diff --git a/README.md b/README.md index 84cf1cb..5789ebc 100644 --- a/README.md +++ b/README.md @@ -47,6 +47,12 @@ Building AI agents is challenging enough without worrying about observability, e pip install gradient-adk ``` +To use local evaluations (powered by DeepEval), install with the eval extra: + +```bash +pip install gradient-adk[eval] +``` + ## Quick Start > **🎥 Watch the [Getting Started Video](https://www.youtube.com/watch?v=23xiqgrGciE)** for a complete walkthrough @@ -82,6 +88,8 @@ gradient agent deploy ### 4. Evaluate Your Agent +Run evaluations against a deployed agent on DigitalOcean: + ```bash gradient agent evaluate \ --test-case-name "my-evaluation" \ @@ -89,6 +97,14 @@ gradient agent evaluate \ --categories correctness,context_quality ``` +Or run evaluations locally using DeepEval (no deployment needed): + +```bash +gradient agent evaluate --local --preset basic --dataset-file dataset.csv +``` + +See the [Local Evaluations](#local-evaluations) section for full details. + ## Usage Examples ### Using LangGraph (Automatic Trace Capture) @@ -192,7 +208,7 @@ gradient agent logs gradient agent traces ``` -### Evaluation +### Remote Evaluation You can evaluate your deployed agent with a number of useful evaluation metrics. See the [DigitalOcean docs](https://docs.digitalocean.com/products/gradient-ai-platform/how-to/create-evaluation-datasets/#evaluation-datasets-for-agents-built-with-agent-development-kit) for details on what belongs in a dataset. @@ -209,6 +225,181 @@ gradient agent evaluate \ --success-threshold 80.0 ``` +## Local Evaluations + +The ADK includes an integrated local evaluation framework powered by [DeepEval](https://github.com/confident-ai/deepeval). Run evaluations locally against your agent code — no deployment required. A judge LLM (via DigitalOcean Serverless Inference or any OpenAI-compatible endpoint) scores your agent's responses across a configurable set of metrics. + +### How It Works + +Local evaluation runs entirely in-process: + +1. `gradient agent evaluate --local` imports your agent module and gets the FastAPI app +2. For each row in the dataset CSV, the runner sends a request to `/run` via ASGI transport (no network, no subprocess) +3. The `@entrypoint` decorator detects eval headers and creates an `EvalRecord` via ContextVar +4. Your agent code calls `eval_record()` to attach retrieval context and tool call data — these are silent no-ops in production +5. After each response, the runner assembles DeepEval `LLMTestCase` objects and evaluates with the judge LLM +6. Metrics that lack required data (e.g. `tool_correctness` without `expected_tools` in the CSV) are auto-skipped with actionable reasons + +### Installation + +```bash +pip install gradient-adk[eval] +``` + +### Instrumenting Your Agent + +Import `eval_record` from `gradient_adk` and call it during request handling to record retrieval context and tool calls. In production (outside evaluation), these calls are silent no-ops with zero overhead. + +```python +from gradient_adk import entrypoint, RequestContext, eval_record + +@entrypoint +async def main(input: dict, context: RequestContext): + prompt = input.get("prompt", "") + rec = eval_record() + + # Record a tool call and retrieval context + chunks = retrieve(prompt) + rec.add_tool_call("retrieve", args={"query": prompt}, output=chunks) + rec.add_context(chunks) + + # ... call LLM, process response ... + + answer = summarize(raw_answer) + rec.add_tool_call("summarize", args={"text": raw_answer}, output=answer) + + return answer +``` + +- `rec.add_context(chunks)` — records retrieval context for faithfulness and contextual metrics +- `rec.add_tool_call(name, args, output)` — records tool invocations for tool correctness metrics + +### Creating a Dataset + +Create a CSV file with at minimum a `query` column. Additional columns enable more metrics: + +| Column | Required | Format | Enables | +|--------|----------|--------|---------| +| `query` | Yes | JSON (e.g. `{"prompt": "What is Python?"}`) | All metrics | +| `expected_output` | No | String | answer_relevancy | +| `expected_context` | No | JSON list of strings | contextual_precision, contextual_recall | +| `expected_tools` | No | JSON list of objects (e.g. `[{"name": "retrieve"}]`) | tool_correctness | + +Example `dataset.csv`: + +```csv +query,expected_output,expected_context,expected_tools +"{""prompt"": ""What is Python?""}",Python is a high-level interpreted programming language.,"[""Python is a high-level, interpreted programming language.""]","[{""name"": ""retrieve""}, {""name"": ""summarize""}]" +``` + +### Configuration + +Create `.gradient/eval.yml` in your project to configure the judge model, presets, and thresholds: + +```yaml +# Judge model for LLM-as-judge evaluation (uses DO serverless inference). +# The "openai/" prefix tells LiteLLM to use the OpenAI-compatible API format. +# All DigitalOcean models are OpenAI API compliant, so the format is: +# openai/ +judge_model: "openai/openai-gpt-oss-120b" +judge_base_url: "https://inference.do-ai.run/v1" +judge_api_key_env: "GRADIENT_MODEL_ACCESS_KEY" + +# Default metric preset: basic | rag | agent | all +preset: "basic" + +# Per-metric threshold overrides (default: 0.5) +thresholds: + answer_relevancy: 0.7 + faithfulness: 0.8 + +# Per-metric judge model overrides +# metrics: +# faithfulness: +# threshold: 0.8 +# judge_model: "openai/meta-llama/Meta-Llama-3.1-405B-Instruct" +``` + +All settings can be overridden via CLI flags. Precedence: CLI args > YAML > defaults. + +> **Note on model names:** The `openai/` prefix is a [LiteLLM routing convention](https://docs.litellm.ai/docs/providers/openai_compatible) that tells the evaluation framework to use the OpenAI-compatible chat completions format. It does not mean the model is hosted by OpenAI. All DigitalOcean Serverless Inference models are OpenAI API compliant, so the format is always `openai/`. + +### Available Metrics + +Metrics are organized into three presets: + +| Preset | Metrics | Data Required | +|--------|---------|---------------| +| `basic` | answer_relevancy, bias, toxicity | Just input/output — zero config | +| `rag` | faithfulness, contextual_relevancy, contextual_precision, contextual_recall | `eval_record().add_context()` + `expected_context` in CSV | +| `agent` | tool_correctness | `eval_record().add_tool_call()` + `expected_tools` in CSV | +| `all` | All 8 metrics | Auto-skips metrics missing required data | + +All scores are normalized so that **1.0 = best** for every metric (including bias and toxicity, where the underlying raw score is inverted). + +### Running Evaluations + +```bash +# All 8 metrics, verbose per-row breakdown: +gradient agent evaluate --local --preset all --dataset-file dataset.csv --verbose + +# Basic metrics only (no retrieval context or tool data needed): +gradient agent evaluate --local --preset basic --dataset-file dataset.csv + +# RAG metrics only: +gradient agent evaluate --local --preset rag --dataset-file dataset.csv + +# Specific metrics: +gradient agent evaluate --local --metrics answer_relevancy,faithfulness,tool_correctness --dataset-file dataset.csv + +# Override judge model and threshold (openai/ prefix = LiteLLM OpenAI-compatible format): +gradient agent evaluate --local --preset basic --dataset-file dataset.csv \ + --judge-model "openai/meta-llama/Meta-Llama-3.1-405B-Instruct" \ + --threshold 0.7 +``` + +#### CLI Options + +| Flag | Description | +|------|-------------| +| `--local` | Run evaluation locally with DeepEval (required for local evals) | +| `--dataset-file ` | Path to the CSV dataset file | +| `--preset ` | Metric preset: `basic`, `rag`, `agent`, or `all` | +| `--metrics ` | Comma-separated list of specific metrics to run | +| `--judge-model ` | Override the judge model (format: `openai/`) | +| `--threshold ` | Global pass/fail threshold (default: 0.5) | +| `--verbose` | Show per-row score breakdown | + +### Example Output + +``` +Judge model: openai/openai-gpt-oss-120b +Preset: all +Dataset: dataset.csv +Entrypoint: agent.py + +============================================================ + Evaluation Results +============================================================ + + Metric Score Threshold Result + -------------------------------------------------------- + answer_relevancy 1.00 0.50 PASS + bias 1.00 0.50 PASS + toxicity 1.00 0.50 PASS + faithfulness 1.00 0.50 PASS + contextual_relevancy 1.00 0.50 PASS + contextual_precision 1.00 0.50 PASS + contextual_recall 1.00 0.50 PASS + tool_correctness 1.00 0.50 PASS + +============================================================ + 8/8 metrics passed across 3 test case(s) + Total time: 11.2s +============================================================ +``` + + ## Tracing The ADK provides comprehensive tracing capabilities to capture and analyze your agent's execution. You can use **decorators** for wrapping functions or **programmatic functions** for manual span creation. @@ -337,7 +528,9 @@ my-agent/ ├── main.py # Agent entrypoint with @entrypoint decorator ├── .gradient/ │ ├── agent.yml # Agent configuration (auto-generated) +│ ├── eval.yml # Local evaluation configuration (optional) │ └── .gradientignore # Controls which files are excluded from deployment +├── dataset.csv # Evaluation dataset (optional) ├── requirements.txt # Python dependencies ├── .env # Environment variables (not committed) ├── agents/ # Agent implementations diff --git a/gradient_adk/__init__.py b/gradient_adk/__init__.py index 70ba0bf..acf0fb2 100644 --- a/gradient_adk/__init__.py +++ b/gradient_adk/__init__.py @@ -4,6 +4,7 @@ """ from .decorator import entrypoint, RequestContext +from .evaluation.record import eval_record, EvalRecord from .tracing import ( # Decorators trace_llm, @@ -18,6 +19,9 @@ __all__ = [ "entrypoint", "RequestContext", + # Evaluation + "eval_record", + "EvalRecord", # Decorators "trace_llm", "trace_retriever", diff --git a/gradient_adk/cli/cli.py b/gradient_adk/cli/cli.py index bf7b719..abf8ef7 100644 --- a/gradient_adk/cli/cli.py +++ b/gradient_adk/cli/cli.py @@ -799,10 +799,43 @@ def agent_evaluate( interactive: bool = typer.Option( True, "--interactive/--no-interactive", help="Interactive prompt mode" ), + # --- Local evaluation flags --- + local: bool = typer.Option( + False, "--local", help="Run evaluation locally using DeepEval" + ), + preset: Optional[str] = typer.Option( + None, "--preset", help="Metric preset: basic, rag, agent, all" + ), + metrics: Optional[str] = typer.Option( + None, "--metrics", help="Comma-separated metric names" + ), + judge_model: Optional[str] = typer.Option( + None, "--judge-model", help="Override judge model" + ), + threshold: Optional[float] = typer.Option( + None, "--threshold", help="Global pass/fail threshold" + ), + verbose: bool = typer.Option( + False, "--verbose", help="Show per-row breakdown in local eval" + ), ): """Run an evaluation test case for the agent.""" import asyncio from pathlib import Path + + # ---- Local evaluation path ---- + if local: + _run_local_eval( + dataset_file=dataset_file, + preset=preset, + metrics=metrics, + judge_model=judge_model, + threshold=threshold, + verbose=verbose, + ) + return + + # ---- Remote evaluation path (existing) ---- from gradient_adk.digital_ocean_api.client_async import AsyncDigitalOceanGenAI from gradient_adk.cli.agent.evaluation_service import EvaluationService from gradient_adk.digital_ocean_api.models import EvaluationMetricValueType @@ -1094,5 +1127,107 @@ async def run_evaluation(): raise typer.Exit(1) +def _run_local_eval( + *, + dataset_file: Optional[str], + preset: Optional[str], + metrics: Optional[str], + judge_model: Optional[str], + threshold: Optional[float], + verbose: bool, +) -> None: + """Run local evaluation using DeepEval.""" + import asyncio + import os + from pathlib import Path + + # Load .env file if present (same pattern as agent init/launch) + env_path = Path(".env") + if env_path.exists(): + for line in env_path.read_text().splitlines(): + line = line.strip() + if line and not line.startswith("#") and "=" in line: + key, _, value = line.partition("=") + os.environ.setdefault(key.strip(), value.strip()) + + # Map common DO key name if GRADIENT_MODEL_ACCESS_KEY isn't set + if not os.environ.get("GRADIENT_MODEL_ACCESS_KEY"): + do_key = os.environ.get("DIGITALOCEAN_INFERENCE_KEY", "") + if do_key: + os.environ["GRADIENT_MODEL_ACCESS_KEY"] = do_key + + try: + from gradient_adk.evaluation.config import load_eval_config + from gradient_adk.evaluation.runner import run_local_evaluation + from gradient_adk.evaluation.results import format_results + except ImportError as e: + typer.echo( + f"Local evaluation requires the 'eval' extra: pip install gradient-adk[eval]\n{e}", + err=True, + ) + raise typer.Exit(1) + + # Get entrypoint from agent config + entrypoint_file = _agent_config_manager.get_entrypoint_file() + if not entrypoint_file: + typer.echo( + "No entrypoint_file found in .gradient/agent.yml. " + "Run 'gradient agent configure' first.", + err=True, + ) + raise typer.Exit(1) + + # Load eval config + eval_yml = Path(".gradient") / "eval.yml" + config = load_eval_config( + yaml_path=eval_yml, + cli_preset=preset, + cli_judge_model=judge_model, + cli_threshold=threshold, + cli_dataset=dataset_file, + ) + + # Resolve dataset path + ds_path = Path(config.dataset or dataset_file or "dataset.csv") + if not ds_path.exists(): + typer.echo(f"Dataset not found: {ds_path}", err=True) + typer.echo("Specify with --dataset-file ", err=True) + raise typer.Exit(1) + + # Resolve metric names + metric_names = None + if metrics: + metric_names = [m.strip() for m in metrics.split(",")] + + api_key = config.judge_api_key + if not api_key: + typer.echo( + f"No API key found. Set {config.judge_api_key_env} in your environment.", + err=True, + ) + raise typer.Exit(1) + + typer.echo(f"Judge model: {config.judge_model}") + typer.echo(f"Preset: {config.preset}") + typer.echo(f"Dataset: {ds_path}") + typer.echo(f"Entrypoint: {entrypoint_file}") + typer.echo() + + async def _run(): + return await run_local_evaluation( + entrypoint_file=entrypoint_file, + config=config, + dataset_path=ds_path, + metric_names=metric_names, + ) + + results = asyncio.run(_run()) + typer.echo(format_results(results, verbose=verbose)) + + all_passed = all(ms.passed for ms in results.metric_summaries) + if not all_passed: + raise typer.Exit(1) + + def run(): app() \ No newline at end of file diff --git a/gradient_adk/cli/templates/eval.yml.template b/gradient_adk/cli/templates/eval.yml.template new file mode 100644 index 0000000..2403e98 --- /dev/null +++ b/gradient_adk/cli/templates/eval.yml.template @@ -0,0 +1,20 @@ +# Gradient ADK — Local Evaluation Configuration + +# Judge model for LLM-as-judge evaluation (uses DO serverless inference) +judge_model: "openai/openai-gpt-oss-120b" +judge_base_url: "https://inference.do-ai.run/v1" +judge_api_key_env: "GRADIENT_MODEL_ACCESS_KEY" + +# Default metric preset: basic | rag | agent | all +preset: "basic" + +# Per-metric threshold overrides (default: 0.5) +# thresholds: +# answer_relevancy: 0.7 +# faithfulness: 0.8 + +# Per-metric judge model overrides +# metrics: +# faithfulness: +# threshold: 0.8 +# judge_model: "openai/meta-llama/Meta-Llama-3.1-405B-Instruct" diff --git a/gradient_adk/decorator.py b/gradient_adk/decorator.py index a9423bd..9c47e28 100644 --- a/gradient_adk/decorator.py +++ b/gradient_adk/decorator.py @@ -166,6 +166,13 @@ async def run(req: Request): is_evaluation = "evaluation-id" in req.headers + # Wire EvalRecord for local evaluation + eval_request_id = req.headers.get("x-eval-request-id") + eval_token = None + if is_evaluation and eval_request_id: + from gradient_adk.evaluation.record import _begin_eval_request + eval_token = _begin_eval_request(eval_request_id) + context = _build_request_context(req) session_id = context.session_id @@ -196,6 +203,9 @@ async def run(req: Request): tr.on_request_end(outputs=None, error=str(e)) except Exception: pass + if eval_token is not None: + from gradient_adk.evaluation.record import _end_eval_request + _end_eval_request(eval_token) logger.error("Error creating generator", error=str(e), exc_info=True) raise HTTPException(status_code=500, detail="Internal server error") @@ -228,6 +238,9 @@ async def run(req: Request): pass headers = {"X-Gradient-Trace-Id": trace_id} if trace_id else {} + if eval_token is not None: + from gradient_adk.evaluation.record import _end_eval_request + _end_eval_request(eval_token) return JSONResponse(content=result, headers=headers) except Exception as e: @@ -238,6 +251,9 @@ async def run(req: Request): await tr._submit() except Exception: pass + if eval_token is not None: + from gradient_adk.evaluation.record import _end_eval_request + _end_eval_request(eval_token) logger.error( "Error in streaming evaluation", error=str(e), exc_info=True ) @@ -273,6 +289,9 @@ async def run(req: Request): tr.on_request_end(outputs=None, error=str(e)) except Exception: pass + if eval_token is not None: + from gradient_adk.evaluation.record import _end_eval_request + _end_eval_request(eval_token) logger.error("Error in /run", error=str(e), exc_info=True) raise HTTPException(status_code=500, detail="Internal server error") @@ -286,6 +305,10 @@ async def run(req: Request): except Exception: pass + if eval_token is not None: + from gradient_adk.evaluation.record import _end_eval_request + _end_eval_request(eval_token) + if trace_id: from fastapi.responses import JSONResponse diff --git a/gradient_adk/evaluation/__init__.py b/gradient_adk/evaluation/__init__.py new file mode 100644 index 0000000..a603633 --- /dev/null +++ b/gradient_adk/evaluation/__init__.py @@ -0,0 +1,10 @@ +""" +Gradient ADK evaluation module. + +Provides EvalRecord for collecting evaluation data within agent code, +and local evaluation support powered by DeepEval. +""" + +from .record import eval_record, EvalRecord + +__all__ = ["eval_record", "EvalRecord"] diff --git a/gradient_adk/evaluation/config.py b/gradient_adk/evaluation/config.py new file mode 100644 index 0000000..de044fa --- /dev/null +++ b/gradient_adk/evaluation/config.py @@ -0,0 +1,108 @@ +""" +Evaluation configuration — loads ``.gradient/eval.yml``, merges with CLI overrides. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Dict, Optional + +import yaml + + +@dataclass +class EvalConfig: + """Resolved evaluation configuration.""" + + judge_model: str = "openai/openai-gpt-oss-120b" + judge_base_url: str = "https://inference.do-ai.run/v1" + judge_api_key_env: str = "GRADIENT_MODEL_ACCESS_KEY" + preset: str = "basic" + thresholds: Dict[str, float] = field(default_factory=dict) + dataset: Optional[str] = None + concurrency: int = 5 + timeout: int = 30 + # Per-metric judge model overrides: metric_name -> model string + metric_judge_models: Dict[str, str] = field(default_factory=dict) + + @property + def judge_api_key(self) -> Optional[str]: + return os.environ.get(self.judge_api_key_env) + + +# --------------------------------------------------------------------------- +# Loading helpers +# --------------------------------------------------------------------------- + +_YAML_PATH = Path(".gradient") / "eval.yml" + + +def _load_yaml(path: Path) -> Dict[str, Any]: + """Load YAML config file, returning empty dict if missing.""" + if not path.is_file(): + return {} + with open(path, "r", encoding="utf-8") as f: + data = yaml.safe_load(f) + return data if isinstance(data, dict) else {} + + +def _apply_yaml(cfg: EvalConfig, data: Dict[str, Any]) -> None: + """Mutate *cfg* with values from a parsed YAML dict.""" + for key in ("judge_model", "judge_base_url", "judge_api_key_env", "preset", "dataset"): + if key in data: + setattr(cfg, key, data[key]) + + if "concurrency" in data: + cfg.concurrency = int(data["concurrency"]) + if "timeout" in data: + cfg.timeout = int(data["timeout"]) + + # Top-level thresholds dict + if isinstance(data.get("thresholds"), dict): + cfg.thresholds.update( + {k: float(v) for k, v in data["thresholds"].items()} + ) + + # Per-metric overrides (may include threshold + judge_model) + if isinstance(data.get("metrics"), dict): + for metric_name, metric_cfg in data["metrics"].items(): + if not isinstance(metric_cfg, dict): + continue + if "threshold" in metric_cfg: + cfg.thresholds[metric_name] = float(metric_cfg["threshold"]) + if "judge_model" in metric_cfg: + cfg.metric_judge_models[metric_name] = metric_cfg["judge_model"] + + +def load_eval_config( + *, + yaml_path: Optional[Path] = None, + cli_preset: Optional[str] = None, + cli_judge_model: Optional[str] = None, + cli_threshold: Optional[float] = None, + cli_dataset: Optional[str] = None, +) -> EvalConfig: + """Build an EvalConfig with precedence: CLI > YAML > defaults.""" + cfg = EvalConfig() + + # Layer 1: YAML + yaml_data = _load_yaml(yaml_path or _YAML_PATH) + _apply_yaml(cfg, yaml_data) + + # Layer 2: CLI overrides + if cli_preset is not None: + cfg.preset = cli_preset + if cli_judge_model is not None: + cfg.judge_model = cli_judge_model + if cli_dataset is not None: + cfg.dataset = cli_dataset + if cli_threshold is not None: + # CLI threshold applies as a global default for every metric + for name in cfg.thresholds: + cfg.thresholds[name] = cli_threshold + # Also store as a sentinel so the runner can use it as fallback + cfg.thresholds.setdefault("__global__", cli_threshold) + + return cfg diff --git a/gradient_adk/evaluation/dataset.py b/gradient_adk/evaluation/dataset.py new file mode 100644 index 0000000..9b70a13 --- /dev/null +++ b/gradient_adk/evaluation/dataset.py @@ -0,0 +1,111 @@ +""" +CSV dataset parsing and validation for local evaluation. + +Extends the validation pattern from ``cli/agent/evaluation_service.py``. +""" + +from __future__ import annotations + +import csv +import json +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple + + +@dataclass +class DatasetRow: + """One parsed row from the evaluation dataset.""" + + query: Any # parsed JSON value + expected_output: Optional[str] = None + expected_context: Optional[List[str]] = None + expected_tools: Optional[List[str]] = None + + +@dataclass +class ParsedDataset: + """Result of parsing an evaluation CSV.""" + + rows: List[DatasetRow] = field(default_factory=list) + available_columns: Set[str] = field(default_factory=set) + + +# Optional columns and whether their value is a JSON list +_OPTIONAL_COLUMNS: Dict[str, bool] = { + "expected_output": False, + "expected_context": True, + "expected_tools": True, +} + + +def parse_dataset(file_path: Path) -> ParsedDataset: + """Parse and validate a CSV dataset. + + Raises ``ValueError`` with a user-friendly message on any problem. + """ + if not file_path.exists(): + raise ValueError(f"Dataset file not found: {file_path}") + if file_path.suffix.lower() != ".csv": + raise ValueError(f"Dataset must be a CSV file, got: {file_path.suffix}") + + rows: List[DatasetRow] = [] + available_columns: Set[str] = set() + + with open(file_path, "r", newline="", encoding="utf-8") as f: + reader = csv.DictReader(f) + + if reader.fieldnames is None: + raise ValueError("CSV file is empty or has no header row") + + if "query" not in reader.fieldnames: + raise ValueError( + f"Missing required column: 'query'. " + f"Found columns: {', '.join(reader.fieldnames)}" + ) + + # Detect which optional columns are present + for col in _OPTIONAL_COLUMNS: + if col in reader.fieldnames: + available_columns.add(col) + + for row_num, row in enumerate(reader, start=2): + query_raw = row.get("query", "").strip() + if not query_raw: + raise ValueError(f"Row {row_num}: empty value in 'query' column") + + try: + query_parsed = json.loads(query_raw) + except json.JSONDecodeError as e: + raise ValueError( + f"Row {row_num}: invalid JSON in 'query' column: {e}" + ) from e + + dr = DatasetRow(query=query_parsed) + + # Parse optional columns + for col, is_json_list in _OPTIONAL_COLUMNS.items(): + raw = row.get(col, "").strip() if col in available_columns else "" + if not raw: + continue + if is_json_list: + try: + parsed = json.loads(raw) + except json.JSONDecodeError as e: + raise ValueError( + f"Row {row_num}: invalid JSON in '{col}' column: {e}" + ) from e + if not isinstance(parsed, list): + raise ValueError( + f"Row {row_num}: '{col}' must be a JSON list" + ) + setattr(dr, col, parsed) + else: + setattr(dr, col, raw) + + rows.append(dr) + + if not rows: + raise ValueError("Dataset has no data rows") + + return ParsedDataset(rows=rows, available_columns=available_columns) diff --git a/gradient_adk/evaluation/metrics.py b/gradient_adk/evaluation/metrics.py new file mode 100644 index 0000000..69c3dcb --- /dev/null +++ b/gradient_adk/evaluation/metrics.py @@ -0,0 +1,175 @@ +""" +Metric registry, presets, and skip-logic for local DeepEval evaluation. + +Metrics are only *instantiated* inside the runner (which imports deepeval); +this module is pure stdlib so it can be imported anywhere. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Dict, FrozenSet, List, Optional, Set, Tuple + + +@dataclass(frozen=True) +class MetricSpec: + """Declarative description of a single DeepEval metric.""" + + name: str + deepeval_class: str + required_fields: FrozenSet[str] + default_threshold: float + description: str + # If True, the raw score is inverted (0 = best). We display 1 - score + # so that higher is always better from the user's perspective. + inverted: bool = False + + +# --------------------------------------------------------------------------- +# Registry +# --------------------------------------------------------------------------- + +METRIC_REGISTRY: Dict[str, MetricSpec] = {} + + +def _register(*specs: MetricSpec) -> None: + for s in specs: + METRIC_REGISTRY[s.name] = s + + +_register( + # --- basic --- + MetricSpec( + name="answer_relevancy", + deepeval_class="AnswerRelevancyMetric", + required_fields=frozenset(), + default_threshold=0.5, + description="Measures how relevant the answer is to the input query.", + ), + MetricSpec( + name="bias", + deepeval_class="BiasMetric", + required_fields=frozenset(), + default_threshold=0.5, + description="Detects social or cognitive bias in the output.", + inverted=True, + ), + MetricSpec( + name="toxicity", + deepeval_class="ToxicityMetric", + required_fields=frozenset(), + default_threshold=0.5, + description="Detects toxic, harmful, or offensive content.", + inverted=True, + ), + # --- rag --- + MetricSpec( + name="faithfulness", + deepeval_class="FaithfulnessMetric", + required_fields=frozenset({"retrieval_context"}), + default_threshold=0.5, + description="Checks whether the answer is faithful to the retrieval context.", + ), + MetricSpec( + name="contextual_relevancy", + deepeval_class="ContextualRelevancyMetric", + required_fields=frozenset({"retrieval_context"}), + default_threshold=0.5, + description="Measures the relevancy of the retrieved context to the query.", + ), + MetricSpec( + name="contextual_precision", + deepeval_class="ContextualPrecisionMetric", + required_fields=frozenset({"retrieval_context", "expected_output"}), + default_threshold=0.5, + description="Measures precision of the retrieval context given expected output.", + ), + MetricSpec( + name="contextual_recall", + deepeval_class="ContextualRecallMetric", + required_fields=frozenset({"retrieval_context", "expected_output"}), + default_threshold=0.5, + description="Measures recall of the retrieval context given expected output.", + ), + # --- agent --- + MetricSpec( + name="tool_correctness", + deepeval_class="ToolCorrectnessMetric", + required_fields=frozenset({"tool_calls", "expected_tools"}), + default_threshold=0.5, + description="Checks whether the agent invoked the correct tools.", + ), +) + +# --------------------------------------------------------------------------- +# Presets +# --------------------------------------------------------------------------- + +PRESETS: Dict[str, List[str]] = { + "basic": ["answer_relevancy", "bias", "toxicity"], + "rag": [ + "faithfulness", + "contextual_relevancy", + "contextual_precision", + "contextual_recall", + ], + "agent": ["tool_correctness"], + "all": list(METRIC_REGISTRY.keys()), +} + + +def resolve_preset(name: str) -> List[str]: + """Return metric names for the given preset, or raise ValueError.""" + if name not in PRESETS: + valid = ", ".join(sorted(PRESETS)) + raise ValueError(f"Unknown preset '{name}'. Valid presets: {valid}") + return list(PRESETS[name]) + + +# --------------------------------------------------------------------------- +# Skip logic +# --------------------------------------------------------------------------- + +@dataclass +class SkippedMetric: + name: str + reason: str + + +_FIELD_HINTS: Dict[str, str] = { + "retrieval_context": "set eval_record().retrieval_context in your agent code", + "expected_output": "add 'expected_output' column to dataset CSV", + "expected_context": "add 'expected_context' column to dataset CSV", + "expected_tools": "add 'expected_tools' column to dataset CSV", + "tool_calls": "use eval_record().add_tool_call() in your agent code", +} + + +def resolve_runnable_metrics( + metric_names: List[str], + available_fields: Set[str], +) -> Tuple[List[MetricSpec], List[SkippedMetric]]: + """Partition *metric_names* into runnable specs and skipped-with-reason. + + ``available_fields`` is the union of dataset columns present and EvalRecord + fields that have been populated (e.g. ``{"expected_output", "retrieval_context"}``). + """ + runnable: List[MetricSpec] = [] + skipped: List[SkippedMetric] = [] + + for name in metric_names: + spec = METRIC_REGISTRY.get(name) + if spec is None: + skipped.append(SkippedMetric(name=name, reason=f"unknown metric '{name}'")) + continue + + missing = spec.required_fields - available_fields + if missing: + parts = [ + f"requires {f} \u2014 {_FIELD_HINTS.get(f, f)}" for f in sorted(missing) + ] + skipped.append(SkippedMetric(name=name, reason="; ".join(parts))) + else: + runnable.append(spec) + + return runnable, skipped diff --git a/gradient_adk/evaluation/record.py b/gradient_adk/evaluation/record.py new file mode 100644 index 0000000..d756747 --- /dev/null +++ b/gradient_adk/evaluation/record.py @@ -0,0 +1,128 @@ +""" +EvalRecord — per-request evaluation data collector. + +Provides a ContextVar-based store so agent code can attach retrieval context +and tool-call metadata during a request. The eval runner retrieves the data +after the response completes. + +In production (no eval context) ``eval_record()`` returns a lightweight NoOp +that silently discards all writes — zero overhead. +""" + +from __future__ import annotations + +import contextvars +from dataclasses import dataclass, field +from typing import Any, Dict, List, Optional + + +# --------------------------------------------------------------------------- +# Data classes +# --------------------------------------------------------------------------- + +@dataclass +class ToolCallRecord: + """A single tool invocation recorded during evaluation.""" + + name: str + args: Dict[str, Any] = field(default_factory=dict) + output: Any = None + + +@dataclass +class EvalRecord: + """Mutable bag of evaluation data populated by agent code.""" + + retrieval_context: Optional[List[str]] = None + tool_calls: List[ToolCallRecord] = field(default_factory=list) + + def add_tool_call( + self, name: str, *, args: Optional[Dict[str, Any]] = None, output: Any = None + ) -> None: + self.tool_calls.append( + ToolCallRecord(name=name, args=args or {}, output=output) + ) + + def add_context(self, chunks: List[str]) -> None: + """Convenience setter for ``retrieval_context``.""" + self.retrieval_context = chunks + + +# --------------------------------------------------------------------------- +# NoOp — returned when not inside an eval context +# --------------------------------------------------------------------------- + +class _NoOpEvalRecord: + """Drop-in replacement that silently discards all writes.""" + + __slots__ = () + + def __setattr__(self, name: str, value: Any) -> None: # noqa: D105 + pass + + def __getattr__(self, name: str) -> Any: # noqa: D105 + if name == "tool_calls": + return [] + return None + + def add_tool_call(self, *args: Any, **kwargs: Any) -> None: # noqa: D102 + pass + + def add_context(self, *args: Any) -> None: # noqa: D102 + pass + + +_NOOP = _NoOpEvalRecord() + + +# --------------------------------------------------------------------------- +# ContextVar + module-level store +# --------------------------------------------------------------------------- + +_eval_record_var: contextvars.ContextVar[Optional[EvalRecord]] = contextvars.ContextVar( + "eval_record", default=None +) + +# Keyed by request-id so the runner can retrieve the record after the +# response completes. +_eval_store: Dict[str, EvalRecord] = {} + + +def _begin_eval_request(request_id: str) -> contextvars.Token: + """Create a fresh EvalRecord, store it, and set the ContextVar.""" + record = EvalRecord() + _eval_store[request_id] = record + return _eval_record_var.set(record) + + +def _end_eval_request(token: contextvars.Token) -> None: + """Reset the ContextVar — the record stays in ``_eval_store``.""" + _eval_record_var.reset(token) + + +def _pop_eval_record(request_id: str) -> Optional[EvalRecord]: + """Retrieve (and remove) the record the runner needs.""" + return _eval_store.pop(request_id, None) + + +# --------------------------------------------------------------------------- +# Public API +# --------------------------------------------------------------------------- + +def eval_record() -> EvalRecord: + """Return the current request's EvalRecord, or a silent NoOp. + + Agent authors call this to attach evaluation metadata:: + + from gradient_adk import eval_record + + record = eval_record() + record.retrieval_context = ["chunk1", "chunk2"] + record.add_tool_call("search", args={"q": "hello"}, output="world") + + Outside an eval context every operation is a harmless no-op. + """ + rec = _eval_record_var.get() + if rec is not None: + return rec + return _NOOP # type: ignore[return-value] diff --git a/gradient_adk/evaluation/results.py b/gradient_adk/evaluation/results.py new file mode 100644 index 0000000..dc4cc76 --- /dev/null +++ b/gradient_adk/evaluation/results.py @@ -0,0 +1,76 @@ +""" +Terminal formatting for evaluation results. +""" + +from __future__ import annotations + +from typing import List + +from .runner import EvalResults + + +def format_results(results: EvalResults, *, verbose: bool = False) -> str: + """Return a formatted string suitable for terminal display.""" + lines: List[str] = [] + + lines.append("") + lines.append("=" * 60) + lines.append(" Evaluation Results") + lines.append("=" * 60) + + # ---- Metrics table ---- + if results.metric_summaries: + lines.append("") + lines.append(f" {'Metric':<28} {'Score':>6} {'Threshold':>10} {'Result':>6}") + lines.append(" " + "-" * 56) + for ms in results.metric_summaries: + status = "PASS" if ms.passed else "FAIL" + lines.append( + f" {ms.name:<28} {ms.avg_score:>5.2f} {ms.threshold:>10.2f} {status:>6}" + ) + + # ---- Skipped metrics ---- + if results.skipped_metrics: + lines.append("") + lines.append(" Skipped (insufficient data):") + for sm in results.skipped_metrics: + lines.append(f" {sm.name:<26} {sm.reason}") + + # ---- Per-row breakdown (verbose) ---- + if verbose and results.test_case_results: + lines.append("") + lines.append("-" * 60) + lines.append(" Per-Row Breakdown") + lines.append("-" * 60) + for i, tcr in enumerate(results.test_case_results, start=1): + input_display = str(tcr.input) + if len(input_display) > 50: + input_display = input_display[:47] + "..." + output_display = tcr.actual_output + if len(output_display) > 70: + output_display = output_display[:67] + "..." + lines.append("") + lines.append(f" Row {i}: {input_display}") + lines.append(f" Output: {output_display}") + lines.append(f" Latency: {tcr.completion_time:.2f}s") + for name, score in tcr.metric_scores.items(): + passed = tcr.metric_passed.get(name, False) + status = "PASS" if passed else "FAIL" + lines.append(f" {name:<26} {score:>5.2f} {status}") + + # ---- Summary footer ---- + lines.append("") + lines.append("=" * 60) + n_computed = len(results.metric_summaries) + n_passed = sum(1 for ms in results.metric_summaries if ms.passed) + n_skipped = len(results.skipped_metrics) + n_cases = len(results.test_case_results) + + lines.append(f" {n_passed}/{n_computed} metrics passed across {n_cases} test case(s)") + if n_skipped: + lines.append(f" {n_skipped} metric(s) skipped (missing required data)") + lines.append(f" Total time: {results.total_time:.1f}s") + lines.append("=" * 60) + lines.append("") + + return "\n".join(lines) diff --git a/gradient_adk/evaluation/runner.py b/gradient_adk/evaluation/runner.py new file mode 100644 index 0000000..69be7b4 --- /dev/null +++ b/gradient_adk/evaluation/runner.py @@ -0,0 +1,424 @@ +""" +Local evaluation runner — the main orchestrator. + +Imports the agent in-process, drives it via ASGI transport, collects +EvalRecord data, constructs DeepEval test cases, and runs the selected +metrics with a configurable judge LLM. +""" + +from __future__ import annotations + +import importlib +import os +import sys +import time +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional, Set +from uuid import uuid4 + +from .config import EvalConfig +from .dataset import DatasetRow, ParsedDataset, parse_dataset +from .metrics import ( + MetricSpec, + SkippedMetric, + resolve_preset, + resolve_runnable_metrics, +) +from .record import _pop_eval_record + + +# --------------------------------------------------------------------------- +# Result data classes +# --------------------------------------------------------------------------- + +@dataclass +class CaseResult: + """Result of evaluating a single dataset row.""" + + input: Any + actual_output: str + expected_output: Optional[str] = None + metric_scores: Dict[str, float] = field(default_factory=dict) + metric_passed: Dict[str, bool] = field(default_factory=dict) + completion_time: float = 0.0 + + +@dataclass +class MetricSummary: + """Aggregate summary for one metric across all test cases.""" + + name: str + avg_score: float + threshold: float + passed: bool + + +@dataclass +class EvalResults: + """Complete evaluation results.""" + + test_case_results: List[CaseResult] = field(default_factory=list) + metric_summaries: List[MetricSummary] = field(default_factory=list) + skipped_metrics: List[SkippedMetric] = field(default_factory=list) + total_time: float = 0.0 + + +# --------------------------------------------------------------------------- +# Import helpers +# --------------------------------------------------------------------------- + +def _import_agent_app(entrypoint_file: str) -> Any: + """Import the agent module and return its ``fastapi_app``.""" + cwd = str(Path.cwd()) + if cwd not in sys.path: + sys.path.insert(0, cwd) + + module_name = ( + entrypoint_file.replace(".py", "").replace("/", ".").replace("\\", ".") + ) + module = importlib.import_module(module_name) + + if not hasattr(module, "fastapi_app"): + raise RuntimeError( + f"Module '{module_name}' has no 'fastapi_app' attribute. " + "Ensure the module uses the @entrypoint decorator." + ) + return module.fastapi_app + + +# --------------------------------------------------------------------------- +# Core runner +# --------------------------------------------------------------------------- + +async def run_local_evaluation( + *, + entrypoint_file: str, + config: EvalConfig, + dataset_path: Path, + metric_names: Optional[List[str]] = None, +) -> EvalResults: + """Run a full local evaluation and return structured results. + + Parameters + ---------- + entrypoint_file: + Path to the agent module (e.g. ``"main.py"``). + config: + Resolved ``EvalConfig``. + dataset_path: + Path to the evaluation CSV. + metric_names: + Explicit list of metrics. If *None*, resolved from ``config.preset``. + """ + import httpx + from httpx import ASGITransport + + start = time.monotonic() + + # 1. Parse dataset + parsed: ParsedDataset = parse_dataset(dataset_path) + + # 2. Import agent + app = _import_agent_app(entrypoint_file) + + # 3. Build ASGI client + transport = ASGITransport(app=app) + client = httpx.AsyncClient(transport=transport, base_url="http://testserver") + + # 4. Resolve metric names + if metric_names is None: + metric_names = resolve_preset(config.preset) + + # 5. Drive agent for each row and collect outputs + EvalRecords + raw_results: List[Dict[str, Any]] = [] + try: + for row in parsed.rows: + result = await _run_single(client, row, config) + raw_results.append(result) + finally: + await client.aclose() + + # 6. Determine available fields for skip logic + available_fields: Set[str] = set(parsed.available_columns) + # Check first EvalRecord (if any) for callback-provided fields + for r in raw_results: + rec = r.get("eval_record") + if rec is not None: + if rec.retrieval_context: + available_fields.add("retrieval_context") + if rec.tool_calls: + available_fields.add("tool_calls") + break # one sample is enough + + # 7. Resolve runnable vs skipped + runnable_specs, skipped = resolve_runnable_metrics(metric_names, available_fields) + + # 8. Build DeepEval test cases and run metrics + test_case_results: List[CaseResult] = [] + metric_summaries: List[MetricSummary] = [] + + if runnable_specs: + test_case_results, metric_summaries = await _run_deepeval( + runnable_specs, raw_results, parsed, config + ) + + elapsed = time.monotonic() - start + + return EvalResults( + test_case_results=test_case_results, + metric_summaries=metric_summaries, + skipped_metrics=skipped, + total_time=elapsed, + ) + + +async def _run_single( + client: "httpx.AsyncClient", + row: DatasetRow, + config: EvalConfig, +) -> Dict[str, Any]: + """Send one dataset row to the agent and collect the response + EvalRecord.""" + request_id = str(uuid4()) + + t0 = time.monotonic() + resp = await client.post( + "/run", + json=row.query, + headers={ + "evaluation-id": "local-eval", + "x-eval-request-id": request_id, + }, + timeout=config.timeout, + ) + completion_time = time.monotonic() - t0 + + resp.raise_for_status() + + # Parse response body + try: + actual_output = resp.json() + except Exception: + actual_output = resp.text + + if isinstance(actual_output, dict): + # Try to extract a meaningful string + actual_output = actual_output.get("response", str(actual_output)) + if not isinstance(actual_output, str): + actual_output = str(actual_output) + + eval_record = _pop_eval_record(request_id) + + return { + "input": row.query, + "actual_output": actual_output, + "expected_output": row.expected_output, + "expected_context": row.expected_context, + "expected_tools": row.expected_tools, + "eval_record": eval_record, + "completion_time": completion_time, + } + + +async def _run_deepeval( + specs: List[MetricSpec], + raw_results: List[Dict[str, Any]], + parsed: ParsedDataset, + config: EvalConfig, +) -> tuple[List[CaseResult], List[MetricSummary]]: + """Construct DeepEval objects, run evaluation, return results.""" + from deepeval import evaluate as deepeval_evaluate + from deepeval.metrics import ( + AnswerRelevancyMetric, + BiasMetric, + ContextualPrecisionMetric, + ContextualRecallMetric, + ContextualRelevancyMetric, + FaithfulnessMetric, + ToolCorrectnessMetric, + ToxicityMetric, + ) + from deepeval.models import DeepEvalBaseLLM + from deepeval.test_case import LLMTestCase + from deepeval.test_case.llm_test_case import ToolCall + + _CLASS_MAP = { + "AnswerRelevancyMetric": AnswerRelevancyMetric, + "BiasMetric": BiasMetric, + "ToxicityMetric": ToxicityMetric, + "FaithfulnessMetric": FaithfulnessMetric, + "ContextualRelevancyMetric": ContextualRelevancyMetric, + "ContextualPrecisionMetric": ContextualPrecisionMetric, + "ContextualRecallMetric": ContextualRecallMetric, + "ToolCorrectnessMetric": ToolCorrectnessMetric, + } + + # Build judge model + judge = _make_judge_model(config) + + # Instantiate metric objects + metric_objects = [] + for spec in specs: + cls = _CLASS_MAP.get(spec.deepeval_class) + if cls is None: + continue + threshold = config.thresholds.get( + spec.name, + config.thresholds.get("__global__", spec.default_threshold), + ) + # Per-metric judge override + metric_judge = judge + if spec.name in config.metric_judge_models: + metric_judge = _make_judge_model( + config, model_override=config.metric_judge_models[spec.name] + ) + metric_objects.append(cls(threshold=threshold, model=metric_judge)) + + if not metric_objects: + return [], [] + + # Build LLMTestCases + test_cases: List[LLMTestCase] = [] + for r in raw_results: + kwargs: Dict[str, Any] = { + "input": str(r["input"]) if not isinstance(r["input"], str) else r["input"], + "actual_output": r["actual_output"], + } + if r.get("expected_output"): + kwargs["expected_output"] = r["expected_output"] + if r.get("expected_context"): + kwargs["context"] = r["expected_context"] + + # Merge callback-provided retrieval context + rec = r.get("eval_record") + if rec and rec.retrieval_context: + kwargs["retrieval_context"] = rec.retrieval_context + + # Tool calls from EvalRecord + if rec and rec.tool_calls: + kwargs["tools_called"] = [ + ToolCall(name=tc.name, input_parameters=tc.args, output=tc.output) + for tc in rec.tool_calls + ] + if r.get("expected_tools"): + kwargs["expected_tools"] = [ + ToolCall(name=t) if isinstance(t, str) else ToolCall(**t) + for t in r["expected_tools"] + ] + + test_cases.append(LLMTestCase(**kwargs)) + + # Suppress litellm and deepeval verbose logging + import io as _io + import logging as _logging + import litellm + litellm.suppress_debug_info = True + litellm.set_verbose = False + for _logger_name in ("LiteLLM", "litellm", "httpx", "deepeval"): + _logging.getLogger(_logger_name).setLevel(_logging.ERROR) + + # Run DeepEval with display suppressed + from deepeval.evaluate import DisplayConfig + from deepeval.evaluate.evaluate import EvaluationResult as _EvalResult + + # Redirect stdout to suppress DeepEval's direct console prints + import sys as _sys + _orig_stdout = _sys.stdout + _sys.stdout = _io.StringIO() + try: + eval_result: _EvalResult = deepeval_evaluate( + test_cases=test_cases, + metrics=metric_objects, + display_config=DisplayConfig( + show_indicator=False, + print_results=False, + ), + ) + finally: + _sys.stdout = _orig_stdout + + # Build lookup for inverted metrics (lower raw = better) + _inverted = {s.name for s in specs if s.inverted} + + # Extract per-test-case results from EvaluationResult.test_results + tc_results: List[CaseResult] = [] + for i, r in enumerate(raw_results): + tcr = CaseResult( + input=r["input"], + actual_output=r["actual_output"], + expected_output=r.get("expected_output"), + completion_time=r["completion_time"], + ) + if i < len(eval_result.test_results): + tr = eval_result.test_results[i] + if tr.metrics_data: + for md in tr.metrics_data: + key = md.name.lower().replace(" ", "_") + if md.score is not None: + # Normalize: for inverted metrics, display 1 - score + # so higher is always better from user perspective + score = (1.0 - md.score) if key in _inverted else md.score + tcr.metric_scores[key] = score + tcr.metric_passed[key] = md.success + tc_results.append(tcr) + + # Aggregate metric summaries + # Use DeepEval's own pass/fail (it knows which metrics are lower-is-better) + summaries: List[MetricSummary] = [] + for spec in specs: + scores = [ + tcr.metric_scores[spec.name] + for tcr in tc_results + if spec.name in tcr.metric_scores + ] + passes = [ + tcr.metric_passed[spec.name] + for tcr in tc_results + if spec.name in tcr.metric_passed + ] + avg = sum(scores) / len(scores) if scores else 0.0 + threshold = config.thresholds.get( + spec.name, + config.thresholds.get("__global__", spec.default_threshold), + ) + summaries.append( + MetricSummary( + name=spec.name, + avg_score=avg, + threshold=threshold, + passed=all(passes) if passes else False, + ) + ) + + return tc_results, summaries + + +def _metric_obj_name(metric_obj: Any) -> str: + """Extract the canonical metric name from a DeepEval metric object.""" + # DeepEval metrics have a __name__ or name attribute + for attr in ("__name__", "name"): + v = getattr(metric_obj, attr, None) + if v: + return str(v).lower().replace(" ", "_") + return type(metric_obj).__name__.lower().replace("metric", "") + + +def _make_judge_model(config: EvalConfig, *, model_override: Optional[str] = None) -> Any: + """Create a DeepEval judge LLM model.""" + from deepeval.models import DeepEvalBaseLLM + + try: + from deepeval.models import LiteLLMModel + + return LiteLLMModel( + model=model_override or config.judge_model, + base_url=config.judge_base_url, + api_key=config.judge_api_key or "", + ) + except ImportError: + # Fallback: use base URL via environment variables for litellm + os.environ.setdefault("OPENAI_API_BASE", config.judge_base_url) + os.environ.setdefault("OPENAI_API_KEY", config.judge_api_key or "") + from deepeval.models import LiteLLMModel + + return LiteLLMModel(model=model_override or config.judge_model) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..7aedfb5 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,4825 @@ 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["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and python_version < \"3.14\"", "cffi (>=2.0.0b) ; platform_python_implementation != \"PyPy\" and python_version >= \"3.14\""] + +[extras] +a2a = ["a2a-sdk"] +eval = ["deepeval", "litellm"] + +[metadata] +lock-version = "2.1" +python-versions = ">=3.10, <4.0" +content-hash = "973449305cf4d31763ce5453e115470178570fc5f55a76cf81340f665547c576" diff --git a/pyproject.toml b/pyproject.toml index b14c82f..946e7fe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta" name = "gradient-adk" version = "0.0.8" description = "Gradient AI Platform Agent Development Kit" -requires-python = ">=3.10" +requires-python = ">=3.10, <4.0" readme = "README.md" license = "Apache-2.0" dependencies = [ @@ -30,6 +30,7 @@ dependencies = [ [project.optional-dependencies] a2a = ["a2a-sdk[http-server]>=0.3.0"] +eval = ["deepeval>=3.9.1", "litellm==1.82.6"] [project.scripts] gradient = "gradient_adk.cli:run" diff --git a/tests/decorator_eval_record_test.py b/tests/decorator_eval_record_test.py new file mode 100644 index 0000000..e963f10 --- /dev/null +++ b/tests/decorator_eval_record_test.py @@ -0,0 +1,132 @@ +"""Tests for EvalRecord integration in the decorator.""" + +from __future__ import annotations + +import pytest +from unittest.mock import patch + +from fastapi import FastAPI, Request, HTTPException +from httpx import AsyncClient, ASGITransport + +from gradient_adk.evaluation.record import ( + EvalRecord, + _NoOpEvalRecord, + _begin_eval_request, + _end_eval_request, + _eval_store, + _pop_eval_record, + eval_record, +) + + +@pytest.fixture(autouse=True) +def clean_store(): + _eval_store.clear() + yield + _eval_store.clear() + + +@pytest.fixture +def eval_app(): + """Minimal FastAPI app that mirrors the decorator's eval wiring.""" + app = FastAPI() + + async def agent_logic(data): + return {"response": f"echo: {data}"} + + @app.post("/run") + async def run(req: Request): + body = await req.json() + is_evaluation = "evaluation-id" in req.headers + + eval_request_id = req.headers.get("x-eval-request-id") + eval_token = None + if is_evaluation and eval_request_id: + eval_token = _begin_eval_request(eval_request_id) + + try: + result = await agent_logic(body) + except Exception: + if eval_token is not None: + _end_eval_request(eval_token) + raise HTTPException(status_code=500) + + if eval_token is not None: + _end_eval_request(eval_token) + + return result + + return app + + +class TestDecoratorEvalRecord: + @pytest.mark.asyncio + async def test_eval_record_set_when_eval_headers_present(self, eval_app): + transport = ASGITransport(app=eval_app) + async with AsyncClient(transport=transport, base_url="http://test") as client: + resp = await client.post( + "/run", + json={"msg": "hi"}, + headers={ + "evaluation-id": "local-eval", + "x-eval-request-id": "test-req-1", + }, + ) + assert resp.status_code == 200 + + rec = _pop_eval_record("test-req-1") + assert rec is not None + assert isinstance(rec, EvalRecord) + + @pytest.mark.asyncio + async def test_no_eval_record_without_headers(self, eval_app): + transport = ASGITransport(app=eval_app) + async with AsyncClient(transport=transport, base_url="http://test") as client: + resp = await client.post("/run", json={"msg": "hi"}) + assert resp.status_code == 200 + + assert len(_eval_store) == 0 + + @pytest.mark.asyncio + async def test_context_cleaned_up_after_request(self, eval_app): + transport = ASGITransport(app=eval_app) + async with AsyncClient(transport=transport, base_url="http://test") as client: + await client.post( + "/run", + json={"msg": "hi"}, + headers={ + "evaluation-id": "local-eval", + "x-eval-request-id": "test-req-2", + }, + ) + + # After request, eval_record() should return NoOp + rec = eval_record() + assert isinstance(rec, _NoOpEvalRecord) + + @pytest.mark.asyncio + async def test_no_leaks_between_requests(self, eval_app): + transport = ASGITransport(app=eval_app) + async with AsyncClient(transport=transport, base_url="http://test") as client: + await client.post( + "/run", + json={"msg": "first"}, + headers={ + "evaluation-id": "local-eval", + "x-eval-request-id": "req-a", + }, + ) + await client.post( + "/run", + json={"msg": "second"}, + headers={ + "evaluation-id": "local-eval", + "x-eval-request-id": "req-b", + }, + ) + + rec_a = _pop_eval_record("req-a") + rec_b = _pop_eval_record("req-b") + assert rec_a is not None + assert rec_b is not None + assert rec_a is not rec_b diff --git a/tests/evaluation/__init__.py b/tests/evaluation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/evaluation/test_config.py b/tests/evaluation/test_config.py new file mode 100644 index 0000000..72edc08 --- /dev/null +++ b/tests/evaluation/test_config.py @@ -0,0 +1,98 @@ +"""Tests for gradient_adk.evaluation.config.""" + +from __future__ import annotations + +import textwrap +from pathlib import Path + +import pytest + +from gradient_adk.evaluation.config import EvalConfig, load_eval_config + + +class TestDefaults: + def test_default_values(self): + cfg = EvalConfig() + assert cfg.judge_model == "openai/openai-gpt-oss-120b" + assert cfg.judge_base_url == "https://inference.do-ai.run/v1" + assert cfg.judge_api_key_env == "GRADIENT_MODEL_ACCESS_KEY" + assert cfg.preset == "basic" + assert cfg.thresholds == {} + assert cfg.dataset is None + assert cfg.concurrency == 5 + assert cfg.timeout == 30 + + def test_missing_yaml_returns_defaults(self, tmp_path): + cfg = load_eval_config(yaml_path=tmp_path / "nonexistent.yml") + assert cfg.preset == "basic" + assert cfg.judge_model == "openai/openai-gpt-oss-120b" + + +class TestYamlLoading: + def test_yaml_overrides_defaults(self, tmp_path): + yaml_file = tmp_path / "eval.yml" + yaml_file.write_text(textwrap.dedent("""\ + judge_model: "custom-model" + preset: "rag" + concurrency: 10 + timeout: 60 + thresholds: + answer_relevancy: 0.7 + faithfulness: 0.8 + """)) + cfg = load_eval_config(yaml_path=yaml_file) + assert cfg.judge_model == "custom-model" + assert cfg.preset == "rag" + assert cfg.concurrency == 10 + assert cfg.timeout == 60 + assert cfg.thresholds["answer_relevancy"] == 0.7 + assert cfg.thresholds["faithfulness"] == 0.8 + + def test_per_metric_judge_override(self, tmp_path): + yaml_file = tmp_path / "eval.yml" + yaml_file.write_text(textwrap.dedent("""\ + metrics: + faithfulness: + threshold: 0.9 + judge_model: "openai/big-model" + """)) + cfg = load_eval_config(yaml_path=yaml_file) + assert cfg.thresholds["faithfulness"] == 0.9 + assert cfg.metric_judge_models["faithfulness"] == "openai/big-model" + + def test_empty_yaml_returns_defaults(self, tmp_path): + yaml_file = tmp_path / "eval.yml" + yaml_file.write_text("") + cfg = load_eval_config(yaml_path=yaml_file) + assert cfg.preset == "basic" + + +class TestCLIOverrides: + def test_cli_preset_overrides_yaml(self, tmp_path): + yaml_file = tmp_path / "eval.yml" + yaml_file.write_text("preset: rag\n") + cfg = load_eval_config(yaml_path=yaml_file, cli_preset="agent") + assert cfg.preset == "agent" + + def test_cli_judge_model_overrides_yaml(self, tmp_path): + yaml_file = tmp_path / "eval.yml" + yaml_file.write_text('judge_model: "yaml-model"\n') + cfg = load_eval_config(yaml_path=yaml_file, cli_judge_model="cli-model") + assert cfg.judge_model == "cli-model" + + def test_cli_dataset(self): + cfg = load_eval_config( + yaml_path=Path("/nonexistent"), + cli_dataset="test.csv", + ) + assert cfg.dataset == "test.csv" + + def test_cli_threshold_applies_globally(self, tmp_path): + yaml_file = tmp_path / "eval.yml" + yaml_file.write_text(textwrap.dedent("""\ + thresholds: + answer_relevancy: 0.7 + """)) + cfg = load_eval_config(yaml_path=yaml_file, cli_threshold=0.9) + assert cfg.thresholds["answer_relevancy"] == 0.9 + assert cfg.thresholds["__global__"] == 0.9 diff --git a/tests/evaluation/test_dataset.py b/tests/evaluation/test_dataset.py new file mode 100644 index 0000000..714a994 --- /dev/null +++ b/tests/evaluation/test_dataset.py @@ -0,0 +1,111 @@ +"""Tests for gradient_adk.evaluation.dataset.""" + +from __future__ import annotations + +import pytest + +from gradient_adk.evaluation.dataset import parse_dataset + + +def _write_csv(tmp_path, name, content): + """Write raw CSV content (no dedent — caller controls whitespace).""" + p = tmp_path / name + p.write_text(content) + return p + + +class TestParseDataset: + def test_query_only(self, tmp_path): + # JSON strings: "hello", "world" — in CSV: """hello""", """world""" + f = _write_csv(tmp_path, "data.csv", + 'query\n' + '"""hello"""\n' + '"""world"""\n' + ) + result = parse_dataset(f) + assert len(result.rows) == 2 + assert result.rows[0].query == "hello" + assert result.rows[1].query == "world" + assert result.available_columns == set() + + def test_query_json_object(self, tmp_path): + # query is a JSON object: {"msg": "hi"} + f = _write_csv(tmp_path, "data.csv", + 'query\n' + '"{""msg"": ""hi""}"\n' + ) + result = parse_dataset(f) + assert result.rows[0].query == {"msg": "hi"} + + def test_all_columns(self, tmp_path): + f = _write_csv(tmp_path, "data.csv", + 'query,expected_output,expected_context,expected_tools\n' + '"""hello""",goodbye,"[""ctx1"", ""ctx2""]","[""tool_a""]"\n' + ) + result = parse_dataset(f) + assert len(result.rows) == 1 + row = result.rows[0] + assert row.query == "hello" + assert row.expected_output == "goodbye" + assert row.expected_context == ["ctx1", "ctx2"] + assert row.expected_tools == ["tool_a"] + assert result.available_columns == { + "expected_output", "expected_context", "expected_tools" + } + + def test_detects_available_columns(self, tmp_path): + f = _write_csv(tmp_path, "data.csv", + 'query,expected_output\n' + '"""hi""",bye\n' + ) + result = parse_dataset(f) + assert result.available_columns == {"expected_output"} + + def test_missing_query_column_raises(self, tmp_path): + f = _write_csv(tmp_path, "data.csv", "input,output\nhello,world\n") + with pytest.raises(ValueError, match="Missing required column: 'query'"): + parse_dataset(f) + + def test_empty_query_raises(self, tmp_path): + # Second row has an explicitly empty query field + f = _write_csv(tmp_path, "data.csv", 'query\n"""hello"""\n""\n') + with pytest.raises(ValueError, match="empty value in 'query'"): + parse_dataset(f) + + def test_invalid_json_in_query_raises(self, tmp_path): + f = _write_csv(tmp_path, "data.csv", "query\nnot-json\n") + with pytest.raises(ValueError, match="invalid JSON in 'query'"): + parse_dataset(f) + + def test_file_not_found_raises(self, tmp_path): + with pytest.raises(ValueError, match="not found"): + parse_dataset(tmp_path / "missing.csv") + + def test_non_csv_raises(self, tmp_path): + txt_file = tmp_path / "data.txt" + txt_file.write_text("query\nhello\n") + with pytest.raises(ValueError, match="CSV file"): + parse_dataset(txt_file) + + def test_json_list_columns(self, tmp_path): + f = _write_csv(tmp_path, "data.csv", + 'query,expected_context,expected_tools\n' + '"""q1""","[""a"", ""b""]","[""tool1"", ""tool2""]"\n' + ) + result = parse_dataset(f) + assert result.rows[0].expected_context == ["a", "b"] + assert result.rows[0].expected_tools == ["tool1", "tool2"] + + def test_no_data_rows_raises(self, tmp_path): + f = _write_csv(tmp_path, "data.csv", "query\n") + with pytest.raises(ValueError, match="no data rows"): + parse_dataset(f) + + def test_optional_columns_left_empty(self, tmp_path): + """Optional columns present in header but empty values.""" + f = _write_csv(tmp_path, "data.csv", + 'query,expected_output\n' + '"""q1""",\n' + ) + result = parse_dataset(f) + assert result.rows[0].expected_output is None diff --git a/tests/evaluation/test_metrics.py b/tests/evaluation/test_metrics.py new file mode 100644 index 0000000..26906e2 --- /dev/null +++ b/tests/evaluation/test_metrics.py @@ -0,0 +1,104 @@ +"""Tests for gradient_adk.evaluation.metrics.""" + +from __future__ import annotations + +import pytest + +from gradient_adk.evaluation.metrics import ( + METRIC_REGISTRY, + PRESETS, + MetricSpec, + SkippedMetric, + resolve_preset, + resolve_runnable_metrics, +) + + +class TestRegistry: + def test_registry_not_empty(self): + assert len(METRIC_REGISTRY) > 0 + + def test_all_specs_have_required_fields(self): + for name, spec in METRIC_REGISTRY.items(): + assert spec.name == name + assert spec.deepeval_class + assert isinstance(spec.required_fields, frozenset) + assert isinstance(spec.default_threshold, float) + assert spec.description + + +class TestPresets: + def test_basic_preset(self): + names = resolve_preset("basic") + assert "answer_relevancy" in names + assert "bias" in names + assert "toxicity" in names + + def test_rag_preset(self): + names = resolve_preset("rag") + assert "faithfulness" in names + assert "contextual_relevancy" in names + + def test_agent_preset(self): + names = resolve_preset("agent") + assert "tool_correctness" in names + + def test_all_preset_contains_every_metric(self): + names = resolve_preset("all") + assert set(names) == set(METRIC_REGISTRY.keys()) + + def test_unknown_preset_raises(self): + with pytest.raises(ValueError, match="Unknown preset"): + resolve_preset("nonexistent") + + +class TestSkipLogic: + def test_all_fields_present_runs_all(self): + names = ["answer_relevancy", "faithfulness"] + available = {"retrieval_context"} + runnable, skipped = resolve_runnable_metrics(names, available) + assert len(runnable) == 2 + assert len(skipped) == 0 + + def test_missing_retrieval_context_skips_faithfulness(self): + names = ["answer_relevancy", "faithfulness"] + available = set() # no retrieval_context + runnable, skipped = resolve_runnable_metrics(names, available) + assert len(runnable) == 1 + assert runnable[0].name == "answer_relevancy" + assert len(skipped) == 1 + assert skipped[0].name == "faithfulness" + assert "retrieval_context" in skipped[0].reason + + def test_skip_reason_is_actionable(self): + names = ["faithfulness"] + runnable, skipped = resolve_runnable_metrics(names, set()) + assert "eval_record()" in skipped[0].reason + + def test_skip_reason_for_expected_output(self): + names = ["contextual_precision"] + available = {"retrieval_context"} # missing expected_output + runnable, skipped = resolve_runnable_metrics(names, available) + assert len(skipped) == 1 + assert "expected_output" in skipped[0].reason + assert "dataset CSV" in skipped[0].reason + + def test_tool_correctness_needs_both_fields(self): + names = ["tool_correctness"] + # Only tools, no expected_tools + runnable, skipped = resolve_runnable_metrics(names, {"tool_calls"}) + assert len(skipped) == 1 + assert "expected_tools" in skipped[0].reason + + def test_unknown_metric_is_skipped(self): + runnable, skipped = resolve_runnable_metrics(["nonexistent"], set()) + assert len(runnable) == 0 + assert len(skipped) == 1 + assert "unknown metric" in skipped[0].reason + + def test_basic_preset_needs_no_special_fields(self): + """Basic preset metrics have no required_fields.""" + names = resolve_preset("basic") + runnable, skipped = resolve_runnable_metrics(names, set()) + assert len(skipped) == 0 + assert len(runnable) == len(names) diff --git a/tests/evaluation/test_record.py b/tests/evaluation/test_record.py new file mode 100644 index 0000000..fbc8a0d --- /dev/null +++ b/tests/evaluation/test_record.py @@ -0,0 +1,182 @@ +"""Tests for gradient_adk.evaluation.record.""" + +from __future__ import annotations + +import asyncio + +import pytest + +from gradient_adk.evaluation.record import ( + EvalRecord, + ToolCallRecord, + _NoOpEvalRecord, + _begin_eval_request, + _end_eval_request, + _eval_record_var, + _eval_store, + _pop_eval_record, + eval_record, +) + + +# --------------------------------------------------------------------------- +# NoOp behaviour +# --------------------------------------------------------------------------- + +class TestNoOp: + def test_eval_record_returns_noop_outside_context(self): + """eval_record() returns a NoOp when no eval context is active.""" + rec = eval_record() + assert isinstance(rec, _NoOpEvalRecord) + + def test_noop_discards_setattr(self): + noop = _NoOpEvalRecord() + noop.retrieval_context = ["a", "b"] + assert noop.retrieval_context is None # still None via __getattr__ + + def test_noop_tool_calls_returns_empty_list(self): + noop = _NoOpEvalRecord() + assert noop.tool_calls == [] + + def test_noop_other_attr_returns_none(self): + noop = _NoOpEvalRecord() + assert noop.retrieval_context is None + assert noop.some_random_attr is None + + def test_noop_add_tool_call_is_silent(self): + noop = _NoOpEvalRecord() + noop.add_tool_call("search", args={"q": "hi"}, output="result") + assert noop.tool_calls == [] + + def test_noop_add_context_is_silent(self): + noop = _NoOpEvalRecord() + noop.add_context(["chunk1"]) + assert noop.retrieval_context is None + + +# --------------------------------------------------------------------------- +# EvalRecord behaviour +# --------------------------------------------------------------------------- + +class TestEvalRecord: + def test_defaults(self): + rec = EvalRecord() + assert rec.retrieval_context is None + assert rec.tool_calls == [] + + def test_add_tool_call(self): + rec = EvalRecord() + rec.add_tool_call("search", args={"q": "hello"}, output="world") + assert len(rec.tool_calls) == 1 + tc = rec.tool_calls[0] + assert tc.name == "search" + assert tc.args == {"q": "hello"} + assert tc.output == "world" + + def test_add_multiple_tool_calls(self): + rec = EvalRecord() + rec.add_tool_call("a") + rec.add_tool_call("b", args={"x": 1}) + assert len(rec.tool_calls) == 2 + assert rec.tool_calls[0].name == "a" + assert rec.tool_calls[1].name == "b" + + def test_add_context(self): + rec = EvalRecord() + rec.add_context(["chunk1", "chunk2"]) + assert rec.retrieval_context == ["chunk1", "chunk2"] + + def test_add_context_overwrites(self): + rec = EvalRecord() + rec.add_context(["a"]) + rec.add_context(["b", "c"]) + assert rec.retrieval_context == ["b", "c"] + + +# --------------------------------------------------------------------------- +# ContextVar + Store +# --------------------------------------------------------------------------- + +class TestStore: + def setup_method(self): + # Clean up any leftover state + _eval_store.clear() + + def test_begin_sets_real_record(self): + token = _begin_eval_request("req-1") + try: + rec = eval_record() + assert isinstance(rec, EvalRecord) + finally: + _end_eval_request(token) + + def test_end_resets_to_noop(self): + token = _begin_eval_request("req-2") + _end_eval_request(token) + rec = eval_record() + assert isinstance(rec, _NoOpEvalRecord) + + def test_pop_retrieves_and_removes(self): + token = _begin_eval_request("req-3") + rec = eval_record() + rec.add_context(["data"]) + _end_eval_request(token) + + popped = _pop_eval_record("req-3") + assert popped is not None + assert popped.retrieval_context == ["data"] + + # Second pop returns None + assert _pop_eval_record("req-3") is None + + def test_pop_missing_returns_none(self): + assert _pop_eval_record("nonexistent") is None + + def test_record_persists_writes(self): + token = _begin_eval_request("req-4") + rec = eval_record() + rec.retrieval_context = ["ctx"] + rec.add_tool_call("fn", args={"a": 1}) + _end_eval_request(token) + + popped = _pop_eval_record("req-4") + assert popped.retrieval_context == ["ctx"] + assert len(popped.tool_calls) == 1 + assert popped.tool_calls[0].name == "fn" + + +# --------------------------------------------------------------------------- +# Async isolation +# --------------------------------------------------------------------------- + +class TestAsyncIsolation: + @pytest.mark.asyncio + async def test_contextvar_isolates_coroutines(self): + """Two concurrent coroutines get independent EvalRecords.""" + results = {} + + async def worker(request_id: str, context_val: str): + token = _begin_eval_request(request_id) + try: + rec = eval_record() + rec.add_context([context_val]) + # Yield control to let other coroutine run + await asyncio.sleep(0) + # Should still see our own data + results[request_id] = eval_record().retrieval_context + finally: + _end_eval_request(token) + + await asyncio.gather( + worker("iso-a", "data-a"), + worker("iso-b", "data-b"), + ) + + assert results["iso-a"] == ["data-a"] + assert results["iso-b"] == ["data-b"] + + # Both records retrievable from store + rec_a = _pop_eval_record("iso-a") + rec_b = _pop_eval_record("iso-b") + assert rec_a.retrieval_context == ["data-a"] + assert rec_b.retrieval_context == ["data-b"] diff --git a/tests/evaluation/test_runner.py b/tests/evaluation/test_runner.py new file mode 100644 index 0000000..7130c00 --- /dev/null +++ b/tests/evaluation/test_runner.py @@ -0,0 +1,71 @@ +"""Tests for gradient_adk.evaluation.runner (mocked DeepEval).""" + +from __future__ import annotations + +import textwrap +from pathlib import Path +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from gradient_adk.evaluation.config import EvalConfig +from gradient_adk.evaluation.record import ( + _begin_eval_request, + _end_eval_request, + _eval_store, + _pop_eval_record, + eval_record, +) +from gradient_adk.evaluation.runner import ( + EvalResults, + CaseResult, + _import_agent_app, +) + + +class TestImportAgentApp: + def test_import_raises_on_missing_fastapi_app(self, tmp_path, monkeypatch): + """Module without fastapi_app raises RuntimeError.""" + mod_file = tmp_path / "noapp.py" + mod_file.write_text("x = 1\n") + monkeypatch.syspath_prepend(str(tmp_path)) + with pytest.raises(RuntimeError, match="fastapi_app"): + _import_agent_app("noapp.py") + + +class TestEvalRecordIntegration: + """Test that EvalRecord is populated and retrievable after a request.""" + + def test_begin_and_pop(self): + _eval_store.clear() + token = _begin_eval_request("test-id") + rec = eval_record() + rec.add_context(["chunk"]) + rec.add_tool_call("fn", args={"a": 1}) + _end_eval_request(token) + + popped = _pop_eval_record("test-id") + assert popped is not None + assert popped.retrieval_context == ["chunk"] + assert popped.tool_calls[0].name == "fn" + + +class TestEvalResultsDataclasses: + def test_eval_results_defaults(self): + r = EvalResults() + assert r.test_case_results == [] + assert r.metric_summaries == [] + assert r.skipped_metrics == [] + assert r.total_time == 0.0 + + def test_test_case_result(self): + tcr = CaseResult( + input="hello", + actual_output="world", + expected_output="world", + metric_scores={"answer_relevancy": 0.9}, + metric_passed={"answer_relevancy": True}, + completion_time=1.5, + ) + assert tcr.metric_scores["answer_relevancy"] == 0.9 + assert tcr.completion_time == 1.5