docs: Upstage embeddings

qdrant-landing/content/documentation/embeddings/_index.md

@@ -3,7 +3,7 @@ title: Embeddings
 weight: 34
 ---
 
-| Embedding                     |
+| Embeddings Providers          |
 | ----------------------------- |
 | [Aleph Alpha](./aleph-alpha/) |
 | [Bedrock](./bedrock/)         |
@@ -16,4 +16,5 @@ weight: 34
 | [OpenAI](./openai/)           |
 | [Prem AI](./premai/)          |
 | [Snowflake](./snowflake/)     |
+| [Upstage](./upstage/)        |
 | [Voyage AI](./voyage/)        |

qdrant-landing/content/documentation/embeddings/upstage.md

@@ -0,0 +1,186 @@
+---
+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,
+});
+```