Adopt a “type-safe, clean, DRY” style for readability and maintainability. - Prefer module-scope imports: don’t re-import a module inside a function if it already exists at file scope. - Keep type annotations precise: - Annotate intermediate locals when introducing them (e.g., `schema_param: ResponseFormatParam = {...}`). - Use accurate container types (e.g., `Dict[str, str]` for headers) rather than `Any`. - Avoid `# type: ignore` when the mismatch can be fixed by correcting the related type(s). - For version-specific utilities, structure branches so type checkers can understand both paths without extra ignores. - When typing wrappers that forward args/kwargs, use `ParamSpec` to preserve call signatures. - Reduce duplication/readability issues: - Factor repeated parsing/processing into a small helper used by both sync and async variants. - When code organization is getting messy due to shared logic, prefer private methods on the owning class over adding more items to generic utility modules (when circular imports allow). - Remove dead/unused code: don’t introduce variables (like a spinner cycle) that aren’t used. Example (import + typed local + helper pattern): ```py # module scope import json def parse_stream_line(line: bytes) -> str: if not line or line == b"data: [DONE]": return "" if hasattr(line, "decode"): line = line.decode("utf-8") if line.startswith("data: "): line = line[len("data: "):] return line def parse_stream(rbody): for line in rbody: out = parse_stream_line(line) if out: yield out def some_function() -> "ResponseFormatParam": schema_param: "ResponseFormatParam" = {"type": "text"} return schema_param ``` Applying these consistently will improve clarity (fewer surprises), maintainability (less duplication), and type-checker effectiveness (fewer ignores and safer refactors).