgentBaseClass, Datapoint +from .tools import LoanAgent_ApplyUiBankLoanInput, LoanAgent_ModifyLoanContractInput, LoanAgent_SendSharepointDocumentViaDocuSignInput, GoogleInput +from .tools import LoanAgent_ApplyUiBankLoan, LoanAgent_ModifyLoanContract, LoanAgent_SendSharepointDocumentViaDocuSign, Google + + +SYSTEM_PROMPT = """ +You are **Loan Agent**, an AI assistant tasked with automating loan creation in UiBank, modifying the loan contract and sending it to be signed via DocuSign. Your responsibilities include managing requests arrived via email. Follow these guidelines: + +1. **Analyze the Email Body**: + - Carefully read and interpret the email body to gather key information for the loan creation process. + +2. **Validate Parameters Before Execution**: + - For each function, ensure you have all the necessary parameters as per the payloadSchema. + - If missing values are detected, initiate a task in the Action Center by calling **EscalationTool**. + +3. **No Data Generation**: + - Do **not** generate or assume missing data. All information must come from the **EmailBody** input. + +4. **Dynamic Tool Selection Based on Email Input**: + - Select the appropriate tool combination for each scenario based on the content of **EmailBody**. + 1. Apply to loan using the **LoanAgent_ApplyUiBankLoan** tool. + 2. Check the loan rate on Google using the **Google** tool. + 3. If the rate is less than the one on Google, modify the loan contract using the **LoanAgent_ModifyLoanContract** tool + send it to be signed via DocuSign**. The location where to save the modified contract is this one: https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e. + 4. Send the loan contract to be signed via DocuSign using the **LoanAgent_SendSharepointDocumentViaDocuSign** tool. + +5. Always escalate to a human, using EscalationTool, if: + - There is any missing information from the request (e.g name, email address, address, loan amount, etc.) + - There are multiple requests in the same email + - The request is not clear + - Input `reason` as a detailed list of missing parameters. + - Input `assignee` using the email of the end-user. + - The email address contains too many special characters before @ or unallowed special characters after @ for it to be considered a genuine address + +6. **Execution Outputs**: + - **ActionCenterTaskCreated**: Set to **True** if an Action Center task is created, otherwise set it to **False**. + - **ActionCenterTaskURL**: Populate with the task URL if a task is created, otherwise leave blank. + - **ExecutionSummary**: Provide a numbered step-by-step summary detailing all actions taken, including input/output values. + +**Goal**: + - Optimize and streamline loan creation operations by efficiently automating client registration with minimal user involvement while maintaining accuracy. Ensure that any escalations and additional information requirements are handled smoothly by creating Action Center tasks and resuming the workflow after tasks are completed. +""" + + +USER_PROMPT = """ +The user received the following email from an loan requester: + {EmailBody} +""" + + +class AgentInputSchema(BaseModel): + EmailBody: str = Field(description="The body of the request email") + + +class AgentOutputSchema(BaseModel): + ExecutionDetails: str = Field(description="The execution details") + ActionCenterTaskCreated: bool | None = Field( + description="True if an Action Center task is created, otherwise set it to None" + ) + ActionCenterTaskURL: str | None = Field( + description="The URL of the Action Center task if it is created, otherwise set it to None" + ) + + +def get_tools() -> List[BaseTool]: + return [ + StructuredTool.from_function( + name="LoanAgent_ApplyUiBankLoan", + description="Apply for a loan at UI Bank", + func=LoanAgent_ApplyUiBankLoan, + args_schema=LoanAgent_ApplyUiBankLoanInput, + ), + StructuredTool.from_function( + name="LoanAgent_ModifyLoanContract", + description="Modify a loan contract", + func=LoanAgent_ModifyLoanContract, + args_schema=LoanAgent_ModifyLoanContractInput, + ), + StructuredTool.from_function( + name="LoanAgent_SendSharepointDocumentViaDocuSign", + description="Send a sharepoint document via DocuSign", + func=LoanAgent_SendSharepointDocumentViaDocuSign, + args_schema=LoanAgent_SendSharepointDocumentViaDocuSignInput, + ), + StructuredTool.from_function( + name="Google", + description="Search the web", + func=Google, + args_schema=GoogleInput, + ), + EscalationTool( + name="EscalationTool", + description="Create an Action Center task", + assign_to="random@uipath.com", + ), + ] + + +def get_datapoints() -> List[Datapoint]: + return [ + Datapoint( + name="CompleteJohnLoanApplicationScenario", + input={ + "EmailBody": "I would like to apply for a loan of $50,000 for 5 years. My email is john.doe@example.com, my name is John Doe, my address is 123 Main St, Anytown, USA, my annual income is $75,000, and I am 35 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "john.doe@example.com", "LoanAmount": 50000, "LoanTerm": 5, "Income": 75000, "Age": 35}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 50000, "BorrowerName": "John Doe", "BorrowerAddress": "123 Main St, Anytown, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "John Doe", "RecipientEmail": "john.doe@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Loan application process completed"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should return a loan application process completed", + ), + Datapoint( + name="CompleteJoeLoanApplicationScenario", + input={ + "EmailBody": "I would like to apply for a loan of $500,000 for 5 years. My email is joe.doe@example.com, my name is Joe Doe, my address is 123 Main St, Anytown, USA, my annual income is $735,000, and I am 55 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "joe.doe@example.com", "LoanAmount": 500000, "LoanTerm": 5, "Income": 735000, "Age": 55}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 500000, "BorrowerName": "Joe Doe", "BorrowerAddress": "123 Main St, Anytown, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Joe Doe", "RecipientEmail": "joe.doe@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Loan application process completed"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should return a loan application process completed", + ), + Datapoint( + name="CompleteBobLoanApplicationScenario", + input={ + "EmailBody": "I would like to apply for a loan of $100,000 for 5 years. My email is bob.smith@example.com, my name is Bob Smith, my annual income is $150,000, and I am 45 years old. My address is 123 Main St, Anytown, USA." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "bob.smith@example.com", "LoanAmount": 100000, "LoanTerm": 5, "Income": 150000, "Age": 45}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 100000, "BorrowerName": "Bob Smith", "BorrowerAddress": "123 Main St, Anytown, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Bob Smith", "RecipientEmail": "bob.smith@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Complete workflow executed successfully"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should return complete workflow executed successfully", + ), + Datapoint( + name="TestMissingInformationScenario", + input={ + "EmailBody": "I would like to apply for a loan, my email is jane.doe@example.com" + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, + "ContainsEvaluator": {"search_text": "escalat"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"EscalationTool": ("=", 1)}}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Missing information escalated"}}, + }, + simulation_instructions="Agent should escalate due to missing information", + ), + Datapoint( + name="TestInvalidAgeScenario", + input={ + "EmailBody": "I would like to apply for a loan of $5000 for 3 years. My email is anna@email.com, my name is Anna, my address is 123 Main St, Anytown, USA, my income is 500$, and I am 17 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, + "ContainsEvaluator": {"search_text": "age"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "EscalationTool": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "anna@email.com", "LoanAmount": 5000, "LoanTerm": 3, "Income": 500, "Age": 17}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Invalid data detected: Age is below the minimum age for a loan."}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should reject due to invalid age", + ), + Datapoint( + name="TestInvalidTermScenario", + input={ + "EmailBody": "I would like to apply for a loan of $5000 for 7 years. My email is anna@email.com, my name is Anna, my address is 123 Main St, Anytown, USA, my income is 500$, and I am 19 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, + "ContainsEvaluator": {"search_text": "term"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "EscalationTool": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "anna@email.com", "LoanAmount": 5000, "LoanTerm": 7, "Income": 500, "Age": 19}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Invalid data detected: Term is above the maximum term for a loan."}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should reject due to invalid term", + ), + Datapoint( + name="OneYearLoanScenario", + input={ + "EmailBody": "I would like to apply for a loan of $20,000 for 1 year. My email is sarah.quick@example.com, my name is Sarah Quick, my address is 567 Short St, Boston, USA, my annual income is $80,000, and I am 32 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "sarah.quick@example.com", "LoanAmount": 20000, "LoanTerm": 1, "Income": 80000, "Age": 32}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 20000, "BorrowerName": "Sarah Quick", "BorrowerAddress": "567 Short St, Boston, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Sarah Quick", "RecipientEmail": "sarah.quick@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Short-term loan application processed successfully"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should return short-term loan processed", + ), + Datapoint( + name="MissingNameScenario", + input={ + "EmailBody": "I would like to apply for a loan of $30,000 for 3 years. My email is missing.name@example.com, my address is 789 Nameless Ave, Chicago, USA, my annual income is $70,000, and I am 40 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, + "ContainsEvaluator": {"search_text": "name"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"EscalationTool": ("=", 1)}}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Missing name information escalated"}}, + }, + simulation_instructions="Agent should escalate due to missing name", + ), + Datapoint( + name="MissingEmailScenario", + input={ + "EmailBody": "I would like to apply for a loan of $45,000 for 5 years. My name is Emily NoEmail, my address is 123 Email-less St, Denver, USA, my annual income is $65,000, and I am 29 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, + "ContainsEvaluator": {"search_text": "email"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"EscalationTool": ("=", 1)}}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Missing email information escalated"}}, + }, + simulation_instructions="Agent should escalate due to missing email", + ), + Datapoint( + name="HighIncomeLowLoanScenario", + input={ + "EmailBody": "I would like to apply for a loan of $10,000 for 3 years. My email is william.wealthy@example.com, my name is William Wealthy, my address is 888 Rich Ave, Manhattan, USA, my annual income is $500,000, and I am 48 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "william.wealthy@example.com", "LoanAmount": 10000, "LoanTerm": 3, "Income": 500000, "Age": 48}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 10000, "BorrowerName": "William Wealthy", "BorrowerAddress": "888 Rich Ave, Manhattan, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "William Wealthy", "RecipientEmail": "william.wealthy@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "High-income small loan application processed"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should process high-income small loan", + ), + Datapoint( + name="LowIncomeLargeRatioLoanScenario", + input={ + "EmailBody": "I would like to apply for a loan of $40,000 for 5 years. My email is peter.stretch@example.com, my name is Peter Stretch, my address is 456 Tight Blvd, Phoenix, USA, my annual income is $45,000, and I am 36 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "peter.stretch@example.com", "LoanAmount": 40000, "LoanTerm": 5, "Income": 45000, "Age": 36}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 40000, "BorrowerName": "Peter Stretch", "BorrowerAddress": "456 Tight Blvd, Phoenix, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Peter Stretch", "RecipientEmail": "peter.stretch@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Low-income high-ratio loan application processed"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should process low-income high-ratio loan", + ), + Datapoint( + name="SeniorApplicantScenario", + input={ + "EmailBody": "I would like to apply for a loan of $25,000 for 3 years. My email is martha.senior@example.com, my name is Martha Senior, my address is 123 Retirement Ln, Miami, USA, my annual income is $55,000, and I am 72 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "martha.senior@example.com", "LoanAmount": 25000, "LoanTerm": 3, "Income": 55000, "Age": 72}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 25000, "BorrowerName": "Martha Senior", "BorrowerAddress": "123 Retirement Ln, Miami, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Martha Senior", "RecipientEmail": "martha.senior@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Senior applicant loan processed"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should process senior applicant loan", + ), + Datapoint( + name="ExactlyMinimumAgeScenario", + input={ + "EmailBody": "I would like to apply for a loan of $15,000 for 3 years. My email is justin.young@example.com, my name is Justin Young, my address is 789 Youth St, Austin, USA, my annual income is $40,000, and I am 19 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "justin.young@example.com", "LoanAmount": 15000, "LoanTerm": 3, "Income": 40000, "Age": 19}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 15000, "BorrowerName": "Justin Young", "BorrowerAddress": "789 Youth St, Austin, USA"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Justin Young", "RecipientEmail": "justin.young@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Minimum age applicant loan processed"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should process minimum age applicant loan", + ), + Datapoint( + name="InternationalAddressScenario", + input={ + "EmailBody": "I would like to apply for a loan of $60,000 for 5 years. My email is olivia.international@example.com, my name is Olivia International, my address is 42 Global Way, London, United Kingdom, my annual income is $90,000, and I am 33 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": False}}, + "ContainsEvaluator": {"search_text": "loan"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "olivia.international@example.com", "LoanAmount": 60000, "LoanTerm": 5, "Income": 90000, "Age": 33}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 60000, "BorrowerName": "Olivia International", "BorrowerAddress": "42 Global Way, London, United Kingdom"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Olivia International", "RecipientEmail": "olivia.international@example.com"}}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "International address application processed"}}, + }, + simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should process international address application", + ), + Datapoint( + name="MultipleRequestsInOneEmailScenario", + input={ + "EmailBody": "I would like to apply for two loans: one of $30,000 for 3 years and another of $50,000 for 5 years. My email is robert.multiple@example.com, my name is Robert Multiple, my address is 555 Double St, Seattle, USA, my annual income is $120,000, and I am 41 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, + "ContainsEvaluator": {"search_text": "Multiple"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"EscalationTool": ("=", 1)}}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Multiple requests clarification needed"}}, + }, + simulation_instructions="Agent should escalate due to multiple requests", + ), + Datapoint( + name="SpecialCharactersInInputScenario", + input={ + "EmailBody": "I would like to apply for a loan of $35,000 for 3 years. My email is xx_dudu!@$_xx@example.com, my name is Dudu, my address is 123 Symbol# St, L@$ Veg@$, USA, my annual income is $85,000, and I am 38 years old." + }, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, + "ContainsEvaluator": {"search_text": "special characters"}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"EscalationTool": ("=", 1)}}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "The email address contains too many special characters"}}, + }, + simulation_instructions="Agent should escalate due to special characters in input", + ), + ] + + +def get_loan_agent() -> AgentBaseClass: + return AgentBaseClass( + input_schema=AgentInputSchema, + output_schema=AgentOutputSchema, + system_prompt=SYSTEM_PROMPT, + user_prompt=USER_PROMPT, + tools=get_tools(), + datapoints=get_datapoints(), + ) diff --git a/samples/gym-sample/src/gym_sample/loan/evals.py b/samples/gym-sample/src/gym_sample/loan/evals.py new file mode 100644 index 00000000..a123418c --- /dev/null +++ b/samples/gym-sample/src/gym_sample/loan/evals.py @@ -0,0 +1,78 @@ +from typing import List +from uipath.eval.evaluators import LegacyBaseEvaluator +from uipath.eval.coded_evaluators import ( + BaseEvaluator, + ExactMatchEvaluator, + ContainsEvaluator, + ToolCallOrderEvaluator, + ToolCallCountEvaluator, + ToolCallArgsEvaluator, + LLMJudgeOutputEvaluator, +) + +def get_evaluators(include_llm_judge: bool = False) -> List[LegacyBaseEvaluator | BaseEvaluator]: + """Create evaluators for loan agent. + + Evaluators match the test case metrics: + - ExactMatchEvaluator: Validates ActionCenterTaskCreated boolean + - ContainsEvaluator: Checks ExecutionDetails contains expected keywords (e.g., "loan", "Applied") + - ToolCallOrderEvaluator: Validates tool calls are in the expected sequence + - ToolCallCountEvaluator: Validates the count of each tool call + - ToolCallArgsEvaluator: Validates tool call arguments match expected values + - LLMJudgeOutputEvaluator: Uses LLM to judge ExecutionDetails factuality (optional) + """ + evaluators: List[LegacyBaseEvaluator | BaseEvaluator] = [ + # Validates ActionCenterTaskCreated matches expected boolean value + ExactMatchEvaluator.model_validate({ + "id": "ExactMatchEvaluator", + "config": { + "negated": False, + "target_output_key": "ActionCenterTaskCreated" + } + }), + # Validates ExecutionDetails contains common keywords found across scenarios + ContainsEvaluator.model_validate({ + "id": "ContainsEvaluator", + "config": { + "negated": False, + "target_output_key": "ExecutionDetails" + } + }), + # Validates tool calls occur in the expected order + ToolCallOrderEvaluator.model_validate({ + "id": "ToolCallOrderEvaluator", + "config": { + "strict": False, + }, + }), + # Validates the count of each tool call matches expectations + ToolCallCountEvaluator.model_validate({ + "id": "ToolCallCountEvaluator", + "config": { + "strict": False, + }, + }), + # Validates tool call arguments match expected values + ToolCallArgsEvaluator.model_validate({ + "id": "ToolCallArgsEvaluator", + "config": { + "strict": False, + "subset": False, + }, + }), + ] + + if include_llm_judge: + # LLM Judge evaluator for ExecutionDetails - matches LLMJudgeFactualityLoanAgent metric + evaluators.extend([ + LLMJudgeOutputEvaluator.model_validate({ + "id": "LLMJudgeOutputEvaluator", + "config": { + "target_output_key": "ExecutionDetails", + "model": "gpt-4o-2024-11-20", + "temperature": 0.0, + }, + }), + ]) + + return evaluators diff --git a/samples/gym-sample/src/gym_sample/loan/test_input.json b/samples/gym-sample/src/gym_sample/loan/test_input.json new file mode 100644 index 00000000..2a800851 --- /dev/null +++ b/samples/gym-sample/src/gym_sample/loan/test_input.json @@ -0,0 +1,3 @@ +{ + "EmailBody": "I would like to apply for a loan of $50,000 for 5 years. My email is john.doe@example.com, my name is John Doe, my address is 123 Main St, Anytown, USA, my annual income is $75,000, and I am 35 years old." +} diff --git a/samples/gym-sample/src/gym_sample/loan/tools.py b/samples/gym-sample/src/gym_sample/loan/tools.py new file mode 100644 index 00000000..8532b6ba --- /dev/null +++ b/samples/gym-sample/src/gym_sample/loan/tools.py @@ -0,0 +1,118 @@ +from typing import Annotated + +from pydantic import BaseModel, Field + +# Add schema class for the add function + + +class LoanAgent_ApplyUiBankLoanInput(BaseModel): + RequestorEmailAddress: str = Field(description="The email of the requester") + LoanAmount: int = Field(description="The loan amount requested (integer)") + LoanTerm: int = Field(description="The loan term (integer)") + Income: int = Field(description="The requester's income (integer)") + Age: int = Field(description="The requester's age (integer)") + + +class LoanAgent_ModifyLoanContractInput(BaseModel): + SharepointFolderURL: str = Field(description="SharePoint URL") + InterestRate: int = Field(description="The interest rate of the loan") + LoanAmount: int = Field(description="The loan amount") + BorrowerName: str = Field(description="The borrower name") + BorrowerAddress: str | None = Field(description="The borrower address") + + +class LoanAgent_SendSharepointDocumentViaDocuSignInput(BaseModel): + SharepointFileURL: str = Field( + description="The SharePoint URL of the file created by the LoanAgent_ModifyLoanContract tool" + ) + RecipientLegalName: str = Field(description="The recipient legal name") + RecipientEmail: str = Field(description="The recipient email") + + +class GoogleInput(BaseModel): + SearchText: str = Field(description="Search text") + + +def LoanAgent_ApplyUiBankLoan( + RequestorEmailAddress: Annotated[str, "The email of the requester"], + LoanAmount: Annotated[int, "The loan amount requested"], + LoanTerm: Annotated[int, "The loan term"], + Income: Annotated[int, "The requester's income"], + Age: Annotated[int, "The requester's age"], +) -> str: + """Apply for a loan at UI Bank. + + Args: + RequestorEmailAddress: The email of the requester + LoanAmount: The loan amount requested (integer) + LoanTerm: The loan term (integer) + Income: The requester's income (integer) + Age: The requester's age (integer) + + Returns: + string: Loan application details + """ + if LoanTerm == 5 or LoanTerm == 3 or LoanTerm == 1: + if Age > 18: + return "Loan application was approved with a rate of 4% APR." + else: + return "The age of the applicant is below the minimum age for a loan." + else: + return "Loan application term is not supported." + + +def LoanAgent_ModifyLoanContract( + SharepointFolderURL: Annotated[str, "SharePoint URL"], + InterestRate: Annotated[int, "The interest rate of the loan"], + LoanAmount: Annotated[int, "The loan amount"], + BorrowerName: Annotated[str, "The borrower name"], + BorrowerAddress: Annotated[str, "The borrower address"], +) -> str: + """Modify a loan contract. + + Args: + SharepointFolderURL: The SharePoint URL of the file + InterestRate: The interest rate of the loan + LoanAmount: The loan amount + BorrowerName: The borrower name + BorrowerAddress: The borrower address + + Returns: + str: Sharepoint file URL + """ + # Implementation goes here + return SharepointFolderURL + "/New_doc.pdf" + + +def LoanAgent_SendSharepointDocumentViaDocuSign( + SharepointFileURL: Annotated[ + str, + "The SharePoint URL of the file", + ], + RecipientLegalName: Annotated[str, "The recipient legal name"], + RecipientEmail: Annotated[str, "The recipient email"], +) -> str: + """Send a sharepoint document via DocuSign. + + Args: + SharepointFileURL: The SharePoint URL of the file + RecipientLegalName: The recipient legal name + RecipientEmail: The recipient email + + Returns: + str: True if document is sent successfully, False otherwise + """ + # Implementation goes here + return "Document New_doc.pdf was sent to: random@uipath.com" + + +def Google(SearchText: Annotated[str, "Search text"]) -> str: + """Search the web for a given text. + + Args: + SearchText: The text to search for + + Returns: + str: Search results + """ + return "The National Bank kept its benchmark interest rate unchanged at 6.5% during its meeting, following two consecutive rate cuts." diff --git a/samples/gym-sample/src/gym_sample/run.py b/samples/gym-sample/src/gym_sample/run.py new file mode 100644 index 00000000..7342bc74 --- /dev/null +++ b/samples/gym-sample/src/gym_sample/run.py @@ -0,0 +1,177 @@ +"""Agent execution and evaluation runner.""" + +import asyncio +import argparse +import json +from typing import Dict, List, Tuple + +from dotenv import find_dotenv, load_dotenv +from langgraph.graph import StateGraph +from uipath.eval.evaluators.base_evaluator import EvaluationResult +from uipath.eval.evaluators import LegacyBaseEvaluator +from uipath.eval.coded_evaluators import BaseEvaluator +from uipath.eval.models import AgentExecution +from gym_sample.graph import agents_with_datapoints, get_all_evaluators +from gym_sample.trace_utils import setup_tracer +from gym_sample.uipath_gym_types import Datapoint + + +async def run_agents_with_tracing(graphs: List[Tuple[StateGraph, Datapoint]], verbose: bool = False) -> List[AgentExecution]: + """Run the agent with OpenTelemetry trace collection across all datapoints. + + Note: This evaluation mode bypasses the CLI entry point and directly builds graphs + for each datapoint. This allows batch evaluation while the CLI mode (uipath run calculator) + accepts properly typed GraphInput models for single executions. + + Args: + graphs: List of (StateGraph, Datapoint) tuples to run + verbose: Whether to print verbose output + + Returns: + List of AgentExecution: Contains agent input, output, and collected traces for each datapoint. + """ + if verbose: + print("Starting agent execution with trace collection...") + + results = [] + + # Set up tracing once for all runs + exporter, _ = setup_tracer() + + # Run the agent through all graphs/datapoints + if not graphs: + raise ValueError(f"No graphs found") + + for i, (graph, datapoint) in enumerate(graphs): + if verbose: + print(f"\nRunning datapoint {i+1}/{len(graphs)}: {datapoint.input}") + + # Clear previous spans before each run + exporter.clear_exported_spans() + + compiled_graph = graph.compile() + # Evaluation graphs have input pre-bound at build time, so pass empty dict + result = await compiled_graph.ainvoke({}) + + # Extract output - with output_schema, result is the typed output directly + if isinstance(result, dict): + # Try to get the output from the result (handle both old and new format) + agent_output = result if 'answer' in result or 'result' not in result else result.get('result', {}) + elif hasattr(result, 'model_dump'): + # Result is a Pydantic model (output_schema) - convert to dict + agent_output = result.model_dump() + else: + agent_output = {} + + agent_trace = exporter.get_exported_spans().copy() # Copy to avoid reference issues + + if verbose: + print(f"Agent completed. Result: {agent_output}") + print(f"Collected {len(agent_trace)} trace spans") + + results.append(AgentExecution( + agent_input=datapoint.input, + agent_output=agent_output, + agent_trace=agent_trace, + simulation_instructions=datapoint.simulation_instructions, + )) + + return results + + +async def run_evaluation(agent_name: str, include_llm_judge: bool = False, verbose: bool = False) -> Dict[str, Dict[str, EvaluationResult]]: + """Run the complete agent evaluation pipeline across all datapoints.""" + print(f"Running evaluation for agent: {agent_name}") + + evaluators_generator = get_all_evaluators()[agent_name] + evaluators = evaluators_generator(include_llm_judge) + + async with agents_with_datapoints(agent_name) as graphs: + agent_executions = await run_agents_with_tracing(graphs, verbose) + datapoints = [datapoint for _, datapoint in graphs] + + if verbose: + print(f"\nAgent executed successfully across {len(agent_executions)} datapoints!") + print("\nRunning evaluations...") + else: + print("\nRunning evaluations...") + + all_results: Dict[str, Dict[str, EvaluationResult]] = {} + + # Get datapoints to access their evaluation criteria + # Run evaluations for each datapoint execution + for i, agent_execution in enumerate(agent_executions): + datapoint = datapoints[i] + datapoint_key = datapoint.name + if verbose: + print(f"\n--- Evaluating Datapoint {i+1}/{len(agent_executions)} ---") + print(f"Input: {agent_execution.agent_input}") + print(f"Output: {agent_execution.agent_output}") + print(f"Collected {len(agent_execution.agent_trace)} trace spans") + + datapoint_results: Dict[str, EvaluationResult] = {} + + # Run each evaluator for this datapoint + for evaluator in evaluators: + if evaluator.name not in datapoint.evaluation_criteria: + if verbose: + print(f" {evaluator.name} not found in datapoint evaluation criteria. Skipping.") + continue + + # Use the datapoint's evaluation criteria - key by evaluator.name + evaluator_criteria = datapoint.evaluation_criteria[evaluator.name] + if verbose: + print(f" Evaluating {evaluator.name} with criteria: {evaluator_criteria}") + + # Use the appropriate evaluate method based on evaluator type + if isinstance(evaluator, LegacyBaseEvaluator): + # Legacy evaluators use evaluate method + result = await evaluator.evaluate( + agent_execution=agent_execution, + evaluation_criteria=evaluator_criteria + ) + elif isinstance(evaluator, BaseEvaluator): + # New evaluators use validate_and_evaluate_criteria + result = await evaluator.validate_and_evaluate_criteria( + agent_execution=agent_execution, + evaluation_criteria=evaluator_criteria + ) + else: + raise ValueError(f"Evaluator {evaluator.__class__.__name__} is not a valid evaluator") + + if verbose: + print(f" Result: {result}") + datapoint_results[evaluator.name] = result + + all_results[datapoint_key] = datapoint_results + + return all_results + + +async def async_main() -> None: + """Main entry point for agent evaluation (async version).""" + parser = argparse.ArgumentParser(description="Run agent evaluation") + parser.add_argument("--verbose", action="store_true", help="Enable verbose output") + parser.add_argument("--agent_name", default="calculator", help="Name of the agent to run") + parser.add_argument("--include_llm_judge", action="store_true", help="Include LLM judge evaluators") + args = parser.parse_args() + + load_dotenv(find_dotenv()) + + results = await run_evaluation(agent_name=args.agent_name, include_llm_judge=args.include_llm_judge, verbose=args.verbose) + + # Print results for all datapoints + summary = {} + for datapoint_key, datapoint_results in results.items(): + summary[datapoint_key] = {evaluator_id: (result.score, str(result.details)) for evaluator_id, result in datapoint_results.items()} + + print(json.dumps(summary, indent=4)) + + +def main() -> None: + """Synchronous entry point for CLI script.""" + asyncio.run(async_main()) + + +if __name__ == "__main__": + main() diff --git a/samples/gym-sample/src/gym_sample/tools.py b/samples/gym-sample/src/gym_sample/tools.py new file mode 100644 index 00000000..160086af --- /dev/null +++ b/samples/gym-sample/src/gym_sample/tools.py @@ -0,0 +1,125 @@ +from typing import Annotated, Any, Dict, List, TypeAlias, override +from langchain_core.tools import BaseTool, StructuredTool +from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage, ToolMessage +from langgraph.graph import add_messages +from pydantic import BaseModel, Field +import jsonschema + +META_DESCRIPTION_ESCALATION_TOOL: str = """This is an escalation. This tool must be called separately from other tool calls (i.e. not in parallel with other tool calls). +This is because you must wait for the response before deciding what tool to call next. +""" + +Message: TypeAlias = BaseMessage | SystemMessage | ToolMessage | HumanMessage | AIMessage + + +class RaiseErrorInput(BaseModel): + message: str = Field( + description="The error message to display to the user. This should be a brief on line message." + ) + details: str | None = Field( + description="Optional additional details about the error. This can be a multiline text with more details. Only populate this if there are relevant details not already captured in the error message." + ) + + +class StateBaseClass(BaseModel): + class Config: + extra = "allow" # Allow extra fields from input_schema (like 'expression') + + messages: Annotated[List[Message], add_messages] = [] + result: Dict[str, Any] = {} + raised_error: RaiseErrorInput | None = None + run_init_state: Dict[str, str] = {} + + +class EndExecutionDefaultOutput(BaseModel): + result: Dict[str, Any] = {} + + +class EndExecutionTool(StructuredTool): + name: str = "end_execution" + description: str = "Use this tool when you have gathered all required information and want to end execution. The input should match the expected output schema." + output_schema: type[BaseModel] | None = None # The final output schema to return + + @override + def run(self, tool_input: StateBaseClass, *args: Any, **kwargs: Any): + last_message = tool_input.messages[-1] + # If the last message is a ToolMessage, this was the result of a validation step, + # so we need to get the previous message. + if isinstance(last_message, ToolMessage): + last_message = tool_input.messages[-2] + if not isinstance(last_message, AIMessage): + raise ValueError( + "The last message passed as input to the EndExecutionTool should have been an AIMessage" + ) + arguments = last_message.tool_calls[0]["args"] + schema = ( + self.args_schema + if isinstance(self.args_schema, dict) + else self.args_schema.model_json_schema() + ) + jsonschema.validate(arguments, schema) + tool_input.result = arguments + + # Return the output_schema if provided, otherwise return EndExecutionOutput + if self.output_schema: + # Convert the result dict to the output_schema type + return self.output_schema.model_validate(tool_input.result) + return EndExecutionDefaultOutput(result=tool_input.result) + + +class RaiseErrorTool(StructuredTool): + name: str = "raise_error" + description: str = "Raises an error and ends the execution of the agent" + + def __init__(self, *args: Any, **kwargs: Any) -> None: + kwargs["args_schema"] = RaiseErrorInput + super().__init__(*args, **kwargs) + + @override + def run(self, agent_state: StateBaseClass, *args: Any, **kwargs: Any): + last_message = agent_state.messages[-1] + # If the last message is a ToolMessage, this was the result of a validation step, + # so we need to get the previous message. + if isinstance(last_message, ToolMessage): + last_message = agent_state.messages[-2] + if not isinstance(last_message, AIMessage): + raise ValueError( + "The last message passed as input to the EndExecutionTool should have been an AIMessage" + ) + arguments = last_message.tool_calls[0]["args"] + arguments_model = RaiseErrorInput.model_validate(arguments) + return arguments_model + + +class EscalationToolInput(BaseModel): + query: str = Field(..., title="Query string") + + +class EscalationTool(BaseTool): + name: str = "escalation_tool" + description: str + assign_to: str + return_message: str | None = None + + def __init__( + self, + description: str = "", + **kwargs: Any, + ): + super().__init__( + description=f"{META_DESCRIPTION_ESCALATION_TOOL}{description}", + args_schema=EscalationToolInput, + **kwargs, + ) + + @override + async def _arun(self, query: str) -> str: + """Async run method required by BaseTool""" + return self._run(query) + + @override + def _run(self, query: str) -> str: + """Synchronous run method required by BaseTool""" + if self.return_message: + return self.return_message + return f'The escalation was successful. A task has been created for this query: "{query}", and assigned to {self.assign_to}. Call the end execution tool.' diff --git a/samples/gym-sample/src/gym_sample/trace_utils.py b/samples/gym-sample/src/gym_sample/trace_utils.py new file mode 100644 index 00000000..5b3f6220 --- /dev/null +++ b/samples/gym-sample/src/gym_sample/trace_utils.py @@ -0,0 +1,59 @@ +"""OpenTelemetry trace collection utilities for agent evaluation.""" + +from collections.abc import Sequence +from typing import List + +from opentelemetry import trace +from opentelemetry.sdk.trace import ReadableSpan, TracerProvider +from opentelemetry.sdk.trace.export import ( + SimpleSpanProcessor, + SpanExporter, + SpanExportResult, +) +from openinference.instrumentation.langchain import LangChainInstrumentor + + +class InMemorySpanExporter(SpanExporter): + """An OpenTelemetry span exporter that stores spans in memory for testing.""" + + def __init__(self): + self.spans = [] + self.is_shutdown = False + + def export(self, spans: Sequence[ReadableSpan]) -> SpanExportResult: + if self.is_shutdown: + return SpanExportResult.FAILURE + + self.spans.extend(spans) + return SpanExportResult.SUCCESS + + def get_exported_spans(self) -> List[ReadableSpan]: + return self.spans + + def clear_exported_spans(self) -> None: + self.spans = [] + + def force_flush(self, timeout_millis: int = 30000) -> bool: + return not self.is_shutdown + + def shutdown(self) -> None: + self.is_shutdown = True + + +def setup_tracer() -> tuple[InMemorySpanExporter, TracerProvider]: + """Set up OpenTelemetry tracing with LangChain instrumentation. + + Returns: + InMemorySpanExporter: The configured span exporter for capturing traces. + TracerProvider: The configured tracer provider for capturing traces. + """ + # Set up OpenTelemetry trace collection + exporter = InMemorySpanExporter() + provider = TracerProvider() + trace.set_tracer_provider(provider) + provider.add_span_processor(SimpleSpanProcessor(exporter)) + + # Initialize LangChain instrumentation (this creates the spans!) + LangChainInstrumentor().instrument() + + return exporter, provider diff --git a/samples/gym-sample/src/gym_sample/uipath_gym_types.py b/samples/gym-sample/src/gym_sample/uipath_gym_types.py new file mode 100644 index 00000000..5c299bba --- /dev/null +++ b/samples/gym-sample/src/gym_sample/uipath_gym_types.py @@ -0,0 +1,262 @@ +from typing import Any, Callable, Dict, List, Literal + +from langchain.tools import BaseTool +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage +from langgraph.graph import END, START, StateGraph +from langgraph.prebuilt import ToolNode +from pydantic import BaseModel, Field, create_model +from pydantic.fields import FieldInfo +from functools import partial +from langchain_core.runnables import Runnable +from .tools import EndExecutionTool, RaiseErrorTool, StateBaseClass + + +class Datapoint(BaseModel): + name: str = Field(description="The name of the datapoint") + input: Dict[str, str | Dict[str, Any]] + evaluation_criteria: Dict[str, Any] + simulation_instructions: str = "" + + +class AgentBaseClass(BaseModel): + system_prompt: str + user_prompt: str + input_schema: type[BaseModel] + output_schema: type[BaseModel] + datapoints: List[Datapoint] = [] + raise_error_tool: RaiseErrorTool = RaiseErrorTool() + tools: List[BaseTool] = [] + + @property + def end_execution_tool(self) -> EndExecutionTool: + return EndExecutionTool(args_schema=self.output_schema, output_schema=self.output_schema) + + +async def chatbot_node( + state: StateBaseClass, + llm: Runnable, + print_trace: bool = True, + parallel_tool_calls: bool = False, +): + if state.messages and isinstance(state.messages[-1], AIMessage) and "claude" in getattr(llm, "model_name", ""): + state.messages.append(HumanMessage(content="Proceed with the next step")) + ai_message = await llm.ainvoke(state.messages) # get AI response + + if print_trace: + print(f"\nAssistant: {ai_message.content}") + if isinstance(ai_message, AIMessage) and ai_message.tool_calls: + if parallel_tool_calls: + for tool_call in ai_message.tool_calls: + tool_name = tool_call["name"] + print(f"Tool Call: {tool_name}\n") + else: + print(f"Tool Call: {ai_message.tool_calls[0]['name']}") + state.messages.append(ai_message) # append the new message to existing messages + return state + + +def should_continue(state: StateBaseClass) -> Literal["tools", "raise_error", "end_execution"]: + messages = state.messages + last_message = messages[-1] + tool_calls = last_message.tool_calls if isinstance(last_message, AIMessage) else [] + + # Check if end_execution tool is called + if any(tool_call.get("name") == "end_execution" for tool_call in tool_calls): + return "end_execution" + # If there are other tool calls, route to tools + elif any(tool_call.get("name") == "raise_error" for tool_call in tool_calls): + return "raise_error" + elif tool_calls: + return "tools" + # If no tool calls, route to tools anyway (agent needs to make a decision) + return "tools" + + +class BasicLoop: + def __init__( + self, + scenario: AgentBaseClass, + llm: BaseChatModel, + print_trace: bool = False, + parallel_tool_calls: bool = False, + debug: bool = False, + ): + self.llm_with_tools = llm.bind_tools( + list(scenario.tools) + [scenario.end_execution_tool, scenario.raise_error_tool], + parallel_tool_calls=parallel_tool_calls, + tool_choice="any", + ) + self.scenario = scenario + self.print_trace = print_trace + self.parallel_tool_calls = parallel_tool_calls + self.debug = debug + + def prepare_input(self, state: BaseModel) -> StateBaseClass: + """Node to handle input preparation - converts typed input to state with messages. + + Used by both CLI mode (direct binding) and evaluation mode (lambda binding). + With input_schema=, LangGraph passes the typed input directly to this node. + + Args: + state: The typed input matching input_schema (e.g., CalculatorInput). + Despite being named 'state', this receives the input object, not a state. + + Returns: + StateBaseClass with messages prepared from the input + """ + if self.debug: + print(f"[DEBUG] prepare_input received: {type(state).__name__}") + print(f"[DEBUG] Input data: {state.model_dump()}") + + # Create new state with messages from the input + new_state = StateBaseClass() + + # Create initial messages using scenario prompts + try: + new_state.messages = [ + SystemMessage(content=self.scenario.system_prompt), + HumanMessage(content=self.scenario.user_prompt.format_map(state.model_dump())) + ] + if self.debug: + print(f"[DEBUG] Created messages from input") + print(f"[DEBUG] Messages: {[m.content[:50] for m in new_state.messages]}") + except KeyError as e: + # If format_map fails, fall back to basic setup + new_state.messages = [ + SystemMessage(content=self.scenario.system_prompt), + HumanMessage(content=f"Help me with: {state}") + ] + if self.debug: + print(f"[DEBUG] Format error {e} - using fallback") + + if self.debug: + print(f"[DEBUG] Final {len(new_state.messages)} messages") + + return new_state + + def end_execution_node(self, state: StateBaseClass) -> BaseModel: + """Wrapper node that calls end_execution tool and returns output_schema.""" + self.scenario.end_execution_tool.run(state) + # Return the output_schema populated from state.result + return self.scenario.output_schema.model_validate(state.result) + + def _create_state_with_input(self) -> type[BaseModel]: + """Create a State class that includes input fields from input_schema. + + This is necessary for CLI mode where LangGraph needs the State to have + the same fields as the input (like ticket-classification pattern). + + Returns: + A new State class that inherits from StateBaseClass and includes input fields + """ + # Extract fields from input_schema and convert to (type, Field) tuples + input_fields: Dict[str, Any] = {} + for field_name, field_info in self.scenario.input_schema.model_fields.items(): + # Get the annotation, defaulting to Any if not present + field_type = field_info.annotation if field_info.annotation is not None else Any + + # Copy the field metadata if using Field(), otherwise create a simple tuple + if field_info.is_required(): + # Required field: use (type, ...) + input_fields[field_name] = (field_type, ...) + else: + # Optional field: use (type, default_value) + input_fields[field_name] = (field_type, field_info.default) + + # Create a new State class that inherits from StateBaseClass and adds input fields + StateWithInput = create_model( + 'StateWith' + self.scenario.input_schema.__name__, + __base__=StateBaseClass, + **input_fields + ) + + return StateWithInput + + def add_worker_graph( + self, + graph: StateGraph, + ) -> None: + """Add all worker nodes and edges to the graph. + + This method contains the common graph structure used by both CLI and evaluation modes. + Override this method to customize the agent loop behavior. + + Args: + graph: The StateGraph to add nodes and edges to + """ + + # Add worker nodes + graph.add_node( + "chatbot", + partial( + chatbot_node, + llm=self.llm_with_tools, + print_trace=self.print_trace, + parallel_tool_calls=self.parallel_tool_calls, + ), + ) + graph.add_node("tools", ToolNode(self.scenario.tools)) + graph.add_node("end_execution", self.end_execution_node) + graph.add_node("raise_error", self.scenario.raise_error_tool) + + # Add edges + graph.add_edge(START, "prepare_input") + graph.add_edge("prepare_input", "chatbot") + graph.add_conditional_edges("chatbot", should_continue) + graph.add_edge("tools", "chatbot") + graph.add_edge("end_execution", END) + graph.add_edge("raise_error", END) + + def build_cli_graph(self) -> StateGraph: + """Build graph for CLI mode - accepts input at runtime (like ticket-classification example). + + Returns a graph that: + - Uses input= and output= parameters (separate from State) + - Dynamically creates a State class that includes input fields + - Has a prepare_input node that converts input → State + - Accepts typed input at runtime via `graph.ainvoke(CalculatorInput(...))` + """ + # Create State class with input fields for CLI mode + StateWithInput = self._create_state_with_input() + + # CLI mode: input and output are separate types from State + graph = StateGraph( + StateWithInput, + input_schema=self.scenario.input_schema, + output_schema=self.scenario.output_schema + ) + graph.add_node("prepare_input", self.prepare_input) + + # Add all worker nodes and edges + self.add_worker_graph(graph) + + return graph + + def build_evaluation_graph(self, agent_input: Dict[str, Any]) -> StateGraph: + """Build graph for evaluation mode - pre-binds input at build time. + + Args: + agent_input: The input data from a datapoint + + Returns a graph that: + - Pre-binds the input at graph build time + - No runtime input needed - just call graph.ainvoke({}) + """ + # Validate input data against schema + final_agent_input = self.scenario.input_schema.model_validate(agent_input) + + # Evaluation mode: use input_schema for validation but pre-bind the data + graph = StateGraph( + StateBaseClass, + input_schema=self.scenario.input_schema, + output_schema=self.scenario.output_schema + ) + + # Lambda captures the pre-bound input and ignores incoming state from LangGraph + graph.add_node("prepare_input", lambda state=None: self.prepare_input(final_agent_input)) + + # Add all worker nodes and edges + self.add_worker_graph(graph) + + return graph diff --git a/samples/gym-sample/uipath.json b/samples/gym-sample/uipath.json new file mode 100644 index 00000000..c0a1048b --- /dev/null +++ b/samples/gym-sample/uipath.json @@ -0,0 +1,71 @@ +{ + "entryPoints": [ + { + "filePath": "calculator", + "uniqueId": "dc614383-5fdd-4a4b-a868-c91a1ec61dad", + "type": "agent", + "input": { + "type": "object", + "properties": { + "expression": { + "title": "Expression", + "type": "string" + } + }, + "required": [ + "expression" + ] + }, + "output": { + "type": "object", + "properties": { + "answer": { + "default": null, + "title": 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+0300 Subject: [PATCH 2/7] amend the judge trajectory renaming --- samples/gym-sample/README.md | 8 + .../src/gym_sample/calculator/evals.py | 7 +- samples/gym-sample/uv.lock | 853 +++++++++--------- 3 files changed, 445 insertions(+), 423 deletions(-) diff --git a/samples/gym-sample/README.md b/samples/gym-sample/README.md index fd5f6f28..260bce60 100644 --- a/samples/gym-sample/README.md +++ b/samples/gym-sample/README.md @@ -101,6 +101,14 @@ uv sync **Important**: After `uv sync`, the `gym_sample` package is properly installed and **no PYTHONPATH configuration is needed**. You can run Python modules directly, debug with F5 in VS Code, or use the convenience shell scripts. +### Updating Git Dependencies + +This project uses a specific commit from the UiPath SDK that includes **tool call evaluators** (`ToolCallArgsEvaluator`, `ToolCallCountEvaluator`, `ToolCallOrderEvaluator`, `ToolCallOutputEvaluator`) and trajectory evaluators. + +When setting up **for the first time**, `uv sync` automatically installs this version. + +**Note:** We use a specific commit (not a branch) because later commits on the `mj/wire-tool` branch have configuration issues that prevent installation with `uv`. Once the upstream issue is resolved, this can be updated to track the branch directly. + ## Authentication Before running any agents, authenticate with UiPath: diff --git a/samples/gym-sample/src/gym_sample/calculator/evals.py b/samples/gym-sample/src/gym_sample/calculator/evals.py index 20f9dd7d..b61b5373 100644 --- a/samples/gym-sample/src/gym_sample/calculator/evals.py +++ b/samples/gym-sample/src/gym_sample/calculator/evals.py @@ -5,6 +5,7 @@ ExactMatchEvaluator, ContainsEvaluator, JsonSimilarityEvaluator, + LLMJudgeTrajectorySimulationEvaluator, ToolCallOrderEvaluator, ToolCallCountEvaluator, ToolCallArgsEvaluator, @@ -12,7 +13,7 @@ LLMJudgeOutputEvaluator, LLMJudgeStrictJSONSimilarityOutputEvaluator, LLMJudgeTrajectoryEvaluator, - LLMJudgeSimulationTrajectoryEvaluator, + LLMJudgeTrajectorySimulationEvaluator, ) def get_evaluators(include_llm_judge: bool = False) -> List[LegacyBaseEvaluator | BaseEvaluator]: @@ -72,8 +73,8 @@ def get_evaluators(include_llm_judge: bool = False) -> List[LegacyBaseEvaluator "temperature": 0.0, }, }), - LLMJudgeSimulationTrajectoryEvaluator.model_validate({ - "id": "LLMJudgeSimulationTrajectoryEvaluator", + LLMJudgeTrajectorySimulationEvaluator.model_validate({ + "id": "LLMJudgeTrajectorySimulationEvaluator", "config": { "model": "gpt-4o-2024-11-20", "temperature": 0.0, diff --git a/samples/gym-sample/uv.lock b/samples/gym-sample/uv.lock index cc3b3f3f..55b351d0 100644 --- a/samples/gym-sample/uv.lock +++ b/samples/gym-sample/uv.lock @@ -2,8 +2,7 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ "python_full_version < '3.12'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", + "python_full_version == '3.12.*'", "python_full_version >= '3.13'", ] @@ -18,7 +17,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.13.0" +version = "3.13.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = 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7 ------- 1 file changed, 7 deletions(-) diff --git a/samples/gym-sample/src/gym_sample/calculator/evals.py b/samples/gym-sample/src/gym_sample/calculator/evals.py index b61b5373..52dd6cf2 100644 --- a/samples/gym-sample/src/gym_sample/calculator/evals.py +++ b/samples/gym-sample/src/gym_sample/calculator/evals.py @@ -59,13 +59,6 @@ def get_evaluators(include_llm_judge: bool = False) -> List[LegacyBaseEvaluator "temperature": 0.0, }, }), - LLMJudgeStrictJSONSimilarityOutputEvaluator.model_validate({ - "id": "LLMJudgeStrictJSONSimilarityOutputEvaluator", - "config": { - "model": "gpt-4o-2024-11-20", - "temperature": 0.0, - }, - }), LLMJudgeTrajectoryEvaluator.model_validate({ "id": "LLMJudgeTrajectoryEvaluator", "config": { From cfdb90f43dbed00c82f640911f57d01d413924f2 Mon Sep 17 00:00:00 2001 From: Mayank Jha Date: Tue, 21 Oct 2025 15:06:01 +0530 Subject: [PATCH 4/7] adding remaining evaluators --- .../src/gym_sample/calculator/agent.py | 155 ++++++++++++++++++ .../gym-sample/src/gym_sample/export_evals.py | 6 + 2 files changed, 161 insertions(+) diff --git a/samples/gym-sample/src/gym_sample/calculator/agent.py b/samples/gym-sample/src/gym_sample/calculator/agent.py index 08a12021..e57f52c5 100644 --- a/samples/gym-sample/src/gym_sample/calculator/agent.py +++ b/samples/gym-sample/src/gym_sample/calculator/agent.py @@ -285,6 +285,161 @@ def get_datapoints() -> List[Datapoint]: "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 311}}, }, ), + Datapoint( + name="TestToolCallOutput1", + input={"expression": "how much is 10 + 15"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "add", "args": {"a": 10, "b": 15}}]}, + "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "add", "output": "25"}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + }, + ), + Datapoint( + name="TestToolCallOutput2", + input={"expression": "how much is 6 * 7"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 42.0}}, + "ContainsEvaluator": {"search_text": "42"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 42.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "multiply", "args": {"a": 6, "b": 7}}]}, + "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "multiply", "output": "42"}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 42}}, + }, + ), + Datapoint( + name="TestToolCallOutput3", + input={"expression": "how much is 100 + 50 * 2"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 200.0}}, + "ContainsEvaluator": {"search_text": "200"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 200.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 50, "b": 2}}, + {"name": "add", "args": {"a": 100, "b": 100}} + ]}, + "ToolCallOutputEvaluator": {"tool_outputs": [ + {"name": "multiply", "output": "100"}, + {"name": "add", "output": "200"} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 200}}, + }, + ), + Datapoint( + name="TestStrictJSONSimilarity1", + input={"expression": "how much is 12 + 13"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 25.0}}, + }, + ), + Datapoint( + name="TestStrictJSONSimilarity2", + input={"expression": "how much is 9 * 9"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 81.0}}, + "ContainsEvaluator": {"search_text": "81"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 81.0}}, + "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 81.0}}, + }, + ), + Datapoint( + name="TestTrajectory1", + input={"expression": "how much is 4 + 5 * 3"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 19.0}}, + "ContainsEvaluator": {"search_text": "19"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 19.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "LLMJudgeTrajectoryEvaluator": { + "expected_agent_behavior": "The agent should follow the order of operations, first multiplying 5 and 3 to get 15, then adding 4 to get 19." + }, + }, + ), + Datapoint( + name="TestTrajectory2", + input={"expression": "how much is (1 + 2) * (3 + 4)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 21.0}}, + "ContainsEvaluator": {"search_text": "21"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 21.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + "LLMJudgeTrajectoryEvaluator": { + "expected_agent_behavior": "The agent should evaluate parentheses first: add 1 and 2 to get 3, add 3 and 4 to get 7, then multiply 3 and 7 to get 21." + }, + }, + ), + Datapoint( + name="TestTrajectory3", + input={"expression": "how much is 2 * 3 + 4 * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 26.0}}, + "ContainsEvaluator": {"search_text": "26"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 26.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 2), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, + "LLMJudgeTrajectoryEvaluator": { + "expected_agent_behavior": "The agent should perform multiplications first (2*3=6 and 4*5=20), then add the results (6+20=26)." + }, + }, + ), + Datapoint( + name="TestTrajectorySimulation1", + input={"expression": "how much is a + 10"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 27.0}}, + "ContainsEvaluator": {"search_text": "27"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 27.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, + "LLMJudgeTrajectorySimulationEvaluator": { + "expected_agent_behavior": "The agent should recognize that 'a' is an unknown variable, escalate to get its value (17), then add 10 to get 27." + }, + }, + simulation_instructions="When the agent asks for the value of 'a', respond with 17.", + ), + Datapoint( + name="TestTrajectorySimulation2", + input={"expression": "how much is m * n + 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 294.0}}, + "ContainsEvaluator": {"search_text": "294"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 294.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, + "LLMJudgeTrajectorySimulationEvaluator": { + "expected_agent_behavior": "The agent should identify two missing variables (m and n), escalate twice to get their values (both 17), multiply them to get 289, then add 5 to get 294." + }, + }, + simulation_instructions="When the agent asks for missing values, respond with 17 for each variable.", + ), + Datapoint( + name="TestTrajectorySimulation3", + input={"expression": "how much is (p + q) * r"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 578.0}}, + "ContainsEvaluator": {"search_text": "578"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 578.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "add", "multiply"]}, + "LLMJudgeTrajectorySimulationEvaluator": { + "expected_agent_behavior": "The agent should recognize three missing variables (p, q, r), escalate three times to get their values (all 17), add p and q (17+17=34), then multiply by r (34*17=578)." + }, + }, + simulation_instructions="When the agent asks for missing values, respond with 17 for each variable (p, q, and r).", + ), ] diff --git a/samples/gym-sample/src/gym_sample/export_evals.py b/samples/gym-sample/src/gym_sample/export_evals.py index 859a351f..110afee5 100644 --- a/samples/gym-sample/src/gym_sample/export_evals.py +++ b/samples/gym-sample/src/gym_sample/export_evals.py @@ -113,6 +113,12 @@ def export_evaluators(agent_name: str, output_dir: Path, only_supported: bool = "JsonSimilarityEvaluator", "LLMJudgeOutputEvaluator", "LLMJudgeStrictJSONSimilarityOutputEvaluator", + "ToolCallOrderEvaluator", + "ToolCallCountEvaluator", + "ToolCallArgsEvaluator", + "ToolCallOutputEvaluator", + "LLMJudgeTrajectoryEvaluator", + "LLMJudgeTrajectorySimulationEvaluator", } evaluators_getter = get_all_evaluators()[agent_name] From 39de4345fc6b309c1bb7e856d71bc5bc0505cb15 Mon Sep 17 00:00:00 2001 From: Andrei Rusu Date: Tue, 28 Oct 2025 18:19:02 +0200 Subject: [PATCH 5/7] update gym sample to be ready for langchain>=1 and to use main uipath package --- samples/gym-sample/README.md | 4 +- samples/gym-sample/pyproject.toml | 4 +- .../src/gym_sample/calculator/agent.py | 772 ++++++++-------- .../src/gym_sample/calculator/evals.py | 3 +- samples/gym-sample/src/gym_sample/graph.py | 3 +- .../gym-sample/src/gym_sample/loan/agent.py | 2 +- .../gym-sample/src/gym_sample/loan/evals.py | 2 +- samples/gym-sample/src/gym_sample/run.py | 2 +- .../src/gym_sample/uipath_gym_types.py | 1 - samples/gym-sample/uv.lock | 861 ++++++++++-------- 10 files changed, 895 insertions(+), 759 deletions(-) diff --git a/samples/gym-sample/README.md b/samples/gym-sample/README.md index 260bce60..323bbdd9 100644 --- a/samples/gym-sample/README.md +++ b/samples/gym-sample/README.md @@ -486,7 +486,7 @@ class MyAgentOutput(BaseModel): ### 2. Create Agent Configuration ```python -from langchain.tools import StructuredTool +from langchain_core.tools import StructuredTool from gym_sample.uipath_gym_types import AgentBaseClass, Datapoint from gym_sample.tools import EndExecutionTool @@ -639,7 +639,7 @@ async with agents_with_datapoints("calculator") as graphs: ### Creating Custom Tools ```python -from langchain.tools import StructuredTool +from langchain_core.tools import StructuredTool from pydantic import BaseModel, Field class DivideInput(BaseModel): diff --git a/samples/gym-sample/pyproject.toml b/samples/gym-sample/pyproject.toml index 64b9a13b..01ab15da 100644 --- a/samples/gym-sample/pyproject.toml +++ b/samples/gym-sample/pyproject.toml @@ -12,8 +12,8 @@ dependencies = [ "langchain-core>=0.3.34", "langgraph-checkpoint-sqlite>=2.0.3", "python-dotenv>=1.0.1", - "uipath-langchain==0.0.138", - "uipath @ git+https://github.com/UiPath/uipath-python.git@release/revamped-evals", + "uipath-langchain>=0.0.140", + "uipath", "pydantic>=2.10.6", "aiohttp>=3.11.12", "typing-extensions>=4.12.2", diff --git a/samples/gym-sample/src/gym_sample/calculator/agent.py b/samples/gym-sample/src/gym_sample/calculator/agent.py index e57f52c5..7ce046d2 100644 --- a/samples/gym-sample/src/gym_sample/calculator/agent.py +++ b/samples/gym-sample/src/gym_sample/calculator/agent.py @@ -1,5 +1,5 @@ from typing import List -from langchain.tools import BaseTool, StructuredTool +from langchain_core.tools import BaseTool, StructuredTool from pydantic import BaseModel from ..uipath_gym_types import AgentBaseClass, Datapoint from ..tools import EscalationTool @@ -55,391 +55,391 @@ def get_datapoints() -> List[Datapoint]: "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 3}}, }, ), - Datapoint( - name="TestCompoundOperation1", - input={"expression": "how much is 2 + 2 * 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 12.0}}, - "ContainsEvaluator": {"search_text": "12"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 12.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 2, "b": 5}}, - {"name": "add", "args": {"a": 2, "b": 10}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 12}}, - }, - ), - Datapoint( - name="TestCompoundOperation2", - input={"expression": "how much is 3 * 5 + 2 * 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - "ContainsEvaluator": {"search_text": "25"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 3, "b": 5}}, - {"name": "multiply", "args": {"a": 2, "b": 5}}, - {"name": "add", "args": {"a": 15, "b": 10}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, - }, - ), - Datapoint( - name="TestCompoundOperation3", - input={"expression": "how much is 3 * (5 + 5) * 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 150.0}}, - "ContainsEvaluator": {"search_text": "150"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 150.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "multiply"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "add", "args": {"a": 5, "b": 5}}, - {"name": "multiply", "args": {"a": 3, "b": 10}}, - {"name": "multiply", "args": {"a": 30, "b": 5}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 150}}, - }, - ), - Datapoint( - name="TestCompoundOperation4", - input={"expression": "how much is 2 + 3 * 4"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 14.0}}, - "ContainsEvaluator": {"search_text": "14"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 14.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 3, "b": 4}}, - {"name": "add", "args": {"a": 2, "b": 12}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 14}}, - }, - ), - Datapoint( - name="TestCompoundOperation5", - input={"expression": "how much is (2 + 3) * (4 + 1)"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - "ContainsEvaluator": {"search_text": "25"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "add", "args": {"a": 2, "b": 3}}, - {"name": "add", "args": {"a": 4, "b": 1}}, - {"name": "multiply", "args": {"a": 5, "b": 5}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, - }, - ), - Datapoint( - name="TestCompoundOperation6", - input={"expression": "how much is 10 * 0.5 + 3"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 8.0}}, - "ContainsEvaluator": {"search_text": "8"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 8.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 10, "b": 0.5}}, - {"name": "add", "args": {"a": 5, "b": 3}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 8}}, - }, - ), - Datapoint( - name="TestCompoundOperation7", - input={"expression": "how much is 5 * 5 + (-10)"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 15.0}}, - "ContainsEvaluator": {"search_text": "15"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 15.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 5, "b": 5}}, - {"name": "add", "args": {"a": 25, "b": -10}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 15}}, - }, - ), - Datapoint( - name="TestCompoundOperation8", - input={"expression": "how much is (8 + (-3)) * (2 + 2)"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 20.0}}, - "ContainsEvaluator": {"search_text": "20"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 20.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "add", "args": {"a": 8, "b": -3}}, - {"name": "add", "args": {"a": 2, "b": 2}}, - {"name": "multiply", "args": {"a": 5, "b": 4}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 20}}, - }, - ), - Datapoint( - name="TestNestedParentheses", - input={"expression": "how much is (2 * (3 + 4)) * (1 + 1)"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 28.0}}, - "ContainsEvaluator": {"search_text": "28"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 28.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 2)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "add", "multiply"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "add", "args": {"a": 3, "b": 4}}, - {"name": "multiply", "args": {"a": 2, "b": 7}}, - {"name": "add", "args": {"a": 1, "b": 1}}, - {"name": "multiply", "args": {"a": 14, "b": 2}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 28}}, - }, - ), - Datapoint( - name="TestMultipleNegatives", - input={"expression": "how much is (-2) * (-3) + (-5)"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 1.0}}, - "ContainsEvaluator": {"search_text": "1"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 1.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": -2, "b": -3}}, - {"name": "add", "args": {"a": 6, "b": -5}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 1}}, - }, - ), - Datapoint( - name="TestDecimalParentheses", - input={"expression": "how much is (0.5 + 1.5) * 2.5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 5.0}}, - "ContainsEvaluator": {"search_text": "5"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 5.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "add", "args": {"a": 0.5, "b": 1.5}}, - {"name": "multiply", "args": {"a": 2, "b": 2.5}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 5}}, - }, - ), - Datapoint( - name="TestMissingNumberSimple", - input={"expression": "how much is x + 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 22.0}}, - "ContainsEvaluator": {"search_text": "22"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 22.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "add", "args": {"a": 17, "b": 5}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 22}}, - }, - ), - Datapoint( - name="TestMissingNumberMedium", - input={"expression": "how much is x + y * 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 102.0}}, - "ContainsEvaluator": {"search_text": "102"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 102.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 17, "b": 5}}, - {"name": "add", "args": {"a": 17, "b": 85}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 102}}, - }, - ), - Datapoint( - name="TestMissingNumberComplex", - input={"expression": "how much is x + y * z + 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 311.0}}, - "ContainsEvaluator": {"search_text": "311"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 311.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "multiply": ("=", 1), "add": ("=", 2)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "multiply", "add", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 17, "b": 17}}, - {"name": "add", "args": {"a": 17, "b": 289}}, - {"name": "add", "args": {"a": 306, "b": 5}} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 311}}, - }, - ), - Datapoint( - name="TestToolCallOutput1", - input={"expression": "how much is 10 + 15"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - "ContainsEvaluator": {"search_text": "25"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [{"name": "add", "args": {"a": 10, "b": 15}}]}, - "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "add", "output": "25"}]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, - }, - ), - Datapoint( - name="TestToolCallOutput2", - input={"expression": "how much is 6 * 7"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 42.0}}, - "ContainsEvaluator": {"search_text": "42"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 42.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply"]}, - "ToolCallArgsEvaluator": {"tool_calls": [{"name": "multiply", "args": {"a": 6, "b": 7}}]}, - "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "multiply", "output": "42"}]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 42}}, - }, - ), - Datapoint( - name="TestToolCallOutput3", - input={"expression": "how much is 100 + 50 * 2"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 200.0}}, - "ContainsEvaluator": {"search_text": "200"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 200.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - "ToolCallArgsEvaluator": {"tool_calls": [ - {"name": "multiply", "args": {"a": 50, "b": 2}}, - {"name": "add", "args": {"a": 100, "b": 100}} - ]}, - "ToolCallOutputEvaluator": {"tool_outputs": [ - {"name": "multiply", "output": "100"}, - {"name": "add", "output": "200"} - ]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 200}}, - }, - ), - Datapoint( - name="TestStrictJSONSimilarity1", - input={"expression": "how much is 12 + 13"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - "ContainsEvaluator": {"search_text": "25"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 25.0}}, - }, - ), - Datapoint( - name="TestStrictJSONSimilarity2", - input={"expression": "how much is 9 * 9"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 81.0}}, - "ContainsEvaluator": {"search_text": "81"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 81.0}}, - "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 81.0}}, - }, - ), - Datapoint( - name="TestTrajectory1", - input={"expression": "how much is 4 + 5 * 3"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 19.0}}, - "ContainsEvaluator": {"search_text": "19"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 19.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - "LLMJudgeTrajectoryEvaluator": { - "expected_agent_behavior": "The agent should follow the order of operations, first multiplying 5 and 3 to get 15, then adding 4 to get 19." - }, - }, - ), - Datapoint( - name="TestTrajectory2", - input={"expression": "how much is (1 + 2) * (3 + 4)"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 21.0}}, - "ContainsEvaluator": {"search_text": "21"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 21.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, - "LLMJudgeTrajectoryEvaluator": { - "expected_agent_behavior": "The agent should evaluate parentheses first: add 1 and 2 to get 3, add 3 and 4 to get 7, then multiply 3 and 7 to get 21." - }, - }, - ), - Datapoint( - name="TestTrajectory3", - input={"expression": "how much is 2 * 3 + 4 * 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 26.0}}, - "ContainsEvaluator": {"search_text": "26"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 26.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 2), "add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, - "LLMJudgeTrajectoryEvaluator": { - "expected_agent_behavior": "The agent should perform multiplications first (2*3=6 and 4*5=20), then add the results (6+20=26)." - }, - }, - ), - Datapoint( - name="TestTrajectorySimulation1", - input={"expression": "how much is a + 10"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 27.0}}, - "ContainsEvaluator": {"search_text": "27"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 27.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, - "LLMJudgeTrajectorySimulationEvaluator": { - "expected_agent_behavior": "The agent should recognize that 'a' is an unknown variable, escalate to get its value (17), then add 10 to get 27." - }, - }, - simulation_instructions="When the agent asks for the value of 'a', respond with 17.", - ), - Datapoint( - name="TestTrajectorySimulation2", - input={"expression": "how much is m * n + 5"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 294.0}}, - "ContainsEvaluator": {"search_text": "294"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 294.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, - "LLMJudgeTrajectorySimulationEvaluator": { - "expected_agent_behavior": "The agent should identify two missing variables (m and n), escalate twice to get their values (both 17), multiply them to get 289, then add 5 to get 294." - }, - }, - simulation_instructions="When the agent asks for missing values, respond with 17 for each variable.", - ), - Datapoint( - name="TestTrajectorySimulation3", - input={"expression": "how much is (p + q) * r"}, - evaluation_criteria={ - "ExactMatchEvaluator": {"expected_output": {"answer": 578.0}}, - "ContainsEvaluator": {"search_text": "578"}, - "JsonSimilarityEvaluator": {"expected_output": {"answer": 578.0}}, - "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "add": ("=", 1), "multiply": ("=", 1)}}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "add", "multiply"]}, - "LLMJudgeTrajectorySimulationEvaluator": { - "expected_agent_behavior": "The agent should recognize three missing variables (p, q, r), escalate three times to get their values (all 17), add p and q (17+17=34), then multiply by r (34*17=578)." - }, - }, - simulation_instructions="When the agent asks for missing values, respond with 17 for each variable (p, q, and r).", - ), + # Datapoint( + # name="TestCompoundOperation1", + # input={"expression": "how much is 2 + 2 * 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 12.0}}, + # "ContainsEvaluator": {"search_text": "12"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 12.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 2, "b": 5}}, + # {"name": "add", "args": {"a": 2, "b": 10}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 12}}, + # }, + # ), + # Datapoint( + # name="TestCompoundOperation2", + # input={"expression": "how much is 3 * 5 + 2 * 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + # "ContainsEvaluator": {"search_text": "25"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 3, "b": 5}}, + # {"name": "multiply", "args": {"a": 2, "b": 5}}, + # {"name": "add", "args": {"a": 15, "b": 10}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + # }, + # ), + # Datapoint( + # name="TestCompoundOperation3", + # input={"expression": "how much is 3 * (5 + 5) * 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 150.0}}, + # "ContainsEvaluator": {"search_text": "150"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 150.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "multiply"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "add", "args": {"a": 5, "b": 5}}, + # {"name": "multiply", "args": {"a": 3, "b": 10}}, + # {"name": "multiply", "args": {"a": 30, "b": 5}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 150}}, + # }, + # ), + # Datapoint( + # name="TestCompoundOperation4", + # input={"expression": "how much is 2 + 3 * 4"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 14.0}}, + # "ContainsEvaluator": {"search_text": "14"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 14.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 3, "b": 4}}, + # {"name": "add", "args": {"a": 2, "b": 12}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 14}}, + # }, + # ), + # Datapoint( + # name="TestCompoundOperation5", + # input={"expression": "how much is (2 + 3) * (4 + 1)"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + # "ContainsEvaluator": {"search_text": "25"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "add", "args": {"a": 2, "b": 3}}, + # {"name": "add", "args": {"a": 4, "b": 1}}, + # {"name": "multiply", "args": {"a": 5, "b": 5}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + # }, + # ), + # Datapoint( + # name="TestCompoundOperation6", + # input={"expression": "how much is 10 * 0.5 + 3"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 8.0}}, + # "ContainsEvaluator": {"search_text": "8"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 8.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 10, "b": 0.5}}, + # {"name": "add", "args": {"a": 5, "b": 3}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 8}}, + # }, + # ), + # Datapoint( + # name="TestCompoundOperation7", + # input={"expression": "how much is 5 * 5 + (-10)"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 15.0}}, + # "ContainsEvaluator": {"search_text": "15"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 15.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 5, "b": 5}}, + # {"name": "add", "args": {"a": 25, "b": -10}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 15}}, + # }, + # ), + # Datapoint( + # name="TestCompoundOperation8", + # input={"expression": "how much is (8 + (-3)) * (2 + 2)"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 20.0}}, + # "ContainsEvaluator": {"search_text": "20"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 20.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "add", "args": {"a": 8, "b": -3}}, + # {"name": "add", "args": {"a": 2, "b": 2}}, + # {"name": "multiply", "args": {"a": 5, "b": 4}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 20}}, + # }, + # ), + # Datapoint( + # name="TestNestedParentheses", + # input={"expression": "how much is (2 * (3 + 4)) * (1 + 1)"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 28.0}}, + # "ContainsEvaluator": {"search_text": "28"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 28.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 2)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "add", "multiply"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "add", "args": {"a": 3, "b": 4}}, + # {"name": "multiply", "args": {"a": 2, "b": 7}}, + # {"name": "add", "args": {"a": 1, "b": 1}}, + # {"name": "multiply", "args": {"a": 14, "b": 2}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 28}}, + # }, + # ), + # Datapoint( + # name="TestMultipleNegatives", + # input={"expression": "how much is (-2) * (-3) + (-5)"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 1.0}}, + # "ContainsEvaluator": {"search_text": "1"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 1.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": -2, "b": -3}}, + # {"name": "add", "args": {"a": 6, "b": -5}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 1}}, + # }, + # ), + # Datapoint( + # name="TestDecimalParentheses", + # input={"expression": "how much is (0.5 + 1.5) * 2.5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 5.0}}, + # "ContainsEvaluator": {"search_text": "5"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 5.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "add", "args": {"a": 0.5, "b": 1.5}}, + # {"name": "multiply", "args": {"a": 2, "b": 2.5}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 5}}, + # }, + # ), + # Datapoint( + # name="TestMissingNumberSimple", + # input={"expression": "how much is x + 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 22.0}}, + # "ContainsEvaluator": {"search_text": "22"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 22.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "add", "args": {"a": 17, "b": 5}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 22}}, + # }, + # ), + # Datapoint( + # name="TestMissingNumberMedium", + # input={"expression": "how much is x + y * 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 102.0}}, + # "ContainsEvaluator": {"search_text": "102"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 102.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 17, "b": 5}}, + # {"name": "add", "args": {"a": 17, "b": 85}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 102}}, + # }, + # ), + # Datapoint( + # name="TestMissingNumberComplex", + # input={"expression": "how much is x + y * z + 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 311.0}}, + # "ContainsEvaluator": {"search_text": "311"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 311.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "multiply": ("=", 1), "add": ("=", 2)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "multiply", "add", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 17, "b": 17}}, + # {"name": "add", "args": {"a": 17, "b": 289}}, + # {"name": "add", "args": {"a": 306, "b": 5}} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 311}}, + # }, + # ), + # Datapoint( + # name="TestToolCallOutput1", + # input={"expression": "how much is 10 + 15"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + # "ContainsEvaluator": {"search_text": "25"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [{"name": "add", "args": {"a": 10, "b": 15}}]}, + # "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "add", "output": "25"}]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + # }, + # ), + # Datapoint( + # name="TestToolCallOutput2", + # input={"expression": "how much is 6 * 7"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 42.0}}, + # "ContainsEvaluator": {"search_text": "42"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 42.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [{"name": "multiply", "args": {"a": 6, "b": 7}}]}, + # "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "multiply", "output": "42"}]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 42}}, + # }, + # ), + # Datapoint( + # name="TestToolCallOutput3", + # input={"expression": "how much is 100 + 50 * 2"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 200.0}}, + # "ContainsEvaluator": {"search_text": "200"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 200.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + # "ToolCallArgsEvaluator": {"tool_calls": [ + # {"name": "multiply", "args": {"a": 50, "b": 2}}, + # {"name": "add", "args": {"a": 100, "b": 100}} + # ]}, + # "ToolCallOutputEvaluator": {"tool_outputs": [ + # {"name": "multiply", "output": "100"}, + # {"name": "add", "output": "200"} + # ]}, + # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 200}}, + # }, + # ), + # Datapoint( + # name="TestStrictJSONSimilarity1", + # input={"expression": "how much is 12 + 13"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + # "ContainsEvaluator": {"search_text": "25"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + # "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 25.0}}, + # }, + # ), + # Datapoint( + # name="TestStrictJSONSimilarity2", + # input={"expression": "how much is 9 * 9"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 81.0}}, + # "ContainsEvaluator": {"search_text": "81"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 81.0}}, + # "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 81.0}}, + # }, + # ), + # Datapoint( + # name="TestTrajectory1", + # input={"expression": "how much is 4 + 5 * 3"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 19.0}}, + # "ContainsEvaluator": {"search_text": "19"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 19.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + # "LLMJudgeTrajectoryEvaluator": { + # "expected_agent_behavior": "The agent should follow the order of operations, first multiplying 5 and 3 to get 15, then adding 4 to get 19." + # }, + # }, + # ), + # Datapoint( + # name="TestTrajectory2", + # input={"expression": "how much is (1 + 2) * (3 + 4)"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 21.0}}, + # "ContainsEvaluator": {"search_text": "21"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 21.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + # "LLMJudgeTrajectoryEvaluator": { + # "expected_agent_behavior": "The agent should evaluate parentheses first: add 1 and 2 to get 3, add 3 and 4 to get 7, then multiply 3 and 7 to get 21." + # }, + # }, + # ), + # Datapoint( + # name="TestTrajectory3", + # input={"expression": "how much is 2 * 3 + 4 * 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 26.0}}, + # "ContainsEvaluator": {"search_text": "26"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 26.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 2), "add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, + # "LLMJudgeTrajectoryEvaluator": { + # "expected_agent_behavior": "The agent should perform multiplications first (2*3=6 and 4*5=20), then add the results (6+20=26)." + # }, + # }, + # ), + # Datapoint( + # name="TestTrajectorySimulation1", + # input={"expression": "how much is a + 10"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 27.0}}, + # "ContainsEvaluator": {"search_text": "27"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 27.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, + # "LLMJudgeTrajectorySimulationEvaluator": { + # "expected_agent_behavior": "The agent should recognize that 'a' is an unknown variable, escalate to get its value (17), then add 10 to get 27." + # }, + # }, + # simulation_instructions="When the agent asks for the value of 'a', respond with 17.", + # ), + # Datapoint( + # name="TestTrajectorySimulation2", + # input={"expression": "how much is m * n + 5"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 294.0}}, + # "ContainsEvaluator": {"search_text": "294"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 294.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, + # "LLMJudgeTrajectorySimulationEvaluator": { + # "expected_agent_behavior": "The agent should identify two missing variables (m and n), escalate twice to get their values (both 17), multiply them to get 289, then add 5 to get 294." + # }, + # }, + # simulation_instructions="When the agent asks for missing values, respond with 17 for each variable.", + # ), + # Datapoint( + # name="TestTrajectorySimulation3", + # input={"expression": "how much is (p + q) * r"}, + # evaluation_criteria={ + # "ExactMatchEvaluator": {"expected_output": {"answer": 578.0}}, + # "ContainsEvaluator": {"search_text": "578"}, + # "JsonSimilarityEvaluator": {"expected_output": {"answer": 578.0}}, + # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "add": ("=", 1), "multiply": ("=", 1)}}, + # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "add", "multiply"]}, + # "LLMJudgeTrajectorySimulationEvaluator": { + # "expected_agent_behavior": "The agent should recognize three missing variables (p, q, r), escalate three times to get their values (all 17), add p and q (17+17=34), then multiply by r (34*17=578)." + # }, + # }, + # simulation_instructions="When the agent asks for missing values, respond with 17 for each variable (p, q, and r).", + # ), ] diff --git a/samples/gym-sample/src/gym_sample/calculator/evals.py b/samples/gym-sample/src/gym_sample/calculator/evals.py index 52dd6cf2..6209b4ef 100644 --- a/samples/gym-sample/src/gym_sample/calculator/evals.py +++ b/samples/gym-sample/src/gym_sample/calculator/evals.py @@ -1,6 +1,6 @@ from typing import List from uipath.eval.evaluators import LegacyBaseEvaluator -from uipath.eval.coded_evaluators import ( +from uipath.eval.evaluators import ( BaseEvaluator, ExactMatchEvaluator, ContainsEvaluator, @@ -11,7 +11,6 @@ ToolCallArgsEvaluator, ToolCallOutputEvaluator, LLMJudgeOutputEvaluator, - LLMJudgeStrictJSONSimilarityOutputEvaluator, LLMJudgeTrajectoryEvaluator, LLMJudgeTrajectorySimulationEvaluator, ) diff --git a/samples/gym-sample/src/gym_sample/graph.py b/samples/gym-sample/src/gym_sample/graph.py index f87159d3..8bc092dd 100644 --- a/samples/gym-sample/src/gym_sample/graph.py +++ b/samples/gym-sample/src/gym_sample/graph.py @@ -1,12 +1,11 @@ from collections.abc import Callable -import os from typing import AsyncGenerator, Dict, List from contextlib import asynccontextmanager from langchain_core.language_models.chat_models import BaseChatModel from langgraph.graph import StateGraph from uipath.eval.evaluators import LegacyBaseEvaluator -from uipath.eval.coded_evaluators import BaseEvaluator +from uipath.eval.evaluators import BaseEvaluator from gym_sample.uipath_gym_types import ( AgentBaseClass, BasicLoop, diff --git a/samples/gym-sample/src/gym_sample/loan/agent.py b/samples/gym-sample/src/gym_sample/loan/agent.py index 4be34709..18ecf632 100644 --- a/samples/gym-sample/src/gym_sample/loan/agent.py +++ b/samples/gym-sample/src/gym_sample/loan/agent.py @@ -1,6 +1,6 @@ from typing import List -from langchain.tools import BaseTool, StructuredTool +from langchain_core.tools import BaseTool, StructuredTool from pydantic import BaseModel, Field from ..tools import EscalationTool diff --git a/samples/gym-sample/src/gym_sample/loan/evals.py b/samples/gym-sample/src/gym_sample/loan/evals.py index a123418c..4a4d452f 100644 --- a/samples/gym-sample/src/gym_sample/loan/evals.py +++ b/samples/gym-sample/src/gym_sample/loan/evals.py @@ -1,6 +1,6 @@ from typing import List from uipath.eval.evaluators import LegacyBaseEvaluator -from uipath.eval.coded_evaluators import ( +from uipath.eval.evaluators import ( BaseEvaluator, ExactMatchEvaluator, ContainsEvaluator, diff --git a/samples/gym-sample/src/gym_sample/run.py b/samples/gym-sample/src/gym_sample/run.py index 7342bc74..c800ae16 100644 --- a/samples/gym-sample/src/gym_sample/run.py +++ b/samples/gym-sample/src/gym_sample/run.py @@ -9,7 +9,7 @@ from langgraph.graph import StateGraph from uipath.eval.evaluators.base_evaluator import EvaluationResult from uipath.eval.evaluators import LegacyBaseEvaluator -from uipath.eval.coded_evaluators import BaseEvaluator +from uipath.eval.evaluators import BaseEvaluator from uipath.eval.models import AgentExecution from gym_sample.graph import agents_with_datapoints, get_all_evaluators from gym_sample.trace_utils import setup_tracer diff --git a/samples/gym-sample/src/gym_sample/uipath_gym_types.py b/samples/gym-sample/src/gym_sample/uipath_gym_types.py index 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a/samples/gym-sample/pyproject.toml b/samples/gym-sample/pyproject.toml index 01ab15da..57744830 100644 --- a/samples/gym-sample/pyproject.toml +++ b/samples/gym-sample/pyproject.toml @@ -12,7 +12,7 @@ dependencies = [ "langchain-core>=0.3.34", "langgraph-checkpoint-sqlite>=2.0.3", "python-dotenv>=1.0.1", - "uipath-langchain>=0.0.140", + "uipath-langchain>=0.0.142.dev1002350000,<0.0.142.dev1002360000", "uipath", "pydantic>=2.10.6", "aiohttp>=3.11.12", @@ -26,5 +26,14 @@ requires-python = ">=3.10" gym-eval = "gym_sample.run:main" gym-export = "gym_sample.export_evals:main" +[[tool.uv.index]] +name = "testpypi" +url = "https://test.pypi.org/simple/" +publish-url = "https://test.pypi.org/legacy/" +explicit = true + +[tool.uv.sources] +uipath-langchain = { index = "testpypi" } + [tool.setuptools.packages.find] where = ["src"] diff --git a/samples/gym-sample/src/gym_sample/loan/agent.py b/samples/gym-sample/src/gym_sample/loan/agent.py index 18ecf632..85cf9091 100644 --- a/samples/gym-sample/src/gym_sample/loan/agent.py +++ b/samples/gym-sample/src/gym_sample/loan/agent.py @@ -171,9 +171,8 @@ def get_datapoints() -> List[Datapoint]: evaluation_criteria={ "ExactMatchEvaluator": {"expected_output": {"ActionCenterTaskCreated": True}}, "ContainsEvaluator": {"search_text": "age"}, - "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "EscalationTool"]}, - "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "EscalationTool": ("=", 1)}}, - "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "anna@email.com", "LoanAmount": 5000, "LoanTerm": 3, "Income": 500, "Age": 17}}]}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["EscalationTool"]}, + "ToolCallCountEvaluator": {"tool_calls_count": {"EscalationTool": ("=", 1)}}, "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "Invalid data detected: Age is below the minimum age 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--- .../src/gym_sample/calculator/agent.py | 770 +++++++++--------- .../gym-sample/src/gym_sample/loan/agent.py | 2 +- 2 files changed, 386 insertions(+), 386 deletions(-) diff --git a/samples/gym-sample/src/gym_sample/calculator/agent.py b/samples/gym-sample/src/gym_sample/calculator/agent.py index 7ce046d2..2b00bfc0 100644 --- a/samples/gym-sample/src/gym_sample/calculator/agent.py +++ b/samples/gym-sample/src/gym_sample/calculator/agent.py @@ -55,391 +55,391 @@ def get_datapoints() -> List[Datapoint]: "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 3}}, }, ), - # Datapoint( - # name="TestCompoundOperation1", - # input={"expression": "how much is 2 + 2 * 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 12.0}}, - # "ContainsEvaluator": {"search_text": "12"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 12.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 2, "b": 5}}, - # {"name": "add", "args": {"a": 2, "b": 10}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 12}}, - # }, - # ), - # Datapoint( - # name="TestCompoundOperation2", - # input={"expression": "how much is 3 * 5 + 2 * 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - # "ContainsEvaluator": {"search_text": "25"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 3, "b": 5}}, - # {"name": "multiply", "args": {"a": 2, "b": 5}}, - # {"name": "add", "args": {"a": 15, "b": 10}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, - # }, - # ), - # Datapoint( - # name="TestCompoundOperation3", - # input={"expression": "how much is 3 * (5 + 5) * 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 150.0}}, - # "ContainsEvaluator": {"search_text": "150"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 150.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "multiply"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "add", "args": {"a": 5, "b": 5}}, - # {"name": "multiply", "args": {"a": 3, "b": 10}}, - # {"name": "multiply", "args": {"a": 30, "b": 5}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 150}}, - # }, - # ), - # Datapoint( - # name="TestCompoundOperation4", - # input={"expression": "how much is 2 + 3 * 4"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 14.0}}, - # "ContainsEvaluator": {"search_text": "14"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 14.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 3, "b": 4}}, - # {"name": "add", "args": {"a": 2, "b": 12}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 14}}, - # }, - # ), - # Datapoint( - # name="TestCompoundOperation5", - # input={"expression": "how much is (2 + 3) * (4 + 1)"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - # "ContainsEvaluator": {"search_text": "25"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "add", "args": {"a": 2, "b": 3}}, - # {"name": "add", "args": {"a": 4, "b": 1}}, - # {"name": "multiply", "args": {"a": 5, "b": 5}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, - # }, - # ), - # Datapoint( - # name="TestCompoundOperation6", - # input={"expression": "how much is 10 * 0.5 + 3"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 8.0}}, - # "ContainsEvaluator": {"search_text": "8"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 8.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 10, "b": 0.5}}, - # {"name": "add", "args": {"a": 5, "b": 3}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 8}}, - # }, - # ), - # Datapoint( - # name="TestCompoundOperation7", - # input={"expression": "how much is 5 * 5 + (-10)"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 15.0}}, - # "ContainsEvaluator": {"search_text": "15"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 15.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 5, "b": 5}}, - # {"name": "add", "args": {"a": 25, "b": -10}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 15}}, - # }, - # ), - # Datapoint( - # name="TestCompoundOperation8", - # input={"expression": "how much is (8 + (-3)) * (2 + 2)"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 20.0}}, - # "ContainsEvaluator": {"search_text": "20"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 20.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "add", "args": {"a": 8, "b": -3}}, - # {"name": "add", "args": {"a": 2, "b": 2}}, - # {"name": "multiply", "args": {"a": 5, "b": 4}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 20}}, - # }, - # ), - # Datapoint( - # name="TestNestedParentheses", - # input={"expression": "how much is (2 * (3 + 4)) * (1 + 1)"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 28.0}}, - # "ContainsEvaluator": {"search_text": "28"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 28.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 2)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "add", "multiply"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "add", "args": {"a": 3, "b": 4}}, - # {"name": "multiply", "args": {"a": 2, "b": 7}}, - # {"name": "add", "args": {"a": 1, "b": 1}}, - # {"name": "multiply", "args": {"a": 14, "b": 2}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 28}}, - # }, - # ), - # Datapoint( - # name="TestMultipleNegatives", - # input={"expression": "how much is (-2) * (-3) + (-5)"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 1.0}}, - # "ContainsEvaluator": {"search_text": "1"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 1.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": -2, "b": -3}}, - # {"name": "add", "args": {"a": 6, "b": -5}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 1}}, - # }, - # ), - # Datapoint( - # name="TestDecimalParentheses", - # input={"expression": "how much is (0.5 + 1.5) * 2.5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 5.0}}, - # "ContainsEvaluator": {"search_text": "5"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 5.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "add", "args": {"a": 0.5, "b": 1.5}}, - # {"name": "multiply", "args": {"a": 2, "b": 2.5}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 5}}, - # }, - # ), - # Datapoint( - # name="TestMissingNumberSimple", - # input={"expression": "how much is x + 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 22.0}}, - # "ContainsEvaluator": {"search_text": "22"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 22.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "add", "args": {"a": 17, "b": 5}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 22}}, - # }, - # ), - # Datapoint( - # name="TestMissingNumberMedium", - # input={"expression": "how much is x + y * 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 102.0}}, - # "ContainsEvaluator": {"search_text": "102"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 102.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 17, "b": 5}}, - # {"name": "add", "args": {"a": 17, "b": 85}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 102}}, - # }, - # ), - # Datapoint( - # name="TestMissingNumberComplex", - # input={"expression": "how much is x + y * z + 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 311.0}}, - # "ContainsEvaluator": {"search_text": "311"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 311.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "multiply": ("=", 1), "add": ("=", 2)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "multiply", "add", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 17, "b": 17}}, - # {"name": "add", "args": {"a": 17, "b": 289}}, - # {"name": "add", "args": {"a": 306, "b": 5}} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 311}}, - # }, - # ), - # Datapoint( - # name="TestToolCallOutput1", - # input={"expression": "how much is 10 + 15"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - # "ContainsEvaluator": {"search_text": "25"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [{"name": "add", "args": {"a": 10, "b": 15}}]}, - # "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "add", "output": "25"}]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, - # }, - # ), - # Datapoint( - # name="TestToolCallOutput2", - # input={"expression": "how much is 6 * 7"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 42.0}}, - # "ContainsEvaluator": {"search_text": "42"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 42.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [{"name": "multiply", "args": {"a": 6, "b": 7}}]}, - # "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "multiply", "output": "42"}]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 42}}, - # }, - # ), - # Datapoint( - # name="TestToolCallOutput3", - # input={"expression": "how much is 100 + 50 * 2"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 200.0}}, - # "ContainsEvaluator": {"search_text": "200"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 200.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - # "ToolCallArgsEvaluator": {"tool_calls": [ - # {"name": "multiply", "args": {"a": 50, "b": 2}}, - # {"name": "add", "args": {"a": 100, "b": 100}} - # ]}, - # "ToolCallOutputEvaluator": {"tool_outputs": [ - # {"name": "multiply", "output": "100"}, - # {"name": "add", "output": "200"} - # ]}, - # "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 200}}, - # }, - # ), - # Datapoint( - # name="TestStrictJSONSimilarity1", - # input={"expression": "how much is 12 + 13"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, - # "ContainsEvaluator": {"search_text": "25"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, - # "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 25.0}}, - # }, - # ), - # Datapoint( - # name="TestStrictJSONSimilarity2", - # input={"expression": "how much is 9 * 9"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 81.0}}, - # "ContainsEvaluator": {"search_text": "81"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 81.0}}, - # "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 81.0}}, - # }, - # ), - # Datapoint( - # name="TestTrajectory1", - # input={"expression": "how much is 4 + 5 * 3"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 19.0}}, - # "ContainsEvaluator": {"search_text": "19"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 19.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, - # "LLMJudgeTrajectoryEvaluator": { - # "expected_agent_behavior": "The agent should follow the order of operations, first multiplying 5 and 3 to get 15, then adding 4 to get 19." - # }, - # }, - # ), - # Datapoint( - # name="TestTrajectory2", - # input={"expression": "how much is (1 + 2) * (3 + 4)"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 21.0}}, - # "ContainsEvaluator": {"search_text": "21"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 21.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, - # "LLMJudgeTrajectoryEvaluator": { - # "expected_agent_behavior": "The agent should evaluate parentheses first: add 1 and 2 to get 3, add 3 and 4 to get 7, then multiply 3 and 7 to get 21." - # }, - # }, - # ), - # Datapoint( - # name="TestTrajectory3", - # input={"expression": "how much is 2 * 3 + 4 * 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 26.0}}, - # "ContainsEvaluator": {"search_text": "26"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 26.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 2), "add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, - # "LLMJudgeTrajectoryEvaluator": { - # "expected_agent_behavior": "The agent should perform multiplications first (2*3=6 and 4*5=20), then add the results (6+20=26)." - # }, - # }, - # ), - # Datapoint( - # name="TestTrajectorySimulation1", - # input={"expression": "how much is a + 10"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 27.0}}, - # "ContainsEvaluator": {"search_text": "27"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 27.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, - # "LLMJudgeTrajectorySimulationEvaluator": { - # "expected_agent_behavior": "The agent should recognize that 'a' is an unknown variable, escalate to get its value (17), then add 10 to get 27." - # }, - # }, - # simulation_instructions="When the agent asks for the value of 'a', respond with 17.", - # ), - # Datapoint( - # name="TestTrajectorySimulation2", - # input={"expression": "how much is m * n + 5"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 294.0}}, - # "ContainsEvaluator": {"search_text": "294"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 294.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, - # "LLMJudgeTrajectorySimulationEvaluator": { - # "expected_agent_behavior": "The agent should identify two missing variables (m and n), escalate twice to get their values (both 17), multiply them to get 289, then add 5 to get 294." - # }, - # }, - # simulation_instructions="When the agent asks for missing values, respond with 17 for each variable.", - # ), - # Datapoint( - # name="TestTrajectorySimulation3", - # input={"expression": "how much is (p + q) * r"}, - # evaluation_criteria={ - # "ExactMatchEvaluator": {"expected_output": {"answer": 578.0}}, - # "ContainsEvaluator": {"search_text": "578"}, - # "JsonSimilarityEvaluator": {"expected_output": {"answer": 578.0}}, - # "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "add": ("=", 1), "multiply": ("=", 1)}}, - # "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "add", "multiply"]}, - # "LLMJudgeTrajectorySimulationEvaluator": { - # "expected_agent_behavior": "The agent should recognize three missing variables (p, q, r), escalate three times to get their values (all 17), add p and q (17+17=34), then multiply by r (34*17=578)." - # }, - # }, - # simulation_instructions="When the agent asks for missing values, respond with 17 for each variable (p, q, and r).", - # ), + Datapoint( + name="TestCompoundOperation1", + input={"expression": "how much is 2 + 2 * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 12.0}}, + "ContainsEvaluator": {"search_text": "12"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 12.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 2, "b": 5}}, + {"name": "add", "args": {"a": 2, "b": 10}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 12}}, + }, + ), + Datapoint( + name="TestCompoundOperation2", + input={"expression": "how much is 3 * 5 + 2 * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 3, "b": 5}}, + {"name": "multiply", "args": {"a": 2, "b": 5}}, + {"name": "add", "args": {"a": 15, "b": 10}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + }, + ), + Datapoint( + name="TestCompoundOperation3", + input={"expression": "how much is 3 * (5 + 5) * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 150.0}}, + "ContainsEvaluator": {"search_text": "150"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 150.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 5, "b": 5}}, + {"name": "multiply", "args": {"a": 3, "b": 10}}, + {"name": "multiply", "args": {"a": 30, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 150}}, + }, + ), + Datapoint( + name="TestCompoundOperation4", + input={"expression": "how much is 2 + 3 * 4"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 14.0}}, + "ContainsEvaluator": {"search_text": "14"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 14.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 3, "b": 4}}, + {"name": "add", "args": {"a": 2, "b": 12}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 14}}, + }, + ), + Datapoint( + name="TestCompoundOperation5", + input={"expression": "how much is (2 + 3) * (4 + 1)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 2, "b": 3}}, + {"name": "add", "args": {"a": 4, "b": 1}}, + {"name": "multiply", "args": {"a": 5, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + }, + ), + Datapoint( + name="TestCompoundOperation6", + input={"expression": "how much is 10 * 0.5 + 3"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 8.0}}, + "ContainsEvaluator": {"search_text": "8"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 8.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 10, "b": 0.5}}, + {"name": "add", "args": {"a": 5, "b": 3}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 8}}, + }, + ), + Datapoint( + name="TestCompoundOperation7", + input={"expression": "how much is 5 * 5 + (-10)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 15.0}}, + "ContainsEvaluator": {"search_text": "15"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 15.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 5, "b": 5}}, + {"name": "add", "args": {"a": 25, "b": -10}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 15}}, + }, + ), + Datapoint( + name="TestCompoundOperation8", + input={"expression": "how much is (8 + (-3)) * (2 + 2)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 20.0}}, + "ContainsEvaluator": {"search_text": "20"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 20.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 8, "b": -3}}, + {"name": "add", "args": {"a": 2, "b": 2}}, + {"name": "multiply", "args": {"a": 5, "b": 4}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 20}}, + }, + ), + Datapoint( + name="TestNestedParentheses", + input={"expression": "how much is (2 * (3 + 4)) * (1 + 1)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 28.0}}, + "ContainsEvaluator": {"search_text": "28"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 28.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply", "add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 3, "b": 4}}, + {"name": "multiply", "args": {"a": 2, "b": 7}}, + {"name": "add", "args": {"a": 1, "b": 1}}, + {"name": "multiply", "args": {"a": 14, "b": 2}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 28}}, + }, + ), + Datapoint( + name="TestMultipleNegatives", + input={"expression": "how much is (-2) * (-3) + (-5)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 1.0}}, + "ContainsEvaluator": {"search_text": "1"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 1.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": -2, "b": -3}}, + {"name": "add", "args": {"a": 6, "b": -5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 1}}, + }, + ), + Datapoint( + name="TestDecimalParentheses", + input={"expression": "how much is (0.5 + 1.5) * 2.5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 5.0}}, + "ContainsEvaluator": {"search_text": "5"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 5.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 0.5, "b": 1.5}}, + {"name": "multiply", "args": {"a": 2, "b": 2.5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 5}}, + }, + ), + Datapoint( + name="TestMissingNumberSimple", + input={"expression": "how much is x + 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 22.0}}, + "ContainsEvaluator": {"search_text": "22"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 22.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "add", "args": {"a": 17, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 22}}, + }, + ), + Datapoint( + name="TestMissingNumberMedium", + input={"expression": "how much is x + y * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 102.0}}, + "ContainsEvaluator": {"search_text": "102"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 102.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 17, "b": 5}}, + {"name": "add", "args": {"a": 17, "b": 85}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 102}}, + }, + ), + Datapoint( + name="TestMissingNumberComplex", + input={"expression": "how much is x + y * z + 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 311.0}}, + "ContainsEvaluator": {"search_text": "311"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 311.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "multiply": ("=", 1), "add": ("=", 2)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "multiply", "add", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 17, "b": 17}}, + {"name": "add", "args": {"a": 17, "b": 289}}, + {"name": "add", "args": {"a": 306, "b": 5}} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 311}}, + }, + ), + Datapoint( + name="TestToolCallOutput1", + input={"expression": "how much is 10 + 15"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "add", "args": {"a": 10, "b": 15}}]}, + "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "add", "output": "25"}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 25}}, + }, + ), + Datapoint( + name="TestToolCallOutput2", + input={"expression": "how much is 6 * 7"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 42.0}}, + "ContainsEvaluator": {"search_text": "42"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 42.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply"]}, + "ToolCallArgsEvaluator": {"tool_calls": [{"name": "multiply", "args": {"a": 6, "b": 7}}]}, + "ToolCallOutputEvaluator": {"tool_outputs": [{"name": "multiply", "output": "42"}]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 42}}, + }, + ), + Datapoint( + name="TestToolCallOutput3", + input={"expression": "how much is 100 + 50 * 2"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 200.0}}, + "ContainsEvaluator": {"search_text": "200"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 200.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "ToolCallArgsEvaluator": {"tool_calls": [ + {"name": "multiply", "args": {"a": 50, "b": 2}}, + {"name": "add", "args": {"a": 100, "b": 100}} + ]}, + "ToolCallOutputEvaluator": {"tool_outputs": [ + {"name": "multiply", "output": "100"}, + {"name": "add", "output": "200"} + ]}, + "LLMJudgeOutputEvaluator": {"expected_output": {"answer": 200}}, + }, + ), + Datapoint( + name="TestStrictJSONSimilarity1", + input={"expression": "how much is 12 + 13"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 25.0}}, + "ContainsEvaluator": {"search_text": "25"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 25.0}}, + "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 25.0}}, + }, + ), + Datapoint( + name="TestStrictJSONSimilarity2", + input={"expression": "how much is 9 * 9"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 81.0}}, + "ContainsEvaluator": {"search_text": "81"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 81.0}}, + "LLMJudgeStrictJSONSimilarityOutputEvaluator": {"expected_output": {"answer": 81.0}}, + }, + ), + Datapoint( + name="TestTrajectory1", + input={"expression": "how much is 4 + 5 * 3"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 19.0}}, + "ContainsEvaluator": {"search_text": "19"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 19.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "add"]}, + "LLMJudgeTrajectoryEvaluator": { + "expected_agent_behavior": "The agent should follow the order of operations, first multiplying 5 and 3 to get 15, then adding 4 to get 19." + }, + }, + ), + Datapoint( + name="TestTrajectory2", + input={"expression": "how much is (1 + 2) * (3 + 4)"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 21.0}}, + "ContainsEvaluator": {"search_text": "21"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 21.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"add": ("=", 2), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["add", "add", "multiply"]}, + "LLMJudgeTrajectoryEvaluator": { + "expected_agent_behavior": "The agent should evaluate parentheses first: add 1 and 2 to get 3, add 3 and 4 to get 7, then multiply 3 and 7 to get 21." + }, + }, + ), + Datapoint( + name="TestTrajectory3", + input={"expression": "how much is 2 * 3 + 4 * 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 26.0}}, + "ContainsEvaluator": {"search_text": "26"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 26.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"multiply": ("=", 2), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["multiply", "multiply", "add"]}, + "LLMJudgeTrajectoryEvaluator": { + "expected_agent_behavior": "The agent should perform multiplications first (2*3=6 and 4*5=20), then add the results (6+20=26)." + }, + }, + ), + Datapoint( + name="TestTrajectorySimulation1", + input={"expression": "how much is a + 10"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 27.0}}, + "ContainsEvaluator": {"search_text": "27"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 27.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "add"]}, + "LLMJudgeTrajectorySimulationEvaluator": { + "expected_agent_behavior": "The agent should recognize that 'a' is an unknown variable, escalate to get its value (17), then add 10 to get 27." + }, + }, + simulation_instructions="When the agent asks for the value of 'a', respond with 17.", + ), + Datapoint( + name="TestTrajectorySimulation2", + input={"expression": "how much is m * n + 5"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 294.0}}, + "ContainsEvaluator": {"search_text": "294"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 294.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 2), "multiply": ("=", 1), "add": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "multiply", "add"]}, + "LLMJudgeTrajectorySimulationEvaluator": { + "expected_agent_behavior": "The agent should identify two missing variables (m and n), escalate twice to get their values (both 17), multiply them to get 289, then add 5 to get 294." + }, + }, + simulation_instructions="When the agent asks for missing values, respond with 17 for each variable.", + ), + Datapoint( + name="TestTrajectorySimulation3", + input={"expression": "how much is (p + q) * r"}, + evaluation_criteria={ + "ExactMatchEvaluator": {"expected_output": {"answer": 578.0}}, + "ContainsEvaluator": {"search_text": "578"}, + "JsonSimilarityEvaluator": {"expected_output": {"answer": 578.0}}, + "ToolCallCountEvaluator": {"tool_calls_count": {"escalation": ("=", 3), "add": ("=", 1), "multiply": ("=", 1)}}, + "ToolCallOrderEvaluator": {"tool_calls_order": ["escalation", "escalation", "escalation", "add", "multiply"]}, + "LLMJudgeTrajectorySimulationEvaluator": { + "expected_agent_behavior": "The agent should recognize three missing variables (p, q, r), escalate three times to get their values (all 17), add p and q (17+17=34), then multiply by r (34*17=578)." + }, + }, + simulation_instructions="When the agent asks for missing values, respond with 17 for each variable (p, q, and r).", + ), ] diff --git a/samples/gym-sample/src/gym_sample/loan/agent.py b/samples/gym-sample/src/gym_sample/loan/agent.py index 85cf9091..1e433924 100644 --- a/samples/gym-sample/src/gym_sample/loan/agent.py +++ b/samples/gym-sample/src/gym_sample/loan/agent.py @@ -306,7 +306,7 @@ def get_datapoints() -> List[Datapoint]: "ToolCallOrderEvaluator": {"tool_calls_order": ["LoanAgent_ApplyUiBankLoan", "Google", "LoanAgent_ModifyLoanContract", "LoanAgent_SendSharepointDocumentViaDocuSign"]}, "ToolCallCountEvaluator": {"tool_calls_count": {"LoanAgent_ApplyUiBankLoan": ("=", 1), "Google": ("=", 1), "LoanAgent_ModifyLoanContract": ("=", 1), "LoanAgent_SendSharepointDocumentViaDocuSign": ("=", 1)}}, "ToolCallArgsEvaluator": {"tool_calls": [{"name": "LoanAgent_ApplyUiBankLoan", "args": {"RequestorEmailAddress": "olivia.international@example.com", "LoanAmount": 60000, "LoanTerm": 5, "Income": 90000, "Age": 33}}, {"name": "LoanAgent_ModifyLoanContract", "args": {"SharepointFolderURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e", "InterestRate": 4, "LoanAmount": 60000, "BorrowerName": "Olivia International", "BorrowerAddress": "42 Global Way, London, United Kingdom"}}, {"name": "LoanAgent_SendSharepointDocumentViaDocuSign", "args": {"SharepointFileURL": "https://uipath-my.sharepoint.com/:f:/r/personal/random_uipath_com/Documents/Documents?csf=1&web=1&e=HDt89e/New_doc.pdf", "RecipientLegalName": "Olivia International", "RecipientEmail": "olivia.international@example.com"}}]}, - "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "International address application processed"}}, + "LLMJudgeOutputEvaluator": {"expected_output": {"ExecutionDetails": "International address loan application processed with a sequence of steps."}}, }, simulation_instructions="Tool LoanAgent_ApplyUiBankLoan should process international address application", ),