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:
$ pip install dynamiq
Retriever node
The QdrantDocumentRetriever node enables efficient retrieval of relevant documents based on vector similarity search.
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.
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: