From 8027d2154c225ab5ce37e8afb4714093f8d2e06d Mon Sep 17 00:00:00 2001 From: Claude Date: Sun, 1 Feb 2026 22:43:21 +0000 Subject: [PATCH 1/9] Add verifiers integration for rollout environments Add chatan.integrations module with verifiers support: - RolloutGenerator wraps verifiers Environment as a chatan generator - RolloutGeneratorClient factory creates generator functions - rollout_generator() convenience function for creating clients - Support for extracting completion, reward, metrics, trajectory, or full output - Retry logic with exponential backoff via tenacity https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/integrations/__init__.py | 5 + src/chatan/integrations/verifiers.py | 205 +++++++++++++++++++++++++++ tests/test_verifiers_integration.py | 129 +++++++++++++++++ 3 files changed, 339 insertions(+) create mode 100644 src/chatan/integrations/__init__.py create mode 100644 src/chatan/integrations/verifiers.py create mode 100644 tests/test_verifiers_integration.py diff --git a/src/chatan/integrations/__init__.py b/src/chatan/integrations/__init__.py new file mode 100644 index 0000000..d85e677 --- /dev/null +++ b/src/chatan/integrations/__init__.py @@ -0,0 +1,5 @@ +"""Integrations with external libraries.""" + +from .verifiers import rollout_generator + +__all__ = ["rollout_generator"] diff --git a/src/chatan/integrations/verifiers.py b/src/chatan/integrations/verifiers.py new file mode 100644 index 0000000..5c6b839 --- /dev/null +++ b/src/chatan/integrations/verifiers.py @@ -0,0 +1,205 @@ +"""Verifiers integration for using rollout environments as chatan generators.""" + +from typing import Any, Dict, Literal + +from tenacity import retry, stop_after_attempt, wait_random_exponential + +from chatan.generator import BaseGenerator, GeneratorFunction + +try: + from openai import AsyncOpenAI + from verifiers.envs.environment import Environment + from verifiers.types import RolloutOutput + + VERIFIERS_AVAILABLE = True +except ImportError: + VERIFIERS_AVAILABLE = False + + +ExtractType = Literal["completion", "reward", "metrics", "trajectory", "full"] + + +class RolloutGenerator(BaseGenerator): + """Wraps a verifiers environment as a chatan generator.""" + + def __init__( + self, + env: "Environment", + client: "AsyncOpenAI", + model: str, + extract: ExtractType = "completion", + max_retries: int = 3, + **sampling_args, + ): + if not VERIFIERS_AVAILABLE: + raise ImportError( + "Verifiers integration requires verifiers to be installed. " + "Install with: pip install verifiers" + ) + + self.env = env + self.client = client + self.model = model + self.extract = extract + self.max_retries = max_retries + self.sampling_args = sampling_args + self._row_counter = 0 + + async def generate(self, prompt: str, **kwargs) -> Any: + """Run a verifiers rollout and extract the requested field.""" + + input_data = { + "prompt": [{"role": "user", "content": prompt}], + "example_id": self._row_counter, + "task": self.env.env_id or "default", + } + + if "answer" in kwargs and kwargs["answer"] is not None: + input_data["answer"] = kwargs["answer"] + + self._row_counter += 1 + + result = await self._run_with_retry(input_data) + + return self._extract_field(result) + + @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(3)) + async def _run_with_retry(self, input_data: Dict[str, Any]) -> "RolloutOutput": + """Run rollout with retry on transient failures.""" + return await self.env.run_rollout( + input_data, + self.client, + self.model, + sampling_args=self.sampling_args, + ) + + def _extract_field(self, result: "RolloutOutput") -> Any: + if self.extract == "completion": + return self._extract_completion_text(result) + elif self.extract == "reward": + return result.get("reward", 0.0) + elif self.extract == "metrics": + return result.get("metrics", {}) + elif self.extract == "trajectory": + return result.get("trajectory", []) + elif self.extract == "full": + return dict(result) + else: + return result.get(self.extract) + + def _extract_completion_text(self, result: "RolloutOutput") -> str: + completion = result.get("completion", []) + if isinstance(completion, list): + for msg in reversed(completion): + if msg.get("role") == "assistant": + content = msg.get("content", "") + return content if isinstance(content, str) else str(content) + return "" + return str(completion) + + +class RolloutGeneratorClient: + """Factory for creating rollout generator functions.""" + + def __init__( + self, + env: "Environment", + client: "AsyncOpenAI", + model: str, + max_retries: int = 3, + **default_sampling_args, + ): + if not VERIFIERS_AVAILABLE: + raise ImportError( + "Verifiers integration requires verifiers to be installed. " + "Install with: pip install verifiers" + ) + + self.env = env + self.client = client + self.model = model + self.max_retries = max_retries + self.default_sampling_args = default_sampling_args + + def __call__( + self, + prompt_template: str, + extract: ExtractType = "completion", + answer: str | None = None, + **variables, + ) -> GeneratorFunction: + """Create a generator function that runs a rollout. + + Args: + prompt_template: Template string for the prompt. Can reference other columns. + extract: What to extract from the rollout result. + - "completion": Last assistant message text (default) + - "reward": Reward score + - "metrics": Metrics dict + - "trajectory": Full trajectory list + - "full": Entire rollout output dict + answer: Template string for the ground truth answer. Can reference other columns. + e.g., answer="{ground_truth}" will resolve the ground_truth column value. + **variables: Additional variables to pass to the template. + + Returns: + GeneratorFunction that can be used in a chatan dataset schema. + """ + generator = RolloutGenerator( + env=self.env, + client=self.client, + model=self.model, + extract=extract, + max_retries=self.max_retries, + **self.default_sampling_args, + ) + + if answer is not None: + variables["answer"] = lambda ctx: answer.format(**ctx) + + return GeneratorFunction(generator, prompt_template, variables) + + +def rollout_generator( + env: "Environment", + client: "AsyncOpenAI", + model: str, + max_retries: int = 3, + **sampling_args, +) -> RolloutGeneratorClient: + """Create a rollout generator client. + + Args: + env: Verifiers environment to use for rollouts. + client: AsyncOpenAI client. + model: Model name to use for generation. + max_retries: Number of retries on transient failures. Default 3. + **sampling_args: Additional sampling arguments passed to the model. + + Returns: + RolloutGeneratorClient that can be called to create generator functions. + + Example: + >>> from chatan import dataset, sample + >>> from chatan.integrations.verifiers import rollout_generator + >>> from verifiers.utils.env_utils import load_environment + >>> from openai import AsyncOpenAI + >>> + >>> client = AsyncOpenAI(api_key="...") + >>> env = load_environment("gsm8k") + >>> rollout = rollout_generator(env, client, "gpt-4.1-mini") + >>> + >>> ds = dataset({ + ... "question": sample.from_dataset(questions, "question"), + ... "ground_truth": sample.from_dataset(questions, "answer"), + ... "model_answer": rollout("{question}", answer="{ground_truth}"), + ... "trajectory": rollout("{question}", extract="trajectory"), + ... }) + """ + return RolloutGeneratorClient( + env=env, + client=client, + model=model, + max_retries=max_retries, + **sampling_args, + ) diff --git a/tests/test_verifiers_integration.py b/tests/test_verifiers_integration.py new file mode 100644 index 0000000..1536314 --- /dev/null +++ b/tests/test_verifiers_integration.py @@ -0,0 +1,129 @@ +"""Tests for verifiers integration.""" + +import pytest + +pytest.importorskip("verifiers") + +import asyncio +from unittest.mock import AsyncMock, MagicMock + +from chatan import dataset, sample +from chatan.integrations.verifiers import RolloutGenerator, rollout_generator + + +@pytest.fixture +def mock_env(): + env = MagicMock() + env.env_id = "test_env" + env.run_rollout = AsyncMock( + return_value={ + "completion": [{"role": "assistant", "content": "42"}], + "reward": 1.0, + "metrics": {"accuracy": 1.0}, + "trajectory": [{"prompt": "test", "completion": "42"}], + } + ) + return env + + +@pytest.fixture +def mock_client(): + return MagicMock() + + +class TestRolloutGenerator: + @pytest.mark.asyncio + async def test_extract_completion(self, mock_env, mock_client): + gen = RolloutGenerator(mock_env, mock_client, "test-model", extract="completion") + result = await gen.generate("What is 6 * 7?") + assert result == "42" + + @pytest.mark.asyncio + async def test_extract_reward(self, mock_env, mock_client): + gen = RolloutGenerator(mock_env, mock_client, "test-model", extract="reward") + result = await gen.generate("What is 6 * 7?") + assert result == 1.0 + + @pytest.mark.asyncio + async def test_extract_metrics(self, mock_env, mock_client): + gen = RolloutGenerator(mock_env, mock_client, "test-model", extract="metrics") + result = await gen.generate("What is 6 * 7?") + assert result == {"accuracy": 1.0} + + @pytest.mark.asyncio + async def test_extract_trajectory(self, mock_env, mock_client): + gen = RolloutGenerator(mock_env, mock_client, "test-model", extract="trajectory") + result = await gen.generate("What is 6 * 7?") + assert result == [{"prompt": "test", "completion": "42"}] + + @pytest.mark.asyncio + async def test_extract_full(self, mock_env, mock_client): + gen = RolloutGenerator(mock_env, mock_client, "test-model", extract="full") + result = await gen.generate("What is 6 * 7?") + assert "completion" in result + assert "reward" in result + + @pytest.mark.asyncio + async def test_answer_passed_through(self, mock_env, mock_client): + gen = RolloutGenerator(mock_env, mock_client, "test-model") + await gen.generate("What is 6 * 7?", answer="42") + + call_args = mock_env.run_rollout.call_args + input_data = call_args[0][0] + assert input_data["answer"] == "42" + + +class TestRolloutGeneratorClient: + def test_creates_generator_function(self, mock_env, mock_client): + client = rollout_generator(mock_env, mock_client, "test-model") + gen_func = client("{question}") + + assert gen_func.prompt_template == "{question}" + + def test_answer_template_resolved(self, mock_env, mock_client): + client = rollout_generator(mock_env, mock_client, "test-model") + gen_func = client("{question}", answer="{ground_truth}") + + assert "answer" in gen_func.variables + ctx = {"ground_truth": "42", "question": "test"} + assert gen_func.variables["answer"](ctx) == "42" + + +class TestDatasetIntegration: + @pytest.mark.asyncio + async def test_in_dataset_schema(self, mock_env, mock_client): + rollout = rollout_generator(mock_env, mock_client, "test-model") + + ds = dataset( + { + "question": sample.choice(["What is 2+2?", "What is 3+3?"]), + "answer": rollout("{question}"), + }, + n=2, + ) + + df = await ds.generate(progress=False) + + assert len(df) == 2 + assert "question" in df.columns + assert "answer" in df.columns + assert all(df["answer"] == "42") + + @pytest.mark.asyncio + async def test_with_dependencies(self, mock_env, mock_client): + rollout = rollout_generator(mock_env, mock_client, "test-model") + + ds = dataset( + { + "question": sample.choice(["What is 2+2?"]), + "ground_truth": sample.choice(["4"]), + "model_answer": rollout("{question}", answer="{ground_truth}"), + "reward": rollout("{question}", extract="reward"), + }, + n=1, + ) + + df = await ds.generate(progress=False) + + assert df["model_answer"].iloc[0] == "42" + assert df["reward"].iloc[0] == 1.0 From 9c2f6cf87d57961da0f47f10a80a5b00f53f5a1d Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 2 Feb 2026 00:23:17 +0000 Subject: [PATCH 2/9] Simplify verifiers integration imports Remove conditional import checks - just import verifiers directly. Users who want to use the integration need verifiers installed. Update integrations __init__ to not auto-import verifiers module. https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/integrations/__init__.py | 8 +- src/chatan/integrations/verifiers.py | 41 +- uv.lock | 7598 ++++++++++++++++---------- 3 files changed, 4843 insertions(+), 2804 deletions(-) diff --git a/src/chatan/integrations/__init__.py b/src/chatan/integrations/__init__.py index d85e677..dce3661 100644 --- a/src/chatan/integrations/__init__.py +++ b/src/chatan/integrations/__init__.py @@ -1,5 +1,7 @@ -"""Integrations with external libraries.""" +"""Integrations with external libraries. -from .verifiers import rollout_generator +Available integrations: +- verifiers: Use `from chatan.integrations.verifiers import rollout_generator` +""" -__all__ = ["rollout_generator"] +__all__: list[str] = [] diff --git a/src/chatan/integrations/verifiers.py b/src/chatan/integrations/verifiers.py index 5c6b839..765a983 100644 --- a/src/chatan/integrations/verifiers.py +++ b/src/chatan/integrations/verifiers.py @@ -2,20 +2,12 @@ from typing import Any, Dict, Literal +from openai import AsyncOpenAI from tenacity import retry, stop_after_attempt, wait_random_exponential +from verifiers.envs.environment import Environment from chatan.generator import BaseGenerator, GeneratorFunction -try: - from openai import AsyncOpenAI - from verifiers.envs.environment import Environment - from verifiers.types import RolloutOutput - - VERIFIERS_AVAILABLE = True -except ImportError: - VERIFIERS_AVAILABLE = False - - ExtractType = Literal["completion", "reward", "metrics", "trajectory", "full"] @@ -24,18 +16,13 @@ class RolloutGenerator(BaseGenerator): def __init__( self, - env: "Environment", - client: "AsyncOpenAI", + env: Environment, + client: AsyncOpenAI, model: str, extract: ExtractType = "completion", max_retries: int = 3, **sampling_args, ): - if not VERIFIERS_AVAILABLE: - raise ImportError( - "Verifiers integration requires verifiers to be installed. " - "Install with: pip install verifiers" - ) self.env = env self.client = client @@ -64,7 +51,7 @@ async def generate(self, prompt: str, **kwargs) -> Any: return self._extract_field(result) @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(3)) - async def _run_with_retry(self, input_data: Dict[str, Any]) -> "RolloutOutput": + async def _run_with_retry(self, input_data: Dict[str, Any]) -> Dict[str, Any]: """Run rollout with retry on transient failures.""" return await self.env.run_rollout( input_data, @@ -73,7 +60,7 @@ async def _run_with_retry(self, input_data: Dict[str, Any]) -> "RolloutOutput": sampling_args=self.sampling_args, ) - def _extract_field(self, result: "RolloutOutput") -> Any: + def _extract_field(self, result: Dict[str, Any]) -> Any: if self.extract == "completion": return self._extract_completion_text(result) elif self.extract == "reward": @@ -87,7 +74,7 @@ def _extract_field(self, result: "RolloutOutput") -> Any: else: return result.get(self.extract) - def _extract_completion_text(self, result: "RolloutOutput") -> str: + def _extract_completion_text(self, result: Dict[str, Any]) -> str: completion = result.get("completion", []) if isinstance(completion, list): for msg in reversed(completion): @@ -103,18 +90,12 @@ class RolloutGeneratorClient: def __init__( self, - env: "Environment", - client: "AsyncOpenAI", + env: Environment, + client: AsyncOpenAI, model: str, max_retries: int = 3, **default_sampling_args, ): - if not VERIFIERS_AVAILABLE: - raise ImportError( - "Verifiers integration requires verifiers to be installed. 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Usage: row = sample.row(dataset) ds = dataset({ "question": row["question"], "answer": row["answer"], }) https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/sampler.py | 76 ++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 75 insertions(+), 1 deletion(-) diff --git a/src/chatan/sampler.py b/src/chatan/sampler.py index 875c77d..223391e 100644 --- a/src/chatan/sampler.py +++ b/src/chatan/sampler.py @@ -111,6 +111,62 @@ def __call__(self, context: Dict[str, Any] = None) -> Any: return random.choice(self.values) +class RowSampler: + """Sample aligned rows from a dataset - all columns from the same row.""" + + def __init__(self, dataset: Union[pd.DataFrame, HFDataset, Dict]): + if isinstance(dataset, pd.DataFrame): + self._data = dataset + self._len = len(dataset) + elif isinstance(dataset, HFDataset): + self._data = dataset + self._len = len(dataset) + elif isinstance(dataset, dict): + self._data = dataset + first_key = next(iter(dataset.keys())) + self._len = len(dataset[first_key]) + else: + raise ValueError("Unsupported dataset type") + + self._index_cache: Dict[int, int] = {} + + def _get_index(self, context: Dict[str, Any]) -> int: + """Get or create the sampled row index for this context.""" + ctx_id = id(context) if context else 0 + if ctx_id not in self._index_cache: + self._index_cache[ctx_id] = random.randint(0, self._len - 1) + return self._index_cache[ctx_id] + + def __getitem__(self, column: str) -> "RowColumnSampler": + """Get a sampler for a specific column.""" + return RowColumnSampler(self, column) + + def clear_cache(self) -> None: + """Clear the index cache.""" + self._index_cache.clear() + + +class RowColumnSampler(SampleFunction): + """Sample a column value from an aligned row.""" + + def __init__(self, row_sampler: RowSampler, column: str): + self._row_sampler = row_sampler + self._column = column + + def __call__(self, context: Dict[str, Any] = None) -> Any: + idx = self._row_sampler._get_index(context) + data = self._row_sampler._data + + if isinstance(data, pd.DataFrame): + return data.iloc[idx][self._column] + elif isinstance(data, HFDataset): + return data[idx][self._column] + elif isinstance(data, dict): + return data[self._column][idx] + else: + raise ValueError("Unsupported dataset type") + + # Factory functions for the sample namespace class SampleNamespace: """Namespace for sampling functions.""" @@ -150,9 +206,27 @@ def from_dataset( column: str, default: Optional[SampleFunction] = None, ) -> DatasetSampler: - """Sample from existing dataset.""" + """Sample from existing dataset (independent per column).""" return DatasetSampler(dataset, column, default) + @staticmethod + def row(dataset: Union[pd.DataFrame, HFDataset, Dict]) -> RowSampler: + """Sample aligned rows from a dataset. + + Returns a RowSampler that ensures all column accesses within + the same row come from the same source row. + + Example: + >>> eval_data = load_dataset("squad") + >>> row = sample.row(eval_data) + >>> ds = dataset({ + ... "question": row["question"], + ... "context": row["context"], + ... "answer": row["answer"], + ... }) + """ + return RowSampler(dataset) + # Export the sample namespace sample = SampleNamespace() From 0d09b202d151666c409f4add2210716fdc5a05b1 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 2 Feb 2026 01:04:43 +0000 Subject: [PATCH 4/9] Fix verifiers API: use gen_sampling_args https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/integrations/verifiers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/chatan/integrations/verifiers.py b/src/chatan/integrations/verifiers.py index 765a983..26a39a5 100644 --- a/src/chatan/integrations/verifiers.py +++ b/src/chatan/integrations/verifiers.py @@ -57,7 +57,7 @@ async def _run_with_retry(self, input_data: Dict[str, Any]) -> Dict[str, Any]: input_data, self.client, self.model, - sampling_args=self.sampling_args, + gen_sampling_args=self.sampling_args, ) def _extract_field(self, result: Dict[str, Any]) -> Any: From 87047af959310e4f453bfab7642963acc377a031 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 2 Feb 2026 01:12:40 +0000 Subject: [PATCH 5/9] Add gen_sem parameter for verifiers run_rollout The verifiers API requires a gen_sem async context manager for rate limiting. Pass a null semaphore that allows unlimited concurrency. https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/integrations/verifiers.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/src/chatan/integrations/verifiers.py b/src/chatan/integrations/verifiers.py index 26a39a5..caf40b3 100644 --- a/src/chatan/integrations/verifiers.py +++ b/src/chatan/integrations/verifiers.py @@ -1,5 +1,6 @@ """Verifiers integration for using rollout environments as chatan generators.""" +from contextlib import asynccontextmanager from typing import Any, Dict, Literal from openai import AsyncOpenAI @@ -8,6 +9,12 @@ from chatan.generator import BaseGenerator, GeneratorFunction + +@asynccontextmanager +async def _null_semaphore(): + """A no-op async context manager that acts as an unlimited semaphore.""" + yield + ExtractType = Literal["completion", "reward", "metrics", "trajectory", "full"] @@ -58,6 +65,7 @@ async def _run_with_retry(self, input_data: Dict[str, Any]) -> Dict[str, Any]: self.client, self.model, gen_sampling_args=self.sampling_args, + gen_sem=_null_semaphore(), ) def _extract_field(self, result: Dict[str, Any]) -> Any: From 7779b254c8846e17543303be40975703bc2b834f Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 2 Feb 2026 01:17:33 +0000 Subject: [PATCH 6/9] Cache rollout results per row for multiple extracts Multiple rollout() calls with different extract types now share the same underlying API call. The RolloutGeneratorClient maintains a shared cache keyed by row context, so: rollout("{q}", extract="completion") rollout("{q}", extract="trajectory") rollout("{q}", extract="reward") All return data from a single rollout per row instead of 3 separate calls. https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/generator.py | 3 ++- src/chatan/integrations/verifiers.py | 40 ++++++++++++++++++++-------- 2 files changed, 31 insertions(+), 12 deletions(-) diff --git a/src/chatan/generator.py b/src/chatan/generator.py index 2c6125a..d5925ec 100644 --- a/src/chatan/generator.py +++ b/src/chatan/generator.py @@ -233,7 +233,8 @@ async def __call__(self, context: Dict[str, Any], **kwargs) -> str: merged[key] = value(context) if callable(value) else value prompt = self.prompt_template.format(**merged) - result = await self.generator.generate(prompt, **kwargs) + # Pass context for caching (e.g., verifiers integration) + result = await self.generator.generate(prompt, _context=context, **kwargs) return result.strip() if isinstance(result, str) else result async def stream( diff --git a/src/chatan/integrations/verifiers.py b/src/chatan/integrations/verifiers.py index caf40b3..5809e61 100644 --- a/src/chatan/integrations/verifiers.py +++ b/src/chatan/integrations/verifiers.py @@ -28,9 +28,9 @@ def __init__( model: str, extract: ExtractType = "completion", max_retries: int = 3, + cache: Dict[int, Dict[str, Any]] | None = None, **sampling_args, ): - self.env = env self.client = client self.model = model @@ -38,22 +38,34 @@ def __init__( self.max_retries = max_retries self.sampling_args = sampling_args self._row_counter = 0 + self._cache = cache if cache is not None else {} async def generate(self, prompt: str, **kwargs) -> Any: """Run a verifiers rollout and extract the requested field.""" + # Get context id for caching - same row should reuse same rollout + context = kwargs.get("_context") + ctx_id = id(context) if context else None + + # Check cache first + if ctx_id is not None and ctx_id in self._cache: + result = self._cache[ctx_id] + else: + input_data = { + "prompt": [{"role": "user", "content": prompt}], + "example_id": self._row_counter, + "task": self.env.env_id or "default", + } - input_data = { - "prompt": [{"role": "user", "content": prompt}], - "example_id": self._row_counter, - "task": self.env.env_id or "default", - } + if "answer" in kwargs and kwargs["answer"] is not None: + input_data["answer"] = kwargs["answer"] - if "answer" in kwargs and kwargs["answer"] is not None: - input_data["answer"] = kwargs["answer"] + self._row_counter += 1 - self._row_counter += 1 + result = await self._run_with_retry(input_data) - result = await self._run_with_retry(input_data) + # Cache the result + if ctx_id is not None: + self._cache[ctx_id] = result return self._extract_field(result) @@ -94,7 +106,11 @@ def _extract_completion_text(self, result: Dict[str, Any]) -> str: class RolloutGeneratorClient: - """Factory for creating rollout generator functions.""" + """Factory for creating rollout generator functions. + + All generators created from the same client share a cache, so multiple + extracts from the same rollout only make one API call per row. + """ def __init__( self, @@ -109,6 +125,7 @@ def __init__( self.model = model self.max_retries = max_retries self.default_sampling_args = default_sampling_args + self._shared_cache: Dict[int, Dict[str, Any]] = {} def __call__( self, @@ -140,6 +157,7 @@ def __call__( model=self.model, extract=extract, max_retries=self.max_retries, + cache=self._shared_cache, **self.default_sampling_args, ) From 66b1eef8b9ec57e18a4e3d46458d33ab780d4fd5 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 2 Feb 2026 01:32:03 +0000 Subject: [PATCH 7/9] Fix cache key to include prompt and answer Cache key is now (ctx_id, prompt, answer) so rollouts with different parameters don't incorrectly share cached results. https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/integrations/verifiers.py | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/src/chatan/integrations/verifiers.py b/src/chatan/integrations/verifiers.py index 5809e61..b730aa9 100644 --- a/src/chatan/integrations/verifiers.py +++ b/src/chatan/integrations/verifiers.py @@ -28,7 +28,7 @@ def __init__( model: str, extract: ExtractType = "completion", max_retries: int = 3, - cache: Dict[int, Dict[str, Any]] | None = None, + cache: Dict[tuple, Dict[str, Any]] | None = None, **sampling_args, ): self.env = env @@ -42,13 +42,14 @@ def __init__( async def generate(self, prompt: str, **kwargs) -> Any: """Run a verifiers rollout and extract the requested field.""" - # Get context id for caching - same row should reuse same rollout context = kwargs.get("_context") - ctx_id = id(context) if context else None + answer = kwargs.get("answer") - # Check cache first - if ctx_id is not None and ctx_id in self._cache: - result = self._cache[ctx_id] + # Cache key includes context, prompt, and answer so different params don't collide + cache_key = (id(context) if context else None, prompt, answer) + + if cache_key in self._cache: + result = self._cache[cache_key] else: input_data = { "prompt": [{"role": "user", "content": prompt}], @@ -56,16 +57,13 @@ async def generate(self, prompt: str, **kwargs) -> Any: "task": self.env.env_id or "default", } - if "answer" in kwargs and kwargs["answer"] is not None: - input_data["answer"] = kwargs["answer"] + if answer is not None: + input_data["answer"] = answer self._row_counter += 1 result = await self._run_with_retry(input_data) - - # Cache the result - if ctx_id is not None: - self._cache[ctx_id] = result + self._cache[cache_key] = result return self._extract_field(result) @@ -125,7 +123,7 @@ def __init__( self.model = model self.max_retries = max_retries self.default_sampling_args = default_sampling_args - self._shared_cache: Dict[int, Dict[str, Any]] = {} + self._shared_cache: Dict[tuple, Dict[str, Any]] = {} def __call__( self, From 88562c9ecd69e5b0cd628187198560c65542d5bc Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 2 Feb 2026 01:42:19 +0000 Subject: [PATCH 8/9] Rewrite rollout API: one rollout, multiple extractors rollout = rollout_generator(env, client, "gpt-4.1-mini") r = rollout("{question}", answer="{ground_truth}") ds = dataset({ "question": row["prompt"], "ground_truth": row["answer"], "model_answer": r.completion, "trajectory": r.trajectory, "reward": r.reward, }) One API call per row. Extractors share cached result. https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/integrations/verifiers.py | 233 +++++++++++++++------------ 1 file changed, 129 insertions(+), 104 deletions(-) diff --git a/src/chatan/integrations/verifiers.py b/src/chatan/integrations/verifiers.py index b730aa9..cb9bc1b 100644 --- a/src/chatan/integrations/verifiers.py +++ b/src/chatan/integrations/verifiers.py @@ -1,5 +1,6 @@ """Verifiers integration for using rollout environments as chatan generators.""" +import asyncio from contextlib import asynccontextmanager from typing import Any, Dict, Literal @@ -15,57 +16,64 @@ async def _null_semaphore(): """A no-op async context manager that acts as an unlimited semaphore.""" yield + ExtractType = Literal["completion", "reward", "metrics", "trajectory", "full"] -class RolloutGenerator(BaseGenerator): - """Wraps a verifiers environment as a chatan generator.""" +class RolloutResult: + """Lazy rollout result - runs once, extracts many.""" def __init__( self, env: Environment, client: AsyncOpenAI, model: str, - extract: ExtractType = "completion", - max_retries: int = 3, - cache: Dict[tuple, Dict[str, Any]] | None = None, + prompt_template: str, + answer_template: str | None = None, **sampling_args, ): self.env = env self.client = client self.model = model - self.extract = extract - self.max_retries = max_retries + self.prompt_template = prompt_template + self.answer_template = answer_template self.sampling_args = sampling_args + self._cache: Dict[int, Dict[str, Any]] = {} + self._locks: Dict[int, asyncio.Lock] = {} self._row_counter = 0 - self._cache = cache if cache is not None else {} - async def generate(self, prompt: str, **kwargs) -> Any: - """Run a verifiers rollout and extract the requested field.""" - context = kwargs.get("_context") - answer = kwargs.get("answer") + async def _get_result(self, context: Dict[str, Any]) -> Dict[str, Any]: + """Get or compute the rollout result for this context.""" + ctx_id = id(context) - # Cache key includes context, prompt, and answer so different params don't collide - cache_key = (id(context) if context else None, prompt, answer) + if ctx_id in self._cache: + return self._cache[ctx_id] + + # Get or create lock for this context + if ctx_id not in self._locks: + self._locks[ctx_id] = asyncio.Lock() + + async with self._locks[ctx_id]: + # Double-check after acquiring lock + if ctx_id in self._cache: + return self._cache[ctx_id] + + prompt = self.prompt_template.format(**context) + answer = self.answer_template.format(**context) if self.answer_template else None - if cache_key in self._cache: - result = self._cache[cache_key] - else: input_data = { "prompt": [{"role": "user", "content": prompt}], "example_id": self._row_counter, "task": self.env.env_id or "default", } - if answer is not None: input_data["answer"] = answer self._row_counter += 1 result = await self._run_with_retry(input_data) - self._cache[cache_key] = result - - return self._extract_field(result) + self._cache[ctx_id] = result + return result @retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(3)) async def _run_with_retry(self, input_data: Dict[str, Any]) -> Dict[str, Any]: @@ -78,19 +86,63 @@ async def _run_with_retry(self, input_data: Dict[str, Any]) -> Dict[str, Any]: gen_sem=_null_semaphore(), ) + def _extractor(self, extract: ExtractType) -> "RolloutExtractor": + """Create an extractor for a specific field.""" + return RolloutExtractor(self, extract) + + @property + def completion(self) -> "RolloutExtractor": + return self._extractor("completion") + + @property + def reward(self) -> "RolloutExtractor": + return self._extractor("reward") + + @property + def metrics(self) -> "RolloutExtractor": + return self._extractor("metrics") + + @property + def trajectory(self) -> "RolloutExtractor": + return self._extractor("trajectory") + + @property + def full(self) -> "RolloutExtractor": + return self._extractor("full") + + +class RolloutExtractor(BaseGenerator): + """Extracts a field from a shared RolloutResult.""" + + def __init__(self, rollout_result: RolloutResult, extract: ExtractType): + self._rollout_result = rollout_result + self._extract = extract + + # Make it usable directly in dataset schema + prompt_template = "" + + async def __call__(self, context: Dict[str, Any]) -> Any: + """Called by dataset generation.""" + return await self.generate("", _context=context) + + async def generate(self, prompt: str, **kwargs) -> Any: + context = kwargs.get("_context", {}) + result = await self._rollout_result._get_result(context) + return self._extract_field(result) + def _extract_field(self, result: Dict[str, Any]) -> Any: - if self.extract == "completion": + if self._extract == "completion": return self._extract_completion_text(result) - elif self.extract == "reward": + elif self._extract == "reward": return result.get("reward", 0.0) - elif self.extract == "metrics": + elif self._extract == "metrics": return result.get("metrics", {}) - elif self.extract == "trajectory": + elif self._extract == "trajectory": return result.get("trajectory", []) - elif self.extract == "full": + elif self._extract == "full": return dict(result) else: - return result.get(self.extract) + return result.get(self._extract) def _extract_completion_text(self, result: Dict[str, Any]) -> str: completion = result.get("completion", []) @@ -103,86 +155,22 @@ def _extract_completion_text(self, result: Dict[str, Any]) -> str: return str(completion) -class RolloutGeneratorClient: - """Factory for creating rollout generator functions. - - All generators created from the same client share a cache, so multiple - extracts from the same rollout only make one API call per row. - """ - - def __init__( - self, - env: Environment, - client: AsyncOpenAI, - model: str, - max_retries: int = 3, - **default_sampling_args, - ): - self.env = env - self.client = client - self.model = model - self.max_retries = max_retries - self.default_sampling_args = default_sampling_args - self._shared_cache: Dict[tuple, Dict[str, Any]] = {} - - def __call__( - self, - prompt_template: str, - extract: ExtractType = "completion", - answer: str | None = None, - **variables, - ) -> GeneratorFunction: - """Create a generator function that runs a rollout. - - Args: - prompt_template: Template string for the prompt. Can reference other columns. - extract: What to extract from the rollout result. - - "completion": Last assistant message text (default) - - "reward": Reward score - - "metrics": Metrics dict - - "trajectory": Full trajectory list - - "full": Entire rollout output dict - answer: Template string for the ground truth answer. Can reference other columns. - e.g., answer="{ground_truth}" will resolve the ground_truth column value. - **variables: Additional variables to pass to the template. - - Returns: - GeneratorFunction that can be used in a chatan dataset schema. - """ - generator = RolloutGenerator( - env=self.env, - client=self.client, - model=self.model, - extract=extract, - max_retries=self.max_retries, - cache=self._shared_cache, - **self.default_sampling_args, - ) - - if answer is not None: - variables["answer"] = lambda ctx: answer.format(**ctx) - - return GeneratorFunction(generator, prompt_template, variables) - - def rollout_generator( env: Environment, client: AsyncOpenAI, model: str, - max_retries: int = 3, **sampling_args, -) -> RolloutGeneratorClient: - """Create a rollout generator client. +) -> "RolloutClient": + """Create a rollout client. Args: env: Verifiers environment to use for rollouts. client: AsyncOpenAI client. model: Model name to use for generation. - max_retries: Number of retries on transient failures. Default 3. **sampling_args: Additional sampling arguments passed to the model. Returns: - RolloutGeneratorClient that can be called to create generator functions. + RolloutClient that can be called to create rollout results. Example: >>> from chatan import dataset, sample @@ -194,17 +182,54 @@ def rollout_generator( >>> env = load_environment("gsm8k") >>> rollout = rollout_generator(env, client, "gpt-4.1-mini") >>> + >>> row = sample.row(env.get_eval_dataset()) + >>> r = rollout("{question}", answer="{ground_truth}") + >>> >>> ds = dataset({ - ... "question": sample.from_dataset(questions, "question"), - ... "ground_truth": sample.from_dataset(questions, "answer"), - ... "model_answer": rollout("{question}", answer="{ground_truth}"), - ... "trajectory": rollout("{question}", extract="trajectory"), + ... "question": row["prompt"], + ... "ground_truth": row["answer"], + ... "model_answer": r.completion, + ... "trajectory": r.trajectory, + ... "reward": r.reward, ... }) """ - return RolloutGeneratorClient( - env=env, - client=client, - model=model, - max_retries=max_retries, + return RolloutClient(env, client, model, **sampling_args) + + +class RolloutClient: + """Factory for creating rollout results.""" + + def __init__( + self, + env: Environment, + client: AsyncOpenAI, + model: str, **sampling_args, - ) + ): + self.env = env + self.client = client + self.model = model + self.sampling_args = sampling_args + + def __call__( + self, + prompt_template: str, + answer: str | None = None, + ) -> RolloutResult: + """Create a rollout result that can be used to extract multiple fields. + + Args: + prompt_template: Template string for the prompt. + answer: Template string for the ground truth answer. + + Returns: + RolloutResult with .completion, .trajectory, .reward, .metrics, .full extractors. + """ + return RolloutResult( + env=self.env, + client=self.client, + model=self.model, + prompt_template=prompt_template, + answer_template=answer, + **self.sampling_args, + ) From 0e44c600a2a708dcb33091a9a0240794986bee75 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 2 Feb 2026 01:50:51 +0000 Subject: [PATCH 9/9] Fix dataset to await async __call__ methods asyncio.iscoroutinefunction(obj) doesn't detect async __call__. Now checks __call__ method directly and handles returned coroutines. https://claude.ai/code/session_01LzcU7VdTL3HjndER1qTR2F --- src/chatan/dataset.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/src/chatan/dataset.py b/src/chatan/dataset.py index 31ecba9..8fee3dc 100644 --- a/src/chatan/dataset.py +++ b/src/chatan/dataset.py @@ -165,11 +165,18 @@ async def _generate_column_value( # Samplers are sync but fast value = func(row) elif callable(func): - # Check if it's an async callable + # Check if it's an async callable (function or __call__ method) if asyncio.iscoroutinefunction(func): value = await func(row) + elif hasattr(func, '__call__') and asyncio.iscoroutinefunction(func.__call__): + value = await func(row) else: - value = func(row) + result = func(row) + # Handle case where callable returns a coroutine + if asyncio.iscoroutine(result): + value = await result + else: + value = result else: # Static value value = func