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fix: satisfy type-discipline gate (Final annotations, no dict literals)

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The repo's LIT002/LIT010 budget gate flags mutable dict-literal
construction and non-Final local assignments. Rewrote both new usage
fallback blocks to read via .get()/isinstance guards instead of
`or {}` defaults, and annotated every new local as Final, matching
the codebase's existing style. The one unavoidable dict literal
(get_usage_as_dict's hot-path return, mirroring the pre-existing
_empty dict in the same function) is marked `# mutable-ok` with a reason.
This commit is contained in:
spions 2026-08-07 16:34:50 +03:00
parent 63cd9e1467
commit de365aa6c7
2 changed files with 30 additions and 15 deletions
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30
litellm/litellm_core_utils/litellm_logging.py
View file

@ -4722,12 +4722,16 @@ class StandardLoggingPayloadSetup:
if not usage:
# RerankResponse has no top-level `usage` field - token usage lives
# under `meta.billed_units`/`meta.tokens` instead (Cohere-style API).
meta = response_obj.get("meta")
meta: Final = response_obj.get("meta")
if isinstance(meta, dict) and ("billed_units" in meta or "tokens" in meta):
billed_units = meta.get("billed_units") or {}
tokens = meta.get("tokens") or {}
total_tokens = billed_units.get("total_tokens", 0) or 0
prompt_tokens = tokens.get("input_tokens", total_tokens) or total_tokens
billed_units: Final = meta.get("billed_units")
tokens: Final = meta.get("tokens")
total_tokens: Final = (
billed_units.get("total_tokens", 0) if isinstance(billed_units, dict) else 0
) or 0
prompt_tokens: Final = (
tokens.get("input_tokens", total_tokens) if isinstance(tokens, dict) else total_tokens
) or total_tokens
return Usage(
prompt_tokens=prompt_tokens,
completion_tokens=0,
@ -4768,13 +4772,17 @@ class StandardLoggingPayloadSetup:
if not _raw:
# RerankResponse has no top-level `usage` field - token usage lives
# under `meta.billed_units`/`meta.tokens` instead (Cohere-style API).
meta = response_obj.get("meta")
meta: Final = response_obj.get("meta")
if isinstance(meta, dict) and ("billed_units" in meta or "tokens" in meta):
billed_units = meta.get("billed_units") or {}
tokens = meta.get("tokens") or {}
total_tokens = billed_units.get("total_tokens", 0) or 0
prompt_tokens = tokens.get("input_tokens", total_tokens) or total_tokens
return {
billed_units: Final = meta.get("billed_units")
tokens: Final = meta.get("tokens")
total_tokens: Final = (
billed_units.get("total_tokens", 0) if isinstance(billed_units, dict) else 0
) or 0
prompt_tokens: Final = (
tokens.get("input_tokens", total_tokens) if isinstance(tokens, dict) else total_tokens
) or total_tokens
return { # mutable-ok: hot-path return type is a plain dict by design, matching `_empty` above
"prompt_tokens": prompt_tokens,
"completion_tokens": 0,
"total_tokens": total_tokens,

15
litellm/llms/hosted_vllm/rerank/transformation.py
View file

@ -175,10 +175,17 @@ class HostedVLLMRerankConfig(BaseRerankConfig):
# Extract usage information - some servers (vLLM/Cohere-style) return a
# top-level `meta` object; others (OpenAI/TEI-style) return `usage`.
# Check both so either shape is picked up.
raw_meta: Final = response.get("meta") or {}
usage_data: Final = response.get("usage", {})
total_tokens: Final = raw_meta.get("billed_units", {}).get("total_tokens") or usage_data.get("total_tokens", 0)
input_tokens: Final = raw_meta.get("tokens", {}).get("input_tokens") or usage_data.get("total_tokens", 0)
raw_meta: Final = response.get("meta")
usage_data: Final = response.get("usage")
meta_billed_units: Final = raw_meta.get("billed_units") if isinstance(raw_meta, dict) else None
meta_tokens: Final = raw_meta.get("tokens") if isinstance(raw_meta, dict) else None
usage_total_tokens: Final = usage_data.get("total_tokens", 0) if isinstance(usage_data, dict) else 0
total_tokens: Final = (
meta_billed_units.get("total_tokens") if isinstance(meta_billed_units, dict) else None
) or usage_total_tokens
input_tokens: Final = (
meta_tokens.get("input_tokens") if isinstance(meta_tokens, dict) else None
) or usage_total_tokens
_billed_units: Final = RerankBilledUnits(total_tokens=total_tokens)
_tokens: Final = RerankTokens(input_tokens=input_tokens)
rerank_meta: Final = RerankResponseMeta(billed_units=_billed_units, tokens=_tokens)