docs: Snowflake embeddings (#834)

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

@@ -16,4 +16,5 @@ is_empty: true
 | [Nomic](./nomic/)             |
 | [Nvidia](./nvidia/)           |
 | [OpenAI](./openai/)           |
+| [Snowflake](./snowflake/)     |
 | [Voyage AI](./voyage/)        |

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

@@ -0,0 +1,133 @@
+---
+title: Snowflake Models
+weight: 1500
+---
+
+# Snowflake
+
+Qdrant supports working with [Snowflake](https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/) text embedding models. You can find all the available models on [HuggingFace](https://huggingface.co/Snowflake).
+
+### Setting up the Qdrant and Snowflake models
+
+```python
+from qdrant_client import QdrantClient
+from fastembed import TextEmbedding
+
+qclient = QdrantClient(":memory:")
+embedding_model = TextEmbedding("snowflake/snowflake-arctic-embed-s")
+
+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';
+import { pipeline } from '@xenova/transformers';
+
+const client = new QdrantClient({ url: 'http://localhost:6333' });
+
+const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-s');
+
+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 [`snowflake-arctic-embed-s`](https://huggingface.co/Snowflake/snowflake-arctic-embed-s) model that generates sentence embeddings of size 384.
+
+### Embedding documents
+
+```python
+embeddings = embedding_model.embed(texts)
+```
+
+```typescript
+const embeddings = await extractor(texts, { normalize: true, pooling: 'cls' });
+```
+
+### Converting the model outputs to Qdrant points
+
+```python
+from qdrant_client.models import PointStruct
+
+points = [
+    PointStruct(
+        id=idx,
+        vector=embedding,
+        payload={"text": text},
+    )
+    for idx, (embedding, text) in enumerate(zip(embeddings, texts))
+]
+```
+
+```typescript
+let points = embeddings.tolist().map((embedding, i) => {
+    return {
+        id: i,
+        vector: embedding,
+        payload: {
+            text: texts[i]
+        }
+    }
+});
+```
+
+### Creating a collection to insert the documents
+
+```python
+from qdrant_client.models import VectorParams, Distance
+
+COLLECTION_NAME = "example_collection"
+
+qclient.create_collection(
+    COLLECTION_NAME,
+    vectors_config=VectorParams(
+        size=384,
+        distance=Distance.COSINE,
+    ),
+)
+qclient.upsert(COLLECTION_NAME, points)
+```
+
+```typescript
+const COLLECTION_NAME = "example_collection"
+
+await client.createCollection(COLLECTION_NAME, {
+    vectors: {
+        size: 384,
+        distance: 'Cosine',
+    }
+});
+
+await client.upsert(COLLECTION_NAME, {
+    wait: true,
+    points
+});
+```
+
+### Searching for documents with Qdrant
+
+Once the documents are added, you can search for the most relevant documents.
+
+```python
+query_embedding = next(embedding_model.query_embed("What is the best to use for vector search scaling?"))
+
+qclient.search(
+    collection_name=COLLECTION_NAME,
+    query_vector=query_embedding,
+)
+```
+
+```typescript
+const query_embedding = await extractor("What is the best to use for vector search scaling?", {
+    normalize: true,
+    pooling: 'cls'
+});
+
+await client.search(COLLECTION_NAME, {
+    vector: query_embedding.tolist()[0],
+});
+```