mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-10-31 09:49:48 +00:00
41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
|
from typing import Any, Dict, List, Optional
|
||
|
|
|
||
|
|
from pydantic import BaseModel
|
||
|
|
|
||
|
|
|
||
|
|
class Answer(BaseModel):
|
||
|
|
answer: Optional[str]
|
||
|
|
question: Optional[str]
|
||
|
|
score: Optional[float] = None
|
||
|
|
probability: Optional[float] = None
|
||
|
|
context: Optional[str]
|
||
|
|
offset_start: int
|
||
|
|
offset_end: int
|
||
|
|
offset_start_in_doc: Optional[int]
|
||
|
|
offset_end_in_doc: Optional[int]
|
||
|
|
document_id: Optional[str] = None
|
||
|
|
meta: Optional[Dict[str, str]]
|
||
|
|
|
||
|
|
|
||
|
|
class AnswersToIndividualQuestion(BaseModel):
|
||
|
|
question: str
|
||
|
|
answers: List[Optional[Answer]]
|
||
|
|
|
||
|
|
@staticmethod
|
||
|
|
def to_elastic_response_dsl(data: Dict[str, Any]):
|
||
|
|
result_dsl = {'hits': {'hits': [], 'total': {'value': len(data["answers"])}}}
|
||
|
|
for answer in data["answers"]:
|
||
|
|
|
||
|
|
record = {"_source": {k: v for k, v in dict(answer).items()}}
|
||
|
|
record["_id"] = record["_source"].pop("document_id", None)
|
||
|
|
record["_score"] = record["_source"].pop("score", None)
|
||
|
|
|
||
|
|
result_dsl['hits']['hits'].append(record)
|
||
|
|
|
||
|
|
return result_dsl
|
||
|
|
|
||
|
|
|
||
|
|
class Answers(BaseModel):
|
||
|
|
results: List[AnswersToIndividualQuestion]
|
||
|
|
|