chore(docs): sync up README from docarray

dev-bot@jina.ai · dev-bot@jina.ai · commit 371c5f360a8d · 2023-08-03T09:36:22.000Z
Signed-off-by: dev-bot@jina.ai &lt;Jina Dev Bot&gt;
diff --git a/README.md b/README.md
@@ -42,6 +42,7 @@ New to DocArray? Depending on your use case and background, there are multiple w
 - [Coming from pure PyTorch or TensorFlow](#coming-from-pytorch)
 - [Coming from Pydantic](#coming-from-pydantic)
 - [Coming from FastAPI](#coming-from-fastapi)
+- [Coming from Jina](#coming-from-jina)
 - [Coming from a vector database](#coming-from-a-vector-database)
 - [Coming from Langchain](#coming-from-langchain)
 
@@ -681,7 +682,7 @@ from fastapi import FastAPI
 from docarray.base_doc import DocArrayResponse
 from docarray import BaseDoc
 from docarray.documents import ImageDoc
-from docarray.typing import NdArray
+from docarray.typing import NdArray, ImageTensor
 
 
 class InputDoc(BaseDoc):
@@ -712,6 +713,7 @@ async def create_item(doc: InputDoc) -> OutputDoc:
     )
     return doc
 
+input_doc = InputDoc(text='', img=ImageDoc(tensor=np.random.random((3, 224, 224))))
 
 async with AsyncClient(app=app, base_url="http://test") as ac:
     response = await ac.post("/embed/", data=input_doc.json())
@@ -721,6 +723,70 @@ Just like a vanilla Pydantic model!
 
 </details>
 
+### Coming from Jina
+
+<details markdown="1">
+  <summary>Click to expand</summary>
+
+Jina has adopted docarray as their library for representing and serializing Documents.
+
+Jina allows to serve models and services that are built with DocArray allowing you to serve and scale these applications
+making full use of DocArray's serialization capabilites. 
+
+```python
+import numpy as np
+from jina import Deployment, Executor, requests
+from docarray import BaseDoc, DocList
+from docarray.documents import ImageDoc
+from docarray.typing import NdArray, ImageTensor
+
+
+class InputDoc(BaseDoc):
+    img: ImageDoc
+    text: str
+
+
+class OutputDoc(BaseDoc):
+    embedding_clip: NdArray
+    embedding_bert: NdArray
+
+
+def model_img(img: ImageTensor) -> NdArray:
+    return np.zeros((100, 1))
+
+
+def model_text(text: str) -> NdArray:
+    return np.zeros((100, 1))
+
+
+class MyEmbeddingExecutor(Executor):
+    @requests(on='/embed')
+    def encode(self, docs: DocList[InputDoc], **kwargs) -> DocList[OutputDoc]:
+        ret = DocList[OutputDoc]()
+        for doc in docs:
+            output = OutputDoc(
+                embedding_clip=model_img(doc.img.tensor),
+                embedding_bert=model_text(doc.text),
+            )
+            ret.append(output)
+        return ret
+
+
+with Deployment(
+    protocols=['grpc', 'http'], ports=[12345, 12346], uses=MyEmbeddingExecutor
+) as dep:
+    resp = dep.post(
+        on='/embed',
+        inputs=DocList[InputDoc](
+            [InputDoc(text='', img=ImageDoc(tensor=np.random.random((3, 224, 224))))]
+        ),
+        return_type=DocList[OutputDoc],
+    )
+    print(resp)
+```
+
+</details>
+
 ### Coming from a vector database
 
 <details markdown="1">
@@ -774,13 +840,12 @@ Currently, DocArray supports the following vector databases:
 - [Qdrant](https://qdrant.tech/)
 - [Elasticsearch](https://www.elastic.co/elasticsearch/) v8 and v7
 - [Redis](https://redis.io/)
+- [Milvus](https://milvus.io)
 - ExactNNMemorySearch as a local alternative with exact kNN search.
 - [HNSWlib](https://github.com/nmslib/hnswlib) as a local-first ANN alternative
 
 An integration of [OpenSearch](https://opensearch.org/) is currently in progress.
 
-DocArray <=0.21 also support [Milvus](https://milvus.io/), but this is not yet supported in the current version.
-
 Of course this is only one of the things that DocArray can do, so we encourage you to check out the rest of this readme!
 
 </details>
