Qdrant Document Index

Install dependencies

To use QdrantDocumentIndex, you need to install extra dependencies with the following command:

pip install "docarray[qdrant]"

The following is a starter script for using the QdrantDocumentIndex, based on the Qdrant vector search engine.

For general usage of a Document Index, see the general user guide.

See all configuration options

To see all configuration options for the QdrantDocumentIndex, you can do the following:

from docarray.index import QdrantDocumentIndex

# the following can be passed to the __init__() method
db_config = QdrantDocumentIndex.DBConfig()
print(db_config)  # shows default values

# the following can be passed to the configure() method
runtime_config = QdrantDocumentIndex.RuntimeConfig()
print(runtime_config)  # shows default values

Note that the collection_name from the DBConfig is an Optional[str] with None as default value. This is because the QdrantDocumentIndex will take the name the Document type that you use as schema. For example, for QdrantDocumentIndexMyDoc the data will be stored in a collection name MyDoc if no specific collection_name is passed in the DBConfig.

import numpy as np

from typing import Optional

from docarray import BaseDoc
from docarray.index import QdrantDocumentIndex
from docarray.typing import NdArray

from qdrant_client.http import models


class MyDocument(BaseDoc):
    title: str
    title_embedding: NdArray[786]
    image_path: Optional[str]
    image_embedding: NdArray[512]


# Creating an in-memory Qdrant document index
qdrant_config = QdrantDocumentIndex.DBConfig(":memory:")
doc_index = QdrantDocumentIndex[MyDocument](qdrant_config)

# Connecting to a local Qdrant instance running as a Docker container
qdrant_config = QdrantDocumentIndex.DBConfig("http://localhost:6333")
doc_index = QdrantDocumentIndex[MyDocument](qdrant_config)

# Connecting to Qdrant Cloud service
qdrant_config = QdrantDocumentIndex.DBConfig(
    "https://YOUR-CLUSTER-URL.aws.cloud.qdrant.io", 
    api_key="<your-api-key>",
)
doc_index = QdrantDocumentIndex[MyDocument](qdrant_config)

# Indexing the documents
doc_index.index(
    [
        MyDocument(
            title=f"My document {i}",
            title_embedding=np.random.random(786),
            image_path=None,
            image_embedding=np.random.random(512),
        )
        for i in range(100)
    ]
)

# Performing a vector search only
results = doc_index.find(
    query=np.random.random(512),
    search_field="image_embedding",
    limit=3,
)

# Connecting to a local Qdrant instance with Scalar Quantization enabled,
# and using non-default collection name to store the datapoints
qdrant_config = QdrantDocumentIndex.DBConfig(
    "http://localhost:6333",
    collection_name="another_collection",
    quantization_config=models.ScalarQuantization(
        scalar=models.ScalarQuantizationConfig(
            type=models.ScalarType.INT8,
            quantile=0.99,
            always_ram=True,
        ),
    ),
)
doc_index = QdrantDocumentIndex[MyDocument](qdrant_config)

# Indexing the documents
doc_index.index(
    [
        MyDocument(
            title=f"My document {i}",
            title_embedding=np.random.random(786),
            image_path=None,
            image_embedding=np.random.random(512),
        )
        for i in range(100)
    ]
)

# Text lookup, without vector search. Using the Qdrant filtering mechanisms:
# https://qdrant.tech/documentation/filtering/
results = doc_index.filter(
    filter_query=models.Filter(
        must=[
            models.FieldCondition(
                key="title",
                match=models.MatchText(text="document 2"),
            ),
        ],
    ),
)

# Vector search with additional filtering. Qdrant has the additional filters
# incorporated directly into the vector search phase, without a need to perform
# pre or post-filtering.
query = (
    index.build_query()
        .find(np.random.random(512), search_field="image_embedding")
        .filter(filter_query=models.Filter(
            must=[
                models.FieldCondition(
                    key="title",
                    match=models.MatchText(text="document 2"),
                ),
            ],
        ))
        .build(limit=5)
)
results = index.execute_query(query)