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Add Path and PathLike support when uploading images (#2514)

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* Add Path and PathLike support when uploading images
Improve raise_for_status in special cases
Move ImageResponse to providers.response module
Improve OpenaiChat and OpenaiAccount providers
Add Sources for web_search in OpenaiChat
Add JsonConversation for import and export conversations to js
Add RequestLogin response type
Add TitleGeneration support in OpenaiChat and gui
* Improve Docker Container Guide in README.md
* Add tool calls api support, add search tool support
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H Lohaus 2024-12-28 16:50:08 +01:00 • committed by GitHub
parent 9918df98b3
commit 86e36efe6b
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34 changed files with 935 additions and 1333 deletions
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28
g4f/client/stubs.py
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@ -1,10 +1,13 @@
from __future__ import annotations
from typing import Optional, List, Dict
from typing import Optional, List, Dict, Any
from time import time
from .helper import filter_none
ToolCalls = Optional[List[Dict[str, Any]]]
Usage = Optional[Dict[str, int]]
try:
from pydantic import BaseModel, Field
except ImportError:
@ -57,10 +60,11 @@ class ChatCompletionChunk(BaseModel):
class ChatCompletionMessage(BaseModel):
role: str
content: str
tool_calls: ToolCalls
@classmethod
def model_construct(cls, content: str):
return super().model_construct(role="assistant", content=content)
def model_construct(cls, content: str, tool_calls: ToolCalls = None):
return super().model_construct(role="assistant", content=content, **filter_none(tool_calls=tool_calls))
class ChatCompletionChoice(BaseModel):
index: int
@ -78,11 +82,11 @@ class ChatCompletion(BaseModel):
model: str
provider: Optional[str]
choices: List[ChatCompletionChoice]
usage: Dict[str, int] = Field(examples=[{
usage: Usage = Field(default={
"prompt_tokens": 0, #prompt_tokens,
"completion_tokens": 0, #completion_tokens,
"total_tokens": 0, #prompt_tokens + completion_tokens,
}])
})
@classmethod
def model_construct(
@ -90,7 +94,9 @@ class ChatCompletion(BaseModel):
content: str,
finish_reason: str,
completion_id: str = None,
created: int = None
created: int = None,
tool_calls: ToolCalls = None,
usage: Usage = None
):
return super().model_construct(
id=f"chatcmpl-{completion_id}" if completion_id else None,
@ -99,14 +105,10 @@ class ChatCompletion(BaseModel):
model=None,
provider=None,
choices=[ChatCompletionChoice.model_construct(
ChatCompletionMessage.model_construct(content),
finish_reason
ChatCompletionMessage.model_construct(content, tool_calls),
finish_reason,
)],
usage={
"prompt_tokens": 0, #prompt_tokens,
"completion_tokens": 0, #completion_tokens,
"total_tokens": 0, #prompt_tokens + completion_tokens,
}
**filter_none(usage=usage)
)
class ChatCompletionDelta(BaseModel):