mirror of
https://github.com/hwchase17/langchain
synced 2024-11-06 03:20:49 +00:00
32 lines
929 B
Python
32 lines
929 B
Python
|
import json
|
||
|
|
|
||
|
|
from pydantic import BaseModel, Field, conint
|
||
|
|
|
||
|
|
|
||
|
|
class LLMPlateResponse(BaseModel):
|
||
|
|
row_start: conint(ge=0) = Field(
|
||
|
|
..., description="The starting row of the plate (0-indexed)"
|
||
|
|
)
|
||
|
|
row_end: conint(ge=0) = Field(
|
||
|
|
..., description="The ending row of the plate (0-indexed)"
|
||
|
|
)
|
||
|
|
col_start: conint(ge=0) = Field(
|
||
|
|
..., description="The starting column of the plate (0-indexed)"
|
||
|
|
)
|
||
|
|
col_end: conint(ge=0) = Field(
|
||
|
|
..., description="The ending column of the plate (0-indexed)"
|
||
|
|
)
|
||
|
|
contents: str
|
||
|
|
|
||
|
|
|
||
|
|
def parse_llm_output(result: str):
|
||
|
|
"""
|
||
|
|
Based on the prompt we expect the result to be a string that looks like:
|
||
|
|
|
||
|
|
'[{"row_start": 12, "row_end": 19, "col_start": 1, \
|
||
|
|
"col_end": 12, "contents": "Entity ID"}]'
|
||
|
|
|
||
|
|
We'll load that JSON and turn it into a Pydantic model
|
||
|
|
"""
|
||
|
|
return [LLMPlateResponse(**plate_r) for plate_r in json.loads(result)]
|