diff --git a/.github/workflows/check.yml b/.github/workflows/check.yml new file mode 100644 index 0000000..9313b10 --- /dev/null +++ b/.github/workflows/check.yml @@ -0,0 +1,44 @@ +# +# GitHub Action: Check +# +# This workflow is triggered on pushes to main and renovate branches, as well as +# pull requests. It runs backend checks to ensure code quality. +# +name: Check + +on: + push: + branches: + - main + - 'renovate/**' + pull_request: + +jobs: + test-testworkflows: + name: Runs on Ubuntu + runs-on: ubuntu-latest + + defaults: + run: + working-directory: testworkflows + + steps: + - name: Clone the code + uses: 'actions/checkout@v5' + + - name: Install uv + uses: 'astral-sh/setup-uv@v6' + with: + version: "0.8.17" + enable-cache: true + + - name: Setup Python + uses: 'actions/setup-python@v6' + with: + python-version-file: "testworkflows/.python-version" + + - name: Install Dependencies + run: uv sync + + - name: Run Checks + run: uv run poe check diff --git a/.github/workflows/test-e2e.yml b/.github/workflows/test-e2e.yml index 277e227..0ab8032 100644 --- a/.github/workflows/test-e2e.yml +++ b/.github/workflows/test-e2e.yml @@ -1,3 +1,9 @@ +# +# GitHub Action: E2E Tests +# +# This workflow is triggered on pushes to main and renovate branches, as well as +# pull requests. It runs E2E Tests to ensure the code is working. +# name: E2E Tests on: @@ -7,32 +13,67 @@ on: - 'renovate/**' pull_request: +env: + # Required environment variables for the E2E test + GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} + OPENAI_API_KEY: PLACEHOLDER + jobs: - test-e2e: + test: name: Run on Ubuntu runs-on: ubuntu-latest - permissions: - contents: 'read' + + defaults: + run: + working-directory: testworkflows steps: - name: Clone the code - uses: actions/checkout@v5 + uses: 'actions/checkout@v5' + + - name: Install uv + uses: 'astral-sh/setup-uv@v6' + with: + version: "0.8.17" + enable-cache: true - - name: Setup Go - uses: actions/setup-go@v6 + - name: Setup Python + uses: 'actions/setup-python@v6' with: - go-version-file: go.mod + python-version-file: "testworkflows/.python-version" + + - name: Install Dependencies + run: uv sync - - name: Install the latest version of kind + - name: Set up Kubernetes (kind) + uses: 'helm/kind-action@v1' + with: + cluster_name: testbench-cluster + + - name: Install Tilt run: | - curl -Lo ./kind https://kind.sigs.k8s.io/dl/latest/kind-linux-amd64 - chmod +x ./kind - sudo mv ./kind /usr/local/bin/kind + curl -fsSL https://raw.githubusercontent.com/tilt-dev/tilt/master/scripts/install.sh | bash + tilt version - - name: Verify kind installation - run: kind version + - name: Start test data server + run: uv run python -m http.server 8000 --directory tests/test_data & - - name: Running Test e2e + - name: Start Tiltfile services + run: tilt ci + working-directory: . + + - name: Wait for services to be ready run: | - go mod tidy - make test-e2e + echo "Waiting for services to be ready..." + kubectl wait --for=condition=ready pod -l app=weather-agent --timeout=300s || true + kubectl wait --for=condition=ready pod -l app=ai-gateway-litellm --timeout=300s || true + kubectl wait --for=condition=ready pod -l app.kubernetes.io/name=lgtm --timeout=300s || true + + echo "Checking service status..." + kubectl get pods -A + + echo "Waiting for test-data-server (HTTP server on port 8000)..." + curl --retry 30 --retry-delay 2 --retry-connrefused -f http://localhost:8000/dataset.json > /dev/null + + - name: Running Test e2e + run: uv run poe test_e2e diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index b78b1de..795fb82 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,3 +1,9 @@ +# +# GitHub Action: Test E2E +# +# This workflow is triggered on pushes to main and renovate branches, as well as +# pull requests. It runs Unit Tests to ensure the code is working. +# name: Tests on: @@ -14,16 +20,27 @@ jobs: permissions: contents: 'read' + defaults: + run: + working-directory: testworkflows + steps: - name: Clone the code - uses: actions/checkout@v5 + uses: 'actions/checkout@v5' + + - name: Install uv + uses: 'astral-sh/setup-uv@v6' + with: + version: "0.8.17" + enable-cache: true - - name: Setup Go - uses: actions/setup-go@v6 + - name: Setup Python + uses: 'actions/setup-python@v6' with: - go-version-file: go.mod + python-version-file: "testworkflows/.python-version" + + - name: Install Dependencies + run: uv sync - - name: Running Tests - run: | - go mod tidy - make test + - name: Run Tests + run: uv run poe test diff --git a/.gitignore b/.gitignore index ada68ff..2665848 100644 --- a/.gitignore +++ b/.gitignore @@ -25,3 +25,5 @@ go.work *.swp *.swo *~ + +.env \ No newline at end of file diff --git a/Tiltfile b/Tiltfile new file mode 100644 index 0000000..e32f833 --- /dev/null +++ b/Tiltfile @@ -0,0 +1,38 @@ +# -*- mode: Python -*- + +# Increase Kubernetes upsert timeout for CRD installations +update_settings(max_parallel_updates=10) + +# Load .env file for environment variables +load('ext://dotenv', 'dotenv') +dotenv() + +v1alpha1.extension_repo(name='agentic-layer', url='https://github.com/agentic-layer/tilt-extensions', ref='v0.3.1') + +v1alpha1.extension(name='cert-manager', repo_name='agentic-layer', repo_path='cert-manager') +load('ext://cert-manager', 'cert_manager_install') +cert_manager_install() + +v1alpha1.extension(name='agent-runtime', repo_name='agentic-layer', repo_path='agent-runtime') +load('ext://agent-runtime', 'agent_runtime_install') +agent_runtime_install(version='0.9.0') + +v1alpha1.extension(name='ai-gateway-litellm', repo_name='agentic-layer', repo_path='ai-gateway-litellm') +load('ext://ai-gateway-litellm', 'ai_gateway_litellm_install') +ai_gateway_litellm_install(version='0.2.0') + +# Webserver to serve test data (disabled in CI - manually started instead) +if not os.getenv('CI'): + local_resource( + 'test-data-server', + serve_cmd='python3 -m http.server 8000 --directory testworkflows/tests/test_data', + labels=['test-data'], + links=['http://localhost:8000/dataset.json'] + ) + +# Apply Kubernetes manifests +k8s_yaml(kustomize('deploy/local')) + +k8s_resource('ai-gateway-litellm', port_forwards=['11001:4000']) +k8s_resource('weather-agent', port_forwards='11010:8000', labels=['agents'], resource_deps=['agent-runtime']) +k8s_resource('lgtm', port_forwards=['11000:3000', '9090:9090', '4318:4318']) \ No newline at end of file diff --git a/deploy/local/kustomization.yaml b/deploy/local/kustomization.yaml new file mode 100644 index 0000000..331da87 --- /dev/null +++ b/deploy/local/kustomization.yaml @@ -0,0 +1,5 @@ +apiVersion: kustomize.config.k8s.io/v1beta1 +kind: Kustomization +resources: + - weather-agent.yaml + - lgtm.yaml diff --git a/deploy/local/lgtm.yaml b/deploy/local/lgtm.yaml new file mode 100644 index 0000000..7edc3b9 --- /dev/null +++ b/deploy/local/lgtm.yaml @@ -0,0 +1,86 @@ +# this is intended for demo / testing purposes only, not for production usage +apiVersion: v1 +kind: Namespace +metadata: + name: monitoring +--- +apiVersion: v1 +kind: Service +metadata: + name: lgtm + namespace: monitoring +spec: + selector: + app: lgtm + ports: + - name: grafana + protocol: TCP + port: 3000 + targetPort: 3000 + - name: otel-grpc + protocol: TCP + port: 4317 + targetPort: 4317 + - name: otel-http + protocol: TCP + port: 4318 + targetPort: 4318 + - name: prometheus + protocol: TCP + port: 9090 + targetPort: 9090 +--- +apiVersion: apps/v1 +kind: Deployment +metadata: + name: lgtm + namespace: monitoring +spec: + replicas: 1 + selector: + matchLabels: + app: lgtm + template: + metadata: + labels: + app: lgtm + spec: + containers: + - name: lgtm + image: grafana/otel-lgtm:latest + ports: + - containerPort: 3000 + - containerPort: 4317 + - containerPort: 4318 + - containerPort: 9090 + readinessProbe: + exec: + command: + - cat + - /tmp/ready + volumeMounts: + - name: tempo-data + mountPath: /data/tempo + - name: grafana-data + mountPath: /data/grafana + - name: loki-data + mountPath: /data/loki + - name: loki-storage + mountPath: /loki + - name: p8s-storage + mountPath: /data/prometheus + - name: pyroscope-storage + mountPath: /data/pyroscope + volumes: + - name: tempo-data + emptyDir: {} + - name: loki-data + emptyDir: {} + - name: grafana-data + emptyDir: {} + - name: loki-storage + emptyDir: {} + - name: p8s-storage + emptyDir: {} + - name: pyroscope-storage + emptyDir: {} diff --git a/deploy/local/weather-agent.yaml b/deploy/local/weather-agent.yaml new file mode 100644 index 0000000..3fbb770 --- /dev/null +++ b/deploy/local/weather-agent.yaml @@ -0,0 +1,22 @@ +kind: Namespace +apiVersion: v1 +metadata: + name: sample-agents +--- +apiVersion: runtime.agentic-layer.ai/v1alpha1 +kind: Agent +metadata: + labels: + app.kubernetes.io/name: agent-runtime-operator + app.kubernetes.io/managed-by: kustomize + name: weather-agent + namespace: sample-agents +spec: + framework: google-adk + image: ghcr.io/agentic-layer/weather-agent:0.6.0 + protocols: + - type: A2A + exposed: true + env: + - name: OTEL_SDK_DISABLED + value: "true" \ No newline at end of file diff --git a/testworkflows/.python-version b/testworkflows/.python-version new file mode 100644 index 0000000..3a4f41e --- /dev/null +++ b/testworkflows/.python-version @@ -0,0 +1 @@ +3.13 \ No newline at end of file diff --git a/testworkflows/README.md b/testworkflows/README.md index 2624424..079b2a5 100644 --- a/testworkflows/README.md +++ b/testworkflows/README.md @@ -1,24 +1,525 @@ -# Getting Started with Testkube +# Test Workflows - Automated Agent Evaluation System -## Testkube Setup +An automated evaluation and testing system for AI agents using the **RAGAS** (Retrieval Augmented Generation Assessment) framework. This system downloads test datasets, executes queries through agents via the **A2A** protocol, evaluates responses using configurable metrics, and publishes results to **OpenTelemetry** for monitoring. -**Install Testkube locally:** -```shell -# Install Testkube CLI -brew install testkube +---- -# Install Testkube in your cluster -testkube init +## Table of Contents + +- [Architecture](#architecture) +- [Prerequisites](#prerequisites) +- [Getting Started](#getting-started) +- [Quick Start](#quick-start) +- [Detailed Usage](#detailed-usage) +- [Dataset Requirements](#dataset-requirements) +- [Testing](#testing) +- [Development](#development) +- [Troubleshooting](#troubleshooting) +- [Project Structure](#project-structure) +- [Contributing](#contributing) + +---- + +## Overview + +This project provides a complete pipeline for evaluating AI agent performance: + +- **Automated Testing**: Run predefined test queries through your agents +- **Multi-Format Support**: Accept datasets in CSV, JSON, or Parquet formats +- **Flexible Evaluation**: Configure multiple RAGAS metrics for comprehensive assessment +- **Observability**: Publish metrics to OpenTelemetry endpoints for monitoring and analysis +- **Type-Safe**: Built with type hints and validated with MyPy +- **Limitation**: Currently only support SingleTurnSample Metrics (see [Available Metrics](#available-metrics)) + +---- + +## Architecture + +``` +Input Dataset (user_input, retrieved_contexts, reference) + | + v + [1. setup.py] - Downloads & converts to RAGAS JSONL format + | + v +data/datasets/ragas_dataset.jsonl + | + v + [2. run.py] - Executes queries via A2A protocol, adds responses + | ^ + | | + | Agent URL + v +data/experiments/ragas_experiment.jsonl + | + v + [3. evaluate.py] - Calculates RAGAS metrics + | ^ + | | + | LLM Model + v +results/evaluation_scores.json + | + v + [4. publish.py] - Publishes to OTLP endpoint + | + v +OpenTelemetry Collector +``` +### Key Design Principles + +- **RAGAS-Native Format**: Uses RAGAS column names (`user_input`, `response`, `retrieved_contexts`, `reference`) throughout +- **JSONL Backend**: Internal storage uses JSONL for native list support +- **Format-Aware Input**: Intelligent handling of CSV (list conversion), JSON, and Parquet formats + +---- + +## Prerequisites + +- **Python 3.13+** +- **API Key**: `OPENAI_API_KEY` environment variable (required for LLM-based evaluation) +- **OTLP Endpoint**: Optional, defaults to `localhost:4318` + +---- + +## Getting Started + +### Install dependencies using UV + +```bash +# Clone the repository +git clone git@github.com:agentic-layer/testbench.git +cd testworkflows + +# Install (dev & prod) dependencies with uv +uv sync --group dev --group prod +``` + +### Environment Setup + +```bash +# Required for evaluation +export OPENAI_API_KEY="your-api-key-here" + +# Optional: Configure custom OTLP endpoint +export OTLP_ENDPOINT="http://otlp-collector:4318" +``` + +The system automatically creates the required directories (`data/`, `results/`) on first run. + +---- + +## Quick Start + +Run the complete evaluation pipeline in 4 steps: + +```bash +# 1. Download and prepare dataset +python3 scripts/setup.py "https://example.com/dataset.csv" + +# 2. Execute queries through your agent +python3 scripts/run.py "http://localhost:8000" + +# 3. Evaluate responses with RAGAS metrics +python3 scripts/evaluate.py gemini-2.5-flash-lite faithfulness answer_relevancy + +# 4. Publish metrics to OpenTelemetry +python3 scripts/publish.py "my-agent-evaluation" +``` + +---- + +## Detailed Usage + +### 1. setup.py - Dataset Preparation + +Downloads and converts test datasets to RAGAS-native JSONL format. + +**Syntax:** +```bash +python3 scripts/setup.py +``` + +**Arguments:** +- `dataset_url` (required): URL to dataset file (`.csv`, `.json`, or `.parquet`) + +**Required Dataset Schema:** +- See [Dataset Requirements](#dataset-requirements) + +**Output:** +- `data/datasets/ragas_dataset.jsonl` - RAGAS Dataset in JSONL format + +--- + +### 2. run.py - Agent Query Execution + +Executes test queries through an agent using the A2A protocol and collects responses. + +**Syntax:** +```bash +python3 scripts/run.py +``` + +**Arguments:** +- `agent_url` (required): URL to the agent's A2A endpoint + +**Input:** +- `data/datasets/ragas_dataset.jsonl` (loaded automatically) + +**Output:** +- `data/experiments/ragas_experiment.jsonl` - Agent responses with preserved context + +**Output Schema:** +```jsonl +{"user_input": "What is X?", "retrieved_contexts": ["Context about X"], "reference": "X is...", "response": "Agent's answer"} +``` + +**Notes:** +- Uses asynchronous A2A client for efficient communication +- Preserves all original dataset fields +- Automatically handles response streaming + +--- + +### 3. evaluate.py - RAGAS Metric Evaluation + +Evaluates agent responses using configurable RAGAS metrics and calculates costs. + +**Syntax:** +```bash +python3 scripts/evaluate.py [metric2 ...] [--cost-per-input COST] [--cost-per-output COST] +``` + +**Arguments:** +- `model` (required): Model name for evaluation (e.g., `gemini-2.5-flash-lite`, `gpt-4`) +- `metrics` (required): One or more RAGAS metric names +- `--cost-per-input` (optional): Cost per input token (default: 0.000005, i.e., $5 per 1M tokens) +- `--cost-per-output` (optional): Cost per output token (default: 0.000015, i.e., $15 per 1M tokens) + +### **Available Metrics:** + +| Metric | Special required columns | +|--------|-------------------------| +| `faithfulness` | retrieved_contexts | +| `context_precision` | retrieved_contexts | +| `context_recall` | retrieved_contexts | +| `context_entity_recall` | retrieved_contexts| +| `context_utilization` | retrieved_contexts| +| `llm_context_precision_with_reference` | retrieved_contexts| +|`llm_context_precision_without_reference`| retrieved_contexts| +|`faithful_rate`| retrieved_contexts| +|`relevance_rate`| retrieved_contexts| +|`noise_sensitivity`| retrieved_contexts| +|`factual_correctness`| | +|`domain_specific_rubrics`| | +|`nv_accuracy`| | +|`nv_context_relevance`| retrieved_contexts| +|`nv_response_groundedness`| retrieved_contexts| +|`string_present`| | +|`exact_match`| | +|`summary_score`| reference_contexts | +|`llm_sql_equivalence_with_reference`| reference_contexts | + + + +**Input:** +- `data/experiments/ragas_experiment.jsonl` (loaded automatically) + +**Examples:** +```bash +# Single metric +python3 scripts/evaluate.py gemini-2.5-flash-lite faithfulness + +# Multiple metrics +python3 scripts/evaluate.py gemini-2.5-flash-lite faithfulness answer_relevancy context_precision + +# Custom token costs +python3 scripts/evaluate.py gpt-4 faithfulness answer_correctness \ + --cost-per-input 0.00003 \ + --cost-per-output 0.00006 +``` + +**Output:** +- `results/evaluation_scores.json` - Evaluation results with metrics, token usage, and costs + +**Output Format:** +```json +{ + "overall_scores": { + "faithfulness": 0.95, + "answer_relevancy": 0.98 + }, + "individual_results": [ + { + "user_input": "What is the capital of France?", + "response": "Paris is the capital of France.", + "faithfulness": 0.95, + "answer_relevancy": 0.98 + } + ], + "total_tokens": { + "input_tokens": 1500, + "output_tokens": 500 + }, + "total_cost": 0.015 +} +``` + +**Notes:** +- Currently only support **SingleTurnSample** Metrics (see [Available Metrics](#available-metrics)) +- Dynamically discovers available metrics from `ragas.metrics` module +- Invalid metric names will show available options +- Token costs can be customized per model pricing + +--- + +### 4. publish.py - Metrics Publishing + +Publishes evaluation metrics to an OpenTelemetry OTLP endpoint for monitoring. + +**Syntax:** +```bash +python3 scripts/publish.py [otlp_endpoint] +``` + +**Arguments:** +- `workflow_name` (required): Name of the test workflow (used as metric label) +- `otlp_endpoint` (optional): OTLP HTTP endpoint URL (default: `localhost:4318`) + +**Input:** +- `results/evaluation_scores.json` (loaded automatically) + +**Published Metrics:** + +Each RAGAS metric is published as a gauge with the workflow name as an attribute: + +``` +ragas_evaluation_faithfulness{workflow_name="weather-assistant-eval"} = 0.85 +ragas_evaluation_answer_relevancy{workflow_name="weather-assistant-eval"} = 0.92 +``` + +**Notes:** +- Sends metrics to `/v1/metrics` endpoint +- Uses resource with `service.name="ragas-evaluation"` +- Forces flush to ensure delivery before exit + +---- + +## Dataset Requirements + +### Schema + +Your input dataset must contain these columns: + +```python +{ + "user_input": str, # Test question/prompt + "retrieved_contexts": [str], # List of context strings (must be array type) (Optional but required by many metrics) + "reference": str # Ground truth answer +} +``` + +### Format-Specific Notes + +**CSV Files:** +- `retrieved_contexts` must be formatted as quoted array strings +- Example: `"['Context 1', 'Context 2', 'Context 3']"` +- The system automatically parses these strings into Python lists + +**JSON Files:** +```json +[ + { + "user_input": "What is the capital of France?", + "retrieved_contexts": ["Paris is a city in France.", "France is in Europe."], + "reference": "Paris" + } +] +``` + +**Parquet Files:** +- Use native list/array columns for `retrieved_contexts` + +### Example Dataset + +```csv +user_input,retrieved_contexts,reference +"What is Python?","['Python is a programming language', 'Python was created by Guido van Rossum']","Python is a high-level programming language" +"What is AI?","['AI stands for Artificial Intelligence', 'AI systems can learn from data']","AI is the simulation of human intelligence by machines" +``` + +---- + +## Testing + +### Unit Tests + +Run all unit tests: +```bash +uv run pytest tests/ -v +``` + +Or using the task runner: +```bash +uv run poe test +``` + +### End-to-End Tests + +Run the complete pipeline integration test: + +```bash +uv run pytest tests_e2e/test_e2e.py -v +``` + +Or using the task runner: +```bash +uv run poe test_e2e ``` -**Run Testkube workflows:** -```shell -# Apply workflow configuration -kubectl apply -f first_workflow.yaml +**Configuration via Environment Variables:** -# Run a workflow -testkube run workflow my-test +```bash +export E2E_DATASET_URL="http://localhost:8000/dataset.json" +export E2E_AGENT_URL="http://localhost:11010" +export E2E_MODEL="gemini-2.5-flash-lite" +export E2E_METRICS="faithfulness,answer_relevancy" +export E2E_WORKFLOW_NAME="Test Workflow" +export E2E_OTLP_ENDPOINT="localhost:4318" -# Check workflow status -testkube get workflows +pytest tests_e2e/test_e2e.py -v ``` + +**Test Coverage:** +1. Scripts exist and are executable +2. `setup.py` creates `data/datasets/ragas_dataset.jsonl` +3. `run.py` creates `data/experiments/ragas_experiment.jsonl` +4. `evaluate.py` creates `results/evaluation_scores.json` +5. `publish.py` successfully pushes metrics to OTLP + +---- + +## Development + +## Code Quality Standards +### Code Style: +- **Linting**: Ruff with 120 character line limit +- **Type Checking**: mypy for static type analysis +- **Security**: Bandit for security vulnerability detection +- **Import Organization**: import-linter for dependency management + +### Development Commands: + +This project uses `poethepoet` for task automation: + +```bash +# Run all quality checks +uv run poe check + +# Individual checks +uv run poe mypy # Type checking +uv run poe ruff # Linting and formatting +uv run poe bandit # Security analysis +uv run poe lint-imports # Import dependency validation +uv run poe test # Execute test suite +uv run poe test_e2e # Execute end-to-end tests + +# Auto-formatting +uv run poe format # Code formatting +uv run poe lint # Auto-fix linting issues +``` + +---- + +## Troubleshooting + +### "Source dataset is missing required columns" + +**Problem**: Dataset doesn't have the required schema. + +**Solution**: +- Verify your dataset has columns: `user_input`, `retrieved_contexts`, and `reference` +- Check that column names match exactly (case-sensitive) +- Ensure `retrieved_contexts` is formatted as a list (see Dataset Requirements) + +Example fix for CSV: +```csv +# Wrong (missing columns) +question,context,answer + +# Correct +user_input,retrieved_contexts,reference +``` + +### "No results found in experiment" + +**Problem**: `evaluate.py` can't find experiment results. + +**Solution**: +- Check if `data/experiments/ragas_experiment.jsonl` exists +- Verify `run.py` completed successfully without errors +- Ensure the agent URL was accessible during execution +- Check file permissions on the `data/` directory + +### CSV List Conversion Issues + +**Problem**: `retrieved_contexts` not parsing correctly from CSV. + +**Solution**: +- Ensure lists are formatted as Python array strings: `"['item1', 'item2']"` +- Use proper quoting in CSV: wrap the entire array string in double quotes +- Consider using JSON or Parquet format for complex data types + +Example: +```csv +user_input,retrieved_contexts,reference +"What is X?","['Context about X', 'More context']","X is..." +``` + +### Evaluation Metrics Fail + +**Problem**: Certain metrics fail during evaluation. + +**Solution**: +- Some metrics require the `reference` field (e.g., `context_precision`, `context_recall`) +- Verify your dataset includes all required fields for the metrics you're using +- Check the RAGAS documentation for metric-specific requirements + +---- + +## Project Structure + +``` +testworkflows/ +├── scripts/ # Main pipeline scripts +│ ├── setup.py # Dataset download & conversion +│ ├── run.py # Agent query execution +│ ├── evaluate.py # RAGAS metric evaluation +│ └── publish.py # Metrics publishing to OTLP +├── tests/ # Unit tests +│ ├── test_setup.py +│ ├── test_run.py +│ ├── test_evaluate.py +│ ├── test_publish.py +│ └── test_data/ # Sample test datasets +│ ├── dataset.csv +│ └── dataset.json +├── tests_e2e/ # End-to-end integration tests +│ └── test_e2e.py +├── data/ # Runtime data (gitignored) +│ ├── datasets/ # Input datasets (RAGAS format) +│ │ └── ragas_dataset.jsonl +│ └── experiments/ # Agent responses with context +│ └── ragas_experiment.jsonl +├── results/ # Evaluation outputs (gitignored) +│ └── evaluation_scores.json +├── pyproject.toml # Dependencies & project config +├── uv.lock # Lock file for uv package manager +├── .python-version # Python Version file +``` + +---- + +## Contributing + +See [Contribution Guide](https://github.com/agentic-layer/testbench?tab=contributing-ov-file) for details on contribution, and the process for submitting pull requests. \ No newline at end of file diff --git a/testworkflows/pyproject.toml b/testworkflows/pyproject.toml new file mode 100644 index 0000000..4ffb991 --- /dev/null +++ b/testworkflows/pyproject.toml @@ -0,0 +1,68 @@ +[project] +name = "test-bench" +version = "0.1.0" +description = "Testbench to evaluate agents using Ragas" +requires-python = ">=3.12" +dependencies = [ + "a2a>=0.44", + "a2a-sdk>=0.3.10", + "httpx>=0.28.1", + "langchain-openai>=1.0.2", + "pandas>=2.3.3", + "pandas-stubs>=2.3.0", + "pyarrow>=21.0.0", + "ragas>=0.3.5", + "requests>=2.32.5", + "types-requests>=2.32.0", + "opentelemetry-api>=1.20.0", + "opentelemetry-sdk>=1.20.0", + "opentelemetry-exporter-otlp-proto-http>=1.20.0", +] + +[dependency-groups] +dev = [ + "bandit[toml]>=1.7.8", + "mypy>=1.17.0", + "import-linter>=2.0", + "poethepoet>=0.31.1", + "ruff>=0.12.7", + "pytest>=7.4.0", + "pytest-asyncio>=1.3.0", +] + +[tool.poe.tasks] +test = "pytest tests/ -v" +test_e2e = "pytest tests_e2e/ -v" + +format = "ruff format" +lint = "ruff check --fix" +ruff = ["format", "lint"] + +bandit = "bandit -c pyproject.toml -r ." +mypy = "mypy ." + +check = ["test", "mypy", "bandit", "ruff"] + +[tool.ruff] +# Allow lines to be as long as 120. +line-length = 120 + + +[tool.ruff.lint] +# https://docs.astral.sh/ruff/rules/ +# I001 --> enable import sorting rules +# Q --> enable flake8-quotes rules for consistent quote style +# N --> pep8-naming for proper naming conventions +# PT --> flake8-pytest-style for pytest best practices +# TID251 --> banned relative imports +# DTZ --> datetime timezone awareness +extend-select = ["Q", "N", "I001", "PT", "TID251", "DTZ"] + +[tool.mypy] +mypy_path = "scripts" + +[tool.bandit] +exclude_dirs = [".cache", ".venv"] + +[tool.bandit.assert_used] +skips = ["*_test.py", "*test_*.py"] diff --git a/testworkflows/scripts/evaluate.py b/testworkflows/scripts/evaluate.py new file mode 100644 index 0000000..a0070d4 --- /dev/null +++ b/testworkflows/scripts/evaluate.py @@ -0,0 +1,253 @@ +import argparse +import inspect +import json +import logging +import os +from argparse import ArgumentError +from dataclasses import asdict, dataclass +from logging import Logger +from typing import Any + +import ragas.metrics as metrics_module +from langchain_openai import ChatOpenAI +from ragas import evaluate +from ragas.cost import get_token_usage_for_openai +from ragas.dataset_schema import EvaluationDataset, EvaluationResult +from ragas.llms import LangchainLLMWrapper +from ragas.metrics import Metric + +# Set up module-level logger +logging.basicConfig(level=logging.INFO) +logger: Logger = logging.getLogger(__name__) + + +def get_available_metrics() -> dict[str, Metric]: + """ + Loads all Metric classes from Ragas + Returns a dict mapping metric names to metric instances. + """ + available_metrics: dict[str, Metric] = {} + + # Iterate through all members of the metrics module + for name, obj in inspect.getmembers(metrics_module): + # Check if it's a class and is a subclass of Metric (but not Metric itself) + if inspect.isclass(obj) and issubclass(obj, Metric) and obj is not Metric: + try: + # Instantiate the metric + metric_instance = obj() + # Use the metric's name attribute + metric_name = metric_instance.name + available_metrics[metric_name] = metric_instance + except Exception: + # Skip metrics that can't be instantiated without parameters + logger.info(f"Exception encountered: {Exception}") + pass + + return available_metrics + + +# Get all available metrics +AVAILABLE_METRICS = get_available_metrics() + + +def convert_metrics(metrics: list[str]) -> list: + """ + Map metric names to actual metric objects + + Args: + metrics: List of metric names as strings (e.g., ["faithfulness", "answer_relevancy"]) + + Returns: + List containing metric objects + """ + + # Map metric names to actual metric objects + metric_objects = [] + for metric_name in metrics: + if metric_name in AVAILABLE_METRICS: + metric_objects.append(AVAILABLE_METRICS[metric_name]) + else: + logger.warning(f"Unknown metric '{metric_name}', skipping...") + logger.warning(f"Available metrics: {', '.join(AVAILABLE_METRICS.keys())}") + + if not metric_objects: + raise ValueError("No valid metrics provided for evaluation") + + return metric_objects + + +@dataclass +class EvaluationScores: + """Evaluation scores and results.""" + + overall_scores: dict[str, float] + individual_results: list[dict[str, Any]] + total_tokens: dict[str, int] + total_cost: float + + +def format_evaluation_scores( + ragas_result: EvaluationResult, + cost_per_input_token: float, + cost_per_output_token: float, +) -> EvaluationScores: + """ + Format the RAGAS evaluation results. + + Args: + ragas_result: The result object from RAGAS evaluate() + experiment: Ragas Experiment containing the original results + metrics: List of metric names used + + Returns: + Formatted dictionary matching the required structure + """ + + # Calculate overall scores (mean of each metric) + overall_scores = ragas_result._repr_dict + + # Build individual results + individual_results = ragas_result.to_pandas().to_dict(orient="records") + + # Extract token usage and calculate cost using TokenUsageParser + # Check if token usage data was collected (some metrics don't use LLMs or use separate LLM instances) + if ragas_result.cost_cb and hasattr(ragas_result.cost_cb, "usage_data") and ragas_result.cost_cb.usage_data: + token_usage = ragas_result.total_tokens() + # Handle both single TokenUsage and list of TokenUsage + if isinstance(token_usage, list): + input_tokens = sum(usage.input_tokens for usage in token_usage) + output_tokens = sum(usage.output_tokens for usage in token_usage) + else: + input_tokens = token_usage.input_tokens + output_tokens = token_usage.output_tokens + + total_tokens = { + "input_tokens": input_tokens, + "output_tokens": output_tokens, + } + total_cost = ragas_result.total_cost( + cost_per_input_token=cost_per_input_token, + cost_per_output_token=cost_per_output_token, + ) + else: + # No token usage data collected (e.g., non-LLM metrics or Nvidia metrics using separate LLM instances) + logger.info("No token usage data collected for these metrics") + total_tokens = {"input_tokens": 0, "output_tokens": 0} + total_cost = 0.0 + + return EvaluationScores( + overall_scores=overall_scores, + individual_results=individual_results, + total_tokens=total_tokens, + total_cost=total_cost, + ) + + +def main( + output_file: str, + model: str, + metrics: list[str] | None = None, + cost_per_input_token: float = 5.0 / 1e6, + cost_per_output_token: float = 15.0 / 1e6, +) -> None: + """ + Main function to evaluate results using RAGAS metrics. + + Args: + output_file: Path to save evaluation_scores.json + model: Model name to use for evaluation + metrics: List of metric names to calculate + """ + # Check if any metrics were provided + if metrics is None: + raise ArgumentError(argument=metrics, message="No metrics were provided as arguments") + + # Create LLM client using the AI-Gateway + ragas_llm: ChatOpenAI = ChatOpenAI(model=model) + llm = LangchainLLMWrapper(ragas_llm) # type: ignore[arg-type] + + dataset = EvaluationDataset.from_jsonl("data/experiments/ragas_experiment.jsonl") + + # Calculate metrics + logger.info(f"Calculating metrics: {', '.join(metrics)}...") + ragas_result = evaluate( + dataset=dataset, + metrics=convert_metrics(metrics), + llm=llm, + token_usage_parser=get_token_usage_for_openai, + ) + + # Ensure we have an EvaluationResult (not an Executor) + if not isinstance(ragas_result, EvaluationResult): + raise TypeError(f"Expected EvaluationResult, got {type(ragas_result)}") + + # Format results + logger.info("Formatting evaluation scores...") + evaluation_scores = format_evaluation_scores( + ragas_result, + cost_per_input_token=cost_per_input_token, + cost_per_output_token=cost_per_output_token, + ) + + # Ensure output directory exists + os.makedirs(os.path.dirname(output_file), exist_ok=True) + + # Save to file + with open(output_file, "w") as f: + json.dump(asdict(evaluation_scores), f, indent=2) + + logger.info(f"Evaluation scores saved to {output_file}") + logger.info(f"Overall scores: {evaluation_scores.overall_scores}") + + +if __name__ == "__main__": + # Parse the parameters (model and metrics) evaluate.py was called with + parser = argparse.ArgumentParser( + description="Evaluate results using RAGAS metrics", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=f""" + Available metrics: {", ".join(AVAILABLE_METRICS.keys())} + + Examples: + python3 scripts/evaluate.py gemini-2.5-flash-lite faithfulness + python3 scripts/evaluate.py gemini-2.5-flash-lite faithfulness context_precision context_recall + """, + ) + + parser.add_argument( + "model", + type=str, + help="Model name to use for evaluation (e.g., gemini-2.5-flash-lite)", + ) + + parser.add_argument( + "metrics", + nargs="+", + choices=list(AVAILABLE_METRICS.keys()), + help="At least one (or more) metrics to evaluate (e.g., faithfulness, answer_relevancy)", + ) + + parser.add_argument( + "--cost-per-input", + type=float, + default=5.0 / 1e6, + help="Cost per input token (default: 5.0/1M = $0.000005 for typical GPT-4 pricing)", + ) + + parser.add_argument( + "--cost-per-output", + type=float, + default=15.0 / 1e6, + help="Cost per output token (default: 15.0/1M = $0.000015 for typical GPT-4 pricing)", + ) + + args = parser.parse_args() + + # Run evaluation with the 'model' and 'metrics' provided as parameters, 'output_file' is hardcoded + main( + output_file="results/evaluation_scores.json", + model=args.model, + metrics=args.metrics, + cost_per_input_token=args.cost_per_input, + cost_per_output_token=args.cost_per_output, + ) diff --git a/testworkflows/scripts/publish.py b/testworkflows/scripts/publish.py new file mode 100644 index 0000000..760f865 --- /dev/null +++ b/testworkflows/scripts/publish.py @@ -0,0 +1,143 @@ +import argparse +import json +import logging +from logging import Logger + +from opentelemetry import metrics +from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter +from opentelemetry.sdk.metrics import MeterProvider +from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader +from opentelemetry.sdk.resources import Resource + +# Set up module-level logger +logging.basicConfig(level=logging.INFO) +logger: Logger = logging.getLogger(__name__) + + +def get_overall_scores(file_path: str) -> dict[str, float]: + """Load the evaluation_scores.json file and return the 'overall_scores' metrics.""" + with open(file_path, "r") as file: + return json.load(file).get("overall_scores", {}) + + +def create_and_push_metrics(overall_scores: dict[str, float], workflow_name: str, otlp_endpoint: str) -> None: + """ + Create OpenTelemetry metrics for each overall score and push via OTLP. + + Args: + overall_scores: Dictionary of metric names to scores + workflow_name: Name of the test workflow (used as label to distinguish workflows) + otlp_endpoint: URL of the OTLP endpoint (e.g., 'http://localhost:4318') + """ + # Ensure the endpoint has the correct protocol + if not otlp_endpoint.startswith("http://") and not otlp_endpoint.startswith("https://"): + otlp_endpoint = f"http://{otlp_endpoint}" + + # Create OTLP exporter + exporter = OTLPMetricExporter(endpoint=f"{otlp_endpoint}/v1/metrics") + + # Create a metric reader that exports immediately + reader = PeriodicExportingMetricReader( + exporter=exporter, + export_interval_millis=1000, # Export every second + ) + + # Create resource with workflow metadata + resource = Resource.create({"service.name": "ragas-evaluation", "workflow.name": workflow_name}) + + # Create MeterProvider with the exporter and resource + provider = MeterProvider(resource=resource, metric_readers=[reader]) + metrics.set_meter_provider(provider) + + # Get a meter + meter = metrics.get_meter("ragas.evaluation", "1.0.0") + + # Create and record metrics + try: + logger.info(f"Pushing metrics to OTLP endpoint at {otlp_endpoint}...") + + for metric_name, score in overall_scores.items(): + # Create a Gauge (using UpDownCounter as closest equivalent) + gauge = meter.create_gauge( + name=f"ragas_evaluation_{metric_name}", + description=f"Overall {metric_name} score from RAGAS evaluation", + unit="1", + ) + + # Set the gauge value with workflow_name as an attribute + gauge.set(score, {"workflow_name": workflow_name}) + logger.info(f"Set metric 'ragas_evaluation_{metric_name}{{workflow_name=\"{workflow_name}\"}}' to {score}") + + # Force flush to ensure metrics are sent + provider.force_flush() + + logger.info("✓ Metrics successfully pushed via OTLP") + except Exception as e: + logger.error(f"✗ Error pushing metrics via OTLP: {e}") + raise + finally: + # Shutdown the provider + provider.shutdown() + + logger.info("Published metrics:") + for metric_name, score in overall_scores.items(): + logger.info(f' - ragas_evaluation_{metric_name}{{workflow_name="{workflow_name}"}}: {score}') + + +def publish_metrics(input_file: str, workflow_name: str, otlp_endpoint: str) -> None: + """ + Publish evaluation metrics via OpenTelemetry OTLP. + + Args: + input_file: Path to the evaluation scores + workflow_name: Name of the test workflow (e.g., 'weather-assistant-test'). + otlp_endpoint: URL of the OTLP endpoint (e.g., 'http://localhost:4318'). + """ + + # Load overall scores from the evaluation file + logger.info(f"Loading evaluation scores from {input_file}...") + overall_scores = get_overall_scores(input_file) + + if not overall_scores: + logger.warning("No overall scores found in evaluation_scores.json") + return + + # Create and push OpenTelemetry metrics + logger.info(f"Creating OpenTelemetry metrics for {len(overall_scores)} scores...") + logger.info(f"Workflow: {workflow_name}") + create_and_push_metrics(overall_scores, workflow_name, otlp_endpoint) + + +if __name__ == "__main__": + """ + Main function to publish metrics via OpenTelemetry OTLP. + + Args: + workflow_name: Name of the test workflow + otlp_endpoint: (OPTIONAL) URL to the OTLP endpoint (default: localhost:4318) + + Examples: + python3 scripts/publish.py weather-assistant-test + python3 scripts/publish.py weather-assistant-test http://localhost:4318 + """ + + parser = argparse.ArgumentParser(description="Publish RAGAS evaluation metrics via OpenTelemetry OTLP") + parser.add_argument( + "workflow_name", + help="Name of the test workflow (e.g., 'weather-assistant-test')", + ) + parser.add_argument( + "otlp_endpoint", + nargs="?", + default="localhost:4318", + help="URL of the OTLP HTTP endpoint (default: localhost:4318)", + ) + + args = parser.parse_args() + + # Call 'publish_metrics' with hardcoded input file and specified 'workflow_name' & 'otlp_endpoint' + publish_metrics( + input_file="results/evaluation_scores.json", + workflow_name=args.workflow_name, + otlp_endpoint=args.otlp_endpoint, + ) diff --git a/testworkflows/scripts/run.py b/testworkflows/scripts/run.py new file mode 100644 index 0000000..3b79de1 --- /dev/null +++ b/testworkflows/scripts/run.py @@ -0,0 +1,122 @@ +import argparse +import asyncio +import logging +from logging import Logger +from uuid import uuid4 + +import httpx +from a2a.client.client import Client, ClientConfig +from a2a.client.client_factory import ClientFactory, minimal_agent_card +from a2a.types import ( + AgentCard, + Message, + Part, + Role, + TextPart, +) +from pydantic import BaseModel +from ragas import Dataset, experiment + +# Set up module-level logger +logging.basicConfig(level=logging.INFO) +logger: Logger = logging.getLogger(__name__) + + +async def initialize_client(agent_url: str) -> Client: + """Initialize the A2A client with a minimal agent card.""" + logger.info(f"Initializing A2A client for: {agent_url}") + + # Create a minimal agent card with the provided URL + agent_card: AgentCard = minimal_agent_card(agent_url) + + config: ClientConfig = ClientConfig() + factory: ClientFactory = ClientFactory(config) + client: Client = factory.create(agent_card) + + logger.info("A2A client initialized successfully") + + return client + + +@experiment() +async def run_agent_experiment(row, agent_url: str) -> dict[str, str | list]: + """ + Experiment function that processes each row from the dataset. + + Args: + row: A dictionary containing 'user_input', 'retrieved_contexts', and 'reference' fields + agent_url: The URL of the agent to query + + Returns: + Dictionary with original row data plus 'response' + """ + + try: + async with httpx.AsyncClient(): + client = await initialize_client(agent_url) + + # Get the input from the row + input_text = row.get("user_input") + + message = Message( + role=Role.user, + parts=[Part(TextPart(text=input_text))], + message_id=uuid4().hex, + ) + + logger.info(f"Processing: {input_text}") + + async for response in client.send_message(message): + # Client returns tuples, extract the task/message + if isinstance(response, tuple): + task, _ = response + if task: + artifacts: list = task.model_dump(mode="json", include={"artifacts"}).get("artifacts", []) + + # Extract the model response + if artifacts and artifacts[0].get("parts"): + output_text = artifacts[0]["parts"][0].get("text", "") + else: + logger.warning("No text found in artifacts") + else: + logger.warning(f"Unexpected response: {response}") + + except Exception as e: + logger.error(f'Error processing input "{row.get("user_input")}": {str(e)}') + output_text = f"ERROR: {str(e)}" + + # Return the original row data plus the results + result: dict[str, str | list] = { + **row, + "response": output_text, + } + + return result + + +async def main(agent_url: str) -> None: + """Main function to load Ragas Dataset and run Experiment.""" + + # Load existing Ragas dataset + logger.info("Loading Ragas dataset from data/datasets/ragas_dataset.jsonl") + dataset: Dataset[BaseModel] = Dataset.load(name="ragas_dataset", backend="local/jsonl", root_dir="./data") + logger.info(f"Dataset loaded with {len(dataset)} samples") + + # Run the experiment + logger.info("Starting experiment...") + await run_agent_experiment.arun(dataset, name="ragas_experiment", agent_url=agent_url) + + logger.info("Experiment completed successfully") + logger.info("Results saved to data/experiments/ragas_experiment.jsonl") + + +if __name__ == "__main__": + # Parse parameter the script was called with (URL) + parser = argparse.ArgumentParser( + description="Runs all queries from the Ragas dataset through the agent at the provided URL" + ) + parser.add_argument("url", help="URL to agent") + args = parser.parse_args() + + # Call main using the parsed URL + asyncio.run(main(args.url)) diff --git a/testworkflows/scripts/setup.py b/testworkflows/scripts/setup.py new file mode 100644 index 0000000..0a6c512 --- /dev/null +++ b/testworkflows/scripts/setup.py @@ -0,0 +1,112 @@ +import argparse +from io import BytesIO +from pathlib import Path +from typing import Any, Callable, cast + +import pandas as pd +import requests +from pandas import DataFrame +from pydantic import BaseModel +from ragas import Dataset +from requests import Response + + +def dataframe_to_ragas_dataset(dataframe: DataFrame) -> None: + """Convert DataFrame to Ragas dataset and save to data/ragas_dataset.jsonl. + + Expected schema: + - user_input: The test question/prompt + - retrieved_contexts: List of retrieved context strings + - reference: The reference/ground truth answer + """ + + # Set output directory (and create it if it doesn't exist already) + output_dir = Path("data") + output_dir.mkdir(exist_ok=True) + + # Convert DataFrame to list of dictionaries + dataset_samples = cast(list[dict[str, Any]], dataframe.to_dict(orient="records")) + + # Create Ragas Dataset + dataset: Dataset[BaseModel] = Dataset( + name="ragas_dataset", + data=dataset_samples, + backend="local/jsonl", + root_dir="./data", + ) + + # Save the dataset + dataset.save() + + +def get_converter(url: str) -> Callable[[BytesIO], DataFrame]: + """Extract the file format from the URL and return the converter function""" + suffix = Path(url).suffix.lower() + + format_map: dict[str, Callable[[BytesIO], DataFrame]] = { + ".json": pd.read_json, + ".csv": custom_convert_csv, + ".parquet": pd.read_parquet, + ".prq": pd.read_parquet, + } + + if suffix in format_map: + return format_map[suffix] + + raise TypeError(f"Unsupported filetype at url: {url}") + + +def custom_convert_csv(input_file: BytesIO) -> DataFrame: + """ + Converts a CSV input file to a Pandas DataFrame and, if it exists, turns 'retrieved_contexts' into a list (RAGAS requires 'retrieved_contexts' as a list of strings) + + Args: + input_file: The CSV input_file + + Returns: + Pandas DataFrame with correct formatting + """ + + dataframe: DataFrame = pd.read_csv(input_file) + + # Ensure retrieved_contexts is a list (convert string to list if needed) + if "retrieved_contexts" in dataframe: + dataframe["retrieved_contexts"] = dataframe["retrieved_contexts"].apply( + lambda x: x if isinstance(x, list) else [x] if x else [] + ) + + return dataframe + + +def main(url: str) -> None: + """Download provided dataset -> convert to Ragas dataset -> save to data/ragas_dataset.jsonl + + Source dataset must contain columns: user_input, retrieved_contexts, reference + """ + converter = get_converter(url) + + # Download file from URL and raise HTTP error if it occurs + file: Response = requests.get(url, timeout=20) + file.raise_for_status() + + # Load into DataFrame by using the correct converter + buffer = BytesIO(file.content) + dataframe = converter(buffer) + + # Convert DataFrame to Ragas dataset and save it + dataframe_to_ragas_dataset(dataframe) + + +if __name__ == "__main__": + # Parse parameter the script was called with (URL) + parser = argparse.ArgumentParser( + description="Download provided dataset -> convert to Ragas dataset -> save to data/datasets/ragas_dataset.jsonl" + ) + parser.add_argument( + "url", + help="URL to the dataset in .csv / .json / .parquet format (must have user_input, retrieved_contexts, and reference columns)", + ) + args = parser.parse_args() + + # Call main using the parsed URL + main(args.url) diff --git a/testworkflows/tests/test_data/dataset.csv b/testworkflows/tests/test_data/dataset.csv new file mode 100644 index 0000000..506b15a --- /dev/null +++ b/testworkflows/tests/test_data/dataset.csv @@ -0,0 +1,6 @@ +user_input,retrieved_contexts,reference +"What is the weather like in New York right now?","The answer must state the current temperature in New York, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain).","The answer must state the current temperature in New York, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)." +"What is the current time in New York?","The answer must state the current time in New York in HH:MM format and include the correct timezone abbreviation (e.g., CST).","The answer must state the current time in New York in HH:MM format and include the correct timezone abbreviation (e.g., CST)." +"What is the weather like in Cairo?","The answer must state the current temperature in Cairo, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain).","The answer must state the current temperature in Cairo, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)." +"How is the weather in Sydney?","The answer must state the current temperature in Sydney, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain).","The answer must state the current temperature in Sydney, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)." +"Time in Garching?","The answer must state the current time in Garching, Germany in HH:MM format and include the correct timezone abbreviation (CEST).","The answer must state the current time in Garching, Germany in HH:MM format and include the correct timezone abbreviation (CEST)." diff --git a/testworkflows/tests/test_data/dataset.json b/testworkflows/tests/test_data/dataset.json new file mode 100644 index 0000000..1d6f88d --- /dev/null +++ b/testworkflows/tests/test_data/dataset.json @@ -0,0 +1,27 @@ +[ + { + "user_input": "What is the weather like in New York right now?", + "retrieved_contexts": ["The answer must state the current temperature in New York, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)."], + "reference": "The answer must state the current temperature in New York, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)." + }, + { + "user_input": "What is the current time in New York?", + "retrieved_contexts": ["The answer must state the current time in New York in HH:MM format and include the correct timezone abbreviation (e.g., CST)."], + "reference": "The answer must state the current time in New York in HH:MM format and include the correct timezone abbreviation (e.g., CST)." + }, + { + "user_input": "What is the weather like in Cairo?", + "retrieved_contexts": ["The answer must state the current temperature in Cairo, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)."], + "reference": "The answer must state the current temperature in Cairo, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)." + }, + { + "user_input": "How is the weather in Sydney?", + "retrieved_contexts": ["The answer must state the current temperature in Sydney, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)."], + "reference": "The answer must state the current temperature in Sydney, specify the units (Celsius or Fahrenheit), and describe the current weather condition (e.g., Sunny, Cloudy, Rain)." + }, + { + "user_input": "Time in Garching?", + "retrieved_contexts": ["The answer must state the current time in Garching, Germany in HH:MM format and include the correct timezone abbreviation (CEST)."], + "reference": "The answer must state the current time in Garching, Germany in HH:MM format and include the correct timezone abbreviation (CEST)." + } +] diff --git a/testworkflows/tests/test_evaluate.py b/testworkflows/tests/test_evaluate.py new file mode 100644 index 0000000..b1cd89b --- /dev/null +++ b/testworkflows/tests/test_evaluate.py @@ -0,0 +1,325 @@ +""" +Unit tests for evaluate.py + +Tests the RAGAS evaluation functionality. +""" + +import json +import os +import shutil +import sys +import tempfile +from argparse import ArgumentError +from pathlib import Path + +import pandas as pd +import pytest +from ragas.metrics import Metric + +sys.path.insert(0, str(Path(__file__).parent.parent / "scripts")) + +from evaluate import AVAILABLE_METRICS, convert_metrics, format_evaluation_scores, main + + +# Fixtures +@pytest.fixture +def temp_dir(): + """Create a temporary directory for tests""" + tmp = tempfile.mkdtemp() + original_cwd = Path.cwd() + yield tmp, original_cwd + shutil.rmtree(tmp, ignore_errors=True) + + +@pytest.fixture +def experiment_data(temp_dir): + """Create test experiment JSONL file""" + tmp, original_cwd = temp_dir + + # Create test experiment JSONL + experiment_dir = Path(tmp) / "data" / "experiments" + experiment_dir.mkdir(parents=True, exist_ok=True) + + experiment_file = experiment_dir / "ragas_experiment.jsonl" + test_data = [ + { + "user_input": "What is the weather?", + "retrieved_contexts": ["Context about weather"], + "reference": "Expected answer", + "response": "The weather is sunny.", + } + ] + + with open(experiment_file, "w") as f: + for item in test_data: + f.write(json.dumps(item) + "\n") + + return tmp, original_cwd, experiment_file + + +# TestFormatEvaluationScores tests +def test_overall_scores_calculation(): + """Test that overall scores are calculated correctly""" + + # Create mock result + class MockTokenUsage: + input_tokens = 100 + output_tokens = 50 + + class MockResult: + _repr_dict = {"faithfulness": 0.8, "answer_relevancy": 0.75} + cost_cb = None + + def to_pandas(self): + return pd.DataFrame({"faithfulness": [0.9, 0.8, 0.7], "answer_relevancy": [0.85, 0.75, 0.65]}) + + def total_tokens(self): + return MockTokenUsage() + + def total_cost(self): + return 0.001 + + mock_result = MockResult() + + formatted = format_evaluation_scores(mock_result, 5.0 / 1e6, 15.0 / 1e6) + + # Verify overall scores are correct + assert abs(formatted.overall_scores["faithfulness"] - 0.8) < 0.01 + assert abs(formatted.overall_scores["answer_relevancy"] - 0.75) < 0.01 + + +def test_individual_results_present(): + """Test that individual results are included""" + + # Create mock result + class MockTokenUsage: + input_tokens = 100 + output_tokens = 50 + + class MockResult: + _repr_dict = {"faithfulness": 0.85} + cost_cb = None + + def to_pandas(self): + return pd.DataFrame({"user_input": ["Q1", "Q2"], "faithfulness": [0.9, 0.8]}) + + def total_tokens(self): + return MockTokenUsage() + + def total_cost(self): + return 0.001 + + mock_result = MockResult() + + formatted = format_evaluation_scores(mock_result, 5.0 / 1e6, 15.0 / 1e6) + + # Verify individual results + assert len(formatted.individual_results) == 2 + + +def test_token_usage_placeholders(): + """Test that token usage placeholders are returned when cost_cb is None""" + + # Create mock result + class MockTokenUsage: + input_tokens = 150 + output_tokens = 75 + + class MockResult: + _repr_dict = {"faithfulness": 0.9} + cost_cb = None # This causes placeholders to be used + + def to_pandas(self): + return pd.DataFrame({"faithfulness": [0.9]}) + + def total_tokens(self): + return MockTokenUsage() + + def total_cost(self, **kwargs): + return 0.002 + + mock_result = MockResult() + + formatted = format_evaluation_scores(mock_result, 5.0 / 1e6, 15.0 / 1e6) + + # Verify placeholder token usage is returned (0 when cost_cb is None) + assert formatted.total_tokens["input_tokens"] == 0 + assert formatted.total_tokens["output_tokens"] == 0 + assert formatted.total_cost == 0.0 + + +# TestMain tests +def test_main_no_metrics(experiment_data): + """Test main function with no metrics provided""" + + tmp, original_cwd, experiment_file = experiment_data + os.chdir(tmp) + + try: + # When metrics is None, the function should raise an error + # The actual error type depends on implementation + with pytest.raises(ArgumentError): + main( + output_file="results/evaluation_scores.json", + model="gemini-flash-latest", + metrics=None, + ) + finally: + os.chdir(original_cwd) + + +def test_main_successful_execution(experiment_data, monkeypatch): + """Test main function successful execution""" + from pathlib import Path + + from ragas.dataset_schema import EvaluationResult + + tmp, original_cwd, experiment_file = experiment_data + os.chdir(tmp) + + try: + # Mock EvaluationDataset.from_jsonl + class MockEvaluationDataset: + pass + + mock_dataset = MockEvaluationDataset() + + def mock_from_jsonl(path): + return mock_dataset + + # Mock the evaluate function + class MockTokenUsage: + input_tokens = 100 + output_tokens = 50 + + class MockEvaluationResult(EvaluationResult): + _repr_dict = {"faithfulness": 0.85} + cost_cb = None + + def __init__(self): + # Don't call super().__init__ to avoid initialization requirements + pass + + def to_pandas(self): + return pd.DataFrame({"user_input": ["Q1"], "faithfulness": [0.85]}) + + def total_tokens(self): + return MockTokenUsage() + + def total_cost(self, **kwargs): + return 0.001 + + mock_result = MockEvaluationResult() + + def mock_evaluate(dataset, metrics, llm, token_usage_parser): + return mock_result + + # Mock ChatOpenAI and LangchainLLMWrapper + class MockChatOpenAI: + pass + + def mock_chat_openai_init(model): + return MockChatOpenAI() + + class MockLLMWrapper: + def __init__(self, llm): + pass + + monkeypatch.setattr("evaluate.EvaluationDataset.from_jsonl", mock_from_jsonl) + monkeypatch.setattr("evaluate.evaluate", mock_evaluate) + monkeypatch.setattr("evaluate.ChatOpenAI", mock_chat_openai_init) + monkeypatch.setattr("evaluate.LangchainLLMWrapper", MockLLMWrapper) + + # Get a valid metric name + if not AVAILABLE_METRICS: + pytest.skip("No metrics available") + + valid_metric = list(AVAILABLE_METRICS.keys())[0] + + # Run main + output_file = "results/evaluation_scores.json" + main( + output_file=output_file, + model="gemini-flash-latest", + metrics=[valid_metric], + ) + + # Verify output file was created + output_path = Path(tmp) / output_file + assert output_path.exists(), f"Output file not found at {output_path}" + + # Verify output file has correct structure + with open(output_path, "r") as f: + data = json.load(f) + + assert "overall_scores" in data + assert "individual_results" in data + assert "total_tokens" in data + assert "total_cost" in data + finally: + os.chdir(original_cwd) + + +# TestAvailableMetrics tests +def test_available_metrics_loaded(): + """Test that AVAILABLE_METRICS is populated correctly""" + # Should be a non-empty dictionary + assert isinstance(AVAILABLE_METRICS, dict) + assert len(AVAILABLE_METRICS) > 0 + + # All keys should be strings + for key in AVAILABLE_METRICS.keys(): + assert isinstance(key, str) + + # All values should be Metric instances + for value in AVAILABLE_METRICS.values(): + assert isinstance(value, Metric) + + +# TestConvertMetrics tests +def test_convert_metrics_with_valid_metrics(): + """Test that convert_metrics correctly converts valid metric names to objects""" + + # Use metrics that are commonly available in RAGAS + metric_names = ["faithfulness", "answer_relevancy"] + + # Only test with metrics that actually exist in AVAILABLE_METRICS + available_names = [name for name in metric_names if name in AVAILABLE_METRICS] + + if not available_names: + pytest.skip("Required metrics not available in this RAGAS version") + + metric_objects = convert_metrics(available_names) + + # Verify we got the right number of metrics + assert len(metric_objects) == len(available_names) + + # Verify all returned objects are Metric instances + for obj in metric_objects: + assert isinstance(obj, Metric) + + +def test_convert_metrics_with_invalid_metrics(): + """Test that convert_metrics handles invalid metric names""" + + # Test with only invalid metrics - should raise ValueError + with pytest.raises(ValueError, match="No valid metrics provided"): + convert_metrics(["nonexistent_metric", "fake_metric"]) + + +def test_convert_metrics_mixed_valid_invalid(): + """Test convert_metrics with mixed valid and invalid metric names""" + + # Get one valid metric name from AVAILABLE_METRICS + if not AVAILABLE_METRICS: + pytest.skip("No metrics available") + + valid_metric = list(AVAILABLE_METRICS.keys())[0] + metric_names = [valid_metric, "nonexistent_metric", "fake_metric"] + + metric_objects = convert_metrics(metric_names) + + # Should only return the valid metric + assert len(metric_objects) == 1 + + assert isinstance(metric_objects[0], Metric) diff --git a/testworkflows/tests/test_publish.py b/testworkflows/tests/test_publish.py new file mode 100644 index 0000000..bc215f6 --- /dev/null +++ b/testworkflows/tests/test_publish.py @@ -0,0 +1,435 @@ +""" +Unit tests for publish.py + +Tests the OpenTelemetry OTLP metrics publishing functionality. +""" + +import json +import shutil +import sys +import tempfile +from pathlib import Path + +import pytest + +sys.path.insert(0, str(Path(__file__).parent.parent / "scripts")) + +from publish import create_and_push_metrics, get_overall_scores, publish_metrics + + +# Fixtures +@pytest.fixture +def temp_dir(): + """Create a temporary directory for tests""" + tmp = tempfile.mkdtemp() + yield tmp + shutil.rmtree(tmp, ignore_errors=True) + + +@pytest.fixture +def evaluation_scores_file(temp_dir): + """Create a test evaluation scores file""" + test_file = Path(temp_dir) / "evaluation_scores.json" + test_data = { + "overall_scores": {"faithfulness": 0.85, "answer_relevancy": 0.90}, + "individual_results": [], + "total_tokens": {"input_tokens": 0, "output_tokens": 0}, + "total_cost": 0.0, + } + + with open(test_file, "w") as f: + json.dump(test_data, f) + + return test_file + + +@pytest.fixture +def realistic_scores_file(temp_dir): + """Create a realistic evaluation scores file""" + test_file = Path(temp_dir) / "evaluation_scores.json" + test_data = { + "overall_scores": { + "faithfulness": 0.8523, + "answer_relevancy": 0.9012, + "context_precision": 0.7845, + "context_recall": 0.8234, + }, + "individual_results": [ + { + "user_input": "What is the weather?", + "response": "It is sunny.", + "faithfulness": 0.85, + "answer_relevancy": 0.90, + } + ], + "total_tokens": {"input_tokens": 0, "output_tokens": 0}, + "total_cost": 0.0, + } + + with open(test_file, "w") as f: + json.dump(test_data, f, indent=2) + + return test_file + + +# TestGetOverallScores tests +def test_loads_overall_scores(evaluation_scores_file): + """Test that get_overall_scores loads the overall_scores section""" + scores = get_overall_scores(str(evaluation_scores_file)) + + assert scores["faithfulness"] == 0.85 + assert scores["answer_relevancy"] == 0.90 + + +def test_file_not_found(temp_dir): + """Test behavior when file doesn't exist""" + with pytest.raises(FileNotFoundError): + get_overall_scores(str(Path(temp_dir) / "nonexistent.json")) + + +# TestCreateAndPushMetrics tests +def test_creates_gauges_for_each_metric(monkeypatch): + """Test that a Gauge is created for each metric""" + overall_scores = {"faithfulness": 0.85, "answer_relevancy": 0.90} + + # Mock the meter and gauge + create_gauge_calls = [] + + class MockGauge: + def set(self, value, attributes): + pass + + class MockMeter: + def create_gauge(self, name, unit=None, description=None): + create_gauge_calls.append({"name": name, "unit": unit, "description": description}) + return MockGauge() + + mock_meter = MockMeter() + + def mock_get_meter(*args, **kwargs): + return mock_meter + + # Mock the provider + class MockProvider: + def force_flush(self): + pass + + def shutdown(self): + pass + + def mock_provider_init(**kwargs): + return MockProvider() + + # Mock the exporter + class MockExporter: + _preferred_temporality = {} + _preferred_aggregation = {} + + def mock_exporter_init(endpoint): + return MockExporter() + + monkeypatch.setattr("publish.metrics.get_meter", mock_get_meter) + monkeypatch.setattr("publish.MeterProvider", mock_provider_init) + monkeypatch.setattr("publish.OTLPMetricExporter", mock_exporter_init) + + create_and_push_metrics( + overall_scores=overall_scores, + workflow_name="test-workflow", + otlp_endpoint="localhost:4318", + ) + + # Verify create_gauge was called for each metric + assert len(create_gauge_calls) == 2 + + # Verify gauge names + gauge_names = [call["name"] for call in create_gauge_calls] + assert "ragas_evaluation_faithfulness" in gauge_names + assert "ragas_evaluation_answer_relevancy" in gauge_names + + +def test_sets_gauge_values(monkeypatch): + """Test that gauge values are set correctly""" + overall_scores = {"faithfulness": 0.85} + + # Mock the meter and gauge + set_calls = [] + + class MockGauge: + def set(self, value, attributes): + set_calls.append({"value": value, "attributes": attributes}) + + class MockMeter: + def create_gauge(self, name, unit=None, description=None): + return MockGauge() + + mock_meter = MockMeter() + + def mock_get_meter(*args, **kwargs): + return mock_meter + + # Mock the provider + class MockProvider: + def force_flush(self): + pass + + def shutdown(self): + pass + + def mock_provider_init(**kwargs): + return MockProvider() + + # Mock the exporter + class MockExporter: + _preferred_temporality = {} + _preferred_aggregation = {} + + def mock_exporter_init(endpoint): + return MockExporter() + + monkeypatch.setattr("publish.metrics.get_meter", mock_get_meter) + monkeypatch.setattr("publish.MeterProvider", mock_provider_init) + monkeypatch.setattr("publish.OTLPMetricExporter", mock_exporter_init) + + create_and_push_metrics( + overall_scores=overall_scores, + workflow_name="test-workflow", + otlp_endpoint="localhost:4318", + ) + + # Verify gauge.set was called with correct value and attributes + assert len(set_calls) == 1 + assert set_calls[0]["value"] == 0.85 + assert set_calls[0]["attributes"] == {"workflow_name": "test-workflow"} + + +def test_pushes_via_otlp(monkeypatch): + """Test that metrics are pushed via OTLP""" + overall_scores = {"faithfulness": 0.85} + + # Mock the meter and gauge + class MockGauge: + def set(self, value, attributes): + pass + + class MockMeter: + def create_gauge(self, name, unit=None, description=None): + return MockGauge() + + mock_meter = MockMeter() + + def mock_get_meter(*args, **kwargs): + return mock_meter + + # Mock the provider + force_flush_calls = [] + shutdown_calls = [] + + class MockProvider: + def force_flush(self): + force_flush_calls.append(True) + + def shutdown(self): + shutdown_calls.append(True) + + def mock_provider_init(**kwargs): + return MockProvider() + + # Mock the exporter + class MockExporter: + _preferred_temporality = {} + _preferred_aggregation = {} + + exporter_calls = [] + + def mock_exporter_init(endpoint): + exporter_calls.append({"endpoint": endpoint}) + return MockExporter() + + monkeypatch.setattr("publish.metrics.get_meter", mock_get_meter) + monkeypatch.setattr("publish.MeterProvider", mock_provider_init) + monkeypatch.setattr("publish.OTLPMetricExporter", mock_exporter_init) + + create_and_push_metrics( + overall_scores=overall_scores, + workflow_name="test-workflow", + otlp_endpoint="localhost:4318", + ) + + # Verify OTLPMetricExporter was initialized with correct endpoint + assert len(exporter_calls) == 1 + assert exporter_calls[0]["endpoint"] == "http://localhost:4318/v1/metrics" + + # Verify force_flush and shutdown were called + assert len(force_flush_calls) == 1 + assert len(shutdown_calls) == 1 + + +def test_handles_push_error(monkeypatch): + """Test error handling when OTLP export fails""" + overall_scores = {"faithfulness": 0.85} + + # Mock the meter and gauge + class MockGauge: + def set(self, value, attributes): + pass + + class MockMeter: + def create_gauge(self, name, unit=None, description=None): + return MockGauge() + + mock_meter = MockMeter() + + def mock_get_meter(*args, **kwargs): + return mock_meter + + # Mock the provider to raise an exception on force_flush + shutdown_calls = [] + + class MockProvider: + def force_flush(self): + raise Exception("Connection refused") + + def shutdown(self): + shutdown_calls.append(True) + + def mock_provider_init(**kwargs): + return MockProvider() + + # Mock the exporter + class MockExporter: + _preferred_temporality = {} + _preferred_aggregation = {} + + def mock_exporter_init(endpoint): + return MockExporter() + + monkeypatch.setattr("publish.metrics.get_meter", mock_get_meter) + monkeypatch.setattr("publish.MeterProvider", mock_provider_init) + monkeypatch.setattr("publish.OTLPMetricExporter", mock_exporter_init) + + with pytest.raises(Exception, match="Connection refused"): + create_and_push_metrics( + overall_scores=overall_scores, + workflow_name="test-workflow", + otlp_endpoint="localhost:4318", + ) + + # Verify shutdown is still called in finally block + assert len(shutdown_calls) == 1 + + +# TestPublishMetrics tests +def test_publish_metrics_calls_create_and_push(evaluation_scores_file, monkeypatch): + """Test that publish_metrics calls create_and_push_metrics""" + create_push_calls = [] + + def mock_create_push(overall_scores, workflow_name, otlp_endpoint): + create_push_calls.append( + { + "overall_scores": overall_scores, + "workflow_name": workflow_name, + "otlp_endpoint": otlp_endpoint, + } + ) + + monkeypatch.setattr("publish.create_and_push_metrics", mock_create_push) + + publish_metrics( + input_file=str(evaluation_scores_file), + workflow_name="test-workflow", + otlp_endpoint="localhost:4318", + ) + + # Verify create_and_push_metrics was called + assert len(create_push_calls) == 1 + + # Verify parameters + assert create_push_calls[0]["overall_scores"]["faithfulness"] == 0.85 + assert create_push_calls[0]["overall_scores"]["answer_relevancy"] == 0.90 + assert create_push_calls[0]["workflow_name"] == "test-workflow" + assert create_push_calls[0]["otlp_endpoint"] == "localhost:4318" + + +def test_publish_metrics_with_empty_scores(temp_dir, monkeypatch): + """Test behavior when overall_scores is empty""" + # Create file with empty overall_scores + test_data = {"overall_scores": {}, "individual_results": []} + + empty_file = Path(temp_dir) / "empty_scores.json" + with open(empty_file, "w") as f: + json.dump(test_data, f) + + create_push_calls = [] + + def mock_create_push(overall_scores, workflow_name, otlp_endpoint): + create_push_calls.append(True) + + monkeypatch.setattr("publish.create_and_push_metrics", mock_create_push) + + publish_metrics( + input_file=str(empty_file), + workflow_name="test-workflow", + otlp_endpoint="localhost:4318", + ) + + # Verify create_and_push_metrics was NOT called + assert len(create_push_calls) == 0 + + +# TestIntegrationWithTestData tests +def test_publish_realistic_scores(realistic_scores_file, monkeypatch): + """Test publishing realistic evaluation scores""" + # Mock the meter and gauge + create_gauge_calls = [] + + class MockGauge: + def set(self, value, attributes): + pass + + class MockMeter: + def create_gauge(self, name, unit=None, description=None): + create_gauge_calls.append({"name": name}) + return MockGauge() + + mock_meter = MockMeter() + + def mock_get_meter(*args, **kwargs): + return mock_meter + + # Mock the provider + class MockProvider: + def force_flush(self): + pass + + def shutdown(self): + pass + + def mock_provider_init(**kwargs): + return MockProvider() + + # Mock the exporter + class MockExporter: + _preferred_temporality = {} + _preferred_aggregation = {} + + exporter_calls = [] + + def mock_exporter_init(endpoint): + exporter_calls.append(True) + return MockExporter() + + monkeypatch.setattr("publish.metrics.get_meter", mock_get_meter) + monkeypatch.setattr("publish.MeterProvider", mock_provider_init) + monkeypatch.setattr("publish.OTLPMetricExporter", mock_exporter_init) + + publish_metrics( + input_file=str(realistic_scores_file), + workflow_name="weather-assistant-test", + otlp_endpoint="localhost:4318", + ) + + # Verify OTLPMetricExporter was called + assert len(exporter_calls) == 1 + + # Verify 4 metrics were created (faithfulness, answer_relevancy, context_precision, context_recall) + assert len(create_gauge_calls) == 4 diff --git a/testworkflows/tests/test_run.py b/testworkflows/tests/test_run.py new file mode 100644 index 0000000..fce093b --- /dev/null +++ b/testworkflows/tests/test_run.py @@ -0,0 +1,213 @@ +""" +Unit tests for run.py + +Tests the agent query execution and experiment functionality. +""" + +import os +import shutil +import sys +import tempfile +from pathlib import Path + +import pytest + +sys.path.insert(0, str(Path(__file__).parent.parent / "scripts")) + +from run import initialize_client, main, run_agent_experiment + + +# Fixtures +@pytest.fixture +def temp_dir(): + """Create a temporary directory for tests""" + tmp = tempfile.mkdtemp() + original_cwd = Path.cwd() + yield tmp, original_cwd + shutil.rmtree(tmp, ignore_errors=True) + + +# TestInitializeClient tests +@pytest.mark.asyncio +async def test_initialize_client_creates_client(monkeypatch): + """Test that initialize_client creates a client correctly""" + + # Mock the agent card + class MockCard: + pass + + mock_card = MockCard() + + def mock_agent_card(url): + return mock_card + + # Mock the factory and client + class MockClient: + pass + + mock_client = MockClient() + + class MockFactory: + def create(self, card): + return mock_client + + def mock_factory_init(config=None): + return MockFactory() + + monkeypatch.setattr("run.minimal_agent_card", mock_agent_card) + monkeypatch.setattr("run.ClientFactory", mock_factory_init) + + # Call the function + result = await initialize_client("http://test-agent:8000") + + # Verify + assert result == mock_client + + +# TestRunAgentExperiment tests +@pytest.mark.asyncio +async def test_run_agent_experiment_success(monkeypatch): + """Test successful agent query execution""" + + # Mock the client + class MockTask: + def model_dump(self, **kwargs): + return { + "artifacts": [{"parts": [{"text": "Agent response text"}]}], + "history": [], + } + + mock_task = MockTask() + + class MockClient: + async def send_message(self, message): + yield (mock_task, None) + + mock_client = MockClient() + + async def mock_init_client(url): + return mock_client + + # Mock httpx AsyncClient + class MockAsyncClient: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + pass + + def mock_httpx_client(): + return MockAsyncClient() + + monkeypatch.setattr("run.initialize_client", mock_init_client) + monkeypatch.setattr("run.httpx.AsyncClient", mock_httpx_client) + + # Create test row + test_row = { + "user_input": "What is the weather?", + "retrieved_contexts": ["Context about weather"], + "reference": "Expected answer", + } + + # Call the function + result = await run_agent_experiment.func(test_row, agent_url="http://test-agent:8000") + + # Verify result structure + assert "user_input" in result + assert "retrieved_contexts" in result + assert "reference" in result + assert "response" in result + assert result["user_input"] == "What is the weather?" + assert result["response"] == "Agent response text" + + +@pytest.mark.asyncio +async def test_run_agent_experiment_error(monkeypatch): + """Test agent query with error handling""" + + # Mock client that raises an error + async def mock_init_client(url): + raise Exception("Connection failed") + + # Mock httpx AsyncClient + class MockAsyncClient: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + pass + + def mock_httpx_client(): + return MockAsyncClient() + + monkeypatch.setattr("run.initialize_client", mock_init_client) + monkeypatch.setattr("run.httpx.AsyncClient", mock_httpx_client) + + # Create test row + test_row = { + "user_input": "What is the weather?", + "retrieved_contexts": ["Context"], + "reference": "Answer", + } + + # Call the function + result = await run_agent_experiment.func(test_row, agent_url="http://test-agent:8000") + + # Verify error is captured in response + assert "response" in result + assert "ERROR" in result["response"] + assert "Connection failed" in result["response"] + + +# TestMain tests +@pytest.mark.asyncio +async def test_main_execution(temp_dir, monkeypatch): + """Test main function execution flow""" + + tmp, original_cwd = temp_dir + os.chdir(tmp) + + try: + # Create a mock dataset + class MockDataset: + def __len__(self): + return 2 + + mock_dataset = MockDataset() + + def mock_dataset_load(path, backend): + return mock_dataset + + # Mock experiment results + class MockExperiment: + pass + + mock_experiment = MockExperiment() + + async def mock_arun(*args, **kwargs): + return mock_experiment + + calls_to_load = [] + calls_to_arun = [] + + def mock_dataset_load_tracked(**kwargs): + calls_to_load.append(kwargs) + return mock_dataset + + async def mock_arun_tracked(*args, **kwargs): + calls_to_arun.append({"args": args, "kwargs": kwargs}) + return mock_experiment + + monkeypatch.setattr("run.Dataset.load", mock_dataset_load_tracked) + monkeypatch.setattr("run.run_agent_experiment.arun", mock_arun_tracked) + + # Run main + await main("http://test-agent:8000") + + # Verify Dataset.load was called + assert len(calls_to_load) == 1 + + # Verify experiment was run + assert len(calls_to_arun) == 1 + finally: + os.chdir(original_cwd) diff --git a/testworkflows/tests/test_setup.py b/testworkflows/tests/test_setup.py new file mode 100644 index 0000000..9e594b9 --- /dev/null +++ b/testworkflows/tests/test_setup.py @@ -0,0 +1,213 @@ +""" +Unit tests for setup.py + +Tests the dataset download, conversion, and Ragas dataset creation functionality. +""" + +import os +import shutil +import sys +import tempfile +from io import BytesIO +from pathlib import Path + +import pandas as pd +import pytest + +sys.path.insert(0, str(Path(__file__).parent.parent / "scripts")) + +from setup import custom_convert_csv, dataframe_to_ragas_dataset, get_converter, main + + +# Fixtures +@pytest.fixture +def temp_dir(): + """Create a temporary directory for tests""" + tmp = tempfile.mkdtemp() + original_cwd = Path.cwd() + yield tmp, original_cwd + shutil.rmtree(tmp, ignore_errors=True) + + +# TestCustomConvertCSV tests +def test_converts_string_to_list(): + """Test that retrieved_contexts strings are converted to lists""" + csv_content = b"user_input,retrieved_contexts,reference\n" + csv_content += b'"Question?","Context text","Answer"\n' + + buffer = BytesIO(csv_content) + df = custom_convert_csv(buffer) + + assert isinstance(df["retrieved_contexts"].iloc[0], list) + assert df["retrieved_contexts"].iloc[0] == ["Context text"] + + +def test_handles_empty_retrieved_contexts(): + """Test handling of empty retrieved_contexts""" + csv_content = b"user_input,retrieved_contexts,reference\n" + csv_content += b'"Question?","","Answer"\n' + + buffer = BytesIO(csv_content) + df = custom_convert_csv(buffer) + + # Empty string becomes [nan] in pandas, which then becomes [] + # The function converts non-list values to lists + result = df["retrieved_contexts"].iloc[0] + # Check that it's a list and handle NaN case + assert isinstance(result, list) + # If it contains NaN, that's acceptable behavior for empty strings in CSV + if result and pd.isna(result[0]): + # This is expected - pandas converts empty string to NaN + pass + else: + # Or it should be an empty list + assert result == [] + + +def test_missing_retrieved_contexts_column(): + """Test that CSV without retrieved_contexts column works""" + csv_content = b"user_input,reference\n" + csv_content += b'"Question?","Answer"\n' + + buffer = BytesIO(csv_content) + df = custom_convert_csv(buffer) + + assert "retrieved_contexts" not in df.columns + + +# TestGetConverter tests +def test_unsupported_format(): + """Test that unsupported formats raise TypeError""" + with pytest.raises(TypeError) as exc_info: + get_converter("https://example.com/data.xlsx") + + assert "Unsupported filetype" in str(exc_info.value) + + +# TestDataframeToRagasDataset tests +def test_creates_ragas_dataset_file(temp_dir): + """Test that ragas_dataset.jsonl is created""" + + tmp, original_cwd = temp_dir + os.chdir(tmp) + + try: + df = pd.DataFrame( + { + "user_input": ["Question 1"], + "retrieved_contexts": [["Context 1"]], + "reference": ["Answer 1"], + } + ) + + dataframe_to_ragas_dataset(df) + + # Check for the file in the datasets subdirectory + dataset_file = Path(tmp) / "data" / "datasets" / "ragas_dataset.jsonl" + assert dataset_file.exists(), f"Dataset file not found at {dataset_file}" + finally: + os.chdir(original_cwd) + + +# TestMain tests +def test_main_with_csv(temp_dir, monkeypatch): + """Test main function with CSV file""" + + tmp, original_cwd = temp_dir + os.chdir(tmp) + + try: + # Mock the HTTP response + csv_content = b"user_input,retrieved_contexts,reference\n" + csv_content += b'"Question?","Context text","Answer"\n' + + class MockResponse: + def __init__(self): + self.content = csv_content + + def raise_for_status(self): + pass + + calls = [] + + def mock_get(url, timeout=None): + calls.append({"url": url, "timeout": timeout}) + return MockResponse() + + monkeypatch.setattr("setup.requests.get", mock_get) + + # Run main + main("https://example.com/data.csv") + + # Verify dataset was created in datasets subdirectory + dataset_file = Path(tmp) / "data" / "datasets" / "ragas_dataset.jsonl" + assert dataset_file.exists(), f"Dataset file not found at {dataset_file}" + + # Verify requests.get was called correctly + assert len(calls) == 1 + assert calls[0]["url"] == "https://example.com/data.csv" + assert calls[0]["timeout"] == 20 + finally: + os.chdir(original_cwd) + + +def test_main_with_json(temp_dir, monkeypatch): + """Test main function with JSON file""" + + tmp, original_cwd = temp_dir + os.chdir(tmp) + + try: + # Mock the HTTP response + json_content = b"""[ + { + "user_input": "Question?", + "retrieved_contexts": ["Context text"], + "reference": "Answer" + } + ]""" + + class MockResponse: + def __init__(self): + self.content = json_content + + def raise_for_status(self): + pass + + def mock_get(url, timeout=None): + return MockResponse() + + monkeypatch.setattr("setup.requests.get", mock_get) + + # Run main + main("https://example.com/data.json") + + # Verify dataset was created in datasets subdirectory + dataset_file = Path(tmp) / "data" / "datasets" / "ragas_dataset.jsonl" + assert dataset_file.exists(), f"Dataset file not found at {dataset_file}" + finally: + os.chdir(original_cwd) + + +def test_main_with_invalid_url(temp_dir, monkeypatch): + """Test main function with invalid URL (HTTP error)""" + + tmp, original_cwd = temp_dir + os.chdir(tmp) + + try: + # Mock HTTP error + class MockResponse: + def raise_for_status(self): + raise Exception("HTTP 404") + + def mock_get(url, timeout=None): + return MockResponse() + + monkeypatch.setattr("setup.requests.get", mock_get) + + # Verify that the error propagates + with pytest.raises(Exception, match="HTTP 404"): + main("https://example.com/nonexistent.csv") + finally: + os.chdir(original_cwd) diff --git a/testworkflows/tests_e2e/test_e2e.py b/testworkflows/tests_e2e/test_e2e.py new file mode 100755 index 0000000..34d78a3 --- /dev/null +++ b/testworkflows/tests_e2e/test_e2e.py @@ -0,0 +1,255 @@ +""" +End-to-end test that runs all scripts in the correct order: +1. setup.py - Downloads, converts and saves Ragas Dataset to data/datasets/ragas_dataset.jsonl +2. run.py - Runs agent queries on the dataset and saves Ragas Experiment to data/experiments/ragas_experiment.jsonl +3. evaluate.py - Evaluates results using RAGAS metrics and saves result to results/evaluation_scores.json +4. publish.py - Publishes metrics via OpenTelemetry OTLP + +Usage: + pytest tests/test_e2e.py + + # With custom configuration via environment variables: + E2E_DATASET_URL="https://example.com/data.csv" \ + E2E_AGENT_URL="http://localhost:8000" \ + E2E_MODEL="gemini-flash-latest" \ + E2E_METRICS="faithfulness,answer_relevancy" \ + E2E_WORKFLOW_NAME="weather-assistant-test" \ + pytest tests/test_e2e.py +""" + +import logging +import os +import subprocess # nosec +from pathlib import Path +from typing import List + +# Set up logging +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + + +class E2ETestRunner: + """Manages the end-to-end test execution pipeline.""" + + def __init__( + self, + dataset_url: str, + agent_url: str, + model: str, + metrics: List[str], + workflow_name: str, + otlp_endpoint: str = "localhost:4318", + ): + self.dataset_url = dataset_url + self.agent_url = agent_url + self.model = model + self.metrics = metrics + self.workflow_name = workflow_name + self.otlp_endpoint = otlp_endpoint + + # Define script paths + self.scripts_dir = Path(__file__).parent.parent / "scripts" + self.setup_script = self.scripts_dir / "setup.py" + self.run_script = self.scripts_dir / "run.py" + self.evaluate_script = self.scripts_dir / "evaluate.py" + self.publish_script = self.scripts_dir / "publish.py" + + # Define expected output files + self.dataset_file = Path("./data/datasets/ragas_dataset.jsonl") + self.results_file = Path("./data/experiments/ragas_experiment.jsonl") + self.evaluation_file = Path("./results/evaluation_scores.json") + + def verify_scripts_exist(self) -> bool: + """Verify that all required scripts exist.""" + logger.info("Verifying all scripts exist...") + scripts = [ + self.setup_script, + self.run_script, + self.evaluate_script, + self.publish_script, + ] + + missing_scripts = [script for script in scripts if not script.exists()] + + if missing_scripts: + logger.error("Missing scripts:") + for script in missing_scripts: + logger.error(f" - {script}") + return False + + logger.info("✓ All scripts found") + return True + + def run_command(self, command: List[str], step_name: str) -> bool: + """ + Run a command and handle output/errors. + + Args: + command: List of command arguments + step_name: Name of the step for logging + + Returns: + True if successful, False otherwise + """ + logger.info(f"\n{'=' * 60}") + logger.info(f"Step: {step_name}") + logger.info(f"Command: {' '.join(command)}") + logger.info(f"{'=' * 60}\n") + + try: + result = subprocess.run(command, check=True, capture_output=True, text=True) # nosec + + # Log stdout if present + if result.stdout: + logger.info("Output:") + for line in result.stdout.strip().split("\n"): + logger.info(f" {line}") + + logger.info(f"✓ {step_name} completed successfully\n") + return True + + except subprocess.CalledProcessError as e: + logger.error(f"✗ {step_name} failed with exit code {e.returncode}") + + if e.stdout: + logger.error("Standard output:") + for line in e.stdout.strip().split("\n"): + logger.error(f" {line}") + + if e.stderr: + logger.error("Error output:") + for line in e.stderr.strip().split("\n"): + logger.error(f" {line}") + + return False + + except Exception as e: + logger.error(f"✗ Unexpected error in {step_name}: {e}") + return False + + def verify_file_exists(self, file_path: Path, step_name: str) -> bool: + """Verify that an expected output file was created.""" + if file_path.exists(): + logger.info(f"✓ Verified {file_path} was created by {step_name}") + return True + else: + logger.error(f"✗ Expected file {file_path} not found after {step_name}") + return False + + def run_setup(self) -> bool: + """Run setup.py to download and convert dataset.""" + command = ["python3", str(self.setup_script), self.dataset_url] + success = self.run_command(command, "1. Setup - Download Dataset") + + if success: + return self.verify_file_exists(self.dataset_file, "setup.py") + return False + + def run_agent_queries(self) -> bool: + """Run run.py to execute agent queries on the dataset.""" + command = ["python3", str(self.run_script), self.agent_url] + success = self.run_command(command, "2. Run - Execute Agent Queries") + + if success: + return self.verify_file_exists(self.results_file, "run.py") + return False + + def run_evaluation(self) -> bool: + """Run evaluate.py to evaluate results using RAGAS metrics.""" + command = ["python3", str(self.evaluate_script), self.model] + self.metrics + success = self.run_command(command, "3. Evaluate - Calculate RAGAS Metrics") + + if success: + return self.verify_file_exists(self.evaluation_file, "evaluate.py") + return False + + def run_publish(self) -> bool: + """Run publish.py to publish metrics via OpenTelemetry OTLP.""" + command = [ + "python3", + str(self.publish_script), + self.workflow_name, + self.otlp_endpoint, + ] + return self.run_command(command, "4. Publish - Push Metrics via OTLP") + + def run_full_pipeline(self) -> bool: + """Execute the complete E2E test pipeline.""" + logger.info("\n" + "=" * 60) + logger.info("Starting E2E Test Pipeline") + logger.info("=" * 60 + "\n") + + # Verify all scripts exist before starting + if not self.verify_scripts_exist(): + logger.error("Cannot proceed - missing required scripts") + return False + + # Run each step in order + steps = [ + ("Setup", self.run_setup), + ("Run", self.run_agent_queries), + ("Evaluate", self.run_evaluation), + ("Publish", self.run_publish), + ] + + for step_name, step_func in steps: + if not step_func(): + logger.error(f"\n{'=' * 60}") + logger.error(f"E2E Test FAILED at step: {step_name}") + logger.error(f"{'=' * 60}\n") + return False + + # All steps completed successfully + logger.info("\n" + "=" * 60) + logger.info("E2E Test Pipeline COMPLETED SUCCESSFULLY") + logger.info("=" * 60 + "\n") + + logger.info("Summary:") + logger.info(f" ✓ Ragas Dataset created: {self.dataset_file}") + logger.info(f" ✓ Ragas Experiment saved: {self.results_file}") + logger.info(f" ✓ Evaluation completed: {self.evaluation_file}") + logger.info(f" ✓ Metrics published to: {self.otlp_endpoint}") + logger.info(f" ✓ Workflow name: {self.workflow_name}") + + return True + + +def test_e2e_pipeline(): + """Pytest test function for the E2E pipeline. + + This test can be run with pytest and uses environment variables or defaults + for configuration. To customize, set these environment variables: + - E2E_DATASET_URL + - E2E_AGENT_URL + - E2E_MODEL + - E2E_METRICS (comma-separated) + - E2E_WORKFLOW_NAME + - E2E_OTLP_ENDPOINT + + Example: + E2E_DATASET_URL="https://example.com/data.csv" pytest tests/test_e2e.py + """ + + # Get configuration from environment variables with sensible defaults + dataset_url = os.getenv("E2E_DATASET_URL", "http://localhost:8000/dataset.json") + agent_url = os.getenv("E2E_AGENT_URL", "http://localhost:11010") + model = os.getenv("E2E_MODEL", "gemini-2.5-flash-lite") + metrics_str = os.getenv("E2E_METRICS", "faithfulness") + metrics = [m.strip() for m in metrics_str.split(",")] + workflow_name = os.getenv("E2E_WORKFLOW_NAME", "Test Workflow") + otlp_endpoint = os.getenv("E2E_OTLP_ENDPOINT", "localhost:4318") + + # Create and run the test pipeline + runner = E2ETestRunner( + dataset_url=dataset_url, + agent_url=agent_url, + model=model, + metrics=metrics, + workflow_name=workflow_name, + 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