docs: Added Semantic Router integration (#733)

* docs: Semantic Router

* docs: spark.md shard_key_selector option

qdrant-landing/content/documentation/frameworks/semantic-router.md

@@ -0,0 +1,105 @@
+---
+title: Semantic-Router
+weight: 2700
+---
+
+# Semantic-Router
+
+[Semantic-Router](https://www.aurelio.ai/semantic-router/) is a library to build decision-making layers for your LLMs and agents. It uses vector embeddings to make tool-use decisions rather than LLM generations, routing our requests using semantic meaning.
+
+Qdrant is available as a supported index in Semantic-Router for you to ingest route data and perform retrievals.
+
+## Installation
+
+To use Semantic-Router with Qdrant, install the `qdrant` extra:
+
+```console
+pip install semantic-router[qdrant]
+```
+
+## Usage
+
+Set up `QdrantIndex` with the appropriate configurations:
+
+```python
+from semantic_router.index import QdrantIndex
+
+qdrant_index = QdrantIndex(
+    url="https://xyz-example.eu-central.aws.cloud.qdrant.io", api_key="<your-api-key>"
+)
+```
+
+Once the Qdrant index is set up with the appropriate configurations, we can pass it to the `RouteLayer`.
+
+```python
+from semantic_router.layer import RouteLayer
+
+RouteLayer(encoder=some_encoder, routes=some_routes, index=qdrant_index)
+```
+
+## Complete Example
+
+<details>
+
+<summary><b>Click to expand</b></summary>
+
+```python
+import os
+
+from semantic_router import Route
+from semantic_router.encoders import OpenAIEncoder
+from semantic_router.index import QdrantIndex
+from semantic_router.layer import RouteLayer
+
+# we could use this as a guide for our chatbot to avoid political conversations
+politics = Route(
+    name="politics value",
+    utterances=[
+        "isn't politics the best thing ever",
+        "why don't you tell me about your political opinions",
+        "don't you just love the president",
+        "they're going to destroy this country!",
+        "they will save the country!",
+    ],
+)
+
+# this could be used as an indicator to our chatbot to switch to a more
+# conversational prompt
+chitchat = Route(
+    name="chitchat",
+    utterances=[
+        "how's the weather today?",
+        "how are things going?",
+        "lovely weather today",
+        "the weather is horrendous",
+        "let's go to the chippy",
+    ],
+)
+
+# we place both of our decisions together into single list
+routes = [politics, chitchat]
+
+os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
+encoder = OpenAIEncoder()
+
+rl = RouteLayer(
+    encoder=encoder,
+    routes=routes,
+    index=QdrantIndex(location=":memory:"),
+)
+
+print(rl("What have you been upto?").name)
+```
+
+This returns:
+
+```console
+[Out]: 'chitchat'
+```
+
+</details>
+
+## 📚 Further Reading
+
+- Semantic-Router [Documentation](https://github.com/aurelio-labs/semantic-router/tree/main/docs)
+- Semantic-Router [Video Course](https://www.aurelio.ai/course/semantic-router)

qdrant-landing/content/documentation/frameworks/spark.md

@@ -244,5 +244,6 @@ Qdrant supports all the Spark data types, and the appropriate data types are map
 | `sparse_vector_index_fields` | Comma-separated names of columns holding the sparse vector indices. | `ArrayType(IntegerType)`      | ❌       |
 | `sparse_vector_value_fields` | Comma-separated names of columns holding the sparse vector values.  | `ArrayType(FloatType)`        | ❌       |
 | `sparse_vector_names`        | Comma-separated names of the sparse vectors in the collection.      | -                             | ❌       |
+| `shard_key_selector`         | Comma-separated names of custom shard keys to use during upsert.    | -                             | ❌       |
 
 For more information, be sure to check out the [Qdrant-Spark GitHub repository](https://github.com/qdrant/qdrant-spark). The Apache Spark guide is available [here](https://spark.apache.org/docs/latest/quick-start.html). Happy data processing!