What does this PR do?

Fixes #16048

EventSerializer's BaseModel branch returned obj.model_dump() directly, so non-finite floats nested inside a pydantic model bypassed the NaN/Inf sanitizer that the dict/list branches already apply. Python's JSON C encoder then emitted bare NaN/Infinity tokens, which strict JSON parsers reject. On the tracing path, any traced pydantic object carrying a NaN/Inf float (common with ML/scoring payloads) produced an event body that failed ingestion.

The fix routes the dumped dict back through default(), so the same sanitization applies at every nesting depth:

from pydantic import BaseModel
from langfuse._utils.serializer import EventSerializer

class Scores(BaseModel):
    confidence: float

# before: {"confidence": NaN}  (bare NaN, invalid JSON)
# after:  {"confidence": "NaN"}
EventSerializer().encode(Scores(confidence=float("nan")))

Type of change

• Bug fix

Verification

pytest tests/unit/test_serializer.py -q -k "not path"     # 29 passed
ruff check langfuse/_utils/serializer.py tests/unit/test_serializer.py  # all checks passed
ruff format --check langfuse/_utils/serializer.py tests/unit/test_serializer.py

The new regression test test_pydantic_model_with_non_finite_float_serializes_to_valid_json fails before the fix (strict parse raises ValueError: NaN) and passes after. test_path fails on this machine only (POSIX-vs-Windows path separators) and passes in CI's Linux runner; the same pre-existing mypy import-not-found note for langchain_core is present on the unmodified file.

Checklist

• I self-reviewed the diff using code_review.md.
• I added or updated tests for behavior changes.
• I updated docs, examples, or .env.template if needed.
• I did not hand-edit generated files; if generated files changed, I used the upstream regeneration path.
• I did not commit secrets or credentials.

Greptile Summary

Routes Pydantic model_dump() output back through EventSerializer so nested non-finite floats become JSON-safe strings.

• Sanitizes nested NaN and infinity values consistently with existing dict/list handling.
• Adds regression coverage using strict JSON parsing for Pydantic payloads.

Confidence Score: 5/5

The PR appears safe to merge, with the intended Pydantic serialization behavior covered by a focused strict-JSON regression test.

The changed branch reuses the established recursive normalization path, ensuring non-finite floats nested in Pydantic models no longer produce invalid bare JSON constants, and no concrete blocking or non-blocking defect remains.

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart LR
  A[Pydantic BaseModel] --> B[model_dump]
  B --> C[EventSerializer recursive normalization]
  C --> D[NaN and Infinity converted to strings]
  D --> E[Valid JSON ingestion payload]

Reviews (1): Last reviewed commit: "fix: serialize NaN/Inf floats nested in ..." | Re-trigger Greptile

Context used:

• Knowledge Base — Utils and Support Types