pinecone-sparse-english-v0

For general-purpose text retrieval, we recommend full-text search: it runs over your raw text fields with BM25 ranking and Lucene query syntax.pinecone-sparse-english-v0 is the right choice for workflows that need a learned sparse-vector representation — for example, when your application already produces sparse vectors upstream of Pinecone, or when you’re pairing it with a dense encoder in a single-index hybrid workflow on the Vectors API.You can call the Generate vectors operation for standalone embedding to turn text into vectors without writing to an index. That differs from integrated embedding on a document index, where Pinecone embeds a field’s text when you upsert or search. To see how many embedding tokens a request consumes, see Embedding tokens.

Built on the innovations of the DeepImpact architecture, the model directly estimates the lexical importance of tokens by using their context, unlike traditional retrieval models like BM25, which rely solely on term frequency. The model outperforms BM25 by up to 44% (average 23%) NDCG@10 on Text Retrieval Conference (TREC) Deep Learning Tracks and up to 24% (8% on average) on BEIR. For more information see our blog post on cascading retrievalWhen using the model to generate embeddings directly, you must specify the input_type as either query or passage. Optionally, you can:

Return the string tokens using "return_tokens": true.
Raise the max input tokens limit from the default of 512 to the maximum of 2048 using "max_tokens_per_sequence": 2048.
Return an error when the input exceeds max_tokens_per_sequence using "truncate": "NONE".

With integrated embedding on a document index, Pinecone sets input_type to passage for writes and query for reads by default.

Installation

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

pip install --upgrade pinecone

npm install @pinecone-database/pinecone@latest

Create index

This creates a document index whose chunk_text field embeds with pinecone-sparse-english-v0. The write_parameters and read_parameters raise the max input tokens from 512 to 2,048 for upserts and queries.

from pinecone import Pinecone, SchemaBuilder

pc = Pinecone(api_key="YOUR_API_KEY")

schema = (
    SchemaBuilder()
      .add_string_field(
          name="chunk_text",
          sparse_embed={
              "model": "pinecone-sparse-english-v0",
              "write_parameters": {"max_tokens_per_sequence": 2048},
              "read_parameters": {"max_tokens_per_sequence": 2048},
          },
      )
      .build()
)
pc.indexes.create(name="pinecone-sparse-english-v0", schema=schema)

index = pc.index(name="pinecone-sparse-english-v0")

import { Pinecone } from '@pinecone-database/pinecone';

const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

await pc.createIndex({
  name: 'pinecone-sparse-english-v0',
  schema: {
    fields: {
      chunk_text: {
        type: 'string',
        sparseEmbed: {
          model: 'pinecone-sparse-english-v0',
          writeParameters: { max_tokens_per_sequence: 2048 },
          readParameters: { max_tokens_per_sequence: 2048 },
        },
      },
    },
  },
  waitUntilReady: true,
});

const index = pc.index({ name: 'pinecone-sparse-english-v0', namespace: 'example-namespace' });

PINECONE_API_KEY="YOUR_API_KEY"

curl -X POST "https://api.pinecone.io/indexes" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "name": "pinecone-sparse-english-v0",
    "schema": {
      "fields": {
        "chunk_text": {
          "type": "string",
          "sparse_embed": {
            "model": "pinecone-sparse-english-v0",
            "write_parameters": { "max_tokens_per_sequence": 2048 },
            "read_parameters": { "max_tokens_per_sequence": 2048 }
          }
        }
      }
    }
  }'

Embed & upsert

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

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."},
    ],
)

await index.documents.upsert({
  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.' },
  ],
});

PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl -X POST "https://$INDEX_HOST/namespaces/example-namespace/documents/upsert" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "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." }
    ]
  }'

Query

Search with a sparse_embed clause. Pinecone embeds the query text with the field’s model and returns the documents that share the most important terms with it.

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

print(response)

const response = await index.documents.search({
  topK: 3,
  scoreBy: [
    { type: 'sparse_embed', fields: ['chunk_text'], query: 'Tell me about the tech company known as Apple.' },
  ],
  includeFields: ['chunk_text'],
});

console.log(response);

PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl -X POST "https://$INDEX_HOST/namespaces/example-namespace/documents/search" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "top_k": 3,
    "score_by": [
      { "type": "sparse_embed", "fields": ["chunk_text"], "query": "Tell me about the tech company known as Apple." }
    ],
    "include_fields": ["chunk_text"]
  }'

​Installation

​Create index

​Embed & upsert

​Query

Installation

Create index

Embed & upsert

Query