Embedding Models

To store text in a vector database, it must first be converted into a vector, also known as an embedding. Typically, this vectorization is done by a third party.

By selecting an embedding model when you create your Upstash Vector database, you can now upsert and query raw string data when using your database instead of converting your text to a vector first. The vectorization is done automatically by your selected model.

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Upstash Embedding Models - Video Guide

Let’s look at how Upstash embeddings work, how the models we offer compare, and which model is best for your use case.

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Models

Upstash Vector comes with a variety of embedding models that score well in the MTEB leaderboard, a benchmark for measuring the performance of embedding models. They support use cases such as classification, clustering, or retrieval.

You can choose the following general purpose models:

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Using a Model

To start using embedding models, create the index with a model of your choice.

Then, you can start upserting and querying raw text data without any extra setup.

• Python
• JavaScript
• Go
• curl

from upstash_vector import Index

index = Index(
    url="UPSTASH_VECTOR_REST_URL",
    token="UPSTASH_VECTOR_REST_TOKEN",
)

index.upsert(
    [("id-0", "Upstash is a serverless data platform.", {"field": "value"})],
)

• Python
• JavaScript
• Go
• curl

from upstash_vector import Index

index = Index(
    url="UPSTASH_VECTOR_REST_URL",
    token="UPSTASH_VECTOR_REST_TOKEN",
)

index.query(
    data="What is Upstash?",
    top_k=1,
    include_metadata=True,
)