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Merge pull request #38884 from BerriAI/litellm_techdebt_20260830

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chore(techdebt): clear fresh debt from the 2026-08-29 and 2026-08-30 windows
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Mateo Wang 2026-08-31 15:08:04 -07:00 • committed by GitHub
commit 63aa51057f
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11 changed files with 8 additions and 21 deletions
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1
litellm/google_genai/adapters/transformation.py
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@ -108,7 +108,6 @@ class GoogleGenAIStreamWrapper(AdapterCompletionStreamWrapper):
def __init__(self, completion_stream: object):
self.sent_first_chunk = False
# State tracking for accumulating partial tool calls
self.accumulated_tool_calls = dict[int, _ToolCallAccumulator]()
self._returned_response = False
super().__init__(completion_stream)

2
litellm/litellm_core_utils/model_response_utils.py
View file

@ -144,7 +144,6 @@ def _is_choice_non_empty(choice: StreamingChoices) -> bool:
# Check model_extra for dynamically added fields on the choice
choice_extra_fields: Final[Mapping[str, object]] = choice.model_extra or {}
for extra_field_name, extra_field_value in choice_extra_fields.items():
# Skip certain structural fields that are just default/None placeholders
if extra_field_name == "index" and extra_field_value == 0:
continue
if extra_field_name in {"finish_reason", "logprobs"} and extra_field_value is None:
@ -192,7 +191,6 @@ def _is_delta_non_empty(delta: Delta) -> bool:
# Check model_extra for dynamically added fields (this is where Pydantic stores them)
delta_extra_fields: Final[Mapping[str, object]] = delta.model_extra or {}
for extra_field_value in delta_extra_fields.values():
# Even structural fields are meaningful if they have actual content
if _has_meaningful_content(extra_field_value):
return True

4
litellm/llms/oci/chat/cohere.py
View file

@ -9,7 +9,7 @@ response parsing, and streaming chunk parsing for models served with
import datetime
import json
from collections.abc import Iterable, Mapping, Sequence
from typing import Any, Final
from typing import Final
import httpx
from pydantic import JsonValue, TypeAdapter, ValidationError
@ -76,7 +76,7 @@ def _content_text(content: str | Iterable[Mapping[str, object]] | None) -> str:
return str(content)
def _extract_text_content(content: Any) -> str:
def _extract_text_content(content: str | Iterable[Mapping[str, object]] | None) -> str:
"""Return the plain-text representation of a message content value."""
return _content_text(content)

2
litellm/llms/runwayml/videos/transformation.py
View file

@ -160,14 +160,12 @@ class RunwayMLVideoConfig(BaseVideoConfig):
**self._prompt_image_param(video_create_optional_params),
**self._ratio_param(video_create_optional_params),
**self._duration_param(video_create_optional_params),
# Pass through other parameters that aren't OpenAI-specific
**{key: value for key, value in video_create_optional_params.items() if key not in supported_openai_params},
}
@staticmethod
def _prompt_image_param(video_create_optional_params: VideoCreateOptionalRequestParams) -> Mapping[str, object]:
# Handle input_reference parameter - map to promptImage
# RunwayML supports URLs and data URIs directly
if "input_reference" in video_create_optional_params:
return {"promptImage": video_create_optional_params["input_reference"]}
return {}

1
litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
View file

@ -182,7 +182,6 @@ class VertexAIGeminiImageEditConfig(BaseImageEditConfig, VertexLLM):
else None
)
# Generation config with proper structure for image editing
generation_config: Final[dict[str, object]] = {
key: value for key, value in (("response_modalities", ["IMAGE"]), ("image_config", image_config)) if value
}

2
litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py
View file

@ -203,7 +203,6 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
if value is not None
}
# Build the request body for Vertex AI RAG API
query_body: Final[Mapping[str, object]] = {
key: value
for key, value in (("text", query), ("rag_retrieval_config", rag_retrieval_config or None))
@ -294,7 +293,6 @@ class VertexVectorStoreConfig(BaseVectorStoreConfig, VertexBase):
# Add metadata if provided
metadata: Final = vector_store_create_optional_params.get("metadata")
# Build the request body for Vertex AI RAG Corpus creation
request_body: Final[dict[str, object]] = {
key: value
for key, value in (

4
litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py
View file

@ -433,7 +433,7 @@ class HeadroomGuardrail(CustomGuardrail):
payload["model"] = model
try:
raw_response: HttpxResponse = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType]
raw_response: HttpxResponse = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped
url=f"{self.headroom_api_base}/v1/compress",
json=payload,
headers=self._request_headers(),
@ -570,7 +570,7 @@ class HeadroomGuardrail(CustomGuardrail):
params["query"] = query
try:
raw_response: HttpxResponse = await self.async_handler.get( # pyright: ignore[reportUnknownMemberType]
raw_response: HttpxResponse = await self.async_handler.get( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.get is untyped
url=f"{self.headroom_api_base}/v1/retrieve/{hash_value}",
params=params,
headers=self._request_headers(),

2
litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py
View file

@ -197,7 +197,7 @@ class RepelloAIGuardrail(CustomGuardrail):
repelloai_response: RepelloAIAnalyzeResponse | None = None
try:
verbose_proxy_logger.debug("RepelloAI Argus request: %s", request)
response: Final[HttpxResponse] = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType]
response: Final[HttpxResponse] = await self.async_handler.post( # pyright: ignore[reportUnknownMemberType] # AsyncHTTPHandler.post is untyped
url=endpoint,
headers={"X-API-Key": self.repelloai_api_key},
json=request,

7
litellm/proxy/response_polling/background_streaming.py
View file

@ -144,10 +144,8 @@ async def background_streaming_task(
# Process streaming response following OpenAI events format
# https://platform.openai.com/docs/api-reference/responses-streaming
output_items: Final = dict[str, _OutputItem]() # Track output items by ID
accumulated_text: Final = dict[
tuple[str, int], str
]() # Track accumulated text deltas by (item_id, content_index)
output_items: Final = dict[str, _OutputItem]()
accumulated_text: Final = dict[tuple[str, int], str]()
# ResponsesAPIResponse fields to extract from response.completed
usage_data = None
@ -262,7 +260,6 @@ async def background_streaming_task(
if "content" in delta_item:
content_list = delta_item["content"]
if content_index < len(content_list):
# Update existing content part with accumulated text
content_entry = content_list[content_index]
if isinstance(content_entry, dict):
content_entry["text"] = accumulated_text[key]

2
litellm/rag/ingestion/vertex_ai_ingestion.py
View file

@ -186,7 +186,6 @@ class VertexAIRAGIngestion(BaseRAGIngestion, VertexBase):
base_url: Final = get_vertex_base_url(self.location)
url: Final = f"{base_url}/v1beta1/projects/{self.project_id}/locations/{self.location}/ragCorpora"
# Build request body with camelCase keys (Vertex AI API format)
vector_db_config: Final = self.vector_store_config.get("vector_db_config")
embedding_model: Final = self.vector_store_config.get("embedding_model")
embedding_model_config: Final = (
@ -447,7 +446,6 @@ class VertexAIRAGIngestion(BaseRAGIngestion, VertexBase):
# Add max embedding requests per minute if specified
max_embedding_qpm: Final = self.vector_store_config.get("max_embedding_requests_per_min")
# Build request body with camelCase keys (Vertex AI API format)
chunking_config: Final = (
{"chunkSize": chunk_size or 1024, "chunkOverlap": chunk_overlap or 200}
if chunk_size or chunk_overlap

2
type-discipline-budget.json
View file

@ -9,7 +9,7 @@
"limit": 269
},
"LIT004": {
"limit": 43
"limit": 40
},
"LIT005": {
"limit": 0