Add jina embeddings to integrations (#388)

* add jina embeddings to integrations

* update url

qdrant-landing/content/documentation/integrations/jina-embeddings.md

@@ -0,0 +1,59 @@
+---
+title: Jina Embeddings
+weight: 800
+---
+
+# Jina Embeddings
+
+Qdrant can also easily work with [Jina embeddings](https://jina.ai/embeddings/) which allow for model input lengths of up to 8192 tokens.
+
+To call their endpoint, all you need is an API key obtainable [here](https://jina.ai/embeddings/).
+
+```python
+import qdrant_client
+import requests
+
+from qdrant_client.http.models import Distance, VectorParams
+from qdrant_client.http.models import Batch
+
+# Provide Jina API key and choose one of the available models.
+# You can get a free trial key here: https://jina.ai/embeddings/
+JINA_API_KEY = "jina_xxxxxxxxxxx"
+MODEL = "jina-embeddings-v2-base-en"  # or "jina-embeddings-v2-base-en"
+EMBEDDING_SIZE = 768  # 512 for small variant
+
+# Get embeddings from the API
+url = "https://api.jina.ai/v1/embeddings"
+
+headers = {
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {JINA_API_KEY}",
+}
+
+data = {
+    "input": ["Your text string goes here", "You can send multiple texts"],
+    "model": MODEL,
+}
+
+response = requests.post(url, headers=headers, json=data)
+embeddings = [d["embedding"] for d in response.json()["data"]]
+
+
+# Index the embeddings into Qdrant
+qdrant_client = qdrant_client.QdrantClient(":memory:")
+qdrant_client.create_collection(
+    collection_name="MyCollection",
+    vectors_config=VectorParams(size=EMBEDDING_SIZE, distance=Distance.DOT),
+)
+
+
+qdrant_client.upsert(
+    collection_name="MyCollection",
+    points=Batch(
+        ids=list(range(len(embeddings))),
+        vectors=embeddings,
+    ),
+)
+
+```
+