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samsja merged 1 commit into from
Apr 17, 2023
Merged

docs: fix style in collapsible box #1396

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18 changes: 9 additions & 9 deletions README.md
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Expand Up @@ -87,7 +87,7 @@ vec = DocVec[MyDocument](
print(vec.image_tensor.shape) # (10, 1704, 2272, 3)
```

<details>
<details markdown="1">
<summary>Click for more details</summary>

So let's take a closer look at how you can represent your data with DocArray:
Expand Down Expand Up @@ -323,7 +323,7 @@ But fret not! DocArray has you covered!
- :bucket: On **[AWS S3](https://aws.amazon.com/de/s3/)**
- :cloud: On **[Jina AI Cloud](https://cloud.jina.ai/)**

<details>
<details markdown="1">
<summary>See Document Store usage</summary>

The Document Store interface lets you push and pull Documents to and from multiple data sources, all with the same user interface.
Expand Down Expand Up @@ -355,7 +355,7 @@ This is useful for:

Currently, DocArray Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdrant](https://qdrant.tech/)**, **[ElasticSearch](https://www.elastic.co/)**, and **[HNSWLib](https://github.com/nmslib/hnswlib)**, with more to come!

<details>
<details markdown="1">
<summary>See Document Index usage</summary>

The Document Index interface lets you index and retrieve Documents from multiple vector databases, all with the same user interface.
Expand Down Expand Up @@ -407,7 +407,7 @@ Choose your own adventure!

## Coming from old DocArray

<details>
<details markdown="1">
<summary>Click to expand</summary>

If you are using DocArray v<0.30.0, you will be familiar with its [dataclass API](https://docarray.jina.ai/fundamentals/dataclass/).
Expand All @@ -432,7 +432,7 @@ For now, Document Indexes support **[Weaviate](https://weaviate.io/)**, **[Qdran

## Coming from Pydantic

<details>
<details markdown="1">
<summary>Click to expand</summary>

If you come from Pydantic, you can see DocArray Documents as juiced up Pydantic models, and DocArray as a collection of goodies around them.
Expand Down Expand Up @@ -509,7 +509,7 @@ except Exception as e:

## Coming from PyTorch

<details>
<details markdown="1">
<summary>Click to expand</summary>

If you come from PyTorch, you can see DocArray mainly as a way of _organizing your data as it flows through your model_.
Expand Down Expand Up @@ -620,7 +620,7 @@ schema definition (see [below](#coming-from-fastapi)). Everything is handled in

## Coming from TensorFlow

<details>
<details markdown="1">
<summary>Click to expand</summary>

Similar to the [PyTorch approach](#coming-from-pytorch), you can also use DocArray with TensorFlow to handle and represent multi-modal data inside your ML model.
Expand Down Expand Up @@ -669,7 +669,7 @@ class MyPodcastModel(tf.keras.Model):

## Coming from FastAPI

<details>
<details markdown="1">
<summary>Click to expand</summary>

Documents are Pydantic Models (with a twist), and as such they are fully compatible with FastAPI!
Expand Down Expand Up @@ -728,7 +728,7 @@ Just like a vanilla Pydantic model!

## Coming from a vector database

<details>
<details markdown="1">
<summary>Click to expand</summary>

If you came across DocArray as a universal vector database client, you can best think of it as **a new kind of ORM for vector databases**.
Expand Down