Releases: qdrant/qdrant-client
Releases · qdrant/qdrant-client
v1.6.2
v1.6.1
Changelog
Features
-
Proper support for Async client - #319
- Now all methods of regular qdrant client are available in async version.
- All method names, parameters and return types are preserved.
- Both gRPC and REST version are available.
-
Improvements of fastembed integration:
- Support the latest v0.1.x version of fastembed: https://github.com/qdrant/fastembed/releases/tag/0.1.0
- Support for encoding of iterators in
addAPI, useful for lazy reading of large dataset - Support for data-level parallelism of the encoding operations
Fixes
- #337 - fix: convert score to float in local mode for pydantic
Async Qdrant client usage example:
from qdrant_client import AsyncQdrantClient, models
import numpy as np
import asyncio
async def main():
client = AsyncQdrantClient(url="http://localhost:6333")
res = await client.search(
collection_name="my_collection",
query_vector=np.random.rand(10).tolist(), # type: ignore
limit=10,
)
print(res)
asyncio.run(main())v1.6.0
Changelog
Imptovements
- Support for Qdrant 1.6.x features
- Support for python 3.12
- Integration of FastEmbed v0.0.5 - https://github.com/qdrant/fastembed/releases/tag/0.0.5
Bug fixes
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)More in Notebook
v1.3.2
Changelog
Features
Bug fixes
v1.2.0
v1.1.2
- Minor fix for ids in local mode
v1.1.1
Local Mode
Introduce a new way to run Qdrant from python, no server required!
Python client allows you to run same code in local mode without running Qdrant server.
Simply initialize client like this:
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
# or
client = QdrantClient(path="path/to/db") # Persists changes to diskLocal mode is useful for development, prototyping and testing.
- You can use it to run tests in your CI/CD pipeline.
- Run it in Colab or Jupyter Notebook, no extra dependencies required. See an example
- When you need to scale, simply switch to server mode.
How it works?
We just implemented Qdrant API in pure Python.
We covered it with tests extensively to be sure it works the same as the server version.