added all code snippets and sidebars.js

docs/examples/docs/examples/qdrant_zendeskdocs/examples/qdrant_zendesk/code/qdrant.py

@@ -0,0 +1,51 @@
+import dlt
+from dlt.destinations.qdrant import qdrant_adapter
+from qdrant_client import QdrantClient
+
+from zendesk import zendesk_support
+
+def main():
+    # 1. Create a pipeline
+    pipeline = dlt.pipeline(
+        pipeline_name="qdrant_zendesk_pipeline",
+        destination="qdrant",
+        dataset_name="zendesk_data_tickets",
+    )
+
+    # 2. Initialize Zendesk source to get the ticket data
+    zendesk_source = zendesk_support(load_all=False)
+    tickets = zendesk_source.tickets
+
+    # 3. Run the dlt pipeline
+    info = pipeline.run(
+        # 4. Here we use a special function to tell Qdrant
+        # which fields to embed
+         qdrant_adapter(
+        tickets,
+        embed=["subject", "description"],
+        )
+    )
+
+    return info
+
+if __name__ == "__main__":
+    load_info = main()
+    print(load_info)
+
+
+    # running the Qdrant client
+    qdrant_client = QdrantClient(
+        url="https://your-qdrant-url",
+        api_key="your-qdrant-api-key",
+    )
+
+    # view Qdrant collections
+    print(qdrant_client.get_collections())
+
+    response = qdrant_client.query(
+        "zendesk_data_tickets",  # collection/dataset name
+        query_text=["cancel", "cancel subscription"],  # prompt to search
+        limit=3  # limit the number of results to the nearest 3 embeddings
+    )
+
+    print(response)

docs/website/docs/examples/_examples-qdrant-header.md

@@ -0,0 +1,21 @@
+import Admonition from "@theme/Admonition";
+import CodeBlock from '@theme/CodeBlock';
+
+<Admonition>
+    The source code for this example can be found in our repository at: <a href={"https://github.com/dlt-hub/dlt/tree/devel/docs/examples/" + props.slug}>{"https://github.com/dlt-hub/dlt/tree/devel/docs/examples/" + props.slug}</a>.
+</Admonition>
+
+## TLDR
+<div>{props.intro}</div>
+
+## Setup: Running this example on your machine
+<CodeBlock language="sh">
+{`# clone the dlt repository
+git clone [email protected]:dlt-hub/dlt.git
+# go to example directory
+cd ./dlt/docs/examples/${props.slug}
+# install dlt with qdrant
+pip install "dlt[qdrant]"
+# run the example script
+python ${props.run_file}.py`}
+</CodeBlock>

docs/website/docs/examples/qdrant_zendesk/code/qdrant-snippets.py

@@ -37,5 +37,29 @@ def main():
         load_info = main()
         print(load_info)
 
-    # @@@DLT_SNIPPET_END main_code
+        # @@@DLT_SNIPPET_END main_code
+
+        # @@@DLT_SNIPPET_START declare_qdrant_client
+        # running the Qdrant client
+        qdrant_client = QdrantClient(
+            url="https://your-qdrant-url",
+            api_key="your-qdrant-api-key",
+        )
+        # @@@DLT_SNIPPET_END declare_qdrant_client
+
+        # @@@DLT_SNIPPET_START view_collections
+        # view Qdrant collections
+        print(qdrant_client.get_collections())
+        # @@@DLT_SNIPPET_END view_collections
+
+        # @@@DLT_SNIPPET_START get_response
+        response = qdrant_client.query(
+            "zendesk_data_tickets",  # collection/dataset name
+            query_text=["cancel", "cancel subscription"],  # prompt to search
+            limit=3  # limit the number of results to the nearest 3 embeddings
+        )
+        # @@@DLT_SNIPPET_END get_response
+
+        print(response)
+
     # @@@DLT_SNIPPET_END example

docs/website/docs/examples/qdrant_zendesk/index.md

@@ -1,13 +1,14 @@
 ---
-title: Load Zendesk tickets data to Qdrant
+title: Similarity Searching with dlt and Qdrant
 description: Learn how to use the dlt source, Zendesk and dlt destination, Qdrant to conduct a similarity search on your tickets data.
 keywords: [similarity search, example]
 ---
+import Header from '../_examples-qdrant-header.md';
 
 <Header
     intro="In this tutorial, you will learn how to do use dlt to store your
-    vectorized Zendesk tickets data in the dlt destination; qdrant. You can
-    use qdrant's vectorization and similarity searching capabilities on your tickets data,
+    vectorized Zendesk tickets data in the dlt destination; Qdrant. You can
+    use Qdrant's vectorization and similarity searching capabilities on your tickets data,
     while using dlt as a medium to automate your pipeline."
     slug="qdrant_zendesk"
     run_file="qdrant-snippets"
@@ -43,44 +44,88 @@ email = "..."
 You are now setup to start writing a custom pipeline to connect your Zendesk ticket data to Qdrant. The following lines of code do exactly that:
 
 <!--@@@DLT_SNIPPET_START ./code/qdrant-snippets.py::main_code-->
+```py
+import dlt
+from dlt.destinations.qdrant import qdrant_adapter
+from qdrant_client import QdrantClient
+
+from zendesk import zendesk_support
+
+def main():
+    # 1. Create a pipeline
+    pipeline = dlt.pipeline(
+        pipeline_name="qdrant_zendesk_pipeline",
+        destination="qdrant",
+        dataset_name="zendesk_data_tickets",
+    )
+
+    # 2. Initialize Zendesk source to get the ticket data
+    zendesk_source = zendesk_support(load_all=False)
+    tickets = zendesk_source.tickets
+
+    # 3. Run the dlt pipeline
+    info = pipeline.run(
+        # 4. Here we use a special function to tell Qdrant
+        # which fields to embed
+         qdrant_adapter(
+        tickets,
+        embed=["subject", "description"],
+        )
+    )
+
+    return info
+
+if __name__ == "__main__":
+    load_info = main()
+    print(load_info)
+```
 <!--@@@DLT_SNIPPET_END ./code/qdrant-snippets.py::main_code-->
 
 Overview of the code above:
-1. We create a pipeline with the name `qdrant_zendesk_pipeline` and the destination Qdrant.
+1. We create a pipeline with the name `qdrant_zendesk_pipeline` and the destination Qdrant. The name of the dataset here will be the same as the "collection" name on Qdrant!
 2. Then, we initialize the Zendesk verified source. We only need to load the tickets data, so we get tickets resource from the source by getting the tickets attribute.
 3. pipeline.run() runs the pipeline and returns information about the load process.
-4. Since Qdrant is a vector database, it specializes in conducting similarity searches within its embedded data. To make that possible, we use the special Qdrant adapter to embed (or vectorize) our data before loading it.
+4. Qdrant being a vector database, specializes in conducting similarity searches within its **embedded data**. To make that possible, we use the special Qdrant adapter to **embed** (or vectorize) our data before loading it.
 
 ## Querying the data
 
 In the code above, we also imported the `QdrantClient`. That is what we will use to connect to Qdrant and see what sort of data we have thus stored so far.
 We connect to the Qdrant client with our credentials:
 
-```python
+<!--@@@DLT_SNIPPET_START ./code/qdrant-snippets.py::declare_qdrant_client-->
+```py
+# running the Qdrant client
 qdrant_client = QdrantClient(
     url="https://your-qdrant-url",
     api_key="your-qdrant-api-key",
 )
 ```
+<!--@@@DLT_SNIPPET_END ./code/qdrant-snippets.py::declare_qdrant_client-->
 
 If needed, you can list all your datasets or Qdrant "collections" with the following function call:
 
-```python
-qdrant_client.get_collections()
+<!--@@@DLT_SNIPPET_START ./code/qdrant-snippets.py::view_collections-->
+```py
+# view Qdrant collections
+print(qdrant_client.get_collections())
 ```
+<!--@@@DLT_SNIPPET_END ./code/qdrant-snippets.py::view_collections-->
 
 You should be able to see your own data there. For the purposes of this article, it would be the same name as the dataset you declared in the pipeline above, i.e. `zendesk_data_tickets`.
 
 Next, we query Qdrant to conduct a similarity search using their "query" function. For example, we would like to see tickets that are similar to the ("subject", "description") pair: ("cancel", "cancel subscription"). It can be queried as follows:
 
-```python
-qdrant_client.query(
-    "zendesk_data_tickets", # collection/dataset name
-    query_text=["cancel", "cancel subscription"], # prompt to search
-    limit=3 # limit the number of results to the nearest 3 embeddings
+<!--@@@DLT_SNIPPET_START ./code/qdrant-snippets.py::get_response-->
+```py
+response = qdrant_client.query(
+    "zendesk_data_tickets",  # collection/dataset name
+    query_text=["cancel", "cancel subscription"],  # prompt to search
+    limit=3  # limit the number of results to the nearest 3 embeddings
 )
 ```
-The query above gives the following response:
+<!--@@@DLT_SNIPPET_END ./code/qdrant-snippets.py::get_response-->
+
+The query above gives stores the following results in the `response` variable:
 ```json
 [QueryResponse(id='6aeacd21-b3d0-5174-97ef-5aaa59486414', embedding=None, metadata={'_dlt_id': 'Nx3wBiL29xTgaQ', '_dlt_load_id': '1700130284.002391', 'allow_attachments': True, 'allow_channelback': False, 'assignee_id': 12765072569105, 'brand_id': 12765073054225, 'created_at': '2023-09-01T11:19:25+00:00', 'custom_status_id': 12765028278545, 'description': 'I have been trying to cancel my subscription but the system won’t let me do it. Can you please help?', 'from_messaging_channel': False, 'generated_timestamp': 1693567167, 'group_id': 12765036328465, 'has_incidents': False, 'id': 12, 'is_public': True, 'organization_id': 12765041119505, 'raw_subject': 'Unable to Cancel Subscription', 'requester_id': 12765072569105, 'status': 'open', 'subject': 'Unable to Cancel Subscription', 'submitter_id': 12765072569105, 'tags': ['test1'], 'test_field': 'test1', 'ticket_form_id': 12765054772497, 'updated_at': '2023-09-01T11:19:25+00:00', 'url': 'https://d3v-dlthub.zendesk.com/api/v2/tickets/12.json', 'via__channel': 'web'}, document='', score=0.89545774),
  QueryResponse(id='a22189c1-70ab-5421-938b-1caae3e7d6d8', embedding=None, metadata={'_dlt_id': 'bc/xloksL89EUg', '_dlt_load_id': '1700130284.002391', 'allow_attachments': True, 'allow_channelback': False, 'assignee_id': 12765072569105, 'brand_id': 12765073054225, 'created_at': '2023-07-18T17:23:42+00:00', 'custom_status_id': 12765028278545, 'description': 'ABCDEF', 'from_messaging_channel': False, 'generated_timestamp': 1689701023, 'group_id': 12765036328465, 'has_incidents': False, 'id': 4, 'is_public': True, 'organization_id': 12765041119505, 'raw_subject': 'What is this ticket', 'requester_id': 12765072569105, 'status': 'open', 'subject': 'What is this ticket', 'submitter_id': 12765072569105, 'tags': ['test1'], 'test_field': 'test1', 'ticket_form_id': 12765054772497, 'updated_at': '2023-07-18T17:23:42+00:00', 'url': 'https://d3v-dlthub.zendesk.com/api/v2/tickets/4.json', 'via__channel': 'web'}, document='', score=0.8643349),

docs/website/sidebars.js

@@ -20,6 +20,7 @@ const sidebars = {
     {
       type: 'category',
       label: 'Integrations',
+      label: 'Integrations',
       link: {
         type: 'generated-index',
         title: 'Integrations',
@@ -240,6 +241,7 @@ const sidebars = {
         'examples/incremental_loading/index',
         'examples/connector_x_arrow/index',
         'examples/chess_production/index',
+        'examples/qdrant_zendesk/index'
       ],
     },
     {