chore: initial public snapshot for github upload
This commit is contained in:
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@@ -0,0 +1,510 @@
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"""
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GigaChat Chat Transformation
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Transforms OpenAI-format requests to GigaChat format and back.
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"""
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import json
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import time
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import uuid
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from typing import TYPE_CHECKING, Any, AsyncIterator, Iterator, List, Optional, Union
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import httpx
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from litellm._logging import verbose_logger
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from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
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from litellm.secret_managers.main import get_secret_str
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from litellm.types.llms.openai import AllMessageValues
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from litellm.types.utils import Choices, Message, ModelResponse, Usage
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from ..authenticator import get_access_token
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from ..file_handler import upload_file_sync
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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# GigaChat API endpoint
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GIGACHAT_BASE_URL = "https://gigachat.devices.sberbank.ru/api/v1"
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def is_valid_json(value: str) -> bool:
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"""Checks whether the value passed is a valid serialized JSON string"""
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try:
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json.loads(value)
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except json.JSONDecodeError:
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return False
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else:
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return True
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class GigaChatError(BaseLLMException):
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"""GigaChat API error."""
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pass
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class GigaChatConfig(BaseConfig):
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"""
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Configuration class for GigaChat API.
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GigaChat is Sber's (Russia's largest bank) LLM API.
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Supported parameters:
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temperature: Sampling temperature (0-2, default 0.87)
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top_p: Nucleus sampling parameter
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max_tokens: Maximum tokens to generate
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repetition_penalty: Repetition penalty factor
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profanity_check: Enable content filtering
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stream: Enable streaming
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"""
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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max_tokens: Optional[int] = None
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repetition_penalty: Optional[float] = None
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profanity_check: Optional[bool] = None
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def __init__(
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self,
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temperature: Optional[float] = None,
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top_p: Optional[float] = None,
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max_tokens: Optional[int] = None,
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repetition_penalty: Optional[float] = None,
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profanity_check: Optional[bool] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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# Instance variables for current request context
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self._current_credentials: Optional[str] = None
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self._current_api_base: Optional[str] = None
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def get_complete_url(
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self,
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api_base: Optional[str],
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api_key: Optional[str],
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model: str,
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optional_params: dict,
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litellm_params: dict,
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stream: Optional[bool] = None,
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) -> str:
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"""Get complete API URL for chat completions."""
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base = api_base or get_secret_str("GIGACHAT_API_BASE") or GIGACHAT_BASE_URL
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return f"{base}/chat/completions"
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def validate_environment(
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self,
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headers: dict,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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) -> dict:
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"""
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Set up headers with OAuth token.
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"""
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# Get access token
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credentials = (
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api_key
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or get_secret_str("GIGACHAT_CREDENTIALS")
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or get_secret_str("GIGACHAT_API_KEY")
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)
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access_token = get_access_token(credentials=credentials)
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# Store credentials for image uploads
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self._current_credentials = credentials
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self._current_api_base = api_base
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headers["Authorization"] = f"Bearer {access_token}"
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headers["Content-Type"] = "application/json"
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headers["Accept"] = "application/json"
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return headers
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def get_supported_openai_params(self, model: str) -> List[str]:
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"""Return list of supported OpenAI parameters."""
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return [
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"stream",
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"temperature",
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"top_p",
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"max_tokens",
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"max_completion_tokens",
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"stop",
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"tools",
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"tool_choice",
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"functions",
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"function_call",
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"response_format",
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]
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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) -> dict:
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"""Map OpenAI parameters to GigaChat parameters."""
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for param, value in non_default_params.items():
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if param == "stream":
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optional_params["stream"] = value
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elif param == "temperature":
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# GigaChat: temperature 0 means use top_p=0 instead
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if value == 0:
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optional_params["top_p"] = 0
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else:
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optional_params["temperature"] = value
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elif param == "top_p":
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optional_params["top_p"] = value
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elif param in ("max_tokens", "max_completion_tokens"):
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optional_params["max_tokens"] = value
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elif param == "stop":
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# GigaChat doesn't support stop sequences
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pass
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elif param == "tools":
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# Convert tools to functions format
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optional_params["functions"] = self._convert_tools_to_functions(value)
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elif param == "tool_choice":
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# Map OpenAI tool_choice to GigaChat function_call
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mapped_choice = self._map_tool_choice(value)
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if mapped_choice is not None:
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optional_params["function_call"] = mapped_choice
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elif param == "functions":
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optional_params["functions"] = value
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elif param == "function_call":
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optional_params["function_call"] = value
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elif param == "response_format":
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# Handle structured output via function calling
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if value.get("type") == "json_schema":
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json_schema = value.get("json_schema", {})
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schema_name = json_schema.get("name", "structured_output")
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schema = json_schema.get("schema", {})
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function_def = {
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"name": schema_name,
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"description": f"Output structured response: {schema_name}",
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"parameters": schema,
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}
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if "functions" not in optional_params:
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optional_params["functions"] = []
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optional_params["functions"].append(function_def)
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optional_params["function_call"] = {"name": schema_name}
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optional_params["_structured_output"] = True
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return optional_params
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def _convert_tools_to_functions(self, tools: List[dict]) -> List[dict]:
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"""Convert OpenAI tools format to GigaChat functions format."""
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functions = []
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for tool in tools:
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if tool.get("type") == "function":
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func = tool.get("function", {})
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functions.append(
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{
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"name": func.get("name", ""),
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"description": func.get("description", ""),
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"parameters": func.get("parameters", {}),
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}
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)
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return functions
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def _map_tool_choice(
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self, tool_choice: Union[str, dict]
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) -> Optional[Union[str, dict]]:
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"""
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Map OpenAI tool_choice to GigaChat function_call format.
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OpenAI format:
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- "auto": Call zero, one, or multiple functions (default)
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- "required": Call one or more functions
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- "none": Don't call any functions
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- {"type": "function", "function": {"name": "get_weather"}}: Force specific function
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GigaChat format:
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- "none": Disable function calls
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- "auto": Automatic mode (default)
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- {"name": "get_weather"}: Force specific function
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Args:
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tool_choice: OpenAI tool_choice value
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Returns:
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GigaChat function_call value or None
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"""
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if tool_choice == "none":
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return "none"
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elif tool_choice == "auto":
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return "auto"
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elif tool_choice == "required":
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# GigaChat doesn't have a direct "required" equivalent
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# Use "auto" as the closest behavior
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return "auto"
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elif isinstance(tool_choice, dict):
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# OpenAI format: {"type": "function", "function": {"name": "func_name"}}
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# GigaChat format: {"name": "func_name"}
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if tool_choice.get("type") == "function":
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func_name = tool_choice.get("function", {}).get("name")
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if func_name:
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return {"name": func_name}
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# Default to None (don't set function_call)
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return None
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def _upload_image(self, image_url: str) -> Optional[str]:
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"""
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Upload image to GigaChat and return file_id.
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Args:
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image_url: URL or base64 data URL of the image
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Returns:
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file_id string or None if upload failed
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"""
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try:
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return upload_file_sync(
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image_url=image_url,
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credentials=self._current_credentials,
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api_base=self._current_api_base,
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)
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except Exception as e:
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verbose_logger.error(f"Failed to upload image: {e}")
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return None
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def transform_request(
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self,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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headers: dict,
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) -> dict:
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"""Transform OpenAI request to GigaChat format."""
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# Transform messages
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giga_messages = self._transform_messages(messages)
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# Build request
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request_data = {
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"model": model.replace("gigachat/", ""),
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"messages": giga_messages,
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}
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# Add optional params
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for key in [
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"temperature",
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"top_p",
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"max_tokens",
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"stream",
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"repetition_penalty",
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"profanity_check",
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]:
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if key in optional_params:
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request_data[key] = optional_params[key]
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# Add functions if present
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if "functions" in optional_params:
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request_data["functions"] = optional_params["functions"]
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if "function_call" in optional_params:
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request_data["function_call"] = optional_params["function_call"]
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return request_data
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def _transform_messages(self, messages: List[AllMessageValues]) -> List[dict]:
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"""Transform OpenAI messages to GigaChat format."""
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transformed = []
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for i, msg in enumerate(messages):
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message = dict(msg)
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# Remove unsupported fields
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message.pop("name", None)
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# Transform roles
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role = message.get("role", "user")
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if role == "developer":
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message["role"] = "system"
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elif role == "system" and i > 0:
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# GigaChat only allows system message as first message
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message["role"] = "user"
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elif role == "tool":
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message["role"] = "function"
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content = message.get("content", "")
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if not isinstance(content, str) or not is_valid_json(content):
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message["content"] = json.dumps(content, ensure_ascii=False)
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# Handle None content
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if message.get("content") is None:
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message["content"] = ""
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# Handle list content (multimodal) - extract text and images
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content = message.get("content")
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if isinstance(content, list):
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texts = []
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attachments = []
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for part in content:
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if isinstance(part, dict):
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if part.get("type") == "text":
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texts.append(part.get("text", ""))
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elif part.get("type") == "image_url":
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# Extract image URL and upload to GigaChat
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image_url = part.get("image_url", {})
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if isinstance(image_url, str):
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url = image_url
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else:
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url = image_url.get("url", "")
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if url:
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file_id = self._upload_image(url)
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if file_id:
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attachments.append(file_id)
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message["content"] = "\n".join(texts) if texts else ""
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if attachments:
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message["attachments"] = attachments
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# Transform tool_calls to function_call
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tool_calls = message.get("tool_calls")
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if tool_calls and isinstance(tool_calls, list) and len(tool_calls) > 0:
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tool_call = tool_calls[0]
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func = tool_call.get("function", {})
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args = func.get("arguments", "{}")
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if isinstance(args, str):
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try:
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args = json.loads(args)
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except json.JSONDecodeError:
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args = {}
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message["function_call"] = {
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"name": func.get("name", ""),
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"arguments": args,
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}
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message.pop("tool_calls", None)
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transformed.append(message)
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return transformed
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def transform_response(
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self,
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model: str,
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raw_response: httpx.Response,
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model_response: ModelResponse,
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logging_obj: LiteLLMLoggingObj,
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request_data: dict,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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encoding: Any,
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api_key: Optional[str] = None,
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json_mode: Optional[bool] = None,
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) -> ModelResponse:
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"""Transform GigaChat response to OpenAI format."""
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try:
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response_json = raw_response.json()
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except Exception:
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raise GigaChatError(
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status_code=raw_response.status_code,
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message=f"Invalid JSON response: {raw_response.text}",
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)
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is_structured_output = optional_params.get("_structured_output", False)
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choices = []
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for choice in response_json.get("choices", []):
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message_data = choice.get("message", {})
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finish_reason = choice.get("finish_reason", "stop")
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# Transform function_call to tool_calls or content
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if finish_reason == "function_call" and message_data.get("function_call"):
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func_call = message_data["function_call"]
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args = func_call.get("arguments", {})
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if is_structured_output:
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# Convert to content for structured output
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||||
if isinstance(args, dict):
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content = json.dumps(args, ensure_ascii=False)
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else:
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content = str(args)
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message_data["content"] = content
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message_data.pop("function_call", None)
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message_data.pop("functions_state_id", None)
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finish_reason = "stop"
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||||
else:
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# Convert to tool_calls format
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||||
if isinstance(args, dict):
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args = json.dumps(args, ensure_ascii=False)
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message_data["tool_calls"] = [
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{
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"id": f"call_{uuid.uuid4().hex[:24]}",
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"type": "function",
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||||
"function": {
|
||||
"name": func_call.get("name", ""),
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||||
"arguments": args,
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||||
},
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||||
}
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||||
]
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||||
message_data.pop("function_call", None)
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finish_reason = "tool_calls"
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||||
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||||
# Clean up GigaChat-specific fields
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||||
message_data.pop("functions_state_id", None)
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||||
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||||
choices.append(
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||||
Choices(
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||||
index=choice.get("index", 0),
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||||
message=Message(
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||||
role=message_data.get("role", "assistant"),
|
||||
content=message_data.get("content"),
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||||
tool_calls=message_data.get("tool_calls"),
|
||||
),
|
||||
finish_reason=finish_reason,
|
||||
)
|
||||
)
|
||||
|
||||
# Build usage
|
||||
usage_data = response_json.get("usage", {})
|
||||
usage = Usage(
|
||||
prompt_tokens=usage_data.get("prompt_tokens", 0),
|
||||
completion_tokens=usage_data.get("completion_tokens", 0),
|
||||
total_tokens=usage_data.get("total_tokens", 0),
|
||||
)
|
||||
|
||||
model_response.id = response_json.get("id", f"chatcmpl-{uuid.uuid4().hex[:12]}")
|
||||
model_response.created = response_json.get("created", int(time.time()))
|
||||
model_response.model = model
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||||
model_response.choices = choices # type: ignore
|
||||
setattr(model_response, "usage", usage)
|
||||
|
||||
return model_response
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||||
|
||||
def get_error_class(
|
||||
self,
|
||||
error_message: str,
|
||||
status_code: int,
|
||||
headers: Union[dict, httpx.Headers],
|
||||
) -> BaseLLMException:
|
||||
"""Return GigaChat error class."""
|
||||
return GigaChatError(
|
||||
status_code=status_code,
|
||||
message=error_message,
|
||||
headers=headers,
|
||||
)
|
||||
|
||||
def get_model_response_iterator(
|
||||
self,
|
||||
streaming_response: Union[Iterator[str], AsyncIterator[str], ModelResponse],
|
||||
sync_stream: bool,
|
||||
json_mode: Optional[bool] = False,
|
||||
):
|
||||
"""Return streaming response iterator."""
|
||||
from .streaming import GigaChatModelResponseIterator
|
||||
|
||||
return GigaChatModelResponseIterator(
|
||||
streaming_response=streaming_response,
|
||||
sync_stream=sync_stream,
|
||||
json_mode=json_mode,
|
||||
)
|
||||
Reference in New Issue
Block a user