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JoanFM merged 2 commits into from
Aug 3, 2023
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docs: update readme #1744

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docs: update readme
Aug 3, 2023
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71 changes: 68 additions & 3 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -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)

Expand Down Expand Up @@ -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):
Expand Down Expand Up @@ -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())
Expand All @@ -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">
Expand Down Expand Up @@ -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>
Expand Down