# Implementation of vector for Tortoise-ORM
This package adds the support of `pgvector` vectors to Tortoise-ORM as a new type of fields.
That way you can filter/order by cosine similarity distances for scementic search using embeddings.
Here's an example for openai's embeddings but this will work with any kind of embeddings.
Usage:
```python
from tortoise.models import Model, QuerySet
from tortoise_vector.field import VectorField
from tortoise_vector.expression import CosineSimilarity
OPENAI_VECTOR_SIZE = 1536
class MyModel(Model):
# vectors have a fixed size, openai uses 1536 dimensions
embedding = VectorField(vector_size=OPENAI_VECTOR_SIZE)
async def get_embedding_from_text(string: str) -> list[float]:
...
async def get_nearst_models(text: str) -> QuerySet[MyModel]:
embedding = await get_embedding_from_text(text)
return (
MyModel
.all()
.annotate(
distance=CosineSimilarity(
"embedding",
embedding,
OPENAI_VECTOR_SIZE
)
)
.order_by("distance")
)
```
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