docs: Superduper integration

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

@@ -32,6 +32,7 @@ partition: build
 | [Rig-rs](/documentation/frameworks/rig-rs/)                   | Rust library for building scalable, modular, and ergonomic LLM-powered applications.                                 |
 | [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search.                             |
 | [Spring AI](/documentation/frameworks/spring-ai/)             | Java AI framework for building with Spring design principles such as portability and modular design.                 |
+| [Superduper](/documentation/frameworks/superduper/)           | Framework for building flexible, compositional AI apps which may be applied directly to databases.                   |
 | [Swarm](/documentation/frameworks/swarm/)                     | Python framework for managing multiple AI agents that can work together.                                             |
 | [Sycamore](/documentation/frameworks/sycamore/)               | Document processing engine for ETL, RAG, LLM-based applications, and analytics on unstructured data.                 |
 | [Testcontainers](/documentation/frameworks/testcontainers/)   | Framework for providing throwaway, lightweight instances of systems for testing                                      |

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

@@ -0,0 +1,87 @@
+---
+title: Superduper
+---
+
+# Superduper
+
+[Superduper](https://superduper.io/) is a framework for building flexible, compositional AI applications which may be applied directly to databases using a declarative programming model. These applications declare and maintain a desired state of the database, and use the database directly to store outputs of AI components, meta-data about the components and data pertaining to the state of the system.
+
+Qdrant is available as a vector search provider in Superduper.
+
+## Installation
+
+```bash
+pip install superduper-framework
+```
+
+## Setup
+
+- To use Qdrant for vector search layer, create a `settings.yaml` file with the following config.
+
+> settings.yaml
+```yaml
+cluster:
+  vector_search:
+    type: qdrant
+
+vector_search_kwargs:
+  url: "http://localhost:6333"
+  api_key: "<YOUR_API_KEY>
+  # Supports all parameters of qdrant_client.QdrantClient
+```
+
+- Set the `SUPERDUPER_CONFIG` env value to the path of the config file.
+
+```bash
+export SUPERDUPER_CONFIG=path/to/settings.yaml
+```
+
+That's all. You can now use Superduper backed by Qdrant.
+
+## Example
+
+Here's an example to run vector search using the configured Qdrant index.
+
+```python
+import json
+import requests 
+from superduper import superduper, Document
+from superduper.ext.sentence_transformers import SentenceTransformer
+
+r = requests.get('https://superduperdb-public-demo.s3.amazonaws.com/text.json')
+
+with open('text.json', 'wb') as f:
+    f.write(r.content)
+
+with open('text.json', 'r') as f:
+    data = json.load(f)        
+
+db = superduper('mongomock://test')
+
+_ = db['documents'].insert_many([Document({'txt': txt}) for txt in data]).execute()
+
+model = SentenceTransformer(
+    identifier="test",
+    predict_kwargs={"show_progress_bar": True},
+    model="all-MiniLM-L6-v2",
+    device="cpu",
+    postprocess=lambda x: x.tolist(),
+)
+
+vector_index = model.to_vector_index(select=db['documents'].find(), key='txt')
+
+db.apply(vector_index)
+
+query = db['documents'].like({'txt': 'Tell me about vector-search'}, vector_index=vector_index.identifier, n=3).find()
+cursor = query.execute()
+
+for r in cursor:
+    print('=' * 100)
+    print(r.unpack()['txt'])
+    print('=' * 100)
+```
+
+## 📚 Further Reading
+
+- Superduper [Intro](https://docs.superduper.io/docs/intro)
+- Superduper [Source](https://github.com/superduper-io/superduper)