Merge pull request #1521 from anastasiasenyk/master

docs: add Dynamiq integration

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

@@ -16,6 +16,7 @@ partition: build
 | [DocArray](/documentation/frameworks/docarray/)               | Python library for managing data in multi-modal AI applications.                                                     |
 | [DSPy](/documentation/frameworks/dspy/)                       | Framework for algorithmically optimizing LM prompts and weights.                                                     |
 | [dsRAG](/documentation/frameworks/dsrag/)                     | High-performance Python retrieval engine for unstructured data.                                                      |
+| [Dynamiq](/documentation/frameworks/dynamiq/)                 | Dynamiq is all-in-one Gen AI framework, designed to streamline the development of AI-powered applications.           |
 | [Feast](/documentation/frameworks/feast/)                     | Open-source feature store to operate production ML systems at scale as a set of features.                            |
 | [Fifty-One](/documentation/frameworks/fifty-one/)             | Toolkit for building high-quality datasets and computer vision models.                                               |
 | [Genkit](/documentation/frameworks/genkit/)                   | Framework to build, deploy, and monitor production-ready AI-powered apps.                                            |

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

@@ -0,0 +1,81 @@
+---
+title: Dynamiq
+---
+
+
+# Dynamiq
+
+Dynamiq is your all-in-one Gen AI framework, designed to streamline the development of AI-powered applications. Dynamiq specializes in orchestrating retrieval-augmented generation (RAG) and large language model (LLM) agents.
+
+Qdrant is a vector database available in Dynamiq, capable of serving multiple roles. It can be used for writing and retrieving documents, acting as memory for agent interactions, and functioning as a retrieval tool that agents can call when needed.
+
+## Installing
+
+First, ensure you have the `dynamiq` library installed:  
+
+```bash
+$ pip install dynamiq
+```  
+
+## Retriever node
+
+The QdrantDocumentRetriever node enables efficient retrieval of relevant documents based on vector similarity search.
+
+```python
+from dynamiq.nodes.retrievers import QdrantDocumentRetriever
+from dynamiq import Workflow
+
+# Define a retriever node to fetch most relevant documents
+retriever_node = QdrantDocumentRetriever(
+    index_name="default",
+    top_k=5,  # Optional: Maximum number of documents to retrieve
+    filters={...}  # Optional: Additional filtering conditions
+)
+
+# Create a workflow and add the retriever node
+wf = Workflow()
+wf.flow.add_nodes(retriever_node)
+
+# Execute retrieval
+result = wf.run(input_data={
+  'embedding': query_embedding  # Provide an embedded query for similarity search
+})
+
+```  
+
+## Writer node
+
+The QdrantDocumentWriter node allows storing documents in the Qdrant vector database.
+
+```python
+from dynamiq.nodes.writers import QdrantDocumentWriter
+
+# Define a writer node to store documents in Qdrant
+writer_node = QdrantDocumentWriter(
+    index_name="default",
+    create_if_not_exist=True
+)
+
+# Create a workflow and add the writer node
+wf = Workflow()
+wf.flow.add_nodes(writer_node)
+
+# Execute writing
+result = wf.run(input_data={
+  'documents': embedded_documents  # Provide embedded documents for storage
+})
+```  
+
+# Additional Tutorials
+
+Discover additional examples and use cases of Qdrant with Dynamiq:  
+
+- [Using Qdrant with Dynamiq – A Hands-on Tutorial](https://colab.research.google.com/drive/1rlZJW4lOM36b7ZxK-dVJv5dE2xrgwxU_?usp=sharing)
+- [End-to-End Application with Qdrant and Dynamiq](https://colab.research.google.com/drive/1RaR25BCj_D5wzQ70ejUQyKzdCM6DUXMF?usp=sharing)
+
+
+
+## For more details, please refer to:
+
+-  [Dynamiq Documentation](https://docs.getdynamiq.ai/)
+-  [Dynamiq GitHub](https://github.com/dynamiq-ai/dynamiq)