diff --git a/pyproject.toml b/pyproject.toml index c7d29ca..62844e0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,6 +11,7 @@ dependencies = [ "google-cloud-core>=2.4.3", "google-genai>=1.59.0", "json-repair~=0.40.0", + "openai>=2.44.0", "pydantic>=2.11.7", "rapidfuzz>=3.13.0", "requests>=2.32.4", diff --git a/src/genai_utils/chunking.py b/src/genai_utils/chunking.py new file mode 100644 index 0000000..102de2f --- /dev/null +++ b/src/genai_utils/chunking.py @@ -0,0 +1,76 @@ +""" +We frequently end up chunking input sentences when we do things with Gen AI. +This has become replicated across multiple projects, +so we made some laughably simple helper functions here. +""" + +import math +from typing import Iterator + + +def calculate_n_chunks(n_sentences: int, chunk_size: int, overlap_size: int) -> int: + """ + Calculates the number of chunks that ``make_chunks`` will produce. + + Parameters + ---------- + n_sentences: int + Total number of sentences. + chunk_size: int + Number of sentences per chunk. + overlap_size: int + Number of overlapping sentences between consecutive chunks. + + Returns + ------- + int + The number of chunks. + """ + if n_sentences == 0: + return 0 + # The first chunk that reaches the end of the input is the last one we + # emit; any further chunk would be a subset of it (see ``make_chunks``). + if n_sentences <= chunk_size: + return 1 + step = chunk_size - overlap_size + return math.ceil((n_sentences - chunk_size) / step) + 1 + + +def make_chunks( + sentences: list[str], chunk_size: int, overlap_size: int +) -> Iterator[list[str]]: + """ + Takes a list of sentences, and yields smaller chunks of sentences, + with overlaps between chunks. + + Parameters + ---------- + text: list[str] + List of sentences to be chunked. + chunk_size: int + Number of sentences to be in each chunk. + overlap_size: int + Size of overlap of consecutive chunks. + + Returns + ------- + Iterator[list[str]] + Yields one chunk of sentences at a time. + + Raises + ------ + ValueError: + if the overlap is negative or the overlap is larger than the chunk size. + """ + if not (0 <= overlap_size < chunk_size): + raise ValueError( + f"overlap_size ({overlap_size}) must be >= 0 and " + f"< chunk_size ({chunk_size})" + ) + step = chunk_size - overlap_size + for i in range(0, len(sentences), step): + yield sentences[i : i + chunk_size] + # Once a chunk reaches the end, stop: any later chunk would start + # inside this one's overlap and be fully contained in it. + if i + chunk_size >= len(sentences): + break diff --git a/src/genai_utils/openai_llm.py b/src/genai_utils/openai_llm.py new file mode 100644 index 0000000..3b3e75a --- /dev/null +++ b/src/genai_utils/openai_llm.py @@ -0,0 +1,148 @@ +""" +This file contains basic functionality for making requests to a hosted +OpenAI API compatible LLM. +""" + +import logging +import time + +import openai +from openai import OpenAI +from tenacity import ( + before_sleep_log, + retry, + retry_if_exception, + stop_after_attempt, + wait_exponential, +) + +_logger = logging.getLogger(__name__) + + +def _is_model_loading(exception: BaseException) -> bool: + # Servers (vLLM, llama.cpp) return 503 while up but not yet ready to + # serve — e.g. still loading the model, or a freshly-started replica. + # Retrying rides out that window; stop_after_attempt bounds the wait. + return ( + isinstance(exception, openai.InternalServerError) + and exception.status_code == 503 + ) + + +def log_tokens_per_second(response: object, elapsed: float) -> None: + """ + Logs generation throughput (tokens/second) at info level. + + Prefers the server's own decode timing when available (llama.cpp reports a + ``timings`` object with ``predicted_per_second``), otherwise falls back to + ``completion_tokens`` over the measured wall-clock time, which works for any + OpenAI-compatible server such as vLLM. + """ + if not _logger.isEnabledFor(logging.INFO): + return + + # llama.cpp: authoritative decode rate from the server, no network overhead. + # The openai client stashes unknown response fields on ``model_extra``. + extra = getattr(response, "model_extra", None) or {} + timings = extra.get("timings") if isinstance(extra, dict) else None + if isinstance(timings, dict) and timings.get("predicted_per_second") is not None: + _logger.info( + "Generation throughput: %.1f tok/s (%d tokens in %.2fs, server timings)", + timings["predicted_per_second"], + timings.get("predicted_n", 0), + timings.get("predicted_ms", 0) / 1000, + ) + return + + # vLLM / generic fallback: completion tokens over wall-clock time. + usage = getattr(response, "usage", None) + completion_tokens = getattr(usage, "completion_tokens", None) + if completion_tokens and elapsed > 0: + _logger.info( + "Generation throughput: %.1f tok/s (%d completion tokens in %.2fs)", + completion_tokens / elapsed, + completion_tokens, + elapsed, + ) + + +class NoOutputError(Exception): + pass + + +class LLM: + """ + An LLM using the Open AI API. + This might be something served via VLLM or Llama.cpp. + Mostly just wraps the methods for sending requests, etc. + If the model needs an API key, you will need to provide one. + """ + + def __init__( + self, + model_url: str, + model_name: str = "local-model", + api_key: str = "not-needed", + ): + """ + Args: + model_url: + The url where the model is hosted. + model_name: + The name of the model. + This will have been set when you deployed the model. + The previous hardcoded name was "local-model" + so that behaviour remains as default. + api_key: + The API key for the model, if you need one. + """ + self.model_url = model_url + self.model_name = model_name + self.client = OpenAI( + base_url=f"{model_url}/v1", + api_key=api_key, + ) + + @retry( + retry=retry_if_exception(_is_model_loading), + wait=wait_exponential(multiplier=2, min=5, max=30), + stop=stop_after_attempt(5), + before_sleep=before_sleep_log(_logger, logging.WARNING), + reraise=True, + ) + def run_message(self, message_content: str, use_thinking: bool) -> str: + """ + Sends the message to the client in the correct formatting. + Returns the result. + Will use thinking if you ask it to. + """ + extra_model_config = ( + { + "chat_template_kwargs": {"enable_thinking": False}, + "reasoning_budget": 0, + } + if not use_thinking + else { + "chat_template_kwargs": {"enable_thinking": True}, + } + ) + # Ask llama.cpp to report per-request timings. Ignored by servers that + # don't support it (e.g. vLLM), so it's safe to always send. + extra_model_config["timings_per_token"] = True + + start = time.perf_counter() + response = self.client.chat.completions.create( + model=self.model_name, + messages=[ + { + "role": "user", + "content": str(message_content), + }, + ], + extra_body=extra_model_config, + ) + log_tokens_per_second(response, time.perf_counter() - start) + response_text = response.choices[0].message.content + if response_text is None: + raise NoOutputError() + return response_text diff --git a/tests/genai_utils/test_chunking.py b/tests/genai_utils/test_chunking.py new file mode 100644 index 0000000..1d13832 --- /dev/null +++ b/tests/genai_utils/test_chunking.py @@ -0,0 +1,98 @@ +from pytest import mark, param, raises + +from genai_utils.chunking import ( + calculate_n_chunks, + make_chunks, +) + + +@mark.parametrize( + "n_sentences,chunk_size,overlap_size,expected_chunk_count", + [ + param(50, 100, 10, 1, id="short transcript with overlap"), + param(350, 100, 10, 4, id="long transcript with overlap"), + param(50, 100, 0, 1, id="short transcript without overlap"), + param(350, 100, 0, 4, id="long transcript without overlap"), + param(350, 100, 50, 6, id="long transcript with big overlap"), + param(10, 100, 15, 1, id="text shorter than overlap"), + param(50, 50, 0, 1, id="sentences same as chunk size"), + param(20, 50, 0, 1, id="sentences shorter than chunk size"), + param(20, 19, 0, 2, id="sentences one longer than chunk size"), + param(0, 100, 0, 0, id="no sentences"), + ], +) +def test_calculate_n_chunks( + n_sentences, chunk_size, overlap_size, expected_chunk_count +) -> None: + assert ( + calculate_n_chunks(n_sentences, chunk_size, overlap_size) + == expected_chunk_count + ) + + +@mark.parametrize( + "n_sentences,chunk_size,overlap_size", + [ + param(50, 100, 10, id="short transcript with overlap"), + param(350, 100, 10, id="long transcript with overlap"), + param(350, 100, 50, id="long transcript with big overlap"), + param(20, 19, 0, id="sentences one longer than chunk size"), + param(0, 100, 0, id="no sentences"), + ], +) +def test_make_chunks_count_matches_calculation( + n_sentences, chunk_size, overlap_size +) -> None: + """``make_chunks`` should yield exactly as many chunks as predicted.""" + sentences = [f"sentence {i}" for i in range(n_sentences)] + chunks = list(make_chunks(sentences, chunk_size, overlap_size)) + assert len(chunks) == calculate_n_chunks(n_sentences, chunk_size, overlap_size) + + +def test_make_chunks_overlap_content() -> None: + """Consecutive chunks should share ``overlap_size`` sentences.""" + sentences = [f"s{i}" for i in range(10)] + chunks = list(make_chunks(sentences, chunk_size=4, overlap_size=2)) + + # first chunk is the start of the input + assert chunks[0] == ["s0", "s1", "s2", "s3"] + # the tail of one chunk is the head of the next + assert chunks[0][-2:] == chunks[1][:2] + assert chunks[1] == ["s2", "s3", "s4", "s5"] + + +def test_make_chunks_no_overlap_partitions_input() -> None: + """With no overlap the chunks should join back into the original list.""" + sentences = [f"s{i}" for i in range(10)] + chunks = list(make_chunks(sentences, chunk_size=3, overlap_size=0)) + flattened = [sentence for chunk in chunks for sentence in chunk] + assert flattened == sentences + + +def test_make_chunks_no_redundant_trailing_chunk() -> None: + """A final chunk fully contained in the previous one should not be emitted.""" + sentences = [f"s{i}" for i in range(10)] + # chunk_size=4, overlap=2, step=2 would step to index 8 and yield a final + # ["s8", "s9"] that is already wholly inside the previous ["s6"..."s9"] chunk. + chunks = list(make_chunks(sentences, chunk_size=4, overlap_size=2)) + assert chunks[-1] == ["s6", "s7", "s8", "s9"] + # every chunk carries new content the previous one didn't have + assert all(chunks[i] != chunks[i - 1] for i in range(1, len(chunks))) + + +def test_make_chunks_empty_input() -> None: + assert list(make_chunks([], chunk_size=5, overlap_size=2)) == [] + + +@mark.parametrize( + "chunk_size,overlap_size", + [ + param(10, 15, id="overlap bigger than chunk"), + param(10, 10, id="overlap same as chunk"), + param(100, -1, id="negative overlap"), + ], +) +def test_make_chunks_invalid_overlap_raises(chunk_size, overlap_size) -> None: + with raises(ValueError): + # wrap in list to force the generator to run + list(make_chunks(["x"] * 50, chunk_size, overlap_size)) diff --git a/tests/genai_utils/test_openai_llm.py b/tests/genai_utils/test_openai_llm.py new file mode 100644 index 0000000..28ba767 --- /dev/null +++ b/tests/genai_utils/test_openai_llm.py @@ -0,0 +1,133 @@ +import logging +from types import SimpleNamespace +from unittest.mock import MagicMock, patch + +import openai +from pytest import mark, param, raises + +from genai_utils.openai_llm import ( + LLM, + NoOutputError, + _is_model_loading, + log_tokens_per_second, +) + +LOGGER_NAME = "genai_utils.openai_llm" + + +def _make_response(content): + """Build a minimal stand-in for an OpenAI chat completion response.""" + message = SimpleNamespace(content=content) + choice = SimpleNamespace(message=message) + return SimpleNamespace(choices=[choice], model_extra={}, usage=None) + + +class _ModelLoadingError(openai.InternalServerError): + """A 503 InternalServerError without the awkward real constructor.""" + + def __init__(self) -> None: + self.status_code = 503 + + +def test_is_model_loading_false_for_other_exceptions() -> None: + assert _is_model_loading(ValueError("nope")) is False + + +def test_is_model_loading_true_for_503() -> None: + assert _is_model_loading(_ModelLoadingError()) is True + + +def test_run_message_retries_while_model_loads() -> None: + """A 503 (model still loading) should be retried, then succeed.""" + llm = LLM(model_url="http://localhost:1234", model_name="test-model") + llm.client = MagicMock() + llm.client.chat.completions.create.side_effect = [ + _ModelLoadingError(), + _make_response("ready now"), + ] + + # patch out tenacity's sleep so the exponential backoff doesn't slow the test + with patch("tenacity.nap.time.sleep"): + result = llm.run_message("hello", use_thinking=False) + + assert result == "ready now" + assert llm.client.chat.completions.create.call_count == 2 + + +def test_run_message_returns_content() -> None: + llm = LLM(model_url="http://localhost:1234", model_name="test-model") + llm.client = MagicMock() + llm.client.chat.completions.create.return_value = _make_response("the answer") + + assert llm.run_message("hello", use_thinking=False) == "the answer" + + +def test_run_message_raises_on_no_output() -> None: + llm = LLM(model_url="http://localhost:1234", model_name="test-model") + llm.client = MagicMock() + llm.client.chat.completions.create.return_value = _make_response(None) + + with raises(NoOutputError): + llm.run_message("hello", use_thinking=False) + + +@mark.parametrize( + "use_thinking,expected_enabled", + [ + param(True, True, id="thinking enabled"), + param(False, False, id="thinking disabled"), + ], +) +def test_run_message_passes_thinking_config(use_thinking, expected_enabled) -> None: + llm = LLM(model_url="http://localhost:1234", model_name="test-model") + llm.client = MagicMock() + llm.client.chat.completions.create.return_value = _make_response("ok") + + llm.run_message("hello", use_thinking=use_thinking) + + _, kwargs = llm.client.chat.completions.create.call_args + extra_body = kwargs["extra_body"] + assert extra_body["chat_template_kwargs"]["enable_thinking"] is expected_enabled + # timing reporting is always requested; harmless for servers that ignore it + assert extra_body["timings_per_token"] is True + # the model name is forwarded to the API + assert kwargs["model"] == "test-model" + + +def test_log_tokens_per_second_silent_when_info_disabled(caplog) -> None: + """Should be a no-op (and never raise) when info logging is off.""" + caplog.set_level(logging.WARNING, logger=LOGGER_NAME) + response = SimpleNamespace(model_extra={}, usage=None) + log_tokens_per_second(response, elapsed=1.0) + assert caplog.records == [] + + +def test_log_tokens_per_second_prefers_server_timings(caplog) -> None: + caplog.set_level(logging.INFO, logger=LOGGER_NAME) + response = SimpleNamespace( + model_extra={ + "timings": { + "predicted_per_second": 42.0, + "predicted_n": 10, + "predicted_ms": 250, + } + }, + usage=SimpleNamespace(completion_tokens=999), + ) + log_tokens_per_second(response, elapsed=5.0) + + assert "throughput" in caplog.text + # the server timing rate wins over the wall-clock fallback + assert "42.0 tok/s" in caplog.text + + +def test_log_tokens_per_second_falls_back_to_usage(caplog) -> None: + caplog.set_level(logging.INFO, logger=LOGGER_NAME) + response = SimpleNamespace( + model_extra={}, + usage=SimpleNamespace(completion_tokens=20), + ) + log_tokens_per_second(response, elapsed=2.0) + + # 20 tokens over 2 seconds => 10 tok/s + assert "10.0 tok/s" in caplog.text diff --git 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