Embedding Models - Upstash Documentation

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.

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.

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 for dense and hybrid indexes:

Name	Dimension	Sequence Length	MTEB
BAAI/bge-large-en-v1.5	1024	512	64.23
BAAI/bge-base-en-v1.5	768	512	63.55
BAAI/bge-small-en-v1.5	384	512	62.17
BAAI/bge-m3	1024	8192	*

The sequence length is not a hard limit. Models truncate the input appropriately when given a raw text data that would result in more tokens than the given sequence length. However, we recommend using appropriate models and not exceeding their sequence length to have more accurate results.

MTEB score for the BAAI/bge-m3 is not fully measured.

For sparse and hybrid indexes, on the following models can be selected:

Name
BAAI/bge-m3
BM25

See Creating Sparse Vectors for the details of the above models.

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.

from upstash_vector import Indexindex = Index(    url="UPSTASH_VECTOR_REST_URL",    token="UPSTASH_VECTOR_REST_TOKEN",)index.upsert(    [("id-0", "Upstash is a serverless data platform.", {"field": "value"})],)

import { Index } from "@upstash/vector"const index = new Index({  url: "UPSTASH_VECTOR_REST_URL",  token: "UPSTASH_VECTOR_REST_TOKEN",})await index.upsert({  id: "id-0",  data: "Upstash is a serverless data platform.",  metadata: {    field: "value",  },})

package mainimport (	"github.com/upstash/vector-go")func main() {	index := vector.NewIndex("UPSTASH_VECTOR_REST_URL", "UPSTASH_VECTOR_REST_TOKEN")	index.UpsertData(vector.UpsertData{		Id:       "id-0",		Data:     "Upstash is a serverless data platform.",		Metadata: map[string]any{"field": "value"},	})}

use Upstash\Vector\Index;use Upstash\Vector\DataUpsert;$index = new Index(  url: 'UPSTASH_VECTOR_REST_URL',  token: 'UPSTASH_VECTOR_REST_TOKEN',);$index->upsertData(new DataUpsert(  id: 'id-0',  data: 'Upstash is a serverless data platform.',  metadata: [    'field' => 'value',  ],));

curl $UPSTASH_VECTOR_REST_URL/upsert-data \  -X POST \  -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN" \  -d '{"id": "1", "data": "Upstash is a serverless data platform.", "metadata": {"field": "value"}}'

from upstash_vector import Indexindex = Index(    url="UPSTASH_VECTOR_REST_URL",    token="UPSTASH_VECTOR_REST_TOKEN",)index.query(    data="What is Upstash?",    top_k=1,    include_metadata=True,)

import { Index } from "@upstash/vector"const index = new Index({  url: "UPSTASH_VECTOR_REST_URL",  token: "UPSTASH_VECTOR_REST_TOKEN",})await index.query({  data: "What is Upstash?",  topK: 1,  includeMetadata: true,})

package mainimport (	"github.com/upstash/vector-go")func main() {	index := vector.NewIndex("UPSTASH_VECTOR_REST_URL", "UPSTASH_VECTOR_REST_TOKEN")	index.QueryData(vector.QueryData{		Data:            "What is Upstash?",		TopK:            1,		IncludeMetadata: true,	})}

use Upstash\Vector\Index;use Upstash\Vector\DataQuery;$index = new Index(  url: 'UPSTASH_VECTOR_REST_URL',  token: 'UPSTASH_VECTOR_REST_TOKEN',);$index->queryData(new DataQuery(  data: 'What is Upstash?',  topK: 1,  includeMetadata: true,));

curl $UPSTASH_VECTOR_REST_URL/query-data \  -H "Authorization: Bearer $UPSTASH_VECTOR_REST_TOKEN" \  -d '{"data": "What is Upstash?", "topK": 1, "includeMetadata": "true"}'