diff --git a/llmclient/__init__.py b/llmclient/__init__.py index ffab2b1..f7fb8af 100644 --- a/llmclient/__init__.py +++ b/llmclient/__init__.py @@ -20,12 +20,10 @@ CommonLLMNames, LiteLLMModel, LLMModel, - MultipleCompletionLLMModel, sum_logprobs, validate_json_completion, ) from .types import ( - Chunk, Embeddable, LLMResult, ) @@ -38,7 +36,6 @@ "EXTRA_TOKENS_FROM_USER_ROLE", "GLOBAL_COST_TRACKER", "MODEL_COST_MAP", - "Chunk", "CommonLLMNames", "Embeddable", "EmbeddingModel", @@ -49,7 +46,6 @@ "LLMResult", "LiteLLMEmbeddingModel", "LiteLLMModel", - "MultipleCompletionLLMModel", "SentenceTransformerEmbeddingModel", "SparseEmbeddingModel", "configure_llm_logs", diff --git a/llmclient/cost_tracker.py b/llmclient/cost_tracker.py index afcfcca..5fa3a39 100644 --- a/llmclient/cost_tracker.py +++ b/llmclient/cost_tracker.py @@ -20,7 +20,12 @@ def __init__(self): self.report_every_usd = 1.0 def record( - self, response: litellm.ModelResponse | litellm.types.utils.EmbeddingResponse + self, + response: ( + litellm.ModelResponse + | litellm.types.utils.EmbeddingResponse + | litellm.types.utils.ModelResponseStream + ), ) -> None: self.lifetime_cost_usd += litellm.cost_calculator.completion_cost( completion_response=response diff --git a/llmclient/llms.py b/llmclient/llms.py index fa1e67e..02fc94a 100644 --- a/llmclient/llms.py +++ b/llmclient/llms.py @@ -2,7 +2,6 @@ "CommonLLMNames", "LLMModel", "LiteLLMModel", - "MultipleCompletionLLMModel", "PassThroughRouter", "rate_limited", "sum_logprobs", @@ -16,24 +15,16 @@ import logging from abc import ABC from collections.abc import ( - AsyncGenerator, AsyncIterable, - AsyncIterator, Awaitable, Callable, + Coroutine, Iterable, Mapping, ) from enum import StrEnum -from inspect import isasyncgenfunction, signature -from typing import ( - Any, - ClassVar, - Self, - TypeAlias, - TypeVar, - cast, -) +from inspect import isasyncgenfunction, isawaitable, signature +from typing import Any, ClassVar, ParamSpec, TypeAlias, cast, overload import litellm from aviary.core import ( @@ -56,14 +47,12 @@ from llmclient.constants import ( CHARACTERS_PER_TOKEN_ASSUMPTION, DEFAULT_VERTEX_SAFETY_SETTINGS, - EXTRA_TOKENS_FROM_USER_ROLE, IS_PYTHON_BELOW_312, ) -from llmclient.cost_tracker import TrackedStreamWrapper, track_costs, track_costs_iter +from llmclient.cost_tracker import track_costs, track_costs_iter from llmclient.exceptions import JSONSchemaValidationError -from llmclient.prompts import default_system_prompt from llmclient.rate_limiter import GLOBAL_LIMITER -from llmclient.types import Chunk, LLMResult +from llmclient.types import LLMResult from llmclient.utils import get_litellm_retrying_config logger = logging.getLogger(__name__) @@ -84,6 +73,7 @@ class CommonLLMNames(StrEnum): # Use these to avoid thinking about exact versions GPT_4O = "gpt-4o-2024-11-20" + GPT_35_TURBO = "gpt-3.5-turbo-0125" CLAUDE_35_SONNET = "claude-3-5-sonnet-20241022" # Use these when trying to think of a somewhat opinionated default @@ -92,30 +82,33 @@ class CommonLLMNames(StrEnum): # Use these in unit testing OPENAI_TEST = "gpt-4o-mini-2024-07-18" # Cheap, fast, and not OpenAI's cutting edge ANTHROPIC_TEST = ( - "claude-3-haiku-20240307" # Cheap, fast, and not Anthropic's cutting edge + "claude-3-5-haiku-20241022" # Cheap, fast, and not Anthropic's cutting edge ) -def sum_logprobs(choice: litellm.utils.Choices) -> float | None: +def sum_logprobs(choice: litellm.utils.Choices | list[float]) -> float | None: """Calculate the sum of the log probabilities of an LLM completion (a Choices object). Args: - choice: A sequence of choices from the completion. + choice: A sequence of choices from the completion or an iterable with logprobs. Returns: The sum of the log probabilities of the choice. """ - try: - logprob_obj = choice.logprobs - except AttributeError: - return None - if isinstance(logprob_obj, dict): - if logprob_obj.get("content"): - return sum( - logprob_info["logprob"] for logprob_info in logprob_obj["content"] - ) - elif choice.logprobs.content: - return sum(logprob_info.logprob for logprob_info in choice.logprobs.content) + if isinstance(choice, litellm.utils.Choices): + try: + logprob_obj = choice.logprobs + except AttributeError: + return None + if isinstance(logprob_obj, dict): + if logprob_obj.get("content"): + return sum( + logprob_info["logprob"] for logprob_info in logprob_obj["content"] + ) + elif choice.logprobs.content: + return sum(logprob_info.logprob for logprob_info in choice.logprobs.content) + elif isinstance(choice, list): + return sum(choice) return None @@ -154,24 +147,30 @@ def validate_json_completion( ) from err -def prepare_args(func: Callable, chunk: str, name: str | None) -> tuple[tuple, dict]: +def prepare_args( + func: Callable, completion: str, name: str | None +) -> tuple[tuple, dict]: with contextlib.suppress(TypeError): if "name" in signature(func).parameters: - return (chunk,), {"name": name} - return (chunk,), {} + return (completion,), {"name": name} + return (completion,), {} async def do_callbacks( async_callbacks: Iterable[Callable[..., Awaitable]], sync_callbacks: Iterable[Callable[..., Any]], - chunk: str, + completion: str, name: str | None, ) -> None: - for f in async_callbacks: - args, kwargs = prepare_args(f, chunk, name) - await f(*args, **kwargs) + await asyncio.gather( + *( + f(*args, **kwargs) + for f in async_callbacks + for args, kwargs in (prepare_args(f, completion, name),) + ) + ) for f in sync_callbacks: - args, kwargs = prepare_args(f, chunk, name) + args, kwargs = prepare_args(f, completion, name) f(*args, **kwargs) @@ -180,284 +179,222 @@ class LLMModel(ABC, BaseModel): llm_type: str | None = None name: str - llm_result_callback: ( - Callable[[LLMResult], None] | Callable[[LLMResult], Awaitable[None]] | None - ) = Field( + llm_result_callback: Callable[[LLMResult], Any | Awaitable[Any]] | None = Field( default=None, description=( "An async callback that will be executed on each" - " LLMResult (different than callbacks that execute on each chunk)" + " LLMResult (different than callbacks that execute on each completion)" ), exclude=True, ) config: dict = Field(default_factory=dict) - async def acomplete(self, prompt: str) -> Chunk: + async def acompletion(self, messages: list[Message], **kwargs) -> list[LLMResult]: """Return the completion as string and the number of tokens in the prompt and completion.""" raise NotImplementedError - async def acomplete_iter(self, prompt: str) -> AsyncIterable[Chunk]: - """Return an async generator that yields chunks of the completion. + async def acompletion_iter( + self, messages: list[Message], **kwargs + ) -> AsyncIterable[LLMResult]: + """Return an async generator that yields completions. Only the last tuple will be non-zero. """ raise NotImplementedError - if False: # type: ignore[unreachable] # pylint: disable=using-constant-test - yield # Trick mypy: https://github.com/python/mypy/issues/5070#issuecomment-1050834495 - - async def achat(self, messages: list[Message]) -> Chunk: - """Return the completion as string and the number of tokens in the prompt and completion.""" - raise NotImplementedError - - async def achat_iter(self, messages: list[Message]) -> AsyncIterable[Chunk]: - """Return an async generator that yields chunks of the completion. - - Only the last tuple will be non-zero. - """ - raise NotImplementedError - if False: # type: ignore[unreachable] # pylint: disable=using-constant-test - yield # Trick mypy: https://github.com/python/mypy/issues/5070#issuecomment-1050834495 - - def infer_llm_type(self) -> str: - return "completion" def count_tokens(self, text: str) -> int: return len(text) // 4 # gross approximation - async def call( + def __str__(self) -> str: + return f"{type(self).__name__} {self.name}" + + # SEE: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice + # > `none` means the model will not call any tool and instead generates a message. + # > `auto` means the model can pick between generating a message or calling one or more tools. + # > `required` means the model must call one or more tools. + NO_TOOL_CHOICE: ClassVar[str] = "none" + MODEL_CHOOSES_TOOL: ClassVar[str] = "auto" + TOOL_CHOICE_REQUIRED: ClassVar[str] = "required" + # None means we won't provide a tool_choice to the LLM API + UNSPECIFIED_TOOL_CHOICE: ClassVar[None] = None + + async def call( # noqa: C901, PLR0915 self, messages: list[Message], callbacks: Iterable[Callable] | None = None, name: str | None = None, - system_prompt: str | None = None, - ) -> LLMResult: + output_type: type[BaseModel] | TypeAdapter | JSONSchema | None = None, + tools: list[Tool] | None = None, + tool_choice: Tool | str | None = TOOL_CHOICE_REQUIRED, + **chat_kwargs, + ) -> list[LLMResult]: """Call the LLM model with the given messages and configuration. - Args: - messages: A list of messages to send to the language model. - callbacks: A list of callback functions to execute - after receiving the response. - name: Optional name for the result. - system_prompt: System prompt to use, or None/empty string to not use one. - This should be passed in the messages if using a chat model - **chat_kwargs: Additional keyword arguments to pass to the chat function. - - Returns: - LLMResult object containing the result of the call. + messages: A list of messages to send to the language model. + callbacks: A list of callback functions to execute + name: Optional name for the result. + output_type: The type of the output. + tools: A list of tools to use. + tool_choice: The tool choice to use. + + Results: A list of LLMResult objects containing the result of the call. + + Raises: + ValueError: If the LLM type is unknown. """ - if self.llm_type is None: - self.llm_type = self.infer_llm_type() - - if self.llm_type == "chat": - return await self._run_chat( - messages=messages, - callbacks=callbacks, - name=name, - ) + n = chat_kwargs.get("n") or self.config.get("n", 1) + if n < 1: + raise ValueError("Number of completions (n) must be >= 1.") - if self.llm_type == "completion": - # Build a static prompt from the messages ignoring roles - prompt = "\n".join(m.content for m in messages if m.content) - return await self._run_completion( - prompt=prompt, - data={}, - callbacks=callbacks, - name=name, - system_prompt=system_prompt, + # deal with tools + if tools: + chat_kwargs["tools"] = ToolsAdapter.dump_python( + tools, exclude_none=True, by_alias=True ) + if tool_choice is not None: + chat_kwargs["tool_choice"] = ( + { + "type": "function", + "function": {"name": tool_choice.info.name}, + } + if isinstance(tool_choice, Tool) + else tool_choice + ) - raise ValueError(f"Unknown llm_type {self.llm_type!r}.") - - async def run_prompt( - self, - prompt: str, - data: dict, - callbacks: Iterable[Callable] | None = None, - name: str | None = None, - system_prompt: str | None = default_system_prompt, - ) -> LLMResult: - messages = None # using prompt, not messages - if self.llm_type is None: - self.llm_type = self.infer_llm_type() + # deal with specifying output type + if isinstance(output_type, Mapping): # Use structured outputs + model_name: str = chat_kwargs.get("model") or self.name + if not litellm.supports_response_schema(model_name, None): + raise ValueError(f"Model {model_name} does not support JSON schema.") - if self.llm_type == "chat": - human_message_prompt = {"role": "user", "content": prompt} + chat_kwargs["response_format"] = { + "type": "json_schema", + "json_schema": { + "strict": True, + # SEE: https://platform.openai.com/docs/guides/structured-outputs#additionalproperties-false-must-always-be-set-in-objects + "schema": dict(output_type) | {"additionalProperties": False}, + "name": output_type["title"], # Required by OpenAI as of 12/3/2024 + }, + } + elif output_type is not None: # Use JSON mode + if isinstance(output_type, TypeAdapter): + schema: str = json.dumps(output_type.json_schema()) + else: + schema = json.dumps(output_type.model_json_schema()) + schema_msg = f"Respond following this JSON schema:\n\n{schema}" + # Get the system prompt and its index, or the index to add it + i, system_prompt = next( + ((i, m) for i, m in enumerate(messages) if m.role == "system"), + (0, None), + ) messages = [ - Message(role=m["role"], content=m["content"].format(**data)) - for m in ( - [{"role": "system", "content": system_prompt}, human_message_prompt] + *messages[:i], + ( + system_prompt.append_text(schema_msg, inplace=False) if system_prompt - else [human_message_prompt] - ) + else Message(role="system", content=schema_msg) + ), + *messages[i + 1 if system_prompt else i :], ] - return await self._run_chat(messages, callbacks, name, system_prompt) - if self.llm_type == "completion": - return await self._run_completion( - prompt, data, callbacks, name, system_prompt - ) - raise ValueError(f"Unknown llm_type {self.llm_type!r}.") - - async def _run_chat( - self, - messages: list[Message], - callbacks: Iterable[Callable] | None = None, - name: str | None = None, - system_prompt: str | None = default_system_prompt, - ) -> LLMResult: - """Run a chat prompt. - - Args: - messages: List of messages to use. - callbacks: Optional functions to call with each chunk of the completion. - name: Optional name for the result. - system_prompt: System prompt to use, or None/empty string to not use one. + chat_kwargs["response_format"] = {"type": "json_object"} - Returns: - Result of the chat. - """ - result = LLMResult( - model=self.name, - name=name, - prompt=messages, - prompt_count=( - sum( - self.count_tokens(m.content) - for m in messages - if m.content is not None - ) - + sum(self.count_tokens(m.role) for m in messages) - ), - ) + messages = [ + ( + m + if not isinstance(m, ToolRequestMessage) or m.tool_calls + # OpenAI doesn't allow for empty tool_calls lists, so downcast empty + # ToolRequestMessage to Message here + else Message(role=m.role, content=m.content) + ) + for m in messages + ] + results: list[LLMResult] = [] start_clock = asyncio.get_running_loop().time() if callbacks is None: - chunk = await self.achat(messages) - output = chunk.text - result.reasoning_content = chunk.reasoning_content + results = await self.acompletion(messages, **chat_kwargs) else: + if tools: + raise NotImplementedError("Using tools with callbacks is not supported") + n = chat_kwargs.get("n") or self.config.get("n", 1) + if n > 1: + raise NotImplementedError( + "Multiple completions with callbacks is not supported" + ) sync_callbacks = [f for f in callbacks if not is_coroutine_callable(f)] async_callbacks = [f for f in callbacks if is_coroutine_callable(f)] - completion = await self.achat_iter(messages) # type: ignore[misc] + stream_results = await self.acompletion_iter(messages, **chat_kwargs) text_result = [] - async for chunk in completion: - if chunk.text: + async for result in stream_results: + if result.text: if result.seconds_to_first_token == 0: result.seconds_to_first_token = ( asyncio.get_running_loop().time() - start_clock ) - text_result.append(chunk.text) + text_result.append(result.text) await do_callbacks( - async_callbacks, sync_callbacks, chunk.text, name + async_callbacks, sync_callbacks, result.text, name ) - output = "".join(text_result) - usage = chunk.prompt_tokens, chunk.completion_tokens - if sum(usage) > 0: - result.prompt_count, result.completion_count = usage - elif output: - result.completion_count = self.count_tokens(output) - result.text = output or "" - result.seconds_to_last_token = asyncio.get_running_loop().time() - start_clock - if self.llm_result_callback: - if is_coroutine_callable(self.llm_result_callback): - await self.llm_result_callback(result) # type: ignore[misc] - else: - self.llm_result_callback(result) - return result + results.append(result) + + for result in results: + usage = result.prompt_count, result.completion_count + if not sum(usage): + result.completion_count = self.count_tokens(cast(str, result.text)) + result.seconds_to_last_token = ( + asyncio.get_running_loop().time() - start_clock + ) + + if self.llm_result_callback: + possibly_awaitable_result = self.llm_result_callback(result) + if isawaitable(possibly_awaitable_result): + await possibly_awaitable_result + return results - async def _run_completion( + async def call_single( self, - prompt: str, - data: dict, + messages: list[Message], callbacks: Iterable[Callable] | None = None, name: str | None = None, - system_prompt: str | None = default_system_prompt, + output_type: type[BaseModel] | TypeAdapter | JSONSchema | None = None, + tools: list[Tool] | None = None, + tool_choice: Tool | str | None = TOOL_CHOICE_REQUIRED, ) -> LLMResult: - """Run a completion prompt. - - Args: - prompt: Prompt to use. - data: Keys for the input variables that will be formatted into prompt. - callbacks: Optional functions to call with each chunk of the completion. - name: Optional name for the result. - system_prompt: System prompt to use, or None/empty string to not use one. - - Returns: - Result of the completion. - """ - formatted_prompt: str = ( - system_prompt + "\n\n" + prompt if system_prompt else prompt - ).format(**data) - result = LLMResult( - model=self.name, - name=name, - prompt=formatted_prompt, - prompt_count=self.count_tokens(formatted_prompt), + results = await self.call( + messages, callbacks, name, output_type, tools, tool_choice, n=1 ) + if not results: + raise ValueError("No results returned from call") + return results[0] - start_clock = asyncio.get_running_loop().time() - if callbacks is None: - chunk = await self.acomplete(formatted_prompt) - output = chunk.text - else: - sync_callbacks = [f for f in callbacks if not is_coroutine_callable(f)] - async_callbacks = [f for f in callbacks if is_coroutine_callable(f)] - completion = self.acomplete_iter(formatted_prompt) - text_result = [] - async for chunk in completion: - if chunk.text: - if result.seconds_to_first_token == 0: - result.seconds_to_first_token = ( - asyncio.get_running_loop().time() - start_clock - ) - text_result.append(chunk.text) - await do_callbacks( - async_callbacks, sync_callbacks, chunk.text, name - ) - output = "".join(text_result) - usage = chunk.prompt_tokens, chunk.completion_tokens - if sum(usage) > 0: - result.prompt_count, result.completion_count = usage - elif output: - result.completion_count = self.count_tokens(output) - result.text = output or "" - result.seconds_to_last_token = asyncio.get_running_loop().time() - start_clock - if self.llm_result_callback: - if is_coroutine_callable(self.llm_result_callback): - await self.llm_result_callback(result) # type: ignore[misc] - else: - self.llm_result_callback(result) - return result +P = ParamSpec("P") -LLMModelOrChild = TypeVar("LLMModelOrChild", bound=LLMModel) +@overload +def rate_limited( + func: Callable[P, Coroutine[Any, Any, list[LLMResult]]], +) -> Callable[P, Coroutine[Any, Any, list[LLMResult]]]: ... +@overload def rate_limited( - func: Callable[[LLMModelOrChild, Any], Awaitable[Chunk] | AsyncIterable[Chunk]], -) -> Callable[ - [LLMModelOrChild, Any, Any], - Awaitable[Chunk | AsyncIterator[Chunk] | AsyncIterator[LLMModelOrChild]], -]: + func: Callable[P, AsyncIterable[LLMResult]], +) -> Callable[P, Coroutine[Any, Any, AsyncIterable[LLMResult]]]: ... + + +def rate_limited(func): """Decorator to rate limit relevant methods of an LLMModel.""" @functools.wraps(func) - async def wrapper( - self: LLMModelOrChild, *args: Any, **kwargs: Any - ) -> Chunk | AsyncIterator[Chunk] | AsyncIterator[LLMModelOrChild]: + async def wrapper(self, *args, **kwargs): if not hasattr(self, "check_rate_limit"): raise NotImplementedError( f"Model {self.name} must have a `check_rate_limit` method." ) # Estimate token count based on input - if func.__name__ in {"acomplete", "acomplete_iter"}: - prompt = args[0] if args else kwargs.get("prompt", "") - token_count = ( - len(prompt) / CHARACTERS_PER_TOKEN_ASSUMPTION - + EXTRA_TOKENS_FROM_USER_ROLE - ) - elif func.__name__ in {"achat", "achat_iter"}: + if func.__name__ in {"acompletion", "acompletion_iter"}: messages = args[0] if args else kwargs.get("messages", []) token_count = len(str(messages)) / CHARACTERS_PER_TOKEN_ASSUMPTION else: @@ -469,10 +406,10 @@ async def wrapper( # portion before yielding if isasyncgenfunction(func): - async def rate_limited_generator() -> AsyncGenerator[LLMModelOrChild, None]: + async def rate_limited_generator() -> AsyncIterable[LLMResult]: async for item in func(self, *args, **kwargs): token_count = 0 - if isinstance(item, Chunk): + if isinstance(item, LLMResult): token_count = int( len(item.text or "") / CHARACTERS_PER_TOKEN_ASSUMPTION ) @@ -481,10 +418,10 @@ async def rate_limited_generator() -> AsyncGenerator[LLMModelOrChild, None]: return rate_limited_generator() - result = await func(self, *args, **kwargs) # type: ignore[misc] - - if func.__name__ in {"acomplete", "achat"} and isinstance(result, Chunk): - await self.check_rate_limit(result.completion_tokens) + # We checked isasyncgenfunction above, so this must be an Awaitable + result = await func(self, *args, **kwargs) + if func.__name__ == "acompletion" and isinstance(result, list): + await self.check_rate_limit(sum(r.completion_count for r in result)) return result return wrapper @@ -506,6 +443,9 @@ async def acompletion(self, *args, **kwargs): class LiteLLMModel(LLMModel): """A wrapper around the litellm library.""" + model_config = ConfigDict(extra="forbid") + + name: str = "gpt-4o-mini" config: dict = Field( default_factory=dict, description=( @@ -522,7 +462,6 @@ class LiteLLMModel(LLMModel): " limits.RateLimitItem string for parsing." ), ) - name: str = "gpt-4o-mini" _router: litellm.Router | None = None @model_validator(mode="before") @@ -536,7 +475,12 @@ def maybe_set_config_attribute(cls, data: dict[str, Any]) -> dict[str, Any]: "model_list": [ { "model_name": data["name"], - "litellm_params": {"model": data["name"]} + "litellm_params": { + "model": data["name"], + "n": data["config"].get("n", 1), + "temperature": data["config"].get("temperature", 0.1), + "max_tokens": data["config"].get("max_tokens", 4096), + } | ( {} if "gemini" not in data["name"] @@ -569,6 +513,16 @@ def maybe_set_config_attribute(cls, data: dict[str, Any]) -> dict[str, Any]: raise ValueError("Only one model name per model list is supported for now.") return data + # SEE: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice + # > `none` means the model will not call any tool and instead generates a message. + # > `auto` means the model can pick between generating a message or calling one or more tools. + # > `required` means the model must call one or more tools. + NO_TOOL_CHOICE: ClassVar[str] = "none" + MODEL_CHOOSES_TOOL: ClassVar[str] = "auto" + TOOL_CHOICE_REQUIRED: ClassVar[str] = "required" + # None means we won't provide a tool_choice to the LLM API + UNSPECIFIED_TOOL_CHOICE: ClassVar[None] = None + def __getstate__(self): # Prevent _router from being pickled, SEE: https://stackoverflow.com/a/2345953 state = super().__getstate__() @@ -598,89 +552,105 @@ async def check_rate_limit(self, token_count: float, **kwargs) -> None: ) @rate_limited - async def acomplete(self, prompt: str) -> Chunk: # type: ignore[override] - response = await track_costs(self.router.atext_completion)( - model=self.name, prompt=prompt + async def acompletion(self, messages: list[Message], **kwargs) -> list[LLMResult]: + # cast is necessary for LiteLLM typing bug: https://github.com/BerriAI/litellm/issues/7641 + prompts = cast( + list[litellm.types.llms.openai.AllMessageValues], + [m.model_dump(by_alias=True) for m in messages if m.content], ) - return Chunk( - text=response.choices[0].text, - prompt_tokens=response.usage.prompt_tokens, - completion_tokens=response.usage.completion_tokens, + completions = await track_costs(self.router.acompletion)( + self.name, prompts, **kwargs ) + results: list[LLMResult] = [] - @rate_limited - async def acomplete_iter( # type: ignore[override] - self, prompt: str - ) -> AsyncIterable[Chunk]: - completion = await track_costs_iter(self.router.atext_completion)( - model=self.name, - prompt=prompt, - stream=True, - stream_options={"include_usage": True}, - ) - async for chunk in completion: - yield Chunk( - text=chunk.choices[0].text, prompt_tokens=0, completion_tokens=0 - ) - if hasattr(chunk, "usage") and hasattr(chunk.usage, "prompt_tokens"): - yield Chunk( - text=chunk.choices[0].text, prompt_tokens=0, completion_tokens=0 - ) + # We are not streaming here, so we can cast to list[litellm.utils.Choices] + choices = cast(list[litellm.utils.Choices], completions.choices) + for completion in choices: + if completion.finish_reason == "tool_calls" or getattr( + completion.message, "tool_calls", None + ): + serialized_message = completion.message.model_dump() + serialized_message["tool_calls"] = ( + serialized_message.get("tool_calls") or [] + ) + output_messages: list[Message | ToolRequestMessage] = [ + ToolRequestMessage(**serialized_message) + ] + else: + output_messages = [Message(**completion.message.model_dump())] - @rate_limited - async def achat(self, messages: list[Message]) -> Chunk: # type: ignore[override] - prompts = [m.model_dump(by_alias=True) for m in messages if m.content] - # type ignore of arg-type is due to https://github.com/BerriAI/litellm/issues/7641 - response = await track_costs(self.router.acompletion)(self.name, prompts) # type: ignore[arg-type] - choice = response.choices[0] - reasoning_content = None - if ( - isinstance(choice, litellm.Choices) - and hasattr(choice.message, "provider_specific_fields") - and isinstance(choice.message.provider_specific_fields, dict) - ): - reasoning_content = choice.message.provider_specific_fields.get( - "reasoning_content", None - ) + reasoning_content = None + if hasattr(completion.message, "provider_specific_fields"): + provider_specific_fields = completion.message.provider_specific_fields + if isinstance(provider_specific_fields, dict): + reasoning_content = provider_specific_fields.get( + "reasoning_content", None + ) - return Chunk( - text=cast(litellm.Choices, choice).message.content, - prompt_tokens=response.usage.prompt_tokens, # type: ignore[attr-defined] - completion_tokens=response.usage.completion_tokens, # type: ignore[attr-defined] - reasoning_content=reasoning_content, - ) + results.append( + LLMResult( + model=self.name, + text=completion.message.content, + prompt=messages, + messages=output_messages, + logprob=sum_logprobs(completion), + prompt_count=completions.usage.prompt_tokens, # type: ignore[attr-defined] + completion_count=completions.usage.completion_tokens, # type: ignore[attr-defined] + system_fingerprint=completions.system_fingerprint, + reasoning_content=reasoning_content, + ) + ) + return results @rate_limited - async def achat_iter( # type: ignore[override] - self, messages: list[Message] - ) -> AsyncIterable[Chunk]: - prompts = [m.model_dump(by_alias=True) for m in messages if m.content] - completion = await track_costs_iter(self.router.acompletion)( # type: ignore[call-overload] + async def acompletion_iter( + self, messages: list[Message], **kwargs + ) -> AsyncIterable[LLMResult]: + # cast is necessary for LiteLLM typing bug: https://github.com/BerriAI/litellm/issues/7641 + prompts = cast( + list[litellm.types.llms.openai.AllMessageValues], + [m.model_dump(by_alias=True) for m in messages if m.content], + ) + stream_completions = await track_costs_iter(self.router.acompletion)( self.name, prompts, stream=True, stream_options={"include_usage": True}, + **kwargs, ) - async for chunk in completion: - yield Chunk( - text=chunk.choices[0].delta.content, - prompt_tokens=0, - completion_tokens=0, - ) - if hasattr(chunk, "usage") and hasattr(chunk.usage, "prompt_tokens"): - yield Chunk( - text=None, - prompt_tokens=chunk.usage.prompt_tokens, - completion_tokens=chunk.usage.completion_tokens, + start_clock = asyncio.get_running_loop().time() + outputs = [] + logprobs = [] + role = None + async for completion in stream_completions: + choice = completion.choices[0] + delta = choice.delta + if hasattr(choice.logprobs, "content"): + logprobs.append(choice.logprobs.content[0].logprob or 0) + outputs.append(delta.content or "") + role = delta.role or role + # NOTE: litellm is not populating provider_specific_fields in streaming mode. + # TODO: Get reasoning_content when this issue is fixed + # https://github.com/BerriAI/litellm/issues/7942 + + text = "".join(outputs) + result = LLMResult( + model=self.name, + text=text, + prompt=messages, + messages=[Message(role=role, content=text)], + logprob=sum_logprobs(logprobs), + ) + + if text: + result.seconds_to_first_token = ( + asyncio.get_running_loop().time() - start_clock ) + if hasattr(completion, "usage"): + result.prompt_count = completion.usage.prompt_tokens + result.completion_count = completion.usage.completion_tokens - def infer_llm_type(self) -> str: - if all( - "text-completion" in m.get("litellm_params", {}).get("model", "") - for m in self.config["model_list"] - ): - return "completion" - return "chat" + yield result def count_tokens(self, text: str) -> int: return litellm.token_counter(model=self.name, text=text) @@ -693,302 +663,3 @@ async def select_tool( model_name=self.name, acompletion=track_costs(self.router.acompletion) ) return await tool_selector(*selection_args, **selection_kwargs) - - -class MultipleCompletionLLMModel(BaseModel): - """Run n completions at once, all starting from the same messages.""" - - model_config = ConfigDict(extra="forbid") - - # this should keep the original model - # if fine-tuned, this should still refer to the base model - name: str = "unknown" - config: dict = Field( - default_factory=lambda: { - "model": "gpt-4o-mini", # TODO: create a field validator - "temperature": 0.1, - }, - description=( - "Configuration of the model:" - "model is the name of the llm model to use," - "temperature is the sampling temperature, and" - "n is the number of completions to generate by default." - ), - ) - encoding: Any | None = None - - def __str__(self) -> str: - return f"{type(self).__name__} {self.name}" - - @model_validator(mode="after") - def set_model_name(self) -> Self: - if (self.config.get("model") is None and self.name != "unknown") or ( - self.name != "unknown" and "model" not in self.config - ): - self.config["model"] = self.name - elif "model" in self.config and self.name == "unknown": - self.name = self.config["model"] - # note we do not consider case where both are set - # because that could be true if the model is fine-tuned - return self - - async def achat( - self, messages: Iterable[Message], **kwargs - ) -> litellm.ModelResponse: - return await track_costs(litellm.acompletion)( - messages=[m.model_dump(by_alias=True) for m in messages], - **(self.config | kwargs), - ) - - async def achat_iter( - self, messages: Iterable[Message], **kwargs - ) -> TrackedStreamWrapper: - return await track_costs_iter(litellm.acompletion)( - messages=[m.model_dump(by_alias=True) for m in messages], - stream=True, - stream_options={ - "include_usage": True, # Included to get prompt token counts - }, - **(self.config | kwargs), - ) - - # SEE: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice - # > `none` means the model will not call any tool and instead generates a message. - # > `auto` means the model can pick between generating a message or calling one or more tools. - # > `required` means the model must call one or more tools. - NO_TOOL_CHOICE: ClassVar[str] = "none" - MODEL_CHOOSES_TOOL: ClassVar[str] = "auto" - TOOL_CHOICE_REQUIRED: ClassVar[str] = "required" - # None means we won't provide a tool_choice to the LLM API - UNSPECIFIED_TOOL_CHOICE: ClassVar[None] = None - - async def call( # noqa: C901, PLR0915 - self, - messages: list[Message], - callbacks: list[Callable] | None = None, - output_type: type[BaseModel] | TypeAdapter | JSONSchema | None = None, - tools: list[Tool] | None = None, - tool_choice: Tool | str | None = TOOL_CHOICE_REQUIRED, - **chat_kwargs, - ) -> list[LLMResult]: - """ - Call the LLM model with the given messages and configuration. - - Args: - messages: A list of messages to send to the language model. - callbacks: A list of callback functions to execute after receiving the response. - output_type: The type of the output model. - tools: A list of tools to use during the call. - tool_choice: The tool or tool identifier to use. - **chat_kwargs: Additional keyword arguments to pass to the chat function. - - Returns: - A list of LLMResult objects containing the results of the call. - - Raises: - ValueError: If the number of completions (n) is invalid. - """ - # add static configuration to kQwargs - chat_kwargs = self.config | chat_kwargs - - start_clock = asyncio.get_running_loop().time() - - # Deal with tools. Note OpenAI throws a 400 response if tools is empty: - # > Invalid 'tools': empty array. Expected an array with minimum length 1, - # > but got an empty array instead. - # So, circumvent this behavior if tools in (None, []) - if tools: - chat_kwargs["tools"] = ToolsAdapter.dump_python( - tools, exclude_none=True, by_alias=True - ) - if tool_choice is not None: - chat_kwargs["tool_choice"] = ( - { - "type": "function", - "function": {"name": tool_choice.info.name}, - } - if isinstance(tool_choice, Tool) - else tool_choice - ) - - # deal with specifying output type - if isinstance(output_type, Mapping): # Use structured outputs - model_name: str = chat_kwargs.get("model", "") - if not litellm.supports_response_schema(model_name, None): - raise ValueError(f"Model {model_name} does not support JSON schema.") - - chat_kwargs["response_format"] = { - "type": "json_schema", - "json_schema": { - "strict": True, - # SEE: https://platform.openai.com/docs/guides/structured-outputs#additionalproperties-false-must-always-be-set-in-objects - "schema": dict(output_type) | {"additionalProperties": False}, - "name": output_type["title"], # Required by OpenAI as of 12/3/2024 - }, - } - elif output_type is not None: # Use JSON mode - if isinstance(output_type, TypeAdapter): - schema: str = json.dumps(output_type.json_schema()) - else: - schema = json.dumps(output_type.model_json_schema()) - schema_msg = f"Respond following this JSON schema:\n\n{schema}" - # Get the system prompt and its index, or the index to add it - i, system_prompt = next( - ((i, m) for i, m in enumerate(messages) if m.role == "system"), - (0, None), - ) - messages = [ - *messages[:i], - ( - system_prompt.append_text(schema_msg, inplace=False) - if system_prompt - else Message(role="system", content=schema_msg) - ), - *messages[i + 1 if system_prompt else i :], - ] - chat_kwargs["response_format"] = {"type": "json_object"} - - n = chat_kwargs.get("n", 1) # number of completions - if n < 1: - raise ValueError("Number of completions (n) must be >= 1.") - - prompt = [ - ( - m - if not isinstance(m, ToolRequestMessage) or m.tool_calls - # OpenAI doesn't allow for empty tool_calls lists, so downcast empty - # ToolRequestMessage to Message here - else Message(role=m.role, content=m.content) - ) - for m in messages - ] - results: list[LLMResult] = [] - - if callbacks is None: - completion = await self.achat(prompt, **chat_kwargs) - if output_type is not None: - validate_json_completion(completion, output_type) - - for choice in completion.choices: - if isinstance(choice, litellm.utils.StreamingChoices): - raise NotImplementedError("Streaming is not yet supported.") - - if ( - tools is not None # Allows for empty tools list - or choice.finish_reason == "tool_calls" - or (getattr(choice.message, "tool_calls", None) is not None) - ): - serialized_choice_message = choice.message.model_dump() - serialized_choice_message["tool_calls"] = ( - serialized_choice_message.get("tool_calls") or [] - ) - output_messages: list[Message | ToolRequestMessage] = [ - ToolRequestMessage(**serialized_choice_message) - ] - else: - output_messages = [Message(**choice.message.model_dump())] - - reasoning_content = None - if hasattr(choice.message, "provider_specific_fields") and isinstance( - choice.message.provider_specific_fields, dict - ): - reasoning_content = choice.message.provider_specific_fields.get( - "reasoning_content", None - ) - - results.append( - LLMResult( - model=self.name, - config=chat_kwargs, - prompt=prompt, - messages=output_messages, - logprob=sum_logprobs(choice), - system_fingerprint=completion.system_fingerprint, - # Note that these counts are aggregated over all choices - completion_count=completion.usage.completion_tokens, # type: ignore[attr-defined,unused-ignore] - prompt_count=completion.usage.prompt_tokens, # type: ignore[attr-defined,unused-ignore] - reasoning_content=reasoning_content, - ) - ) - else: - if tools: - raise NotImplementedError("Using tools with callbacks is not supported") - if n > 1: - raise NotImplementedError( - "Multiple completions with callbacks is not supported" - ) - result = LLMResult(model=self.name, config=chat_kwargs, prompt=prompt) - - sync_callbacks = [f for f in callbacks if not is_coroutine_callable(f)] - async_callbacks = [f for f in callbacks if is_coroutine_callable(f)] - stream_completion = await self.achat_iter(messages, **chat_kwargs) - text_result = [] - role = "assistant" - - async for chunk in stream_completion: - delta = chunk.choices[0].delta - role = delta.role or role - if delta.content: - s = delta.content - if result.seconds_to_first_token == 0: - result.seconds_to_first_token = ( - asyncio.get_running_loop().time() - start_clock - ) - text_result.append(s) - [await f(s) for f in async_callbacks] - [f(s) for f in sync_callbacks] - if hasattr(chunk, "usage"): - result.prompt_count = chunk.usage.prompt_tokens - - output = "".join(text_result) - result.completion_count = litellm.token_counter( - model=self.name, - text=output, - ) - # TODO: figure out how tools stream, and log probs - result.messages = [Message(role=role, content=output)] - results.append(result) - - if not results: - # This happens in unit tests. We should probably not keep this block around - # long-term. Previously, we would emit an empty ToolRequestMessage if - # completion.choices were empty, so I am replicating that here. - results.append( - LLMResult( - model=self.name, - config=chat_kwargs, - prompt=prompt, - messages=[ToolRequestMessage(tool_calls=[])], - ) - ) - - end_clock = asyncio.get_running_loop().time() - - for result in results: - # Manually update prompt count if not set, which can - # happen if the target model doesn't support 'include_usage' - if not result.prompt_count and result.messages: - result.prompt_count = litellm.token_counter( - model=self.name, - messages=[m.model_dump() for m in result.messages], - ) - - # update with server-side counts - result.seconds_to_last_token = end_clock - start_clock - - return results - - async def call_single( - self, - messages: list[Message], - callbacks: list[Callable] | None = None, - output_type: type[BaseModel] | TypeAdapter | None = None, - tools: list[Tool] | None = None, - tool_choice: Tool | str | None = TOOL_CHOICE_REQUIRED, - **chat_kwargs, - ) -> LLMResult: - return ( - await self.call( - messages, callbacks, output_type, tools, tool_choice, n=1, **chat_kwargs - ) - )[0] diff --git a/llmclient/prompts.py b/llmclient/prompts.py deleted file mode 100644 index 43e5c55..0000000 --- a/llmclient/prompts.py +++ /dev/null @@ -1,5 +0,0 @@ -default_system_prompt = ( - "Answer in a direct and concise tone. " - "Your audience is an expert, so be highly specific. " - "If there are ambiguous terms or acronyms, first define them." -) diff --git a/llmclient/types.py b/llmclient/types.py index 0f8363b..f32d692 100644 --- a/llmclient/types.py +++ b/llmclient/types.py @@ -32,20 +32,6 @@ class Embeddable(BaseModel): embedding: list[float] | None = Field(default=None, repr=False) -class Chunk(BaseModel): - model_config = ConfigDict(extra="forbid", frozen=True) - - text: str | None - prompt_tokens: int - completion_tokens: int - reasoning_content: str | None = Field( - default=None, description="Reasoning content from LLMs such as DeepSeek-R1." - ) - - def __str__(self): - return self.text - - class LLMResult(BaseModel): """A class to hold the result of a LLM completion. @@ -76,7 +62,7 @@ class LLMResult(BaseModel): default=None, description="Optional prompt or list of serialized prompts.", ) - text: str = "" + text: str | None = None messages: list[Message] | None = Field( default=None, description="Messages received from the LLM." ) @@ -98,7 +84,7 @@ class LLMResult(BaseModel): ) def __str__(self) -> str: - return self.text + return self.text or "" @computed_field # type: ignore[prop-decorator] @property diff --git a/llmclient/utils.py b/llmclient/utils.py index 22e52d5..d2bdc68 100644 --- a/llmclient/utils.py +++ b/llmclient/utils.py @@ -45,12 +45,12 @@ def get_litellm_retrying_config(timeout: float = 60.0) -> dict[str, Any]: def prepare_args( - func: Callable, chunk: str, name: str | None = None + func: Callable, completion: str, name: str | None = None ) -> tuple[tuple, dict]: with contextlib.suppress(TypeError): if "name" in signature(func).parameters: - return (chunk,), {"name": name} - return (chunk,), {} + return (completion,), {"name": name} + return (completion,), {} def partial_format(value: str, **formats: dict[str, Any]) -> str: diff --git a/pyproject.toml b/pyproject.toml index 4284b81..d968673 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,11 +26,10 @@ dependencies = [ "coredis", "fhaviary>=0.14.0", # For multi-image support "limits", + "litellm>=1.59.3", "pydantic~=2.0,>=2.10.1,<2.10.2", "tiktoken>=0.4.0", "typing-extensions; python_version <= '3.11'", # for typing.override - 'litellm; python_version < "3.13"', # NOTE: paper-qa==5.3 doesn't support 3.13 yet - 'litellm>=1.49.1; python_version >= "3.13"', # For removal of imghdr ] description = "A client to provide LLM responses for FutureHouse applications." dynamic = ["version"] @@ -60,6 +59,7 @@ dev = [ "pytest>=8", # Pin to keep recent "python-dotenv", "refurb>=2", # Pin to keep recent + "typeguard", ] local = [ "numpy", @@ -238,7 +238,7 @@ min-similarity-lines = 12 [tool.pytest.ini_options] # Add the specified OPTS to the set of command line arguments as if they had been # specified by the user. -addopts = "--doctest-modules" +addopts = "--doctest-modules --typeguard-packages=llmclient" # Sets a list of filters and actions that should be taken for matched warnings. # By default all warnings emitted during the test session will be displayed in # a summary at the end of the test session. diff --git a/tests/cassettes/TestLiteLLMModel.test_call[Anthropic-model].yaml b/tests/cassettes/TestLiteLLMModel.test_call[Anthropic-model].yaml new file mode 100644 index 0000000..eba40bb --- /dev/null +++ b/tests/cassettes/TestLiteLLMModel.test_call[Anthropic-model].yaml @@ -0,0 +1,168 @@ +interactions: + - request: + body: + '{"model": "claude-3-5-haiku-20241022", "messages": [{"role": "user", "content": + [{"type": "text", "text": "What is the meaning of the universe?"}]}], "temperature": + 0, "max_tokens": 56, "system": [{"type": "text", "text": "Respond with single + words."}]}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + anthropic-version: + - "2023-06-01" + connection: + - keep-alive + content-length: + - "253" + content-type: + - application/json + host: + - api.anthropic.com + user-agent: + - litellm/1.57.1 + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: !!binary | + H4sIAAAAAAAAA2SOQUvEMBSE/8ucU2njLi65KYhnDyuiSAjpoxvavnSTF7SU/nfp4oLiaWC+mWEW + hBYGY+5s3Tyfu7f7x1E/vczTYZ7618R3hwcoyDzRlqKcXUdQSHHYDJdzyOJYoDDGlgYY+MGVlqrb + al+dXOhLpWu9a2qtoeAjC7HAvC/XTaGvrX0RgyP3HD/5BuuHQpY42UQuR4YBcWulJMYPyHQuxJ5g + uAyDQrlcMwsCT0WsxJ44w+hGwTt/IusTOQmR7d9AfeWJXPufxSK/nf26fgMAAP//AwBsFDw7MwEA + AA== + headers: + CF-Cache-Status: + - DYNAMIC + CF-RAY: + - 906127f99b5dcf16-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Wed, 22 Jan 2025 17:10:13 GMT + Server: + - cloudflare + Transfer-Encoding: + - chunked + X-Robots-Tag: + - none + anthropic-ratelimit-input-tokens-limit: + - "5000000" + anthropic-ratelimit-input-tokens-remaining: + - "5000000" + anthropic-ratelimit-input-tokens-reset: + - "2025-01-22T17:10:13Z" + anthropic-ratelimit-output-tokens-limit: + - "1000000" + anthropic-ratelimit-output-tokens-remaining: + - "1000000" + anthropic-ratelimit-output-tokens-reset: + - "2025-01-22T17:10:13Z" + anthropic-ratelimit-requests-limit: + - "5000" + anthropic-ratelimit-requests-remaining: + - "4999" + anthropic-ratelimit-requests-reset: + - "2025-01-22T17:10:12Z" + anthropic-ratelimit-tokens-limit: + - "6000000" + anthropic-ratelimit-tokens-remaining: + - "6000000" + anthropic-ratelimit-tokens-reset: + - "2025-01-22T17:10:13Z" + request-id: + - req_01413iQoXX2MNuM768NzgapY + via: + - 1.1 google + status: + code: 200 + message: OK + - request: + body: + '{"model": "claude-3-5-haiku-20241022", "messages": [{"role": "user", "content": + [{"type": "text", "text": "What is the meaning of the universe?"}]}], "temperature": + 0, "max_tokens": 56, "system": [{"type": "text", "text": "Respond with single + words."}]}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + anthropic-version: + - "2023-06-01" + connection: + - keep-alive + content-length: + - "253" + content-type: + - application/json + host: + - api.anthropic.com + user-agent: + - litellm/1.57.1 + method: POST + uri: https://api.anthropic.com/v1/messages + response: + body: + string: !!binary | + H4sIAAAAAAAAA2SOT0vEMBTEv8ucU2nj1oUcddf7HhREJMTkuVvafekmL/6h9LtLFxcUTwPzmxlm + QhdgcMx7Wzeb+91run17vFvfrENL26fN6V33UJCvkZYU5ez2BIUUh8VwOXdZHAsUjjHQAAM/uBKo + uq7a6uC6vlS61qum1hoKPrIQC8zzdNkU+lzaZzF44J7jB19hflHIEkebyOXIMCAOVkpi/IBMp0Ls + CYbLMCiU8zUzoeOxiJXYE2cY3Sh45w9kfSInXWT7N1BfeCIX/rNY5LfTzvM3AAAA//8DAPhEJhoz + AQAA + headers: + CF-Cache-Status: + - 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Respond only with the color - name."}]}], "model": "gpt-4o-mini-2024-07-18", "n": 2}' + name."}]}], "model": "gpt-4o-mini-2024-07-18", "max_tokens": 4096, "n": 2, "temperature": + 0.1}' headers: accept: - application/json @@ -13,13 +14,13 @@ interactions: connection: - keep-alive content-length: - - "381" + - "429" content-type: - application/json host: - api.openai.com user-agent: - - AsyncOpenAI/Python 1.46.1 + - AsyncOpenAI/Python 1.59.7 x-stainless-arch: - arm64 x-stainless-async: @@ -29,9 +30,11 @@ interactions: x-stainless-os: - MacOS x-stainless-package-version: - - 1.46.1 + - 1.59.7 x-stainless-raw-response: - "true" + x-stainless-retry-count: + - "0" x-stainless-runtime: - CPython x-stainless-runtime-version: @@ -41,18 +44,19 @@ interactions: response: body: string: !!binary | - H4sIAAAAAAAAA9STP2/CMBDF93wKyzOpQqBA2bqgVuqEVNqqqiJjH8Hg+Cz7kPpHfPfKIZAgWqlr - lwz3u/fyfGd/JYxxrfiUcbkWJCtn0tvnz8XLbL6dfcJGDhZLhLunhw096vsqk7wXFbjcgKSj6kpi - 5QyQRnvA0oMgiK798SAfjYajfFiDChWYKCsdpUNMK211mmf5MM3GaX/SqNeoJQQ+Za8JY4x91d+Y - 0yp451OW9Y6VCkIQJfDpqYkx7tHEChch6EDCEu+1UKIlsHX0Oagu8bDaBRHT2Z0xTX1/+pXB0nlc - hoaf6ittdVgXHkRAG20DoeNJR3yRv/9f8ieMvdUr2Z2l5M5j5agg3IKNhpPrvNkJb69Ci/OGEZIw - Z6ojOTMsFJDQJnSmwqWQa1Cttr0CYqc0dkB38pdpfvI+HF3b8i/2LZASHIEqnAel5fmJ2zYP8aX8 - 1nYacx2Yh49AUBUrbUvwzuvDnleuGCjo55NJNrrhyT75BgAA//8DAHMIZsi1AwAA + H4sIAAAAAAAAA9STMW/bMBCFd/0K4markC3DSrw1Hdsh6NAAKQKBJk/yNRTJkqcgRpD/XlCWLRlJ + gaxZNNx37+nxjnzJhADSsBWg9pJV503+9a95ejbfbu7Lu8fvh5sft8WvW0OHVXGI93ewSAq3+4OK + T6ovynXeIJOzR6wCSsbkuqzKarkuNlU1gM5pNEnWes7XLu/IUr4qVuu8qPLl1ajeO1IYYSt+Z0II + 8TJ8U06r8Rm2olicKh3GKFuE7blJCAjOpArIGCmytAyLCSpnGe0Q/SfqOQnY9FGmdLY3Zqy/nn9l + XOuD28WRn+sNWYr7OqCMzibbyM5DNhO/yb/8LPkzIR6GlfQXKcEH13mu2T2iTYbrzbgSmG7CjI6M + HUszF53AhV2tkSWZOJsJKKn2qCfpdAFkr8nNwHzub8O85308ONn2I/YTUAo9o659QE3q8sBTW8D0 + Tv7Xdh7yEBgihidSWDNhSIvQ2MjeHLcP8RAZu7oh22LwgY5XoPH1Tl+V5eq62ZSQvWb/AAAA//8D + AGIB7tjQAwAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - - 8e8dbe02c857176b-SJC + - 90389cad9eca15f1-SJC Connection: - keep-alive Content-Encoding: @@ -60,14 +64,14 @@ interactions: Content-Type: - application/json Date: - - Tue, 26 Nov 2024 23:43:44 GMT + - Fri, 17 Jan 2025 19:04:37 GMT Server: - cloudflare Set-Cookie: - - __cf_bm=FLcfXsLHocORp9BaX8WRXlKmbbwQLQT1aaZHgSHfOwM-1732664624-1.0.1.1-EMMzivzKoNvNES87qYj.8X3tabrd2Y0z7mUhxOXUBl_ApeNQMRYqOeomdi8VD9YY1AAfg9NvNYhuESasFB2r1Q; - path=/; expires=Wed, 27-Nov-24 00:13:44 GMT; domain=.api.openai.com; HttpOnly; + - __cf_bm=ZfT56ILiGQaiUV1fZUQtcyD_VjDrf6KFpQZ0JIY73Ug-1737140677-1.0.1.1-IB0Z9RIHOT0nS9n6q_7OuX_IQ5BiU_Dsz2gVZYbc7iXAfDEf1Wuf3NL9jZY25yG0gr8tY2fozes9O6_EFKCBLA; + path=/; expires=Fri, 17-Jan-25 19:34:37 GMT; domain=.api.openai.com; HttpOnly; Secure; SameSite=None - - _cfuvid=xB_0vUwejpcRnbBxzZuL293zZT452oWlLskSKDKBGqE-1732664624374-0.0.1.1-604800000; + - _cfuvid=jCfiShabyOv98ckIt5vSjhex84Ms1rlyVq.ShghRr7U-1737140677579-0.0.1.1-604800000; path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None Transfer-Encoding: - chunked @@ -80,7 +84,7 @@ interactions: openai-organization: - future-house-xr4tdh openai-processing-ms: - - "1744" + - "729" openai-version: - "2020-10-01" strict-transport-security: @@ -96,15 +100,15 @@ interactions: x-ratelimit-remaining-requests: - "29999" x-ratelimit-remaining-tokens: - - "149999187" + - "149991027" x-ratelimit-reset-input-images: - 1ms x-ratelimit-reset-requests: - 2ms x-ratelimit-reset-tokens: - - 0s + - 3ms x-request-id: - - req_d887de09a40a121d235f796b4dc6a0c6 + - req_b1bec1891df837a1b9ee6915584a3bef status: code: 200 message: OK diff --git a/tests/cassettes/TestMultipleCompletionLLMModel.test_output_schema[json-mode-type-adapter].yaml b/tests/cassettes/TestMultipleCompletionLLMModel.test_output_schema[json-mode-type-adapter].yaml deleted file mode 100644 index ed39207..0000000 --- a/tests/cassettes/TestMultipleCompletionLLMModel.test_output_schema[json-mode-type-adapter].yaml +++ /dev/null @@ -1,103 +0,0 @@ -interactions: - - request: - body: - '{"messages":[{"role":"system","content":"Respond following this JSON schema:\n\n{\"properties\": - {\"name\": {\"title\": \"Name\", \"type\": \"string\"}, \"age\": {\"description\": - \"Age in years.\", \"title\": \"Age\", \"type\": \"integer\"}}, \"required\": - [\"name\", \"age\"], \"title\": \"DummyOutputSchema\", \"type\": \"object\"}"},{"role":"user","content":"My - name is Claude and I am 1 year old. 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words."}],"max_tokens":4096,"model":"claude-3-haiku-20240307"}' - headers: - accept: - - application/json - accept-encoding: - - gzip, deflate - anthropic-version: - - "2023-06-01" - connection: - - keep-alive - content-length: - - "202" - content-type: - - application/json - host: - - api.anthropic.com - user-agent: - - litellm/1.53.3 - method: POST - uri: https://api.anthropic.com/v1/messages - response: - body: - string: !!binary | - H4sIAAAAAAAAA0yOzWqEQBCE36XO4+LfYpxb8gA5BdwlBBm0MbJjj7F7wCC+e1ASyKngqx9qw9jD - YpKhTbPXItPhpcqeFwlV2az3p9tb3cBAv2c6UiTiBoLBEvwBnMgo6lhhMIWePCw672JPSZF8uvER - kzzNy7RIKxh0gZVYYd+3v0Wl9eieYtGQ9xfsHwaiYW4XchIYFsR9q3Fh/BpCX5G4I1iO3hvE85Xd - MPIctdXwIBbYrDYIUf+j677/AAAA//8DAHETe7LyAAAA - headers: - CF-Cache-Status: - - DYNAMIC - CF-RAY: - - 8ed700088ad5942c-SJC - Connection: - - keep-alive - Content-Encoding: - - gzip - Content-Type: - - application/json - Date: - - Thu, 05 Dec 2024 21:06:27 GMT - Server: - - cloudflare - Transfer-Encoding: - - chunked - 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true, "stream_options": + {"include_usage": true}}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - "255" + content-type: + - application/json + host: + - api.deepseek.com + user-agent: + - AsyncOpenAI/Python 1.60.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - async:asyncio + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.60.0 + x-stainless-raw-response: + - "true" + x-stainless-retry-count: + - "0" + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.11.10 + method: POST + uri: https://api.deepseek.com/v1/chat/completions + response: + body: + string: + "data: 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__cf_bm=sLhWYYe80.jTobz7CGSRkC_AeFzKo3QwS_KDQbk2dVg-1737663813-1.0.1.1-R.C5N6qI1Y1JLiHLPYe_HEVNMnF0p.yoRc.wj1acpKtjQHfuVDvKDcWede2REFTxDUwhgxHLEVXDVVGU7XZnZQ; + path=/; expires=Thu, 23-Jan-25 20:53:33 GMT; domain=.deepseek.com; HttpOnly; + Secure; SameSite=None + Transfer-Encoding: + - chunked + access-control-allow-credentials: + - "true" + strict-transport-security: + - max-age=31536000; includeSubDomains; preload + vary: + - origin, access-control-request-method, access-control-request-headers + x-content-type-options: + - nosniff + x-ds-trace-id: + - 694153eaf3eff584bf685d19e9546362 + status: + code: 200 + message: OK version: 1 diff --git a/tests/test_cost_tracking.py b/tests/test_cost_tracking.py index c396db2..94ba130 100644 --- a/tests/test_cost_tracking.py +++ b/tests/test_cost_tracking.py @@ -8,8 +8,7 @@ from llmclient import cost_tracking_ctx from llmclient.cost_tracker import GLOBAL_COST_TRACKER from llmclient.embeddings import LiteLLMEmbeddingModel -from llmclient.llms import CommonLLMNames, LiteLLMModel, MultipleCompletionLLMModel -from llmclient.types import LLMResult +from llmclient.llms import CommonLLMNames, LiteLLMModel from .conftest import VCR_DEFAULT_MATCH_ON @@ -38,35 +37,35 @@ class TestLiteLLMModel: [ pytest.param( { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, "max_tokens": 56, }, } ], }, - id="chat-model", + id="OpenAI-model", ), pytest.param( { - "model_name": "gpt-3.5-turbo-instruct", + "model_name": CommonLLMNames.ANTHROPIC_TEST.value, "model_list": [ { - "model_name": "gpt-3.5-turbo-instruct", + "model_name": CommonLLMNames.ANTHROPIC_TEST.value, "litellm_params": { - "model": "gpt-3.5-turbo-instruct", + "model": CommonLLMNames.ANTHROPIC_TEST.value, "temperature": 0, "max_tokens": 56, }, } ], }, - id="completion-model", + id="Anthropic-model", ), ], ) @@ -87,7 +86,7 @@ async def ac(x) -> None: with cost_tracking_ctx(): with assert_costs_increased(): - llm = LiteLLMModel(name="gpt-4o") + llm = LiteLLMModel(name=CommonLLMNames.GPT_4O.value) image = np.zeros((32, 32, 3), dtype=np.uint8) image[:] = [255, 0, 0] messages = [ @@ -117,9 +116,9 @@ async def ac(x) -> None: { "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, "max_tokens": 56, }, @@ -138,72 +137,24 @@ async def ac(x) -> None: ], ) @pytest.mark.asyncio - async def test_run_prompt(self, config: dict[str, Any]) -> None: + async def test_call_single(self, config: dict[str, Any]) -> None: with cost_tracking_ctx(), assert_costs_increased(): - llm = LiteLLMModel(name="gpt-4o-mini", config=config) + llm = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value, config=config) outputs = [] def accum(x) -> None: outputs.append(x) - await llm.run_prompt( - prompt="The {animal} says", - data={"animal": "duck"}, - system_prompt=None, - callbacks=[accum], - ) - - -class TestMultipleCompletionLLMModel: - async def call_model( - self, model: MultipleCompletionLLMModel, *args, **kwargs - ) -> list[LLMResult]: - return await model.call(*args, **kwargs) - - @pytest.mark.parametrize( - "model_name", ["gpt-3.5-turbo", CommonLLMNames.ANTHROPIC_TEST.value] - ) - @pytest.mark.asyncio - async def test_achat(self, model_name: str) -> None: - with cost_tracking_ctx(): - with assert_costs_increased(): - model = MultipleCompletionLLMModel(name=model_name) - await model.achat( - messages=[ - Message(content="What are three things I should do today?"), - ] - ) - - with assert_costs_increased(): - async for _ in await model.achat_iter( - messages=[ - Message(content="What are three things I should do today?"), - ] - ): - pass - - @pytest.mark.parametrize("model_name", [CommonLLMNames.OPENAI_TEST.value]) - @pytest.mark.asyncio - @pytest.mark.vcr - async def test_text_image_message(self, model_name: str) -> None: - with cost_tracking_ctx(), assert_costs_increased(): - model = MultipleCompletionLLMModel(name=model_name, config={"n": 2}) - - # An RGB image of a red square - image = np.zeros((32, 32, 3), dtype=np.uint8) - # (255 red, 0 green, 0 blue) is maximum red in RGB - image[:] = [255, 0, 0] + prompt = "The {animal} says" + data = {"animal": "duck"} + system_prompt = "You are a helpful assistant." + messages = [ + Message(role="system", content=system_prompt), + Message(role="user", content=prompt.format(**data)), + ] - await self.call_model( - model, - messages=[ - Message.create_message( - text=( - "What color is this square? Respond only with the color" - " name." - ), - images=image, - ) - ], + await llm.call_single( + messages=messages, + callbacks=[accum], ) diff --git a/tests/test_llms.py b/tests/test_llms.py index 75ef270..29b726b 100644 --- a/tests/test_llms.py +++ b/tests/test_llms.py @@ -1,5 +1,6 @@ import pathlib import pickle +from collections.abc import AsyncIterator from typing import Any, ClassVar from unittest.mock import Mock, patch @@ -11,10 +12,8 @@ from llmclient.exceptions import JSONSchemaValidationError from llmclient.llms import ( - Chunk, CommonLLMNames, LiteLLMModel, - MultipleCompletionLLMModel, validate_json_completion, ) from llmclient.types import LLMResult @@ -28,35 +27,36 @@ class TestLiteLLMModel: [ pytest.param( { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, "max_tokens": 56, + "logprobs": True, }, } ], }, - id="chat-model", + id="OpenAI-model", ), pytest.param( { - "model_name": "gpt-3.5-turbo-instruct", + "model_name": CommonLLMNames.ANTHROPIC_TEST.value, "model_list": [ { - "model_name": "gpt-3.5-turbo-instruct", + "model_name": CommonLLMNames.ANTHROPIC_TEST.value, "litellm_params": { - "model": "gpt-3.5-turbo-instruct", + "model": CommonLLMNames.ANTHROPIC_TEST.value, "temperature": 0, "max_tokens": 56, }, } ], }, - id="completion-model", + id="Anthropic-model", ), ], ) @@ -68,17 +68,25 @@ async def test_call(self, config: dict[str, Any]) -> None: Message(role="user", content="What is the meaning of the universe?"), ] results = await llm.call(messages) - assert isinstance(results.prompt, list) - assert isinstance(results.prompt[1], Message) - assert all(isinstance(msg, Message) for msg in results.prompt) - assert len(results.prompt) == 2 - assert results.prompt[1].content - assert results.text - - # @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) + assert isinstance(results, list) + + result = results[0] + assert isinstance(result, LLMResult) + assert isinstance(result.prompt, list) + assert isinstance(result.prompt[1], Message) + assert all(isinstance(msg, Message) for msg in result.prompt) + assert len(result.prompt) == 2 # role + user messages + assert result.prompt[1].content + assert result.text + assert result.logprob is None or result.logprob <= 0 + + result = await llm.call_single(messages) + assert isinstance(result, LLMResult) + + # @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON]) @pytest.mark.asyncio async def test_call_w_figure(self) -> None: - llm = LiteLLMModel(name="gpt-4o") + llm = LiteLLMModel(name=CommonLLMNames.GPT_4O.value) image = np.zeros((32, 32, 3), dtype=np.uint8) image[:] = [255, 0, 0] messages = [ @@ -90,34 +98,42 @@ async def test_call_w_figure(self) -> None: text="What color is this square? Show me your chain of reasoning.", images=image, ), - ] # TODO: It's not decoding the image. It's trying to guess the color from the encoded image string. + ] results = await llm.call(messages) - assert isinstance(results.prompt, list) - assert all(isinstance(msg, Message) for msg in results.prompt) - assert isinstance(results.prompt[1], Message) - assert len(results.prompt) == 2 - assert results.prompt[1].content - assert "red" in results.text.lower() - assert results.seconds_to_last_token > 0 - assert results.prompt_count > 0 - assert results.completion_count > 0 - assert results.cost > 0 + assert isinstance(results, list) + for result in results: + assert isinstance(result, LLMResult) + assert isinstance(result.prompt, list) + assert all(isinstance(msg, Message) for msg in result.prompt) + assert isinstance(result.prompt[1], Message) + assert len(result.prompt) == 2 + assert result.prompt[1].content + assert isinstance(result.text, str) + assert "red" in result.text.lower() + assert result.seconds_to_last_token > 0 + assert result.prompt_count > 0 + assert result.completion_count > 0 + assert result.cost > 0 # Also test with a callback async def ac(x) -> None: pass results = await llm.call(messages, [ac]) - assert isinstance(results.prompt, list) - assert all(isinstance(msg, Message) for msg in results.prompt) - assert isinstance(results.prompt[1], Message) - assert len(results.prompt) == 2 - assert results.prompt[1].content - assert "red" in results.text.lower() - assert results.seconds_to_first_token > 0 - assert results.prompt_count > 0 - assert results.completion_count > 0 - assert results.cost > 0 + assert isinstance(results, list) + for result in results: + assert isinstance(result, LLMResult) + assert isinstance(result.prompt, list) + assert all(isinstance(msg, Message) for msg in result.prompt) + assert isinstance(result.prompt[1], Message) + assert len(result.prompt) == 2 + assert result.prompt[1].content + assert isinstance(result.text, str) + assert "red" in result.text.lower() + assert result.seconds_to_last_token > 0 + assert result.prompt_count > 0 + assert result.completion_count > 0 + assert result.cost > 0 @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) @pytest.mark.parametrize( @@ -125,16 +141,18 @@ async def ac(x) -> None: [ pytest.param( { + "model_name": CommonLLMNames.OPENAI_TEST.value, "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, "max_tokens": 56, + "logprobs": True, }, } - ] + ], }, id="with-router", ), @@ -148,33 +166,36 @@ async def ac(x) -> None: ], ) @pytest.mark.asyncio - async def test_run_prompt(self, config: dict[str, Any]) -> None: - llm = LiteLLMModel(name="gpt-4o-mini", config=config) + async def test_call_single(self, config: dict[str, Any]) -> None: + llm = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value, config=config) outputs = [] def accum(x) -> None: outputs.append(x) - completion = await llm.run_prompt( - prompt="The {animal} says", - data={"animal": "duck"}, - system_prompt=None, + prompt = "The {animal} says" + data = {"animal": "duck"} + system_prompt = "You are a helpful assistant." + messages = [ + Message(role="system", content=system_prompt), + Message(role="user", content=prompt.format(**data)), + ] + + completion = await llm.call_single( + messages=messages, callbacks=[accum], ) - assert completion.model == "gpt-4o-mini" - assert completion.seconds_to_first_token > 0 + assert completion.model == CommonLLMNames.OPENAI_TEST.value + assert completion.seconds_to_last_token > 0 assert completion.prompt_count > 0 assert completion.completion_count > 0 assert str(completion) == "".join(outputs) assert completion.cost > 0 - completion = await llm.run_prompt( - prompt="The {animal} says", - data={"animal": "duck"}, - system_prompt=None, + completion = await llm.call_single( + messages=messages, ) - assert completion.seconds_to_first_token == 0 assert completion.seconds_to_last_token > 0 assert completion.cost > 0 @@ -182,10 +203,8 @@ def accum(x) -> None: async def ac(x) -> None: pass - completion = await llm.run_prompt( - prompt="The {animal} says", - data={"animal": "duck"}, - system_prompt=None, + completion = await llm.call_single( + messages=messages, callbacks=[accum, ac], ) assert completion.cost > 0 @@ -198,8 +217,11 @@ async def ac(x) -> None: { "model_list": [ { - "model_name": "gpt-4o-mini", - "litellm_params": {"model": "gpt-4o-mini", "max_tokens": 3}, + "model_name": CommonLLMNames.OPENAI_TEST.value, + "litellm_params": { + "model": CommonLLMNames.OPENAI_TEST.value, + "max_tokens": 3, + }, } ] }, @@ -217,32 +239,35 @@ async def ac(x) -> None: async def test_max_token_truncation( self, config: dict[str, Any], bypassed_router: bool ) -> None: - llm = LiteLLMModel(name="gpt-4o-mini", config=config) + llm = LiteLLMModel(name=CommonLLMNames.OPENAI_TEST.value, config=config) with patch( - "litellm.Router.atext_completion", - side_effect=litellm.Router.atext_completion, + "litellm.Router.acompletion", + side_effect=litellm.Router.acompletion, autospec=True, - ) as mock_atext_completion: - chunk = await llm.acomplete("Please tell me a story") # type: ignore[call-arg] + ) as mock_completion: + completions = await llm.acompletion( + [Message(role="user", content="Please tell me a story")] + ) if bypassed_router: - mock_atext_completion.assert_not_awaited() + mock_completion.assert_not_awaited() else: - mock_atext_completion.assert_awaited_once() - assert isinstance(chunk, Chunk) - assert chunk.completion_tokens == 3 - assert chunk.text - assert len(chunk.text) < 20 + mock_completion.assert_awaited_once() + assert isinstance(completions, list) + completion = completions[0] + assert completion.completion_count == 3 + assert completion.text + assert len(completion.text) < 20 def test_pickling(self, tmp_path: pathlib.Path) -> None: pickle_path = tmp_path / "llm_model.pickle" llm = LiteLLMModel( - name="gpt-4o-mini", + name=CommonLLMNames.OPENAI_TEST.value, config={ "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, "max_tokens": 56, }, @@ -269,40 +294,47 @@ def name_and_age(self) -> str: # So we can test computed_field is not included return f"{self.name}, {self.age}" -class TestMultipleCompletionLLMModel: +class TestMultipleCompletion: NUM_COMPLETIONS: ClassVar[int] = 2 DEFAULT_CONFIG: ClassVar[dict] = {"n": NUM_COMPLETIONS} - MODEL_CLS: ClassVar[type[MultipleCompletionLLMModel]] = MultipleCompletionLLMModel + MODEL_CLS: ClassVar[type[LiteLLMModel]] = LiteLLMModel - async def call_model( - self, model: MultipleCompletionLLMModel, *args, **kwargs - ) -> list[LLMResult]: + async def call_model(self, model: LiteLLMModel, *args, **kwargs) -> list[LLMResult]: return await model.call(*args, **kwargs) @pytest.mark.parametrize( - "model_name", ["gpt-3.5-turbo", CommonLLMNames.ANTHROPIC_TEST.value] + "model_name", + [CommonLLMNames.GPT_35_TURBO.value, CommonLLMNames.ANTHROPIC_TEST.value], ) @pytest.mark.asyncio - async def test_achat(self, model_name: str) -> None: - model = MultipleCompletionLLMModel(name=model_name) - response = await model.achat( - messages=[ - Message(content="What are three things I should do today?"), - ] - ) + async def test_acompletion(self, model_name: str) -> None: + model = self.MODEL_CLS(name=model_name) + messages = [ + Message(content="What are three things I should do today?"), + ] + response = await model.acompletion(messages) - assert len(response.choices) == 1 + assert isinstance(response, list) + assert len(response) == 1 + assert isinstance(response[0], LLMResult) - # Check we can iterate through the response - async for chunk in await model.achat_iter( - messages=[ - Message(content="What are three things I should do today?"), - ] - ): - assert len(chunk.choices) == 1 + @pytest.mark.parametrize( + "model_name", + [CommonLLMNames.OPENAI_TEST.value, CommonLLMNames.ANTHROPIC_TEST.value], + ) + @pytest.mark.asyncio + async def test_acompletion_iter(self, model_name: str) -> None: + model = self.MODEL_CLS(name=model_name) + messages = [Message(content="What are three things I should do today?")] + responses = await model.acompletion_iter(messages) + assert isinstance(responses, AsyncIterator) + + async for response in responses: + assert isinstance(response, LLMResult) + assert isinstance(response.prompt, list) @pytest.mark.vcr(match_on=[*VCR_DEFAULT_MATCH_ON, "body"]) - @pytest.mark.parametrize("model_name", ["gpt-3.5-turbo"]) + @pytest.mark.parametrize("model_name", [CommonLLMNames.GPT_35_TURBO.value]) @pytest.mark.asyncio async def test_model(self, model_name: str) -> None: # Make model_name an arg so that TestLLMModel can parametrize it @@ -322,7 +354,8 @@ async def test_model(self, model_name: str) -> None: assert result.logprob is None or result.logprob <= 0 @pytest.mark.parametrize( - "model_name", [CommonLLMNames.ANTHROPIC_TEST.value, "gpt-3.5-turbo"] + "model_name", + [CommonLLMNames.ANTHROPIC_TEST.value, CommonLLMNames.GPT_35_TURBO.value], ) @pytest.mark.asyncio async def test_streaming(self, model_name: str) -> None: @@ -352,7 +385,9 @@ def play(move: int | None) -> None: """ results = await self.call_model( - self.MODEL_CLS(name="gpt-3.5-turbo", config=self.DEFAULT_CONFIG), + self.MODEL_CLS( + name=CommonLLMNames.GPT_35_TURBO.value, config=self.DEFAULT_CONFIG + ), messages=[Message(content="Please win.")], tools=[Tool.from_function(play)], ) @@ -369,12 +404,20 @@ def play(move: int | None) -> None: @pytest.mark.parametrize( ("model_name", "output_type"), [ - pytest.param("gpt-3.5-turbo", DummyOutputSchema, id="json-mode-base-model"), pytest.param( - "gpt-4o", TypeAdapter(DummyOutputSchema), id="json-mode-type-adapter" + CommonLLMNames.GPT_35_TURBO.value, + DummyOutputSchema, + id="json-mode-base-model", ), pytest.param( - "gpt-4o", DummyOutputSchema.model_json_schema(), id="structured-outputs" + CommonLLMNames.GPT_4O.value, + TypeAdapter(DummyOutputSchema), + id="json-mode-type-adapter", + ), + pytest.param( + CommonLLMNames.GPT_4O.value, + DummyOutputSchema.model_json_schema(), + id="structured-outputs", ), ], ) @@ -427,7 +470,8 @@ async def test_text_image_message(self, model_name: str) -> None: assert "red" in result.messages[-1].content.lower() @pytest.mark.parametrize( - "model_name", [CommonLLMNames.ANTHROPIC_TEST.value, "gpt-3.5-turbo"] + "model_name", + [CommonLLMNames.ANTHROPIC_TEST.value, CommonLLMNames.GPT_35_TURBO.value], ) @pytest.mark.asyncio @pytest.mark.vcr @@ -530,4 +574,14 @@ async def test_deepseek_model(): Message(content="What is the meaning of life?"), ] results = await llm.call(messages) - assert results.reasoning_content + for result in results: + assert result.reasoning_content + + outputs: list[str] = [] + results = await llm.call(messages, callbacks=[outputs.append]) + for result in results: + # TODO: Litellm is not populating provider_specific_fields in streaming mode. + # https://github.com/BerriAI/litellm/issues/7942 + # I'm keeping this test as a reminder to fix this. + # once the issue is fixed. + assert not result.reasoning_content diff --git a/tests/test_rate_limiter.py b/tests/test_rate_limiter.py index 912d12e..009d102 100644 --- a/tests/test_rate_limiter.py +++ b/tests/test_rate_limiter.py @@ -9,59 +9,62 @@ from llmclient.constants import CHARACTERS_PER_TOKEN_ASSUMPTION from llmclient.embeddings import LiteLLMEmbeddingModel -from llmclient.llms import ( - Chunk, - LiteLLMModel, -) +from llmclient.llms import CommonLLMNames, LiteLLMModel from llmclient.types import LLMResult LLM_CONFIG_W_RATE_LIMITS = [ # following ensures that "short-form" rate limits are also supported # where the user doesn't specify the model_list { - "name": "gpt-4o-mini", + "name": CommonLLMNames.OPENAI_TEST.value, "config": { - "rate_limit": {"gpt-4o-mini": RateLimitItemPerSecond(20, 3)}, + "rate_limit": { + CommonLLMNames.OPENAI_TEST.value: RateLimitItemPerSecond(20, 3) + }, }, }, { - "name": "gpt-4o-mini", + "name": CommonLLMNames.OPENAI_TEST.value, "config": { "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, }, } ], - "rate_limit": {"gpt-4o-mini": RateLimitItemPerSecond(20, 1)}, + "rate_limit": { + CommonLLMNames.OPENAI_TEST.value: RateLimitItemPerSecond(20, 1) + }, }, }, { - "name": "gpt-4o-mini", + "name": CommonLLMNames.OPENAI_TEST.value, "config": { "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, }, } ], - "rate_limit": {"gpt-4o-mini": RateLimitItemPerSecond(1_000_000, 1)}, + "rate_limit": { + CommonLLMNames.OPENAI_TEST.value: RateLimitItemPerSecond(1_000_000, 1) + }, }, }, { - "name": "gpt-4o-mini", + "name": CommonLLMNames.OPENAI_TEST.value, "config": { "model_list": [ { - "model_name": "gpt-4o-mini", + "model_name": CommonLLMNames.OPENAI_TEST.value, "litellm_params": { - "model": "gpt-4o-mini", + "model": CommonLLMNames.OPENAI_TEST.value, "temperature": 0, }, } @@ -74,21 +77,13 @@ LLM_METHOD_AND_INPUTS = [ { - "method": "acomplete", - "kwargs": {"prompt": RATE_LIMITER_PROMPT}, - }, - { - "method": "acomplete_iter", - "kwargs": {"prompt": RATE_LIMITER_PROMPT}, - }, - { - "method": "achat", + "method": "acompletion", "kwargs": { "messages": [Message.create_message(role="user", text=RATE_LIMITER_PROMPT)] }, }, { - "method": "achat_iter", + "method": "acompletion_iter", "kwargs": { "messages": [Message.create_message(role="user", text=RATE_LIMITER_PROMPT)] }, @@ -136,13 +131,13 @@ async def time_n_llm_methods( character_count = 0 token_count = 0 - if isinstance(outputs[0], Chunk | LLMResult): + if isinstance(outputs[0], LLMResult): character_count = sum(len(o.text or "") for o in outputs) else: character_count = sum(len(o) for o in outputs) - if hasattr(outputs[0], "prompt_tokens"): - token_count = sum(o.prompt_tokens + o.completion_tokens for o in outputs) + if hasattr(outputs[0], "prompt_count"): + token_count = sum(o.prompt_count + o.completion_count for o in outputs) return ( (character_count / CHARACTERS_PER_TOKEN_ASSUMPTION) @@ -153,7 +148,7 @@ async def time_n_llm_methods( @pytest.mark.parametrize("llm_config_w_rate_limits", LLM_CONFIG_W_RATE_LIMITS) @pytest.mark.asyncio -async def test_rate_limit_on_run_prompt( +async def test_rate_limit_on_call_single( llm_config_w_rate_limits: dict[str, Any], ) -> None: @@ -164,20 +159,26 @@ async def test_rate_limit_on_run_prompt( def accum(x) -> None: outputs.append(x) + prompt = "The {animal} says" + data = {"animal": "duck"} + system_prompt = "You are a helpful assistant." + messages = [ + Message(role="system", content=system_prompt), + Message(role="user", content=prompt.format(**data)), + ] + estimated_tokens_per_second = await time_n_llm_methods( llm, - "run_prompt", + "call_single", 3, - prompt="The {animal} says", - data={"animal": "duck"}, - system_prompt=None, + messages=messages, callbacks=[accum], ) if "rate_limit" in llm.config: max_tokens_per_second = ( - llm.config["rate_limit"]["gpt-4o-mini"].amount - / llm.config["rate_limit"]["gpt-4o-mini"].multiples + llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].amount + / llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].multiples ) assert estimated_tokens_per_second / max_tokens_per_second < ( 1.0 + ACCEPTABLE_RATE_LIMIT_ERROR @@ -192,19 +193,17 @@ def accum2(x) -> None: estimated_tokens_per_second = await time_n_llm_methods( llm, - "run_prompt", + "call_single", 3, use_gather=True, - prompt="The {animal} says", - data={"animal": "duck"}, - system_prompt=None, + messages=messages, callbacks=[accum2], ) if "rate_limit" in llm.config: max_tokens_per_second = ( - llm.config["rate_limit"]["gpt-4o-mini"].amount - / llm.config["rate_limit"]["gpt-4o-mini"].multiples + llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].amount + / llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].multiples ) assert estimated_tokens_per_second / max_tokens_per_second < ( 1.0 + ACCEPTABLE_RATE_LIMIT_ERROR @@ -233,8 +232,8 @@ async def test_rate_limit_on_sequential_completion_litellm_methods( ) if "rate_limit" in llm.config: max_tokens_per_second = ( - llm.config["rate_limit"]["gpt-4o-mini"].amount - / llm.config["rate_limit"]["gpt-4o-mini"].multiples + llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].amount + / llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].multiples ) assert estimated_tokens_per_second / max_tokens_per_second < ( 1.0 + ACCEPTABLE_RATE_LIMIT_ERROR @@ -264,8 +263,8 @@ async def test_rate_limit_on_parallel_completion_litellm_methods( ) if "rate_limit" in llm.config: max_tokens_per_second = ( - llm.config["rate_limit"]["gpt-4o-mini"].amount - / llm.config["rate_limit"]["gpt-4o-mini"].multiples + llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].amount + / llm.config["rate_limit"][CommonLLMNames.OPENAI_TEST.value].multiples ) assert estimated_tokens_per_second / max_tokens_per_second < ( 1.0 + ACCEPTABLE_RATE_LIMIT_ERROR diff --git a/uv.lock b/uv.lock index 80832a1..ea04f51 100644 --- a/uv.lock +++ b/uv.lock @@ -1,10 +1,8 @@ version = 1 requires-python = ">=3.11" resolution-markers = [ - "python_full_version >= '3.13' and platform_python_implementation == 'PyPy'", - "python_full_version >= '3.13' and platform_python_implementation != 'PyPy'", - "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy'", - "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy'", + "python_full_version >= '3.12' and platform_python_implementation == 'PyPy'", + "python_full_version >= '3.12' and platform_python_implementation != 'PyPy'", "python_full_version < '3.12' and platform_python_implementation != 'PyPy'", "python_full_version < '3.12' and platform_python_implementation == 'PyPy'", ] @@ -537,7 +535,7 @@ wheels = [ [[package]] name = "fh-llm-client" -version = "0.0.11.dev12+g069526c.d20250123" +version = "0.0.11.dev3+ge27c993.d20250123" source = { editable = "." } dependencies = [ { name = "coredis" }, @@ -572,6 +570,7 @@ dev = [ { name = "python-dotenv" }, { name = "refurb" }, { name = "sentence-transformers" }, + { name = "typeguard" }, ] local = [ { name = "numpy" }, @@ -595,8 +594,7 @@ requires-dist = [ { name = "fhaviary", extras = ["xml"], marker = "extra == 'dev'" }, { name = "ipython", marker = "extra == 'dev'", specifier = ">=8" }, { name = "limits" }, - { name = "litellm", marker = "python_full_version < '3.13'" }, - { name = "litellm", marker = "python_full_version >= '3.13'", specifier = ">=1.49.1" }, + { name = "litellm", specifier = ">=1.59.3" }, { name = "litellm", marker = "extra == 'deepseek'", specifier = ">1.59.3" }, { name = "mypy", marker = "extra == 'dev'", specifier = ">=1.8" }, { name = "numpy", marker = "extra == 'local'" }, @@ -615,6 +613,7 @@ requires-dist = [ { name = "refurb", marker = "extra == 'dev'", specifier = ">=2" }, { name = "sentence-transformers", marker = "extra == 'local'" }, { name = "tiktoken", specifier = ">=0.4.0" }, + { name = "typeguard", marker = "extra == 'dev'" }, { name = "typing-extensions", marker = "python_full_version < '3.12'" }, ] @@ -2676,13 +2675,25 @@ name = "triton" version = "3.1.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "filelock", marker = "python_full_version < '3.13'" }, + { name = "filelock" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/86/17/d9a5cf4fcf46291856d1e90762e36cbabd2a56c7265da0d1d9508c8e3943/triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f34f6e7885d1bf0eaaf7ba875a5f0ce6f3c13ba98f9503651c1e6dc6757ed5c", size = 209506424 }, { url = "https://files.pythonhosted.org/packages/78/eb/65f5ba83c2a123f6498a3097746607e5b2f16add29e36765305e4ac7fdd8/triton-3.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c8182f42fd8080a7d39d666814fa36c5e30cc00ea7eeeb1a2983dbb4c99a0fdc", size = 209551444 }, ] +[[package]] +name = "typeguard" +version = "4.4.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "typing-extensions" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/62/c3/400917dd37d7b8c07e9723f3046818530423e1e759a56a22133362adab00/typeguard-4.4.1.tar.gz", hash = "sha256:0d22a89d00b453b47c49875f42b6601b961757541a2e1e0ef517b6e24213c21b", size = 74959 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f2/53/9465dedf2d69fe26008e7732cf6e0a385e387c240869e7d54eed49782a3c/typeguard-4.4.1-py3-none-any.whl", hash = "sha256:9324ec07a27ec67fc54a9c063020ca4c0ae6abad5e9f0f9804ca59aee68c6e21", size = 35635 }, +] + [[package]] name = "typeshed-client" version = "2.7.0"