v1.5.0

Changelog

Features

• #280, 6f8c517 - Compatibility updates for Qdrant v1.5.x
• #210 - fastembed integration. Enables lightweight, fast, Python library built for retrieval embedding generation.
• #243 - Migration tool, allows easy data migration from one instance to another

Bugfix

• #258 - disable forcing of http2 for cloud connections
• #268 - fix values count & is_empty & is_null conditions for local mode

Important Notes

• Python 3.7 is no longer supported

---

Use fastembed library to easily encode & index documents into qdrant

pip install fastembed qdrant-client

from qdrant_client import QdrantClient

# Initialize the client
client = QdrantClient(":memory:")  # or QdrantClient(path="path/to/db")

# Prepare your documents, metadata, and IDs
docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"]
metadata = [
    {"source": "Langchain-docs"},
    {"source": "Linkedin-docs"},
]
ids = [42, 2]

# Use the new add method
client.add(
    collection_name="demo_collection",
    documents=docs,
    metadata=metadata,
    ids=ids
)

search_result = client.query(
    collection_name="demo_collection",
    query_text="This is a query document"
)
print(search_result)

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