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_types.py
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import json
from base64 import b64decode, b64encode
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
from pydantic import (
BaseModel,
ByteSize,
ConfigDict,
Field,
model_serializer,
)
from pydantic.json_schema import JsonSchemaValue
from typing_extensions import Annotated, Literal
class SubscriptableBaseModel(BaseModel):
def __getitem__(self, key: str) -> Any:
"""
>>> msg = Message(role='user')
>>> msg['role']
'user'
>>> msg = Message(role='user')
>>> msg['nonexistent']
Traceback (most recent call last):
KeyError: 'nonexistent'
"""
if key in self:
return getattr(self, key)
raise KeyError(key)
def __setitem__(self, key: str, value: Any) -> None:
"""
>>> msg = Message(role='user')
>>> msg['role'] = 'assistant'
>>> msg['role']
'assistant'
>>> tool_call = Message.ToolCall(function=Message.ToolCall.Function(name='foo', arguments={}))
>>> msg = Message(role='user', content='hello')
>>> msg['tool_calls'] = [tool_call]
>>> msg['tool_calls'][0]['function']['name']
'foo'
"""
setattr(self, key, value)
def __contains__(self, key: str) -> bool:
"""
>>> msg = Message(role='user')
>>> 'nonexistent' in msg
False
>>> 'role' in msg
True
>>> 'content' in msg
False
>>> msg.content = 'hello!'
>>> 'content' in msg
True
>>> msg = Message(role='user', content='hello!')
>>> 'content' in msg
True
>>> 'tool_calls' in msg
False
>>> msg['tool_calls'] = []
>>> 'tool_calls' in msg
True
>>> msg['tool_calls'] = [Message.ToolCall(function=Message.ToolCall.Function(name='foo', arguments={}))]
>>> 'tool_calls' in msg
True
>>> msg['tool_calls'] = None
>>> 'tool_calls' in msg
True
>>> tool = Tool()
>>> 'type' in tool
True
"""
if key in self.model_fields_set:
return True
if key in self.model_fields:
return self.model_fields[key].default is not None
return False
def get(self, key: str, default: Any = None) -> Any:
"""
>>> msg = Message(role='user')
>>> msg.get('role')
'user'
>>> msg = Message(role='user')
>>> msg.get('nonexistent')
>>> msg = Message(role='user')
>>> msg.get('nonexistent', 'default')
'default'
>>> msg = Message(role='user', tool_calls=[ Message.ToolCall(function=Message.ToolCall.Function(name='foo', arguments={}))])
>>> msg.get('tool_calls')[0]['function']['name']
'foo'
"""
return self[key] if key in self else default
class Options(SubscriptableBaseModel):
# load time options
numa: Optional[bool] = None
num_ctx: Optional[int] = None
num_batch: Optional[int] = None
num_gpu: Optional[int] = None
main_gpu: Optional[int] = None
low_vram: Optional[bool] = None
f16_kv: Optional[bool] = None
logits_all: Optional[bool] = None
vocab_only: Optional[bool] = None
use_mmap: Optional[bool] = None
use_mlock: Optional[bool] = None
embedding_only: Optional[bool] = None
num_thread: Optional[int] = None
# runtime options
num_keep: Optional[int] = None
seed: Optional[int] = None
num_predict: Optional[int] = None
top_k: Optional[int] = None
top_p: Optional[float] = None
tfs_z: Optional[float] = None
typical_p: Optional[float] = None
repeat_last_n: Optional[int] = None
temperature: Optional[float] = None
repeat_penalty: Optional[float] = None
presence_penalty: Optional[float] = None
frequency_penalty: Optional[float] = None
mirostat: Optional[int] = None
mirostat_tau: Optional[float] = None
mirostat_eta: Optional[float] = None
penalize_newline: Optional[bool] = None
stop: Optional[Sequence[str]] = None
class BaseRequest(SubscriptableBaseModel):
model: Annotated[str, Field(min_length=1)]
'Model to use for the request.'
class BaseStreamableRequest(BaseRequest):
stream: Optional[bool] = None
'Stream response.'
class BaseGenerateRequest(BaseStreamableRequest):
options: Optional[Union[Mapping[str, Any], Options]] = None
'Options to use for the request.'
format: Optional[Union[Literal['', 'json'], JsonSchemaValue]] = None
'Format of the response.'
keep_alive: Optional[Union[float, str]] = None
'Keep model alive for the specified duration.'
class Image(BaseModel):
value: Union[str, bytes, Path]
@model_serializer
def serialize_model(self):
if isinstance(self.value, (Path, bytes)):
return b64encode(self.value.read_bytes() if isinstance(self.value, Path) else self.value).decode()
if isinstance(self.value, str):
try:
if Path(self.value).exists():
return b64encode(Path(self.value).read_bytes()).decode()
except Exception:
# Long base64 string can't be wrapped in Path, so try to treat as base64 string
pass
# String might be a file path, but might not exist
if self.value.split('.')[-1] in ('png', 'jpg', 'jpeg', 'webp'):
raise ValueError(f'File {self.value} does not exist')
try:
# Try to decode to check if it's already base64
b64decode(self.value)
return self.value
except Exception:
raise ValueError('Invalid image data, expected base64 string or path to image file') from Exception
class GenerateRequest(BaseGenerateRequest):
prompt: Optional[str] = None
'Prompt to generate response from.'
suffix: Optional[str] = None
'Suffix to append to the response.'
system: Optional[str] = None
'System prompt to prepend to the prompt.'
template: Optional[str] = None
'Template to use for the response.'
context: Optional[Sequence[int]] = None
'Tokenized history to use for the response.'
raw: Optional[bool] = None
images: Optional[Sequence[Image]] = None
'Image data for multimodal models.'
class BaseGenerateResponse(SubscriptableBaseModel):
model: Optional[str] = None
'Model used to generate response.'
created_at: Optional[str] = None
'Time when the request was created.'
done: Optional[bool] = None
'True if response is complete, otherwise False. Useful for streaming to detect the final response.'
done_reason: Optional[str] = None
'Reason for completion. Only present when done is True.'
total_duration: Optional[int] = None
'Total duration in nanoseconds.'
load_duration: Optional[int] = None
'Load duration in nanoseconds.'
prompt_eval_count: Optional[int] = None
'Number of tokens evaluated in the prompt.'
prompt_eval_duration: Optional[int] = None
'Duration of evaluating the prompt in nanoseconds.'
eval_count: Optional[int] = None
'Number of tokens evaluated in inference.'
eval_duration: Optional[int] = None
'Duration of evaluating inference in nanoseconds.'
class GenerateResponse(BaseGenerateResponse):
"""
Response returned by generate requests.
"""
response: str
'Response content. When streaming, this contains a fragment of the response.'
context: Optional[Sequence[int]] = None
'Tokenized history up to the point of the response.'
class Message(SubscriptableBaseModel):
"""
Chat message.
"""
role: Literal['user', 'assistant', 'system', 'tool']
"Assumed role of the message. Response messages has role 'assistant' or 'tool'."
content: Optional[str] = None
'Content of the message. Response messages contains message fragments when streaming.'
images: Optional[Sequence[Image]] = None
"""
Optional list of image data for multimodal models.
Valid input types are:
- `str` or path-like object: path to image file
- `bytes` or bytes-like object: raw image data
Valid image formats depend on the model. See the model card for more information.
"""
class ToolCall(SubscriptableBaseModel):
"""
Model tool calls.
"""
class Function(SubscriptableBaseModel):
"""
Tool call function.
"""
name: str
'Name of the function.'
arguments: Mapping[str, Any]
'Arguments of the function.'
function: Function
'Function to be called.'
tool_calls: Optional[Sequence[ToolCall]] = None
"""
Tools calls to be made by the model.
"""
class Tool(SubscriptableBaseModel):
type: Optional[Literal['function']] = 'function'
class Function(SubscriptableBaseModel):
name: Optional[str] = None
description: Optional[str] = None
class Parameters(SubscriptableBaseModel):
type: Optional[Literal['object']] = 'object'
required: Optional[Sequence[str]] = None
class Property(SubscriptableBaseModel):
model_config = ConfigDict(arbitrary_types_allowed=True)
type: Optional[str] = None
description: Optional[str] = None
properties: Optional[Mapping[str, Property]] = None
parameters: Optional[Parameters] = None
function: Optional[Function] = None
class ChatRequest(BaseGenerateRequest):
messages: Optional[Sequence[Union[Mapping[str, Any], Message]]] = None
'Messages to chat with.'
tools: Optional[Sequence[Tool]] = None
'Tools to use for the chat.'
class ChatResponse(BaseGenerateResponse):
"""
Response returned by chat requests.
"""
message: Message
'Response message.'
class EmbedRequest(BaseRequest):
input: Union[str, Sequence[str]]
'Input text to embed.'
truncate: Optional[bool] = None
'Truncate the input to the maximum token length.'
options: Optional[Union[Mapping[str, Any], Options]] = None
'Options to use for the request.'
keep_alive: Optional[Union[float, str]] = None
class EmbedResponse(BaseGenerateResponse):
"""
Response returned by embed requests.
"""
embeddings: Sequence[Sequence[float]]
'Embeddings of the inputs.'
class EmbeddingsRequest(BaseRequest):
prompt: Optional[str] = None
'Prompt to generate embeddings from.'
options: Optional[Union[Mapping[str, Any], Options]] = None
'Options to use for the request.'
keep_alive: Optional[Union[float, str]] = None
class EmbeddingsResponse(SubscriptableBaseModel):
"""
Response returned by embeddings requests.
"""
embedding: Sequence[float]
'Embedding of the prompt.'
class PullRequest(BaseStreamableRequest):
"""
Request to pull the model.
"""
insecure: Optional[bool] = None
'Allow insecure (HTTP) connections.'
class PushRequest(BaseStreamableRequest):
"""
Request to pull the model.
"""
insecure: Optional[bool] = None
'Allow insecure (HTTP) connections.'
class CreateRequest(BaseStreamableRequest):
@model_serializer(mode='wrap')
def serialize_model(self, nxt):
output = nxt(self)
if 'from_' in output:
output['from'] = output.pop('from_')
return output
"""
Request to create a new model.
"""
quantize: Optional[str] = None
from_: Optional[str] = None
files: Optional[Dict[str, str]] = None
adapters: Optional[Dict[str, str]] = None
template: Optional[str] = None
license: Optional[Union[str, List[str]]] = None
system: Optional[str] = None
parameters: Optional[Union[Mapping[str, Any], Options]] = None
messages: Optional[Sequence[Union[Mapping[str, Any], Message]]] = None
class ModelDetails(SubscriptableBaseModel):
parent_model: Optional[str] = None
format: Optional[str] = None
family: Optional[str] = None
families: Optional[Sequence[str]] = None
parameter_size: Optional[str] = None
quantization_level: Optional[str] = None
class ListResponse(SubscriptableBaseModel):
class Model(SubscriptableBaseModel):
model: Optional[str] = None
modified_at: Optional[datetime] = None
digest: Optional[str] = None
size: Optional[ByteSize] = None
details: Optional[ModelDetails] = None
models: Sequence[Model]
'List of models.'
class DeleteRequest(BaseRequest):
"""
Request to delete a model.
"""
class CopyRequest(BaseModel):
"""
Request to copy a model.
"""
source: str
'Source model to copy.'
destination: str
'Destination model to copy to.'
class StatusResponse(SubscriptableBaseModel):
status: Optional[str] = None
class ProgressResponse(StatusResponse):
completed: Optional[int] = None
total: Optional[int] = None
digest: Optional[str] = None
class ShowRequest(BaseRequest):
"""
Request to show model information.
"""
class ShowResponse(SubscriptableBaseModel):
modified_at: Optional[datetime] = None
template: Optional[str] = None
modelfile: Optional[str] = None
license: Optional[str] = None
details: Optional[ModelDetails] = None
modelinfo: Optional[Mapping[str, Any]] = Field(alias='model_info')
parameters: Optional[str] = None
class ProcessResponse(SubscriptableBaseModel):
class Model(SubscriptableBaseModel):
model: Optional[str] = None
name: Optional[str] = None
digest: Optional[str] = None
expires_at: Optional[datetime] = None
size: Optional[ByteSize] = None
size_vram: Optional[ByteSize] = None
details: Optional[ModelDetails] = None
models: Sequence[Model]
class RequestError(Exception):
"""
Common class for request errors.
"""
def __init__(self, error: str):
super().__init__(error)
self.error = error
'Reason for the error.'
class ResponseError(Exception):
"""
Common class for response errors.
"""
def __init__(self, error: str, status_code: int = -1):
try:
# try to parse content as JSON and extract 'error'
# fallback to raw content if JSON parsing fails
error = json.loads(error).get('error', error)
except json.JSONDecodeError:
...
super().__init__(error)
self.error = error
'Reason for the error.'
self.status_code = status_code
'HTTP status code of the response.'
def __str__(self) -> str:
return f'{self.error} (status code: {self.status_code})'