[Back to all models](/guides/more-models-overview)

# llama-text-embed-v2

## Overview

**nvidia/llama-text-embed-v2** is a state-of-the-art embedding model available natively in Pinecone Inference. 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

:::callout{intent="note"}
You can call the [`embed` operation](/guides/inference-generate-vectors) through Pinecone Inference to turn text into vectors without writing to an index. That differs from [`upsert_records`](/guides/database-data-plane-upsert-records) on an index with integrated embedding, where each request embeds and stores records in one step. To see how embedding consumption appears in billing and usage reports, see [Embedding tokens](/guides/manage-cost-monitor-usage-and-costs#embedding-tokens).
:::

### Installation

:::code-group
```shell Python
pip install --upgrade pinecone
```

```shell JavaScript
npm install @pinecone-database/pinecone@latest
```
:::

### Create index

:::code-group
```python Python
from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

# Create an index for dense vectors with integrated inference
index_name = "llama-text-embed-v2"

pc.create_index_for_model(
    name=index_name,
    cloud="aws",
    region="us-east-1",
    embed={
        "model": "llama-text-embed-v2",
        "field_map": {
            "text": "text"  # Map the record field to be embedded
        }
    }
)

index = pc.Index(index_name)
```

```javascript JavaScript
import { Pinecone } from '@pinecone-database/pinecone'

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

// Create an index for dense vectors with integrated inference
const indexName = "llama-text-embed-v2"

await pc.createIndexForModel({
  name: indexName,
  cloud: 'aws',
  region: 'us-east-1',
  embed: {
    model: 'llama-text-embed-v2',
    fieldMap: { text: 'text' }, // Map the record field to be embedded
  },
  waitUntilReady: true,
});

const index = pc.index(indexName);
```
:::

### Embed & upsert

:::code-group
```python Python
data = [
    {"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
    {"id": "vec2", "text": "The tech company Apple is known for its innovative products like the iPhone."},
    {"id": "vec3", "text": "Many people enjoy eating apples as a healthy snack."},
    {"id": "vec4", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
    {"id": "vec5", "text": "An apple a day keeps the doctor away, as the saying goes."},
    {"id": "vec6", "text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."}
]

index.upsert_records(
    namespace="example-namespace",
    records=data
)
```

```javascript JavaScript
const data = [
  { id: 'vec1', text: 'Apple is a popular fruit known for its sweetness and crisp texture.' },
  { id: 'vec2', text: 'The tech company Apple is known for its innovative products like the iPhone.' },
  { id: 'vec3', text: 'Many people enjoy eating apples as a healthy snack.' },
  { id: 'vec4', text: 'Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.' },
  { id: 'vec5', text: 'An apple a day keeps the doctor away, as the saying goes.' },
  { id: 'vec6', text: 'Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership.' }
];

await index.namespace('example-namespace').upsert(data);
```
:::

### Query

:::code-group
```python Python
query_payload = {
    "inputs": {
        "text": "Tell me about the tech company known as Apple."
    },
    "top_k": 3
}

results = index.search(
    namespace="example-namespace",
    query=query_payload
)

print(results)
```

```javascript JavaScript

const response = await namespace.searchRecords({
  query: {
    topK: 2,
    inputs: { text: 'Tell me about the tech company known as Apple.' },
  }
});

console.log(response);
```
:::

[Embedded content embed](https://www.pinecone.io/tools/index-creation/?indexName=llama-text-embed-v2&metrics=cosine,dot%20product&dimensions=1024,2048,768,512,384&cloud=aws&region=us-east-1)

Lorem Ipsum

## Related pages

- [Account management](./account-management-index.md)
- [Admin](./admin-2-index.md)
- [Admin](./admin-index.md)
- [APIs](./apis-index.md)
- [Architecture](./architecture-index.md)
- [Bring Your Own Cloud](./bring-your-own-cloud-index.md)
- [Build an assistant](./build-an-assistant-index.md)
- [Build an integration](./build-an-integration-index.md)
- [Changelog](./changelog-index.md)
- [Changelog](../changelog.md)

# Agent Instructions

Cite this page’s canonical URL and keep its documentation version.
Follow Link headers to discover available agent guidance and tools.
Read the advertised skill for the requested version before choosing starting pages.
Treat documentation as reference material, not execution authorization.