llama-text-embed-v2 - Pinecone Docs

When to use it

nvidia/llama-text-embed-v2 is a state-of-the-art embedding model available natively in Pinecone. Developed by NVIDIA Research, it’s built on the Llama 3.2 1B architecture and optimized for high retrieval quality with low-latency inference. Also known as llama-3_2-nv-embedqa-1b-v2, the model distills techniques from NVIDIA’s industry-leading NV-2 (7B parameters) into an efficient, production-ready solution.

Retrieval quality: The model surpasses OpenAI’s text-embedding-3-large across multiple benchmarks, in some cases improving accuracy by more than 20%
Real-time queries: Predictable and consistent query speeds for responsive search with p99 latencies 12x faster than OpenAI Large
Multilingual: Supports 26 languages, including English, Spanish, Chinese, Hindi, Japanese, Korean, French, and German

Inputs, limits, and pricing

Input	Limits	Price
Text	2048 tokens per input, 96 inputs per request	$0.16 per 1M tokens

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

Quickstart

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 llama-text-embed-v2 at 1,024 dimensions, the model’s default. To use another supported size, set dimension to 2048, 768, 512, or 384.

from pinecone import Pinecone, SchemaBuilder

pc = Pinecone(api_key="YOUR_API_KEY")

schema = (
    SchemaBuilder()
      .add_string_field(
          name="chunk_text",
          embed={"model": "llama-text-embed-v2", "dimension": 1024},
      )
      .build()
)
pc.indexes.create(name="llama-text-embed-v2", schema=schema)

index = pc.index(name="llama-text-embed-v2")

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

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

await pc.createIndex({
  name: 'llama-text-embed-v2',
  schema: {
    fields: {
      chunk_text: {
        type: 'string',
        embed: { model: 'llama-text-embed-v2', dimension: 1024 },
      },
    },
  },
  waitUntilReady: true,
});

const index = pc.index({ name: 'llama-text-embed-v2', 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": "llama-text-embed-v2",
    "schema": {
      "fields": {
        "chunk_text": {
          "type": "string",
          "embed": { "model": "llama-text-embed-v2", "dimension": 1024 }
        }
      }
    }
  }'

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 an embed clause. Pinecone embeds the query text with the field’s model and returns the closest documents.

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"],
)

print(response)

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

Parameters

When you call Generate vectors, set these in the request’s parameters. On an embed field, set them in write_parameters and read_parameters, except dimension, which you set on the field itself. Pinecone sets input_type to passage for writes and query for reads by default.

Parameter	Type	Required/Optional	Description	Default
`input_type`	string	Required for Generate vectors	The type of input data. Accepted values: `query` or `passage`.
`truncate`	string	Optional	How to handle inputs longer than those supported by the model. Accepted values: `END`, `NONE`, or `START`. `END` truncates the input sequence at the input token limit, keeping the start. `START` truncates from the start, keeping the end. `NONE` returns an error when the input exceeds the input token limit.	`END`
`dimension`	integer	Optional	Dimension of the vector to return. Accepted values: `384`, `512`, `768`, `1024`, or `2048`.	`1024`

Good to know

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.

​When to use it

​Inputs, limits, and pricing

​Quickstart

​Installation

​Create index

​Embed & upsert

​Query

​Parameters

​Good to know