---
title: Upstage
weight: 1700
---

# Upstage

Qdrant supports working with the Solar Embeddings API from [Upstage](https://upstage.ai/).

[Solar Embeddings](https://developers.upstage.ai/docs/apis/embeddings) API features dual models for user queries and document embedding, within a unified vector space, designed for performant text processing.

You can generate an API key to authenticate the requests from the [Upstage Console](<https://console.upstage.ai/api-keys>).

### Setting up the Qdrant client and Upstage session

```python
import requests
from qdrant_client import QdrantClient

UPSTAGE_BASE_URL = "https://api.upstage.ai/v1/solar/embeddings"

UPSTAGE_API_KEY = "<YOUR_API_KEY>"

upstage_session = requests.Session()

client = QdrantClient(url="http://localhost:6333")

headers = {
    "Authorization": f"Bearer {UPSTAGE_API_KEY}",
    "Accept": "application/json",
}

texts = [
    "Qdrant is the best vector search engine!",
    "Loved by Enterprises and everyone building for low latency, high performance, and scale.",
]
```

```typescript
import { QdrantClient } from '@qdrant/js-client-rest';

const UPSTAGE_BASE_URL = "https://api.upstage.ai/v1/solar/embeddings"
const UPSTAGE_API_KEY = "<YOUR_API_KEY>"

const client = new QdrantClient({ url: 'http://localhost:6333' });

const headers = {
    "Authorization": "Bearer " + UPSTAGE_API_KEY,
    "Accept": "application/json",
    "Content-Type": "application/json"
}

const texts = [
    "Qdrant is the best vector search engine!",
    "Loved by Enterprises and everyone building for low latency, high performance, and scale.",
]
```

The following example shows how to embed documents with the recommended `solar-embedding-1-large-passage` and `solar-embedding-1-large-query` models that generates sentence embeddings of size 4096.

### Embedding documents

```python
body = {
    "input": texts,
    "model": "solar-embedding-1-large-passage",
}

response_body = upstage_session.post(
    UPSTAGE_BASE_URL, headers=headers, json=body
).json()
```

```typescript
let body = {
    "input": texts,
    "model": "solar-embedding-1-large-passage",
}

let response = await fetch(UPSTAGE_BASE_URL, {
    method: "POST",
    body: JSON.stringify(body),
    headers
});

let response_body = await response.json()
```

### Converting the model outputs to Qdrant points

```python
from qdrant_client.models import PointStruct

points = [
    PointStruct(
        id=idx,
        vector=data["embedding"],
        payload={"text": text},
    )
    for idx, (data, text) in enumerate(zip(response_body["data"], texts))
]
```

```typescript
let points = response_body.data.map((data, i) => {
    return {
        id: i,
        vector: data.embedding,
        payload: {
            text: texts[i]
        }
    }
})
```

### Creating a collection to insert the documents

```python
from qdrant_client.models import VectorParams, Distance

collection_name = "example_collection"

client.create_collection(
    collection_name,
    vectors_config=VectorParams(
        size=4096,
        distance=Distance.COSINE,
    ),
)
client.upsert(collection_name, points)
```

```typescript
const COLLECTION_NAME = "example_collection"

await client.createCollection(COLLECTION_NAME, {
    vectors: {
        size: 4096,
        distance: 'Cosine',
    }
});

await client.upsert(COLLECTION_NAME, {
    wait: true,
    points
})
```

## Searching for documents with Qdrant

Once all the documents are added, you can search for the most relevant documents.

```python
body = {
    "input": "What is the best to use for vector search scaling?",
    "model": "solar-embedding-1-large-query",
}

response_body = upstage_session.post(
    UPSTAGE_BASE_URL, headers=headers, json=body
).json()

client.search(
    collection_name=collection_name,
    query_vector=response_body["data"][0]["embedding"],
)
```

```typescript
body = {
    "input": "What is the best to use for vector search scaling?",
    "model": "solar-embedding-1-large-query",
}

response = await fetch(UPSTAGE_BASE_URL, {
    method: "POST",
    body: JSON.stringify(body),
    headers
});

response_body = await response.json()

await client.search(COLLECTION_NAME, {
    vector: response_body.data[0].embedding,
});
```
