From 57d6b75d7f2417248e6e438a9379edeb8ba975a4 Mon Sep 17 00:00:00 2001
From: Alaeddine Abdessalem <alaeddine-13@live.fr>
Date: Tue, 15 Mar 2022 16:31:34 +0100
Subject: [PATCH] docs: add weaviate minimum working example

---
 docs/advanced/document-store/weaviate.md | 66 ++++++++++++++++++++++++
 1 file changed, 66 insertions(+)

diff --git a/docs/advanced/document-store/weaviate.md b/docs/advanced/document-store/weaviate.md
index 32141a72830..67301f46c46 100644
--- a/docs/advanced/document-store/weaviate.md
+++ b/docs/advanced/document-store/weaviate.md
@@ -81,3 +81,69 @@ The following configs can be set:
 | `protocol`         | protocol to be used. Can be 'http' or 'https'                                          | 'http'                      |
 | `name`             | Weaviate class name; the class name of Weaviate object to presesent this DocumentArray | None                        |
 | `serialize_config` | [Serialization config of each Document](../../fundamentals/document/serialization.md)  | None                        |
+
+## Minimum Example
+
+The following example shows how to use DocArray with Weaviate Document Store in order to index and search text 
+Documents.
+
+First, let's run the create the `DocumentArray` instance (make sure a Weaviate server is up and running):
+
+```python
+from docarray import DocumentArray
+
+da = DocumentArray(
+    storage="weaviate",
+    config={
+        "name": "Persisted",
+        "host": "localhost",
+        "port": 8080}
+)
+```
+
+Then, we can index some Documents:
+
+```python
+from docarray import Document
+
+da.extend([
+    Document(text='Persist Documents with Weaviate.'),
+    Document(text='And enjoy fast nearest neighbor search.'),
+    Document(text='All while using DocArray API.'),
+])
+```
+
+Now, we can generate embeddings inside the database using BERT model:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+model = AutoModel.from_pretrained('bert-base-uncased')
+
+def collate_fn(da):
+    return tokenizer(
+        da.texts,
+        return_tensors='pt',
+        truncation=True,
+        padding=True
+    )
+
+da.embed(model, collate_fn=collate_fn)
+```
+
+
+Finally, we can query the database and print the results:
+
+```python
+results = da.find(
+    DocumentArray([Document(text='How to persist Documents')]).embed(model, collate_fn=collate_fn),
+    limit=1
+)
+
+print(results[0].text)
+```
+
+```text
+Persist Documents with Weaviate.
+```