mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-12-06 02:30:41 -08:00
106 lines
No EOL
4.3 KiB
Python
106 lines
No EOL
4.3 KiB
Python
from __future__ import annotations
|
|
|
|
from typing import Optional
|
|
from functools import partial
|
|
from dataclasses import dataclass, field
|
|
|
|
from pydantic_ai import ModelResponsePart, ThinkingPart, ToolCallPart
|
|
from pydantic_ai.models import Model, ModelResponse, KnownModelName, infer_model
|
|
from pydantic_ai.usage import RequestUsage
|
|
from pydantic_ai.models.openai import OpenAIChatModel
|
|
from pydantic_ai.models.openai import OpenAISystemPromptRole, _now_utc, split_content_into_text_and_thinking, replace
|
|
|
|
import pydantic_ai.models.openai
|
|
pydantic_ai.models.openai.NOT_GIVEN = None
|
|
|
|
from ..client import AsyncClient, ChatCompletion
|
|
|
|
@dataclass(init=False)
|
|
class AIModel(OpenAIChatModel):
|
|
"""A model that uses the G4F API."""
|
|
|
|
client: AsyncClient = field(repr=False)
|
|
system_prompt_role: OpenAISystemPromptRole | None = field(default=None)
|
|
|
|
_model_name: str = field(repr=False)
|
|
_provider: str = field(repr=False)
|
|
_system: Optional[str] = field(repr=False)
|
|
|
|
def __init__(
|
|
self,
|
|
model_name: str,
|
|
provider: str | None = None,
|
|
*,
|
|
system_prompt_role: OpenAISystemPromptRole | None = None,
|
|
system: str | None = 'g4f',
|
|
**kwargs
|
|
):
|
|
"""Initialize an AI model.
|
|
|
|
Args:
|
|
model_name: The name of the AI model to use. List of model names available
|
|
[here](https://github.com/openai/openai-python/blob/v1.54.3/src/openai/types/chat_model.py#L7)
|
|
(Unfortunately, despite being ask to do so, OpenAI do not provide `.inv` files for their API).
|
|
system_prompt_role: The role to use for the system prompt message. If not provided, defaults to `'system'`.
|
|
In the future, this may be inferred from the model name.
|
|
system: The model provider used, defaults to `openai`. This is for observability purposes, you must
|
|
customize the `base_url` and `api_key` to use a different provider.
|
|
"""
|
|
self._model_name = model_name
|
|
self._provider = getattr(provider, '__name__', provider)
|
|
self.client = AsyncClient(provider=provider, **kwargs)
|
|
self.system_prompt_role = system_prompt_role
|
|
self._system = system
|
|
|
|
def name(self) -> str:
|
|
if self._provider:
|
|
return f'g4f:{self._provider}:{self._model_name}'
|
|
return f'g4f:{self._model_name}'
|
|
|
|
def _process_response(self, response: ChatCompletion | str) -> ModelResponse:
|
|
"""Process a non-streamed response, and prepare a message to return."""
|
|
choice = response.choices[0]
|
|
items: list[ModelResponsePart] = []
|
|
|
|
if reasoning := getattr(choice.message, 'reasoning', None):
|
|
items.append(ThinkingPart(id='reasoning', content=reasoning, provider_name=self.system))
|
|
|
|
if choice.message.content:
|
|
items.extend(
|
|
(replace(part, id='content', provider_name=self.system) if isinstance(part, ThinkingPart) else part)
|
|
for part in split_content_into_text_and_thinking(choice.message.content, self.profile.thinking_tags)
|
|
)
|
|
if choice.message.tool_calls is not None:
|
|
for c in choice.message.tool_calls:
|
|
items.append(ToolCallPart(c.function.name, c.function.arguments, tool_call_id=c.id))
|
|
usage = RequestUsage(
|
|
input_tokens=response.usage.prompt_tokens,
|
|
output_tokens=response.usage.completion_tokens,
|
|
)
|
|
|
|
return ModelResponse(
|
|
parts=items,
|
|
usage=usage,
|
|
model_name=response.model,
|
|
timestamp=_now_utc(),
|
|
provider_details=None,
|
|
provider_response_id=response.id,
|
|
provider_name=self._provider,
|
|
finish_reason=choice.finish_reason,
|
|
)
|
|
|
|
def new_infer_model(model: Model | KnownModelName, api_key: str = None) -> Model:
|
|
if isinstance(model, Model):
|
|
return model
|
|
if model.startswith("g4f:"):
|
|
model = model[4:]
|
|
if ":" in model:
|
|
provider, model = model.split(":", 1)
|
|
return AIModel(model, provider=provider, api_key=api_key)
|
|
return AIModel(model)
|
|
return infer_model(model)
|
|
|
|
def patch_infer_model(api_key: str | None = None):
|
|
import pydantic_ai.models
|
|
|
|
pydantic_ai.models.infer_model = partial(new_infer_model, api_key=api_key) |