text-embedding-3-small

OpenAI’s text-embedding-3-small is a compact, low-cost text embedding model that you can use for integrated embedding on Pinecone document indexes.

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When to use it

text-embedding-3-small is a compact dense text embedding model from OpenAI. Pinecone offers it for integrated embedding on document indexes, so you upsert and search with text and Pinecone embeds it through Microsoft Azure.

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Inputs, limits, and pricing

Input	Limits	Price
Text	8192 tokens per input, 96 inputs per request, 10,000 bytes per search query	$0.02 per 1M tokens in `global`, $0.022 in us

Monthly and per-minute limits depend on your plan. See Rate limits.

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Quickstart

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Installation

Integrated embedding on document indexes requires Python SDK 10.1.0, Node.js SDK 9.1.0, or Go SDK 7.1.0 or later.

Python

pip install --upgrade pinecone

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Create index

This creates a document index whose chunk_text field embeds with text-embedding-3-small at its default 1,536 dimensions.

Python

import time
from pinecone import Pinecone, SchemaBuilder

pc = Pinecone(api_key="YOUR_API_KEY")

schema = (
    SchemaBuilder()
      .add_string_field(
          name="chunk_text",
          embed={"model": "text-embedding-3-small"},
      )
      .build()
)
pc.indexes.create(name="text-embedding-3-small", schema=schema)

while not pc.indexes.describe(name="text-embedding-3-small").status.ready:
    time.sleep(2)

index = pc.index(name="text-embedding-3-small")

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Embed & upsert

Upsert documents with plain text on the embedded field. Pinecone embeds each value on write, up to 96 documents per request.

Python

index.documents.upsert(
    namespace="example-namespace",
    documents=[
        {"_id": "doc1", "chunk_text": "Apple is a popular fruit known for its sweetness and crisp texture."},
        {"_id": "doc2", "chunk_text": "The tech company Apple is known for its innovative products like the iPhone."},
        {"_id": "doc3", "chunk_text": "Many people enjoy eating apples as a healthy snack."},
        {"_id": "doc4", "chunk_text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
        {"_id": "doc5", "chunk_text": "An apple a day keeps the doctor away, as the saying goes."},
        {"_id": "doc6", "chunk_text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."},
    ],
)

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Query

Search with an embed clause, and Pinecone embeds the query text with the same model.

Python

response = index.documents.search(
    namespace="example-namespace",
    top_k=3,
    score_by=[
        {"type": "embed", "fields": ["chunk_text"], "query": "Tell me about the tech company known as Apple."}
    ],
    include_fields=["chunk_text"],
)

for match in response.matches:
    print(match._id, match._score, match.chunk_text)

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Parameters

Set these in the field’s write_parameters and read_parameters. Set dimension and processing_location on the embed object itself, as embed.dimension and embed.processing_location. Putting either one in write_parameters or read_parameters returns a 400.

Parameter	Type	Required/Optional	Description	Default
truncate	string	Optional	How to handle inputs longer than those supported by the model. Accepted values: END or NONE. END truncates the input sequence at the input token limit. NONE returns an error when the input exceeds the input token limit.	END

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Good to know

• Dimension: 1,536 by default, or any integer from 1 to 1,536. The dimension is fixed when you create the index. Very low dimensions keep little of the text’s meaning. At dimension 1, every cosine score is 1 or -1, so results fall into two tied groups regardless of the query. OpenAI’s benchmarks of shortened text-embedding-3 embeddings go down to 256 dimensions, so treat a few hundred as a practical minimum.
• Metric: cosine by default, or dotproduct or euclidean.
• Processing location: global (default) or us. The Starter and Builder plans can use only global. See Processing locations.
• Availability: Integrated embedding on document indexes only, on every plan.