Swarmauri Persistent Qdrant Vector Store
Project description
Swarmauri Qdrant Vector Store
A vector store implementation using Qdrant as the backend, supporting both persistent local storage and cloud-based operations for document storage and retrieval.
Installation
pip install swarmauri_vectorstore_qdrant
Usage
from swarmauri.documents.Document import Document
from swarmauri.vectorstores.PersistentQdrantVectorStore import PersistentQdrantVectorStore
from swarmauri.vector_stores.CloudQdrantVectorStore import CloudQdrantVectorStore
# Local Persistent Storage
local_store = PersistentQdrantVectorStore(
collection_name="my_collection",
vector_size=100,
path="http://localhost:6333"
)
local_store.connect()
# Cloud Storage
cloud_store = CloudQdrantVectorStore(
api_key="your_api_key",
collection_name="my_collection",
vector_size=100,
url="your_qdrant_cloud_url"
)
cloud_store.connect()
# Add documents
documents = [
Document(content="sample text 1"),
Document(content="sample text 2")
]
local_store.add_documents(documents)
# Retrieve similar documents
results = local_store.retrieve("sample query", top_k=5)
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