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added support for clip to natural language search #965

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added support for clip to natural language search #965

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10 changes: 7 additions & 3 deletions encord/metadata_schema.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
import json
from enum import Enum
from enum import Enum, StrEnum
from typing import Dict, Literal, Sequence, Union

from pydantic import BaseModel, Field
Expand Down Expand Up @@ -41,9 +41,13 @@ class _ClientMetadataSchemaTypeEnum(BaseModel):
values: Sequence[str] = Field([], min_length=1, max_length=256)


class ClientMetadataSchemaTypeEmbeddingModel(StrEnum):
CLIP = "CLIP"

class _ClientMetadataSchemaTypeEmbedding(BaseModel):
ty: Literal["embedding"] = "embedding"
size: int = Field(gt=0, le=4096)
model: ClientMetadataSchemaTypeEmbeddingModel | None = None


class _ClientMetadataSchemaTypeText(BaseModel):
Expand Down Expand Up @@ -214,7 +218,7 @@ def save(self) -> None:
)
self._dirty = False

def add_embedding(self, k: str, *, size: int) -> None:
def add_embedding(self, k: str, *, size: int, embedding_model: ClientMetadataSchemaTypeEmbeddingModel | None = None) -> None:
"""Adds a new embedding to the metadata schema.

**Parameters:**
Expand All @@ -229,7 +233,7 @@ def add_embedding(self, k: str, *, size: int) -> None:
if k in self._schema:
raise MetadataSchemaError(f"{k} is already defined")
_assert_valid_metadata_key(k)
self._schema[k] = _ClientMetadataSchemaOption(root=_ClientMetadataSchemaTypeEmbedding(size=size))
self._schema[k] = _ClientMetadataSchemaOption(root=_ClientMetadataSchemaTypeEmbedding(size=size, model=encord_supported_model if encord_supported_model else None))
self._dirty = True

def add_enum(self, k: str, *, values: Sequence[str]) -> None:
Expand Down