Query Engines

Overview

Query engines are used to perform text queries against various modalities.

Vector

Used to query vector storages. You can set a custom prompt_driver and vector_store_driver. Uses LocalVectorStoreDriver by default.

Use the upsert_text_artifacts into vector storage with an optional namespace.

Use the VectorQueryEngine method to query the vector storage.

from griptape.drivers import OpenAiChatPromptDriver, LocalVectorStoreDriver, OpenAiEmbeddingDriver
from griptape.engines import VectorQueryEngine
from griptape.loaders import WebLoader

engine = VectorQueryEngine(
    prompt_driver=OpenAiChatPromptDriver(model="gpt-3.5-turbo"),
    vector_store_driver=LocalVectorStoreDriver(embedding_driver=OpenAiEmbeddingDriver())
)

engine.upsert_text_artifacts(
    WebLoader().load("https://www.griptape.ai"), namespace="griptape"
)

engine.query("what is griptape?", namespace="griptape")

Image

The Image Query Engine allows you to perform natural language queries on the contents of images. You can specify the provider and model used to query the image by providing the Engine with a particular Image Query Driver.

All Image Query Drivers default to a max_tokens of 256. You can tune this value based on your use case and the Image Query Driver you are providing.

from griptape.drivers import OpenAiImageQueryDriver
from griptape.engines import ImageQueryEngine
from griptape.loaders import ImageLoader

driver = OpenAiImageQueryDriver(
    model="gpt-4o",
    max_tokens=256
)

engine = ImageQueryEngine(
    image_query_driver=driver
)

with open("tests/resources/mountain.png", "rb") as f:
    image_artifact = ImageLoader().load(f.read())

engine.run("Describe the weather in the image", [image_artifact])