Qdrant-client

Changelog

Features

* https://github.com/qdrant/qdrant-client/pull/280, https://github.com/qdrant/qdrant-client/commit/6f8c5172d77a1775474d6ae145e7552543803806 - Compatibility updates for Qdrant v1.5.x
* https://github.com/qdrant/qdrant-client/pull/210 - [fastembed](https://github.com/qdrant/fastembed) integration. Enables lightweight, fast, Python library built for retrieval embedding generation.
* https://github.com/qdrant/qdrant-client/pull/243 - Migration tool, allows easy data migration from one instance to another

Bugfix

* https://github.com/qdrant/qdrant-client/pull/258 - disable forcing of http2 for cloud connections
* https://github.com/qdrant/qdrant-client/pull/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


python
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](https://github.com/qdrant/fastembed/blob/main/docs/examples/Usage_With_Qdrant.ipynb)

Changelog
---
Features
- Pydantic v2 support 214 224
- Forbid extra fields in pydantic models 222
---
Bug fixes
- Local mode cosine normalization 213
- Handle `Dict[str, np.ndarray]` in uploading collection in local mode 183
- Fix grpc.insecure_connection arguments 216
- Add missing init_from param in recreate_collection 205

Change log

Features

* Support for Qdrant v1.2.0 features:
* Nested filters
* group-by
* Optional vectors
* On-disk vecors

Bug fixes

* https://github.com/qdrant/qdrant-client/issues/175

+ Minor fix for ids in local mode

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:

python
from qdrant_client import QdrantClient

client = QdrantClient(":memory:")
or
client = QdrantClient(path="path/to/db") Persists changes to disk


Local 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](https://colab.research.google.com/drive/1Bz8RSVHwnNDaNtDwotfPj0w7AYzsdXZ-?usp=sharing)
- 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.

Support for Qdrant v1.1.0 features:

- Quantization
- Match Any condition

+ Minor improvements