chore: initial public snapshot for github upload
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"""
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Streaming utilities for ChatGPT provider.
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Normalizes non-spec-compliant tool_call chunks from the ChatGPT backend API.
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"""
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from typing import Any, Dict, Optional
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class ChatGPTToolCallNormalizer:
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"""
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Wraps a streaming response and fixes tool_call index/dedup issues.
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The ChatGPT backend API (chatgpt.com/backend-api) sends non-spec-compliant
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streaming tool call chunks:
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1. `index` is always 0, even for multiple parallel tool calls
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2. `id` and `name` get repeated in "closing" chunks that shouldn't exist
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This wrapper normalizes the stream to match the OpenAI spec before yielding
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chunks to the consumer.
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"""
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def __init__(self, stream: Any):
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self._stream = stream
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self._seen_ids: Dict[str, int] = {} # tool_call_id -> assigned_index
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self._next_index: int = 0
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self._last_id: Optional[
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str
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] = None # tracks which tool call the next delta belongs to
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def __getattr__(self, name: str) -> Any:
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return getattr(self._stream, name)
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def __iter__(self):
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return self
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def __aiter__(self):
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return self
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def __next__(self):
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while True:
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chunk = next(self._stream)
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result = self._normalize(chunk)
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if result is not None:
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return result
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async def __anext__(self):
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while True:
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chunk = await self._stream.__anext__()
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result = self._normalize(chunk)
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if result is not None:
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return result
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def _normalize(self, chunk: Any) -> Any:
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"""Fix tool_calls in the chunk. Returns None to skip duplicate chunks."""
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if not chunk.choices:
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return chunk
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delta = chunk.choices[0].delta
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if delta is None or not delta.tool_calls:
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return chunk
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normalized = []
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for tc in delta.tool_calls:
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if tc.id and tc.id not in self._seen_ids:
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# New tool call — assign correct index
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self._seen_ids[tc.id] = self._next_index
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tc.index = self._next_index
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self._last_id = tc.id
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self._next_index += 1
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normalized.append(tc)
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elif tc.id and tc.id in self._seen_ids:
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# Duplicate "closing" chunk — skip it
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continue
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else:
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# Continuation delta (id=None) — fix index
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if self._last_id:
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tc.index = self._seen_ids[self._last_id]
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normalized.append(tc)
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if not normalized:
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return None # all tool_calls were duplicates, skip chunk
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delta.tool_calls = normalized
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return chunk
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@@ -0,0 +1,79 @@
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from typing import Any, List, Optional, Tuple
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from litellm.exceptions import AuthenticationError
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from litellm.llms.openai.openai import OpenAIConfig
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from litellm.types.llms.openai import AllMessageValues
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from ..authenticator import Authenticator
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from ..common_utils import (
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GetAccessTokenError,
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ensure_chatgpt_session_id,
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get_chatgpt_default_headers,
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)
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from .streaming_utils import ChatGPTToolCallNormalizer
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class ChatGPTConfig(OpenAIConfig):
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def __init__(
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self,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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custom_llm_provider: str = "openai",
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) -> None:
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super().__init__()
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self.authenticator = Authenticator()
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def _get_openai_compatible_provider_info(
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self,
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model: str,
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api_base: Optional[str],
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api_key: Optional[str],
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custom_llm_provider: str,
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) -> Tuple[Optional[str], Optional[str], str]:
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dynamic_api_base = self.authenticator.get_api_base()
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try:
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dynamic_api_key = self.authenticator.get_access_token()
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except GetAccessTokenError as e:
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raise AuthenticationError(
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model=model,
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llm_provider=custom_llm_provider,
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message=str(e),
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)
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return dynamic_api_base, dynamic_api_key, custom_llm_provider
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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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validated_headers = super().validate_environment(
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headers, model, messages, optional_params, litellm_params, api_key, api_base
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)
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account_id = self.authenticator.get_account_id()
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session_id = ensure_chatgpt_session_id(litellm_params)
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default_headers = get_chatgpt_default_headers(
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api_key or "", account_id, session_id
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)
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return {**default_headers, **validated_headers}
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def post_stream_processing(self, stream: Any) -> Any:
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return ChatGPTToolCallNormalizer(stream)
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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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optional_params = super().map_openai_params(
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non_default_params, optional_params, model, drop_params
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)
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optional_params.setdefault("stream", False)
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return optional_params
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