From 65a5dd201b02e4e058a0a80e6e65999dd19ebd48 Mon Sep 17 00:00:00 2001 From: Zack Date: Thu, 16 Nov 2023 13:03:05 -0600 Subject: [PATCH] fix: Fix open ai chat completions Former-commit-id: 3b9e335a80103a3d8c7ba79bd677e98aa6717e09 --- swarms/models/openai_chat.py | 676 +++++++++++++++++++++++++++++++++ swarms/models/openai_models.py | 5 +- 2 files changed, 679 insertions(+), 2 deletions(-) create mode 100644 swarms/models/openai_chat.py diff --git a/swarms/models/openai_chat.py b/swarms/models/openai_chat.py new file mode 100644 index 00000000..d53658da --- /dev/null +++ b/swarms/models/openai_chat.py @@ -0,0 +1,676 @@ +from __future__ import annotations + +import logging +import os +import sys +from typing import ( + TYPE_CHECKING, + Any, + AsyncIterator, + Callable, + Dict, + Iterator, + List, + Mapping, + Optional, + Sequence, + Tuple, + Type, + Union, +) + +from langchain.adapters.openai import convert_dict_to_message, convert_message_to_dict +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain.chat_models.base import ( + BaseChatModel, +) +from langchain.llms.base import create_base_retry_decorator +from langchain.pydantic_v1 import BaseModel, Field, root_validator +from langchain.schema import ChatGeneration, ChatResult +from langchain.schema.language_model import LanguageModelInput +from langchain.schema.messages import ( + AIMessageChunk, + BaseMessage, + BaseMessageChunk, + ChatMessageChunk, + FunctionMessageChunk, + HumanMessageChunk, + SystemMessageChunk, + ToolMessageChunk, +) +from langchain.schema.output import ChatGenerationChunk +from langchain.schema.runnable import Runnable +from langchain.utils import ( + get_from_dict_or_env, + get_pydantic_field_names, +) +from langchain.utils.openai import is_openai_v1 + +if TYPE_CHECKING: + import tiktoken + + +logger = logging.getLogger(__name__) + + +def _generate_from_stream(stream: Iterator[ChatGenerationChunk]) -> ChatResult: + generation: Optional[ChatGenerationChunk] = None + for chunk in stream: + if generation is None: + generation = chunk + else: + generation += chunk + assert generation is not None + return ChatResult(generations=[generation]) + + +async def _agenerate_from_stream( + stream: AsyncIterator[ChatGenerationChunk], +) -> ChatResult: + generation: Optional[ChatGenerationChunk] = None + async for chunk in stream: + if generation is None: + generation = chunk + else: + generation += chunk + assert generation is not None + return ChatResult(generations=[generation]) + + +def _import_tiktoken() -> Any: + try: + import tiktoken + except ImportError: + raise ValueError( + "Could not import tiktoken python package. " + "This is needed in order to calculate get_token_ids. " + "Please install it with `pip install tiktoken`." + ) + return tiktoken + + +def _create_retry_decorator( + llm: OpenAIChat, + run_manager: Optional[ + Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] + ] = None, +) -> Callable[[Any], Any]: + import openai + + errors = [ + openai.Timeout, + openai.APIError, + openai.APIConnectionError, + openai.RateLimitError, + openai.ServiceUnavailableError, + ] + return create_base_retry_decorator( + error_types=errors, max_retries=llm.max_retries, run_manager=run_manager + ) + + +async def acompletion_with_retry( + llm: OpenAIChat, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, +) -> Any: + """Use tenacity to retry the async completion call.""" + if is_openai_v1(): + return await llm.async_client.create(**kwargs) + + retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) + + @retry_decorator + async def _completion_with_retry(**kwargs: Any) -> Any: + # Use OpenAI's async api https://github.com/openai/openai-python#async-api + return await llm.client.acreate(**kwargs) + + return await _completion_with_retry(**kwargs) + + +def _convert_delta_to_message_chunk( + _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk] +) -> BaseMessageChunk: + role = _dict.get("role") + content = _dict.get("content") or "" + additional_kwargs: Dict = {} + if _dict.get("function_call"): + function_call = dict(_dict["function_call"]) + if "name" in function_call and function_call["name"] is None: + function_call["name"] = "" + additional_kwargs["function_call"] = function_call + if _dict.get("tool_calls"): + additional_kwargs["tool_calls"] = _dict["tool_calls"] + + if role == "user" or default_class == HumanMessageChunk: + return HumanMessageChunk(content=content) + elif role == "assistant" or default_class == AIMessageChunk: + return AIMessageChunk(content=content, additional_kwargs=additional_kwargs) + elif role == "system" or default_class == SystemMessageChunk: + return SystemMessageChunk(content=content) + elif role == "function" or default_class == FunctionMessageChunk: + return FunctionMessageChunk(content=content, name=_dict["name"]) + elif role == "tool" or default_class == ToolMessageChunk: + return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"]) + elif role or default_class == ChatMessageChunk: + return ChatMessageChunk(content=content, role=role) + else: + return default_class(content=content) + + +class OpenAIChat(BaseChatModel): + """`OpenAI` Chat large language models API. + + To use, you should have the ``openai`` python package installed, and the + environment variable ``OPENAI_API_KEY`` set with your API key. + + Any parameters that are valid to be passed to the openai.create call can be passed + in, even if not explicitly saved on this class. + + Example: + .. code-block:: python + + from swarms.models import ChatOpenAI + openai = ChatOpenAI(model_name="gpt-3.5-turbo") + """ + + @property + def lc_secrets(self) -> Dict[str, str]: + return {"openai_api_key": "OPENAI_API_KEY"} + + @property + def lc_attributes(self) -> Dict[str, Any]: + attributes: Dict[str, Any] = {} + + if self.openai_organization: + attributes["openai_organization"] = self.openai_organization + + if self.openai_api_base: + attributes["openai_api_base"] = self.openai_api_base + + if self.openai_proxy: + attributes["openai_proxy"] = self.openai_proxy + + return attributes + + @classmethod + def is_lc_serializable(cls) -> bool: + """Return whether this model can be serialized by Langchain.""" + return True + + client: Any = None #: :meta private: + async_client: Any = None #: :meta private: + model_name: str = Field(default="gpt-3.5-turbo", alias="model") + """Model name to use.""" + temperature: float = 0.7 + """What sampling temperature to use.""" + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Holds any model parameters valid for `create` call not explicitly specified.""" + # When updating this to use a SecretStr + # Check for classes that derive from this class (as some of them + # may assume openai_api_key is a str) + # openai_api_key: Optional[str] = Field(default=None, alias="api_key") + openai_api_key: Optional[str] = Field(default=None, alias="api_key") + """Automatically inferred from env var `OPENAI_API_KEY` if not provided.""" + openai_api_base: Optional[str] = Field(default=None, alias="base_url") + """Base URL path for API requests, leave blank if not using a proxy or service + emulator.""" + openai_organization: Optional[str] = Field(default=None, alias="organization") + """Automatically inferred from env var `OPENAI_ORG_ID` if not provided.""" + # to support explicit proxy for OpenAI + openai_proxy: Optional[str] = None + request_timeout: Union[float, Tuple[float, float], Any, None] = Field( + default=None, alias="timeout" + ) + """Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or + None.""" + max_retries: int = 2 + """Maximum number of retries to make when generating.""" + streaming: bool = False + """Whether to stream the results or not.""" + n: int = 1 + """Number of chat completions to generate for each prompt.""" + max_tokens: Optional[int] = None + """Maximum number of tokens to generate.""" + tiktoken_model_name: Optional[str] = None + """The model name to pass to tiktoken when using this class. + Tiktoken is used to count the number of tokens in documents to constrain + them to be under a certain limit. By default, when set to None, this will + be the same as the embedding model name. However, there are some cases + where you may want to use this Embedding class with a model name not + supported by tiktoken. This can include when using Azure embeddings or + when using one of the many model providers that expose an OpenAI-like + API but with different models. In those cases, in order to avoid erroring + when tiktoken is called, you can specify a model name to use here.""" + default_headers: Union[Mapping[str, str], None] = None + default_query: Union[Mapping[str, object], None] = None + # Configure a custom httpx client. See the + # [httpx documentation](https://www.python-httpx.org/api/#client) for more details. + http_client: Union[Any, None] = None + """Optional httpx.Client.""" + + class Config: + """Configuration for this pydantic object.""" + + allow_population_by_field_name = True + + @root_validator(pre=True) + def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Build extra kwargs from additional params that were passed in.""" + all_required_field_names = get_pydantic_field_names(cls) + extra = values.get("model_kwargs", {}) + for field_name in list(values): + if field_name in extra: + raise ValueError(f"Found {field_name} supplied twice.") + if field_name not in all_required_field_names: + logger.warning( + f"""WARNING! {field_name} is not default parameter. + {field_name} was transferred to model_kwargs. + Please confirm that {field_name} is what you intended.""" + ) + extra[field_name] = values.pop(field_name) + + invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) + if invalid_model_kwargs: + raise ValueError( + f"Parameters {invalid_model_kwargs} should be specified explicitly. " + f"Instead they were passed in as part of `model_kwargs` parameter." + ) + + values["model_kwargs"] = extra + return values + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key and python package exists in environment.""" + if values["n"] < 1: + raise ValueError("n must be at least 1.") + if values["n"] > 1 and values["streaming"]: + raise ValueError("n must be 1 when streaming.") + + values["openai_api_key"] = get_from_dict_or_env( + values, "openai_api_key", "OPENAI_API_KEY" + ) + # Check OPENAI_ORGANIZATION for backwards compatibility. + values["openai_organization"] = ( + values["openai_organization"] + or os.getenv("OPENAI_ORG_ID") + or os.getenv("OPENAI_ORGANIZATION") + ) + values["openai_api_base"] = values["openai_api_base"] or os.getenv( + "OPENAI_API_BASE" + ) + values["openai_proxy"] = get_from_dict_or_env( + values, + "openai_proxy", + "OPENAI_PROXY", + default="", + ) + try: + import openai + + except ImportError: + raise ImportError( + "Could not import openai python package. " + "Please install it with `pip install openai`." + ) + + if is_openai_v1(): + client_params = { + "api_key": values["openai_api_key"], + "organization": values["openai_organization"], + "base_url": values["openai_api_base"], + "timeout": values["request_timeout"], + "max_retries": values["max_retries"], + "default_headers": values["default_headers"], + "default_query": values["default_query"], + "http_client": values["http_client"], + } + values["client"] = openai.OpenAI(**client_params).chat.completions + values["async_client"] = openai.AsyncOpenAI( + **client_params + ).chat.completions + else: + values["client"] = openai.ChatCompletion + return values + + @property + def _default_params(self) -> Dict[str, Any]: + """Get the default parameters for calling OpenAI API.""" + params = { + "model": self.model_name, + "stream": self.streaming, + "n": self.n, + "temperature": self.temperature, + **self.model_kwargs, + } + if self.max_tokens is not None: + params["max_tokens"] = self.max_tokens + if self.request_timeout is not None and not is_openai_v1(): + params["request_timeout"] = self.request_timeout + return params + + def completion_with_retry( + self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any + ) -> Any: + """Use tenacity to retry the completion call.""" + if is_openai_v1(): + return self.client.create(**kwargs) + + retry_decorator = _create_retry_decorator(self, run_manager=run_manager) + + @retry_decorator + def _completion_with_retry(**kwargs: Any) -> Any: + return self.client.create(**kwargs) + + return _completion_with_retry(**kwargs) + + def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: + overall_token_usage: dict = {} + system_fingerprint = None + for output in llm_outputs: + if output is None: + # Happens in streaming + continue + token_usage = output["token_usage"] + for k, v in token_usage.items(): + if k in overall_token_usage: + overall_token_usage[k] += v + else: + overall_token_usage[k] = v + if system_fingerprint is None: + system_fingerprint = output.get("system_fingerprint") + combined = {"token_usage": overall_token_usage, "model_name": self.model_name} + if system_fingerprint: + combined["system_fingerprint"] = system_fingerprint + return combined + + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs, "stream": True} + + default_chunk_class = AIMessageChunk + for chunk in self.completion_with_retry( + messages=message_dicts, run_manager=run_manager, **params + ): + if not isinstance(chunk, dict): + chunk = chunk.dict() + if len(chunk["choices"]) == 0: + continue + choice = chunk["choices"][0] + chunk = _convert_delta_to_message_chunk( + choice["delta"], default_chunk_class + ) + finish_reason = choice.get("finish_reason") + generation_info = ( + dict(finish_reason=finish_reason) if finish_reason is not None else None + ) + default_chunk_class = chunk.__class__ + chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) + yield chunk + if run_manager: + run_manager.on_llm_new_token(chunk.text, chunk=chunk) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + stream: Optional[bool] = None, + **kwargs: Any, + ) -> ChatResult: + should_stream = stream if stream is not None else self.streaming + if should_stream: + stream_iter = self._stream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return _generate_from_stream(stream_iter) + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs} + response = self.completion_with_retry( + messages=message_dicts, run_manager=run_manager, **params + ) + return self._create_chat_result(response) + + def _create_message_dicts( + self, messages: List[BaseMessage], stop: Optional[List[str]] + ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: + params = self._client_params + if stop is not None: + if "stop" in params: + raise ValueError("`stop` found in both the input and default params.") + params["stop"] = stop + message_dicts = [convert_message_to_dict(m) for m in messages] + return message_dicts, params + + def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult: + generations = [] + if not isinstance(response, dict): + response = response.dict() + for res in response["choices"]: + message = convert_dict_to_message(res["message"]) + gen = ChatGeneration( + message=message, + generation_info=dict(finish_reason=res.get("finish_reason")), + ) + generations.append(gen) + token_usage = response.get("usage", {}) + llm_output = { + "token_usage": token_usage, + "model_name": self.model_name, + "system_fingerprint": response.get("system_fingerprint", ""), + } + return ChatResult(generations=generations, llm_output=llm_output) + + async def _astream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs, "stream": True} + + default_chunk_class = AIMessageChunk + async for chunk in await acompletion_with_retry( + self, messages=message_dicts, run_manager=run_manager, **params + ): + if not isinstance(chunk, dict): + chunk = chunk.dict() + if len(chunk["choices"]) == 0: + continue + choice = chunk["choices"][0] + chunk = _convert_delta_to_message_chunk( + choice["delta"], default_chunk_class + ) + finish_reason = choice.get("finish_reason") + generation_info = ( + dict(finish_reason=finish_reason) if finish_reason is not None else None + ) + default_chunk_class = chunk.__class__ + chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) + yield chunk + if run_manager: + await run_manager.on_llm_new_token(token=chunk.text, chunk=chunk) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + stream: Optional[bool] = None, + **kwargs: Any, + ) -> ChatResult: + should_stream = stream if stream is not None else self.streaming + if should_stream: + stream_iter = self._astream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return await _agenerate_from_stream(stream_iter) + + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs} + response = await acompletion_with_retry( + self, messages=message_dicts, run_manager=run_manager, **params + ) + return self._create_chat_result(response) + + @property + def _identifying_params(self) -> Dict[str, Any]: + """Get the identifying parameters.""" + return {**{"model_name": self.model_name}, **self._default_params} + + @property + def _client_params(self) -> Dict[str, Any]: + """Get the parameters used for the openai client.""" + openai_creds: Dict[str, Any] = { + "model": self.model_name, + } + if not is_openai_v1(): + openai_creds.update( + { + "api_key": self.openai_api_key, + "api_base": self.openai_api_base, + "organization": self.openai_organization, + } + ) + if self.openai_proxy: + import openai + + raise Exception("The 'openai.proxy' option isn't read in the client API. You will need to pass it when you instantiate the client, e.g. 'OpenAI(proxy={"http": self.openai_proxy, "https": self.openai_proxy})'") # type: ignore[assignment] # noqa: E501 + return {**self._default_params, **openai_creds} + + def _get_invocation_params( + self, stop: Optional[List[str]] = None, **kwargs: Any + ) -> Dict[str, Any]: + """Get the parameters used to invoke the model.""" + return { + "model": self.model_name, + **super()._get_invocation_params(stop=stop), + **self._default_params, + **kwargs, + } + + @property + def _llm_type(self) -> str: + """Return type of chat model.""" + return "openai-chat" + + def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]: + tiktoken_ = _import_tiktoken() + if self.tiktoken_model_name is not None: + model = self.tiktoken_model_name + else: + model = self.model_name + if model == "gpt-3.5-turbo": + # gpt-3.5-turbo may change over time. + # Returning num tokens assuming gpt-3.5-turbo-0301. + model = "gpt-3.5-turbo-0301" + elif model == "gpt-4": + # gpt-4 may change over time. + # Returning num tokens assuming gpt-4-0314. + model = "gpt-4-0314" + # Returns the number of tokens used by a list of messages. + try: + encoding = tiktoken_.encoding_for_model(model) + except KeyError: + logger.warning("Warning: model not found. Using cl100k_base encoding.") + model = "cl100k_base" + encoding = tiktoken_.get_encoding(model) + return model, encoding + + def get_token_ids(self, text: str) -> List[int]: + """Get the tokens present in the text with tiktoken package.""" + # tiktoken NOT supported for Python 3.7 or below + if sys.version_info[1] <= 7: + return super().get_token_ids(text) + _, encoding_model = self._get_encoding_model() + return encoding_model.encode(text) + + def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int: + """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package. + + Official documentation: https://github.com/openai/openai-cookbook/blob/ + main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb""" + if sys.version_info[1] <= 7: + return super().get_num_tokens_from_messages(messages) + model, encoding = self._get_encoding_model() + if model.startswith("gpt-3.5-turbo-0301"): + # every message follows {role/name}\n{content}\n + tokens_per_message = 4 + # if there's a name, the role is omitted + tokens_per_name = -1 + elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"): + tokens_per_message = 3 + tokens_per_name = 1 + else: + raise NotImplementedError( + f"get_num_tokens_from_messages() is not presently implemented " + f"for model {model}." + "See https://github.com/openai/openai-python/blob/main/chatml.md for " + "information on how messages are converted to tokens." + ) + num_tokens = 0 + messages_dict = [convert_message_to_dict(m) for m in messages] + for message in messages_dict: + num_tokens += tokens_per_message + for key, value in message.items(): + # Cast str(value) in case the message value is not a string + # This occurs with function messages + num_tokens += len(encoding.encode(str(value))) + if key == "name": + num_tokens += tokens_per_name + # every reply is primed with assistant + num_tokens += 3 + return num_tokens + + def bind_functions( + self, + functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], + function_call: Optional[str] = None, + **kwargs: Any, + ) -> Runnable[LanguageModelInput, BaseMessage]: + """Bind functions (and other objects) to this chat model. + + Args: + functions: A list of function definitions to bind to this chat model. + Can be a dictionary, pydantic model, or callable. Pydantic + models and callables will be automatically converted to + their schema dictionary representation. + function_call: Which function to require the model to call. + Must be the name of the single provided function or + "auto" to automatically determine which function to call + (if any). + kwargs: Any additional parameters to pass to the + :class:`~swarms.runnable.Runnable` constructor. + """ + from langchain.chains.openai_functions.base import convert_to_openai_function + + formatted_functions = [convert_to_openai_function(fn) for fn in functions] + if function_call is not None: + if len(formatted_functions) != 1: + raise ValueError( + "When specifying `function_call`, you must provide exactly one " + "function." + ) + if formatted_functions[0]["name"] != function_call: + raise ValueError( + f"Function call {function_call} was specified, but the only " + f"provided function was {formatted_functions[0]['name']}." + ) + function_call_ = {"name": function_call} + kwargs = {**kwargs, "function_call": function_call_} + return super().bind( + functions=formatted_functions, + **kwargs, + ) diff --git a/swarms/models/openai_models.py b/swarms/models/openai_models.py index f420173a..615bfb0e 100644 --- a/swarms/models/openai_models.py +++ b/swarms/models/openai_models.py @@ -93,7 +93,7 @@ def _create_retry_decorator( import openai errors = [ - openai.error.Timeout, + openai.Timeout, openai.error.APIError, openai.error.APIConnectionError, openai.error.RateLimitError, @@ -594,7 +594,8 @@ class BaseOpenAI(BaseLLM): if self.openai_proxy: import openai - openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # noqa: E501 + # TODO: The 'openai.proxy' option isn't read in the client API. You will need to pass it when you instantiate the client, e.g. 'OpenAI(proxy={"http": self.openai_proxy, "https": self.openai_proxy})' + # openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # noqa: E501 return {**openai_creds, **self._default_params} @property