diff --git a/mise.toml b/mise.toml index 36da1f4..4fd48a6 100644 --- a/mise.toml +++ b/mise.toml @@ -1,7 +1,7 @@ monorepo_root = true [monorepo] -config_roots = [".", "cli", "typescript", "python"] +config_roots = [".", "cli", "typescript", "python", "python-examples"] [tools] # keep this version inline with the version declared in .github/workflows/typescript diff --git a/python-examples/.python-version b/python-examples/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/python-examples/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/python-examples/README.md b/python-examples/README.md new file mode 100644 index 0000000..90f1778 --- /dev/null +++ b/python-examples/README.md @@ -0,0 +1,10 @@ +# Quantiles Python SDK Examples + +This directory in the Quantiles monorepo contains examples to illustrate how to build [`custom_code` evals](https://quantiles.io/documentation/custom-evaluations) on the Quantiles platform using the [Python SDK](https://quantiles.io/documentation/reference/python-sdk). + +The following `custom_code` evals are available along with a [`quantiles.toml` configuration file](https://quantiles.io/documentation/configuration) to make them easily runnable. They are detailed below: + +| Eval | `qt run` command | Source file | Notes | +| --- | --- | --- | --- | +| PubMedQA | `qt run custom_pubmedqa` | [`src/pubmedqa.py`](./src/pubmedqa.py) | [PubMedQA](https://pubmedqa.github.io/) is a biomedical question-answering benchmark. This file shows how a custom evaluation can be implemented, but PubMedQA is also available as a built-in Quantiles benchmark: `qt run pubmedqa`. | +| Example prompt eval | `qt run custom_prompt_eval` | [`src/prompt_eval.py`](./src/prompt_eval.py) | This is a [`custom_code`](https://quantiles.io/documentation/custom-evaluations) eval to illustrate how you might use Quantiles to evaluate the performance of a customer support chatbot with various system prompts | diff --git a/python-examples/mise.toml b/python-examples/mise.toml new file mode 100644 index 0000000..dc0367f --- /dev/null +++ b/python-examples/mise.toml @@ -0,0 +1,5 @@ +[tasks.run-pubmedqa] +run = "qt run custom_pubmedqa" + +[tasks.run_prompt_eval] +run = "qt run custom_prompt_eval" diff --git a/python-examples/pyproject.toml b/python-examples/pyproject.toml new file mode 100644 index 0000000..3d128bb --- /dev/null +++ b/python-examples/pyproject.toml @@ -0,0 +1,10 @@ +[project] +name = "python-examples" +version = "0.1.0" +description = "Example custom evals using the Quantiles Python SDK" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "pydantic>=2.13.4", + "quantiles>=0.1.0,<1.0.0", +] diff --git a/python-examples/quantiles.toml b/python-examples/quantiles.toml new file mode 100644 index 0000000..b5cefc2 --- /dev/null +++ b/python-examples/quantiles.toml @@ -0,0 +1,10 @@ +# PubMedQA built with the Python SDK (not using the built-in benchmark) +[benchmarks.custom_pubmedqa] +type = "custom_code" +command = ["uv", "run", "src/pubmedqa.py"] +input = { model_name = "openai:gpt_5_nano", num_examples = 500 } + +# PubMedQA built with the Python SDK (not using the built-in benchmark) +[benchmarks.custom_prompt_eval] +type = "custom_code" +command = ["uv", "run", "src/prompt_eval.py"] diff --git a/python-examples/src/prompt_eval.py b/python-examples/src/prompt_eval.py new file mode 100644 index 0000000..627105c --- /dev/null +++ b/python-examples/src/prompt_eval.py @@ -0,0 +1,120 @@ +from typing import Literal, TypedDict, cast + +from quantiles import ( + JsonValue, + Statistics, + emit, + entrypoint, + step, + workflow, +) +from quantiles.workflow_context import WorkflowContext + +Label = Literal["billing", "bug", "how_to"] + + +class EvalInput(TypedDict): + prompt_version: str + + +class EvalCase(TypedDict): + id: str + ticket: str + expected: Label + + +class CaseResult(TypedDict): + id: str + expected: Label + prediction: Label + correct: bool + tokens_used: int + + +CASES: list[EvalCase] = [ + { + "id": "double-charge", + "ticket": "I was charged twice for my subscription renewal.", + "expected": "billing", + }, + { + "id": "csv-crash", + "ticket": "The app crashes every time I upload a CSV file.", + "expected": "bug", + }, + { + "id": "invite-teammate", + "ticket": "Can you show me how to invite another teammate?", + "expected": "how_to", + }, +] + + +async def evaluate_case( + ctx: WorkflowContext, + item: EvalCase, + prompt_version: str, +) -> CaseResult: + prediction = cast( + Label, + await step( + ctx, + step_key=f"case:{item['id']}", + input_value={ + "sample_id": item["id"], + "ticket": item["ticket"], + "expected": item["expected"], + "prompt_version": prompt_version, + }, + execute=lambda: call_model(item["ticket"]), + ), + ) + + return { + "id": item["id"], + "expected": item["expected"], + "prediction": prediction, + "correct": prediction == item["expected"], + "tokens_used": len(item["ticket"].split()), + } + + +async def handler(input_value: EvalInput | None, ctx: WorkflowContext) -> JsonValue: + prompt_version = (input_value or {}).get("prompt_version", "A") + results: list[CaseResult] = [] + for item in CASES: + results.append(await evaluate_case(ctx, item, prompt_version)) + + correct = sum(1 for result in results if result["correct"]) + accuracy = Statistics.accuracy(correct, len(results)) + tokens_used = sum(result["tokens_used"] for result in results) + + await emit(ctx, "accuracy", accuracy) + await emit(ctx, "correct_count", float(correct)) + await emit(ctx, "total_count", float(len(results))) + await emit(ctx, "tokens_used", float(tokens_used), "tokens") + + return cast( + JsonValue, + # the below value is a valid JsonValue + { + "accuracy": accuracy, + "correct_count": correct, + "total_count": len(results), + "results": results, + }, + ) + + +async def call_model(ticket: str) -> Label: + text = ticket.lower() + if "crash" in text: + return "bug" + if "invite" in text or "show me" in text: + return "how_to" + return "billing" + + +if __name__ == "__main__": + support_triage = workflow("custom_prompt_eval", handler) + entrypoint(support_triage) diff --git a/python-examples/src/pubmedqa.py b/python-examples/src/pubmedqa.py new file mode 100644 index 0000000..a0d7ef7 --- /dev/null +++ b/python-examples/src/pubmedqa.py @@ -0,0 +1,285 @@ +""" +This file demonstrates how to use the Quantiles Python SDK to implement a new benchmark for the `qt` CLI. + +It uses PubMedQA as an illustrative example, implementing the benchmark from scratch even though PubMedQA is +already available as a built-in benchmark and can be run directly with `qt run pubmedqa`. + +The steps shown in this example can be used to add new benchmarks or build custom evaluation workflows in Quantiles. +""" + +import hashlib +import re +from collections.abc import Awaitable, Callable +from typing import TypedDict, cast + +from pydantic import BaseModel, ConfigDict, ValidationError +from quantiles import ( + LLMMessage, + ModelProvider, + Statistics, + call_llm, + collect_async_iter, + dataset, + emit, + entrypoint, + map_dataset, + step, + workflow, +) +from quantiles.types import JsonValue +from quantiles.workflow_context import WorkflowContext + +_ALLOWED_LABELS = {"yes", "no", "maybe"} + + +class PubmedQARow(BaseModel): + sample_id: str + question: str + context: str + gold_answer: str + + +class EvalResult(TypedDict): + sample_id: str + question: str + gold_answer: str + prediction: str | None + is_correct: bool + model_response: str + tokens: int + + +class RawPubmedQARowModel(BaseModel): + model_config = ConfigDict(extra="allow") + + question: JsonValue = None + query: JsonValue = None + prompt: JsonValue = None + input: JsonValue = None + + context: JsonValue = None + abstract: JsonValue = None + passage: JsonValue = None + long_answer: JsonValue = None + evidence: JsonValue = None + contexts: JsonValue = None + + final_decision: JsonValue = None + finalDecision: JsonValue = None + answer: JsonValue = None + label: JsonValue = None + target: JsonValue = None + + id: JsonValue = None + qid: JsonValue = None + question_id: JsonValue = None + + +def _normalize_label(value: JsonValue) -> str | None: + if not isinstance(value, str): + return None + normalized = value.strip().lower() + if normalized in _ALLOWED_LABELS: + return normalized + return None + + +def _coerce_text(value: JsonValue) -> str: + if isinstance(value, str): + return value.strip() + if isinstance(value, list): + return "\n".join( + _coerce_text(item) for item in value if _coerce_text(item) != "" + ).strip() + if isinstance(value, dict): + return "\n".join( + _coerce_text(item) for item in value.values() if _coerce_text(item) != "" + ).strip() + return "" + + +def _extract_context(row: RawPubmedQARowModel) -> str: + direct_keys = [ + row.context, + row.abstract, + row.passage, + row.long_answer, + row.evidence, + ] + for value in direct_keys: + text = _coerce_text(value) + if text: + return text + + contexts = row.contexts + if isinstance(contexts, list): + return "\n".join( + part for part in (_coerce_text(item) for item in contexts) if part + ) + if isinstance(contexts, dict): + ordered_keys = ["label", "contexts", "context", "abstract", "title"] + values = [_coerce_text(contexts.get(key)) for key in ordered_keys] + values.extend( + _coerce_text(value) + for key, value in contexts.items() + if key not in ordered_keys + ) + return "\n".join(part for part in values if part) + + return "" + + +def _transform_pubmedqa_row(raw: dict[str, JsonValue]) -> PubmedQARow: + try: + row = RawPubmedQARowModel.model_validate(raw) + except ValidationError as e: + raise ValueError(f"Row validation failed: {e}") from e + + question = _coerce_text(row.question or row.query or row.prompt or row.input) + context = _extract_context(row) + gold_answer = ( + _normalize_label(row.final_decision) + or _normalize_label(row.finalDecision) + or _normalize_label(row.answer) + or _normalize_label(row.label) + or _normalize_label(row.target) + ) + if question == "" or gold_answer is None: + raise ValueError("Missing question or gold_answer") + + sample_id = _coerce_text(row.id or row.qid or row.question_id) + if sample_id == "": + sample_id = hashlib.sha256( + f"{question}|{context}|{gold_answer}".encode() + ).hexdigest()[:16] + + return PubmedQARow( + sample_id=sample_id, + question=question, + context=context, + gold_answer=gold_answer, + ) + + +def _extract_label_from_response(content: str) -> str | None: + lowered = content.strip().lower() + if lowered in _ALLOWED_LABELS: + return lowered + + match = re.search(r"\b(yes|no|maybe)\b", lowered) + if match is None: + return None + return match.group(1) + + +def _build_messages(question: str, context: str) -> list[LLMMessage]: + system_prompt = "You are grading a biomedical QA item. Reply with exactly one label: yes, no, or maybe." + user_prompt = ( + f"Question:\n{question}\n\n" + f"Context:\n{context or '(no context provided)'}\n\n" + "Answer with exactly one token: yes, no, or maybe." + ) + return [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ] + + +async def _measure_row( + row: PubmedQARow, model_provider: ModelProvider, model_id: str +) -> EvalResult: + messages = _build_messages(row.question, row.context) + llm_result = await call_llm(model_provider, model_id, messages) + prediction = _extract_label_from_response(llm_result["content"]) + is_correct = prediction == row.gold_answer + + return { + "sample_id": row.sample_id, + "question": row.question, + "gold_answer": row.gold_answer, + "prediction": prediction, + "is_correct": is_correct, + "model_response": llm_result["content"], + "tokens": llm_result["tokens"], + } + + +async def _pubmedqa_handler( + input_data: dict[str, JsonValue], + ctx: WorkflowContext, +) -> JsonValue: + model_name = input_data.get("model_name", "openai:gpt_5_nano") + num_examples = int(cast(int, input_data.get("num_examples", 25))) + + if ":" not in str(model_name): + raise ValueError( + f"model_name must be in 'provider:model_id' format, got: {model_name}" + ) + provider, raw_model_id = str(model_name).split(":", 1) + if provider != "openai": + raise ValueError(f"Unsupported provider: {provider}") + model_provider: ModelProvider = "openai" + model_id = raw_model_id.replace("_", "-") + + ds = await dataset( + ctx, + source="huggingface://quantiles/PubMedQA", + row_type=PubmedQARow, + batch_size=25, + config="pqa_labeled", + split="train", + max_rows=num_examples, + on_error="skip", + transform=_transform_pubmedqa_row, + ) + + async def _eval_row(row: PubmedQARow) -> EvalResult: + result_raw = await step( + ctx, + step_key=f"eval-{row.sample_id}", + input_value=cast( + JsonValue, + { + "sample_id": row.sample_id, + "question": row.question, + "context": row.context, + "gold_answer": row.gold_answer, + "model_id": model_id, + }, + ), + execute=cast( + Callable[[], Awaitable[JsonValue]], + lambda r=row: _measure_row(r, model_provider, model_id), + ), + ) + return cast(EvalResult, result_raw) + + results: list[EvalResult] = await collect_async_iter( + map_dataset(ds, _eval_row), + ) + + correct_count = sum(1 for r in results if r["is_correct"]) + total = len(results) + accuracy = Statistics.accuracy(correct_count, total) + total_tokens = sum(r["tokens"] for r in results) + + await emit(ctx, "accuracy", accuracy) + await emit(ctx, "correct_count", float(correct_count)) + await emit(ctx, "total_count", float(total)) + await emit(ctx, "total_tokens", float(total_tokens)) + + return cast( + JsonValue, + { + "accuracy": accuracy, + "correct_count": correct_count, + "total_count": total, + "total_tokens": total_tokens, + "num_rows": num_examples, + }, + ) + + +if __name__ == "__main__": + pubmedqa_eval = workflow("custom_pubmedqa", 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2026 Quantiles + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/python/README.md b/python/README.md index 11645b1..c56b698 100644 --- a/python/README.md +++ b/python/README.md @@ -5,7 +5,7 @@ Python SDK for the [quantiles](https://quantiles.io) local AI workload observabi ## Installation ```bash -uv pip install quantiles +uv add quantiles ``` ## Usage diff --git a/python/examples/prompt_eval.py b/python/examples/prompt_eval.py deleted file mode 100644 index df7c396..0000000 --- a/python/examples/prompt_eval.py +++ /dev/null @@ -1,119 +0,0 @@ -from typing import Literal, TypedDict, cast - -from quantiles import ( - JsonValue, - Statistics, - emit, - entrypoint, - step, - workflow, -) -from quantiles.workflow_context import WorkflowContext - -Label = Literal["billing", "bug", "how_to"] - - -class EvalInput(TypedDict): - prompt_version: str - - -class EvalCase(TypedDict): - id: str - ticket: str - expected: Label - - -class CaseResult(TypedDict): - id: str - expected: Label - prediction: Label - correct: bool - tokens_used: int - - -CASES: list[EvalCase] = [ - { - "id": "double-charge", - "ticket": "I was charged twice for my subscription renewal.", - "expected": "billing", - }, - { - "id": "csv-crash", - "ticket": "The app crashes every time I upload a CSV file.", - "expected": "bug", - }, - { - "id": "invite-teammate", - "ticket": "Can you show me how to invite another teammate?", - "expected": "how_to", - }, -] - - -async def evaluate_case( - ctx: WorkflowContext, - item: EvalCase, - prompt_version: str, -) -> CaseResult: - prediction = cast( - Label, - await step( - ctx, - step_key=f"case:{item['id']}", - input_value={ - "sample_id": item["id"], - "ticket": item["ticket"], - "expected": item["expected"], - "prompt_version": prompt_version, - }, - execute=lambda: call_model(item["ticket"]), - ), - ) - - return { - "id": item["id"], - "expected": item["expected"], - "prediction": prediction, - "correct": prediction == item["expected"], - "tokens_used": len(item["ticket"].split()), - } - - -async def handler(input_value: EvalInput | None, ctx: WorkflowContext) -> JsonValue: - prompt_version = (input_value or {}).get("prompt_version", "A") - results: list[CaseResult] = [] - for item in CASES: - results.append(await evaluate_case(ctx, item, prompt_version)) - - correct = sum(1 for result in results if result["correct"]) - accuracy = Statistics.accuracy(correct, len(results)) - tokens_used = sum(result["tokens_used"] for result in results) - - await emit(ctx, "accuracy", accuracy) - await emit(ctx, "correct_count", float(correct)) - await emit(ctx, "total_count", float(len(results))) - await emit(ctx, "tokens_used", float(tokens_used), "tokens") - - return cast( - JsonValue, - # the below value is a valid JsonValue - { - "accuracy": accuracy, - "correct_count": correct, - "total_count": len(results), - "results": results, - }, - ) - - -async def call_model(ticket: str) -> Label: - text = ticket.lower() - if "crash" in text: - return "bug" - if "invite" in text or "show me" in text: - return "how_to" - return "billing" - - -support_triage = workflow("support-triage", handler) -entrypoint(support_triage) diff --git a/python/examples/pubmedqa.py b/python/examples/pubmedqa.py deleted file mode 100644 index 11b9bff..0000000 --- a/python/examples/pubmedqa.py +++ /dev/null @@ -1,274 +0,0 @@ -""" -This file demonstrates how to implement a custom Quantiles eval using PubMedQA. - -Although PubMedQA is available as a built-in benchmark in the `qt` CLI, this example is not used by -the built-in implementation. -""" - -import hashlib -import re -from collections.abc import Awaitable, Callable -from typing import TypedDict, cast - -from pydantic import BaseModel, ConfigDict, ValidationError - -from quantiles import ( - LLMMessage, - ModelProvider, - Statistics, - call_llm, - collect_async_iter, - dataset, - emit, - entrypoint, - map_dataset, - step, - workflow, -) -from quantiles.types import JsonValue -from quantiles.workflow_context import WorkflowContext - -_ALLOWED_LABELS = {"yes", "no", "maybe"} - - -class PubmedQARow(BaseModel): - sample_id: str - question: str - context: str - gold_answer: str - - -class EvalResult(TypedDict): - sample_id: str - question: str - gold_answer: str - prediction: str | None - is_correct: bool - model_response: str - tokens: int - - -class RawPubmedQARowModel(BaseModel): - model_config = ConfigDict(extra="allow") - - question: JsonValue = None - query: JsonValue = None - prompt: JsonValue = None - input: JsonValue = None - - context: JsonValue = None - abstract: JsonValue = None - passage: JsonValue = None - long_answer: JsonValue = None - evidence: JsonValue = None - contexts: JsonValue = None - - final_decision: JsonValue = None - finalDecision: JsonValue = None - answer: JsonValue = None - label: JsonValue = None - target: JsonValue = None - - id: JsonValue = None - qid: JsonValue = None - question_id: JsonValue = None - - -def _normalize_label(value: JsonValue) -> str | None: - if not isinstance(value, str): - return None - normalized = value.strip().lower() - if normalized in _ALLOWED_LABELS: - return normalized - return None - - -def _coerce_text(value: JsonValue) -> str: - if isinstance(value, str): - return value.strip() - if isinstance(value, list): - return "\n".join(_coerce_text(item) for item in value if _coerce_text(item) != "").strip() - if isinstance(value, dict): - return "\n".join( - _coerce_text(item) for item in value.values() if _coerce_text(item) != "" - ).strip() - return "" - - -def _extract_context(row: RawPubmedQARowModel) -> str: - direct_keys = [ - row.context, - row.abstract, - row.passage, - row.long_answer, - row.evidence, - ] - for value in direct_keys: - text = _coerce_text(value) - if text: - return text - - contexts = row.contexts - if isinstance(contexts, list): - return "\n".join(part for part in (_coerce_text(item) for item in contexts) if part) - if isinstance(contexts, dict): - ordered_keys = ["label", "contexts", "context", "abstract", "title"] - values = [_coerce_text(contexts.get(key)) for key in ordered_keys] - values.extend(_coerce_text(value) for key, value in contexts.items() if key not in ordered_keys) - return "\n".join(part for part in values if part) - - return "" - - -def _transform_pubmedqa_row(raw: dict[str, JsonValue]) -> PubmedQARow: - try: - row = RawPubmedQARowModel.model_validate(raw) - except ValidationError as e: - raise ValueError(f"Row validation failed: {e}") from e - - question = _coerce_text(row.question or row.query or row.prompt or row.input) - context = _extract_context(row) - gold_answer = ( - _normalize_label(row.final_decision) - or _normalize_label(row.finalDecision) - or _normalize_label(row.answer) - or _normalize_label(row.label) - or _normalize_label(row.target) - ) - if question == "" or gold_answer is None: - raise ValueError("Missing question or gold_answer") - - sample_id = _coerce_text(row.id or row.qid or row.question_id) - if sample_id == "": - sample_id = hashlib.sha256(f"{question}|{context}|{gold_answer}".encode()).hexdigest()[:16] - - return PubmedQARow( - sample_id=sample_id, - question=question, - context=context, - gold_answer=gold_answer, - ) - - -def _extract_label_from_response(content: str) -> str | None: - lowered = content.strip().lower() - if lowered in _ALLOWED_LABELS: - return lowered - - match = re.search(r"\b(yes|no|maybe)\b", lowered) - if match is None: - return None - return match.group(1) - - -def _build_messages(question: str, context: str) -> list[LLMMessage]: - system_prompt = ( - "You are grading a biomedical QA item. Reply with exactly one label: yes, no, or maybe." - ) - user_prompt = ( - f"Question:\n{question}\n\n" - f"Context:\n{context or '(no context provided)'}\n\n" - "Answer with exactly one token: yes, no, or maybe." - ) - return [ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ] - - -async def _measure_row( - row: PubmedQARow, model_provider: ModelProvider, model_id: str -) -> EvalResult: - messages = _build_messages(row.question, row.context) - llm_result = await call_llm(model_provider, model_id, messages) - prediction = _extract_label_from_response(llm_result["content"]) - is_correct = prediction == row.gold_answer - - return { - "sample_id": row.sample_id, - "question": row.question, - "gold_answer": row.gold_answer, - "prediction": prediction, - "is_correct": is_correct, - "model_response": llm_result["content"], - "tokens": llm_result["tokens"], - } - - -async def _pubmedqa_handler( - input_data: dict[str, JsonValue], - ctx: WorkflowContext, -) -> JsonValue: - model_name = input_data.get("model_name", "openai:gpt_5_nano") - num_examples = int(cast(int, input_data.get("num_examples", 25))) - - if ":" not in str(model_name): - raise ValueError(f"model_name must be in 'provider:model_id' format, got: {model_name}") - provider, raw_model_id = str(model_name).split(":", 1) - if provider != "openai": - raise ValueError(f"Unsupported provider: {provider}") - model_provider: ModelProvider = "openai" - model_id = raw_model_id.replace("_", "-") - - ds = await dataset( - ctx, - source="huggingface://quantiles/PubMedQA", - row_type=PubmedQARow, - batch_size=25, - config="pqa_labeled", - split="train", - max_rows=num_examples, - on_error="skip", - transform=_transform_pubmedqa_row, - ) - - async def _eval_row(row: PubmedQARow) -> EvalResult: - result_raw = await step( - ctx, - step_key=f"eval-{row.sample_id}", - input_value=cast( - JsonValue, - { - "sample_id": row.sample_id, - "question": row.question, - "context": row.context, - "gold_answer": row.gold_answer, - "model_id": model_id, - }, - ), - execute=cast( - Callable[[], Awaitable[JsonValue]], - lambda r=row: _measure_row(r, model_provider, model_id), - ), - ) - return cast(EvalResult, result_raw) - - results: list[EvalResult] = await collect_async_iter( - map_dataset(ds, _eval_row), - ) - - correct_count = sum(1 for r in results if r["is_correct"]) - total = len(results) - accuracy = Statistics.accuracy(correct_count, total) - total_tokens = sum(r["tokens"] for r in results) - - await emit(ctx, "accuracy", accuracy) - await emit(ctx, "correct_count", float(correct_count)) - await emit(ctx, "total_count", float(total)) - await emit(ctx, "total_tokens", float(total_tokens)) - - return cast( - JsonValue, - { - "accuracy": accuracy, - "correct_count": correct_count, - "total_count": total, - "total_tokens": total_tokens, - "num_rows": num_examples, - }, - ) - - -if __name__ == "__main__": - pubmedqa_eval = workflow("pubmedqa", _pubmedqa_handler) - entrypoint(pubmedqa_eval) diff --git a/python/mise.toml b/python/mise.toml index 77ed55f..7f7eb64 100644 --- a/python/mise.toml +++ b/python/mise.toml @@ -16,8 +16,6 @@ run = [ [tasks.typecheck] run = "uv run ty check" -[tasks.example-pubmedqa] -run = "qt run --input '{\"model_name\": \"openai:gpt_5_nano\", \"num_examples\": 500}' pubmedqa -- uv run python examples/pubmedqa.py" [tasks.all] depends = ["fmt", "lint", "typecheck", "test"] @@ -36,10 +34,8 @@ depends = [ "test", ] -# requires that the CLI is installed already -[tasks.e2e] -run = [ - "qt run --input '{\"iterations\":3}' e2e-smoke-py -- uv run python tests/e2e/smoke.py", - "python_output=$(sqlite3 .quantiles/quantiles.sqlite \"SELECT output FROM workflow_runs WHERE workflow_name = 'e2e-smoke-py'\")" - # TODO: check output -] +[tasks.wheel-build] +run = "uv build" + +[tasks.wheel-publish] +run = "uv publish --token $PYPI_TOKEN" diff --git a/python/pyproject.toml b/python/pyproject.toml index 10c81a8..c710093 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -4,10 +4,32 @@ version = "0.1.0" description = "Python SDK for the Quantiles local AI workload observability server" readme = "README.md" requires-python = ">=3.12" +license = "Apache-2.0" +license-files = ["LICENSE"] +authors = [ + { name = "Aaron Schlesinger", email = "aaron@quantiles.io" }, + { name = "Golda Manuel", email = "golda@quantiles.io" }, +] +keywords = ["evals", "llm", "observability", "ai", "quantiles"] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: Python :: 3 :: Only", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Software Development :: Libraries :: Python Modules", +] dependencies = ["aiohttp~=3.11", "numpy~=2.4.4", "pydantic~=2.11", "scipy>=1.12.0,<2.0.0", "openai~=2.36.0"] +[project.urls] +Homepage = "https://quantiles.io" +Documentation = "https://quantiles.io/docs" +Repository = "https://github.com/quantiles-evals/quantiles" +Issues = "https://github.com/quantiles-evals/quantiles/issues" + [build-system] -requires = ["uv_build>=0.9.18,<0.10.0"] +requires = ["uv_build>=0.9.18,<1.0.0"] build-backend = "uv_build" [tool.ruff] diff --git a/python/src/quantiles/__init__.py b/python/src/quantiles/__init__.py index 28fd9f5..c5b9c81 100644 --- a/python/src/quantiles/__init__.py +++ b/python/src/quantiles/__init__.py @@ -13,6 +13,8 @@ from .workflow import emit, entrypoint, step, workflow from .workflow_context import WorkflowContext +__version__ = "0.1.0" + __all__ = [ "Classification", "Dataset", diff --git a/typescript/mise.toml b/typescript/mise.toml index 0377cef..16f9b55 100644 --- a/typescript/mise.toml +++ b/typescript/mise.toml @@ -22,7 +22,7 @@ run = "bun run fmt" [tasks.example-prompt-eval] depends = ["install-frozen"] -run = "qt run support-triage -- bun run examples/prompt_eval.ts" +run = "qt run support-triage" [tasks.ci] run = [ @@ -37,6 +37,6 @@ depends = ["typecheck", "lint"] [tasks.e2e] depends = ["install-frozen"] run = [ - "qt run --input '{\"iterations\": 3}' e2e-smoke-ts -- bunx tsx tests/e2e/smoke.ts" + "qt run --input '{\"iterations\": 3}' e2e-smoke-ts" # TODO: check output ]