| 1 | from typing import Any, Protocol |
| 2 | |
| 3 | from pydantic import BaseModel, ValidationError |
| 4 | |
| 5 | |
| 6 | class LlmProvider(Protocol): |
| 7 | def structured_extract(self, prompt: str, schema: type[BaseModel]) -> BaseModel | None: |
| 8 | """Return schema-validated extraction output or None without fabricating missing data.""" |
| 9 | |
| 10 | |
| 11 | class OllamaProvider: |
| 12 | def structured_extract(self, prompt: str, schema: type[BaseModel]) -> BaseModel | None: |
| 13 | return None |
| 14 | |
| 15 | |
| 16 | class FutureOpenAIProvider: |
| 17 | def structured_extract(self, prompt: str, schema: type[BaseModel]) -> BaseModel | None: |
| 18 | return None |
| 19 | |
| 20 | |
| 21 | class FutureAzureOpenAIProvider: |
| 22 | def structured_extract(self, prompt: str, schema: type[BaseModel]) -> BaseModel | None: |
| 23 | return None |
| 24 | |
| 25 | |
| 26 | def validate_structured_output(payload: dict[str, Any], schema: type[BaseModel]) -> BaseModel | None: |
| 27 | try: |
| 28 | return schema.model_validate(payload) |
| 29 | except ValidationError: |
| 30 | return None |