Former-commit-id: 699c943394
clean-history
Kye
1 year ago
parent
091be7d4bb
commit
d6b037c211
@ -1,8 +1,6 @@
|
||||
from swarms.models import Dalle3
|
||||
|
||||
dalle3 = Dalle3(
|
||||
openai_api_key=""
|
||||
)
|
||||
dalle3 = Dalle3(openai_api_key="")
|
||||
task = "A painting of a dog"
|
||||
image_url = dalle3(task)
|
||||
print(image_url)
|
||||
print(image_url)
|
||||
|
||||
@ -1,676 +0,0 @@
|
||||
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.error.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 <im_start>{role/name}\n{content}<im_end>\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 <im_start>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,
|
||||
)
|
||||
@ -1,148 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Optional
|
||||
|
||||
import tiktoken
|
||||
from attr import Factory, define, field
|
||||
|
||||
|
||||
@define(frozen=True)
|
||||
class BaseTokenizer(ABC):
|
||||
DEFAULT_STOP_SEQUENCES = ["Observation:"]
|
||||
|
||||
stop_sequences: list[str] = field(
|
||||
default=Factory(lambda: BaseTokenizer.DEFAULT_STOP_SEQUENCES),
|
||||
kw_only=True,
|
||||
)
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def max_tokens(self) -> int:
|
||||
...
|
||||
|
||||
def count_tokens_left(self, text: str) -> int:
|
||||
diff = self.max_tokens - self.count_tokens(text)
|
||||
|
||||
if diff > 0:
|
||||
return diff
|
||||
else:
|
||||
return 0
|
||||
|
||||
@abstractmethod
|
||||
def count_tokens(self, text: str) -> int:
|
||||
...
|
||||
|
||||
|
||||
@define(frozen=True)
|
||||
class OpenAITokenizer(BaseTokenizer):
|
||||
DEFAULT_OPENAI_GPT_3_COMPLETION_MODEL = "text-davinci-003"
|
||||
DEFAULT_OPENAI_GPT_3_CHAT_MODEL = "gpt-3.5-turbo"
|
||||
DEFAULT_OPENAI_GPT_4_MODEL = "gpt-4"
|
||||
DEFAULT_ENCODING = "cl100k_base"
|
||||
DEFAULT_MAX_TOKENS = 2049
|
||||
TOKEN_OFFSET = 8
|
||||
|
||||
MODEL_PREFIXES_TO_MAX_TOKENS = {
|
||||
"gpt-4-32k": 32768,
|
||||
"gpt-4": 8192,
|
||||
"gpt-3.5-turbo-16k": 16384,
|
||||
"gpt-3.5-turbo": 4096,
|
||||
"gpt-35-turbo-16k": 16384,
|
||||
"gpt-35-turbo": 4096,
|
||||
"text-davinci-003": 4097,
|
||||
"text-davinci-002": 4097,
|
||||
"code-davinci-002": 8001,
|
||||
"text-embedding-ada-002": 8191,
|
||||
"text-embedding-ada-001": 2046,
|
||||
}
|
||||
|
||||
EMBEDDING_MODELS = ["text-embedding-ada-002", "text-embedding-ada-001"]
|
||||
|
||||
model: str = field(kw_only=True)
|
||||
|
||||
@property
|
||||
def encoding(self) -> tiktoken.Encoding:
|
||||
try:
|
||||
return tiktoken.encoding_for_model(self.model)
|
||||
except KeyError:
|
||||
return tiktoken.get_encoding(self.DEFAULT_ENCODING)
|
||||
|
||||
@property
|
||||
def max_tokens(self) -> int:
|
||||
tokens = next(
|
||||
v
|
||||
for k, v in self.MODEL_PREFIXES_TO_MAX_TOKENS.items()
|
||||
if self.model.startswith(k)
|
||||
)
|
||||
offset = 0 if self.model in self.EMBEDDING_MODELS else self.TOKEN_OFFSET
|
||||
|
||||
return (tokens if tokens else self.DEFAULT_MAX_TOKENS) - offset
|
||||
|
||||
def count_tokens(self, text: str | list, model: Optional[str] = None) -> int:
|
||||
"""
|
||||
Handles the special case of ChatML. Implementation adopted from the official OpenAI notebook:
|
||||
https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
|
||||
"""
|
||||
if isinstance(text, list):
|
||||
model = model if model else self.model
|
||||
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model)
|
||||
except KeyError:
|
||||
logging.warning("model not found. Using cl100k_base encoding.")
|
||||
|
||||
encoding = tiktoken.get_encoding("cl100k_base")
|
||||
|
||||
if model in {
|
||||
"gpt-3.5-turbo-0613",
|
||||
"gpt-3.5-turbo-16k-0613",
|
||||
"gpt-4-0314",
|
||||
"gpt-4-32k-0314",
|
||||
"gpt-4-0613",
|
||||
"gpt-4-32k-0613",
|
||||
}:
|
||||
tokens_per_message = 3
|
||||
tokens_per_name = 1
|
||||
elif model == "gpt-3.5-turbo-0301":
|
||||
# every message follows <|start|>{role/name}\n{content}<|end|>\n
|
||||
tokens_per_message = 4
|
||||
# if there's a name, the role is omitted
|
||||
tokens_per_name = -1
|
||||
elif "gpt-3.5-turbo" in model or "gpt-35-turbo" in model:
|
||||
logging.info(
|
||||
"gpt-3.5-turbo may update over time. Returning num tokens assuming"
|
||||
" gpt-3.5-turbo-0613."
|
||||
)
|
||||
return self.count_tokens(text, model="gpt-3.5-turbo-0613")
|
||||
elif "gpt-4" in model:
|
||||
logging.info(
|
||||
"gpt-4 may update over time. Returning num tokens assuming"
|
||||
" gpt-4-0613."
|
||||
)
|
||||
return self.count_tokens(text, model="gpt-4-0613")
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f"""token_count() is not implemented 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
|
||||
|
||||
for message in text:
|
||||
num_tokens += tokens_per_message
|
||||
for key, value in message.items():
|
||||
num_tokens += len(encoding.encode(value))
|
||||
if key == "name":
|
||||
num_tokens += tokens_per_name
|
||||
|
||||
# every reply is primed with <|start|>assistant<|message|>
|
||||
num_tokens += 3
|
||||
|
||||
return num_tokens
|
||||
else:
|
||||
return len(
|
||||
self.encoding.encode(text, allowed_special=set(self.stop_sequences))
|
||||
)
|
||||
Loading…
Reference in new issue