Vecto Python SDK Documentation
Here is the documentation for our Python SDK.
Installation
To install the Python SDK, simply run
pip install vecto-sdk
For the token, sign up for your access here.
Quickstart
For first time users, we recommend using our VectorSpace
interface.
Find Nearest Neighbors
import vecto
vecto.api_key = os.getenv("VECTO_API_KEY", "")
vector_space = vecto.VectorSpace("my-cool-ai")
for animal in ["lion", "wolf", "cheetah", "giraffe", "elephant", "rhinoceros", "hyena", "zebrah"]:
vector_space.ingest_text(animal, { 'text': animal, 'region': 'Africa' })
similar_animals = vector_space.lookup_text("cat", top_k=3)
for animal in similar_animals:
print(f"{animal.attributes['text']} similarity: {animal.similarity:.2%}")
# Prints: "lion similarity: 84.91%"
Ingest Text or Images
import vecto
from pathlib import Path
vecto.api_key = os.getenv("VECTO_API_KEY", "")
vector_space = vecto.VectorSpace("my-cool-image-ai")
if not vector_space.exists():
vector_space.create(model='CLIP', modality='IMAGE')
for animal in ["lion.png", "wolf.png", "cheetah.png", "giraffe.png", "elephant.png", "rhinoceros.png", "hyena.png", "zebra.png"]:
vector_space.ingest_image(Path(animal), { 'text': animal.replace('.png', ''), 'region': 'Africa' })
similar_animals = vector_space.lookup_image(Path("cat.png"), top_k=1)
for animal in similar_animals:
print(f"{animal.attributes['text']}")
# Prints: lion
Looking up by Analogy
import vecto
vecto.api_key = os.getenv("VECTO_API_KEY", "")
vector_space = vecto.VectorSpace("word_space")
if not vector_space.exists():
vector_space.create(model='SBERT', modality='TEXT')
for word in ["man", "woman", "child", "mother", "father", "boy", "girl", "king", "queen"]:
vector_space.ingest_text(word, { 'text': word })
analogy = vector_space.compute_text_analogy("king", { 'start': 'man', 'end': 'woman' }, top_k=3)
for word in analogy:
print(f"{word.attributes['text']} similarity: {word.similarity:.2%}")
# Prints: "queen similarity: 93.41%"
For more advanced capabilities including management access, we recommend using the core Vecto class.