chore(techdebt): clear fresh debt from the 2026-08-29 window

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
2026-09-09 22:31:41 +00:00 · 2026-08-30 07:57:16 +00:00 · 2026-08-30 07:57:16 +00:00 · 693279afb4
commit 693279afb4
parent 5e4b3838aa
10 changed files with 10 additions and 17 deletions
--- a/basedpyright-code-budget.json
+++ b/basedpyright-code-budget.json
@ -1,6 +1,6 @@
 {
  "reportAny": {
-    "limit": 16389
+    "limit": 16387
  },
  "reportArgumentType": {
    "limit": 2229
@ -24,7 +24,7 @@
    "limit": 19
  },
  "reportExplicitAny": {
-    "limit": 5242
+    "limit": 5241
  },
  "reportFunctionMemberAccess": {
    "limit": 7
--- a/litellm/litellm_core_utils/model_response_utils.py
+++ b/litellm/litellm_core_utils/model_response_utils.py
@ -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

--- a/litellm/llms/oci/chat/cohere.py
+++ b/litellm/llms/oci/chat/cohere.py
@ -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)

--- a/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
+++ b/litellm/llms/vertex_ai/image_edit/vertex_gemini_transformation.py
@ -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
        }
--- a/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py
+++ b/litellm/llms/vertex_ai/vector_stores/rag_api/transformation.py
@ -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 (
--- a/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/headroom/headroom.py
@ -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(),
--- a/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py
+++ b/litellm/proxy/guardrails/guardrail_hooks/repelloai/repelloai.py
@ -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,
--- a/litellm/rag/ingestion/vertex_ai_ingestion.py
+++ b/litellm/rag/ingestion/vertex_ai_ingestion.py
@ -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
--- a/ruff-strict-budget.json
+++ b/ruff-strict-budget.json
@ -24,7 +24,7 @@
    "limit": 133
  },
  "ANN401": {
-    "limit": 597
+    "limit": 596
  },
  "ASYNC230": {
    "limit": 11
@ -231,7 +231,7 @@
    "limit": 5
  },
  "TID251": {
-    "limit": 1111
+    "limit": 1110
  },
  "TRY002": {
    "limit": 524
--- a/type-discipline-budget.json
+++ b/type-discipline-budget.json
@ -9,7 +9,7 @@
    "limit": 269
  },
  "LIT004": {
-    "limit": 43
+    "limit": 40
  },
  "LIT005": {
    "limit": 0