Skip to main content

Swarmauri Persistent Qdrant Vector Store

Project description

Swarmauri Logo

PyPI - Downloads PyPI - Python Version PyPI - License PyPI - Version


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)

Want to help?

If you want to contribute to swarmauri-sdk, read up on our guidelines for contributing that will help you get started.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

swarmauri_vectorstore_qdrant-0.6.1.dev6.tar.gz (8.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file swarmauri_vectorstore_qdrant-0.6.1.dev6.tar.gz.

File metadata

File hashes

Hashes for swarmauri_vectorstore_qdrant-0.6.1.dev6.tar.gz
Algorithm Hash digest
SHA256 50d77bb0482c1848eeaf6aaa877718a25bff8e5e300bcb080ec3e2de8839be9a
MD5 271c5fadcafe677b41f7a899595cf1a3
BLAKE2b-256 68658552c9c30f39a58590dd4b30edcdcd9de5a98762fd10dfa2c3f9aae7cfac

See more details on using hashes here.

File details

Details for the file swarmauri_vectorstore_qdrant-0.6.1.dev6-py3-none-any.whl.

File metadata

File hashes

Hashes for swarmauri_vectorstore_qdrant-0.6.1.dev6-py3-none-any.whl
Algorithm Hash digest
SHA256 c03b7cf623b4cf50e6c4969092f7d76690d93b2b78ca576bccaa2288cffb82d6
MD5 b42fa7cdaf5a061ec9bcd176632e53ed
BLAKE2b-256 844bb540bcdfbd8d1f4747ffca7a7a7fd5ccc8d14163ed210cd9841357beea15

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page