Skip to main content

Swarmauri Persistent Qdrant Vector Store

Project description

Swamauri Logo

PyPI - Downloads Hits PyPI - Python Version PyPI - License PyPI - swarmauri_vectorstore_qdrant


Swarmauri Vectorstore Qdrant

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-0.7.4.tar.gz (8.6 kB view details)

Uploaded Source

Built Distribution

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

swarmauri_vectorstore_qdrant-0.7.4-py3-none-any.whl (11.8 kB view details)

Uploaded Python 3

File details

Details for the file swarmauri_vectorstore_qdrant-0.7.4.tar.gz.

File metadata

File hashes

Hashes for swarmauri_vectorstore_qdrant-0.7.4.tar.gz
Algorithm Hash digest
SHA256 e5e236794cd09215f7faad04008488188399dc955f76068f4ba5a1358bd98c5f
MD5 c365a02e750aac65e4612cec27b2be0a
BLAKE2b-256 25db1ab97a860942282570681b24f3a2cd80ff95282a78de982235a1cc9324c5

See more details on using hashes here.

File details

Details for the file swarmauri_vectorstore_qdrant-0.7.4-py3-none-any.whl.

File metadata

File hashes

Hashes for swarmauri_vectorstore_qdrant-0.7.4-py3-none-any.whl
Algorithm Hash digest
SHA256 585fb40635adc40f62375199bf358bdce6a4713f47932051ba90b1b4cde6af29
MD5 dbb80d35a307508944167fabcaf6b4a3
BLAKE2b-256 831c935e36bf7bd95ab7a0e68b33dbcf80dfc6e7786cfe65c0c4bbbd55501871

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