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protocol.py
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protocol.py
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import json
import uuid
import time
from openai._models import BaseModel as OpenAIObject
from typing import Dict, List, Literal, Optional, Tuple, Union
def _generate_id(): # private helper function
return "chatcmpl-" + str(uuid.uuid4())
class Function(OpenAIObject):
arguments: str
name: Optional[
str
] # can be None - openai e.g.: ChoiceDeltaToolCallFunction(arguments='{"', name=None), type=None)
def __init__(
self,
arguments: Optional[Union[Dict, str]],
name: Optional[str] = None,
**params,
):
if arguments is None:
arguments = ""
elif isinstance(arguments, Dict):
arguments = json.dumps(arguments)
else:
arguments = arguments
name = name
# Build a dictionary with the structure your BaseModel expects
data = {"arguments": arguments, "name": name, **params}
super(Function, self).__init__(**data)
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def __setitem__(self, key, value):
# Allow dictionary-style assignment of attributes
setattr(self, key, value)
class FunctionCall(OpenAIObject):
arguments: str
name: Optional[str] = None
class ChatCompletionMessageToolCall(OpenAIObject):
def __init__(
self,
function: Union[Dict, Function],
id: Optional[str] = None,
type: Optional[str] = None,
**params,
):
super(ChatCompletionMessageToolCall, self).__init__(**params)
if isinstance(function, Dict):
self.function = Function(**function)
else:
self.function = function
if id is not None:
self.id = id
else:
self.id = f"{uuid.uuid4()}"
if type is not None:
self.type = type
else:
self.type = "function"
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def __setitem__(self, key, value):
# Allow dictionary-style assignment of attributes
setattr(self, key, value)
class ChatCompletionDeltaToolCall(OpenAIObject):
id: Optional[str] = None
function: Function
type: Optional[str] = None
index: int
class Message(OpenAIObject):
content: Optional[str]
role: Literal["assistant"]
tool_calls: Optional[List[ChatCompletionMessageToolCall]]
function_call: Optional[FunctionCall]
def __init__(
self,
content: Optional[str] = None,
role: Literal["assistant"] = "assistant",
function_call=None,
tool_calls=None,
**params,
):
init_values = {
"content": content,
"role": "assistant",
"function_call": (
FunctionCall(**function_call) if function_call is not None else None
),
"tool_calls": (
[ChatCompletionMessageToolCall(**tool_call) for tool_call in tool_calls]
if tool_calls is not None
else None
),
}
super(Message, self).__init__(
**init_values,
**params,
)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def __setitem__(self, key, value):
# Allow dictionary-style assignment of attributes
setattr(self, key, value)
def json(self, **kwargs):
try:
return self.model_dump() # noqa
except:
# if using pydantic v1
return self.dict()
class TopLogprob(OpenAIObject):
token: str
bytes: Optional[List[int]] = None
logprob: float
class ChatCompletionTokenLogprob(OpenAIObject):
token: str
bytes: Optional[List[int]] = None
logprob: float
top_logprobs: List[TopLogprob]
class ChoiceLogprobs(OpenAIObject):
content: Optional[List[ChatCompletionTokenLogprob]] = None
class Delta(OpenAIObject):
def __init__(
self,
content=None,
role=None,
function_call=None,
tool_calls=None,
**params,
):
super(Delta, self).__init__(**params)
self.content = content
self.role = role
if function_call is not None and isinstance(function_call, dict):
self.function_call = FunctionCall(**function_call)
else:
self.function_call = function_call
if tool_calls is not None and isinstance(tool_calls, list):
self.tool_calls = []
for tool_call in tool_calls:
if isinstance(tool_call, dict):
if tool_call.get("index", None) is None:
tool_call["index"] = 0
self.tool_calls.append(ChatCompletionDeltaToolCall(**tool_call))
elif isinstance(tool_call, ChatCompletionDeltaToolCall):
self.tool_calls.append(tool_call)
else:
self.tool_calls = tool_calls
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def __setitem__(self, key, value):
# Allow dictionary-style assignment of attributes
setattr(self, key, value)
class Choices(OpenAIObject):
def __init__(
self,
finish_reason=None,
index=0,
message: Optional[Union[Message, dict]] = None,
logprobs=None,
enhancements=None,
**params,
):
super(Choices, self).__init__(**params)
if finish_reason is not None:
self.finish_reason = finish_reason
else:
self.finish_reason = "stop"
self.index = index
if message is None:
self.message = Message()
else:
if isinstance(message, Message):
self.message = message
elif isinstance(message, dict):
self.message = Message(**message)
if logprobs is not None:
self.logprobs = logprobs
if enhancements is not None:
self.enhancements = enhancements
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def __setitem__(self, key, value):
# Allow dictionary-style assignment of attributes
setattr(self, key, value)
class Usage(OpenAIObject):
def __init__(
self, prompt_tokens=None, completion_tokens=None, total_tokens=None, **params
):
super(Usage, self).__init__(**params)
if prompt_tokens:
self.prompt_tokens = prompt_tokens
if completion_tokens:
self.completion_tokens = completion_tokens
if total_tokens:
self.total_tokens = total_tokens
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def __setitem__(self, key, value):
# Allow dictionary-style assignment of attributes
setattr(self, key, value)
class StreamingChoices(OpenAIObject):
def __init__(
self,
finish_reason=None,
index=0,
delta: Optional[Delta] = None,
logprobs=None,
enhancements=None,
**params,
):
super(StreamingChoices, self).__init__(**params)
if finish_reason:
self.finish_reason = finish_reason
else:
self.finish_reason = None
self.index = index
if delta is not None:
if isinstance(delta, Delta):
self.delta = delta
elif isinstance(delta, dict):
self.delta = Delta(**delta)
else:
self.delta = Delta()
if enhancements is not None:
self.enhancements = enhancements
if logprobs is not None and isinstance(logprobs, dict):
self.logprobs = ChoiceLogprobs(**logprobs)
else:
self.logprobs = logprobs # type: ignore
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def __setitem__(self, key, value):
# Allow dictionary-style assignment of attributes
setattr(self, key, value)
class ModelResponse(OpenAIObject):
id: str
"""A unique identifier for the completion."""
choices: List[Union[Choices, StreamingChoices]]
"""The list of completion choices the model generated for the input prompt."""
created: int
"""The Unix timestamp (in seconds) of when the completion was created."""
model: Optional[str] = None
"""The model used for completion."""
object: str
"""The object type, which is always "text_completion" """
system_fingerprint: Optional[str] = None
"""This fingerprint represents the backend configuration that the model runs with.
Can be used in conjunction with the `seed` request parameter to understand when
backend changes have been made that might impact determinism.
"""
_hidden_params: dict = {}
def __init__(
self,
id=None,
choices=None,
created=None,
model=None,
object=None,
system_fingerprint=None,
usage=None,
stream=None,
stream_options=None,
response_ms=None,
hidden_params=None,
**params,
) -> None:
if stream is not None and stream is True:
object = "chat.completion.chunk"
if choices is not None and isinstance(choices, list):
new_choices = []
for choice in choices:
if isinstance(choice, StreamingChoices):
_new_choice = choice
elif isinstance(choice, dict):
_new_choice = StreamingChoices(**choice)
new_choices.append(_new_choice)
choices = new_choices
else:
choices = [StreamingChoices()]
else:
object = "chat.completion"
if choices is not None and isinstance(choices, list):
new_choices = []
for choice in choices:
if isinstance(choice, Choices):
_new_choice = choice # type: ignore
elif isinstance(choice, dict):
_new_choice = Choices(**choice) # type: ignore
else:
_new_choice = choice
new_choices.append(_new_choice)
choices = new_choices
else:
choices = [Choices()]
if id is None:
id = _generate_id()
else:
id = id
if created is None:
created = int(time.time())
else:
created = created
model = model
if usage is not None:
if isinstance(usage, dict):
usage = Usage(**usage)
else:
usage = usage
elif stream is None or stream is False:
usage = Usage()
if hidden_params:
self._hidden_params = hidden_params
init_values = {
"id": id,
"choices": choices,
"created": created,
"model": model,
"object": object,
"system_fingerprint": system_fingerprint,
}
if usage is not None:
init_values["usage"] = usage
super().__init__(
**init_values,
**params,
)
def __contains__(self, key):
# Define custom behavior for the 'in' operator
return hasattr(self, key)
def get(self, key, default=None):
# Custom .get() method to access attributes with a default value if the attribute doesn't exist
return getattr(self, key, default)
def __getitem__(self, key):
# Allow dictionary-style access to attributes
return getattr(self, key)
def json(self, **kwargs):
try:
return self.model_dump() # noqa
except:
# if using pydantic v1
return self.dict()