```
* 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-2026-07-generate-vectors) through Pinecone Inference to turn text into vectors without writing to an index. That differs from [`upsert_records`](/guides/database-2026-07-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 theme={null}
pip install --upgrade pinecone
```

```shell JavaScript theme={null}
npm install @pinecone-database/pinecone@latest
```
:::

```
### Create index

```

:::code-group
```python Python theme={null}
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 theme={null}
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 theme={null}
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 theme={null}
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 theme={null}
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 theme={null}

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)
- [Assistants](./assistants-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)

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