landing_page/qdrant-landing/content/documentation/headless/snippets/tutorial-semantic-search-101/python.py

# @hide-start
# mypy: disable-error-code="arg-type"
QDRANT_URL=""
QDRANT_API_KEY=""
# @hide-end
# @block-start client-connection
from qdrant_client import QdrantClient, models

client = QdrantClient(
    url=QDRANT_URL,
    api_key=QDRANT_API_KEY,
    cloud_inference=True
)
# @block-end client-connection

# @block-start create-collection
COLLECTION_NAME="my_books"

client.create_collection(
    collection_name=COLLECTION_NAME,
    vectors_config=models.VectorParams(
        size=384,  # Vector size is defined by used model
        distance=models.Distance.COSINE,
    ),
)
# @block-end create-collection

# @block-start upload-data
documents = [
    {
        "name": "The Time Machine",
        "description": "A man travels through time and witnesses the evolution of humanity.",
        "author": "H.G. Wells",
        "year": 1895,
    },
    {
        "name": "Ender's Game",
        "description": "A young boy is trained to become a military leader in a war against an alien race.",
        "author": "Orson Scott Card",
        "year": 1985,
    },
    {
        "name": "Brave New World",
        "description": "A dystopian society where people are genetically engineered and conditioned to conform to a strict social hierarchy.",
        "author": "Aldous Huxley",
        "year": 1932,
    },
    {
        "name": "The Hitchhiker's Guide to the Galaxy",
        "description": "A comedic science fiction series following the misadventures of an unwitting human and his alien friend.",
        "author": "Douglas Adams",
        "year": 1979,
    },
    {
        "name": "Dune",
        "description": "A desert planet is the site of political intrigue and power struggles.",
        "author": "Frank Herbert",
        "year": 1965,
    },
    {
        "name": "Foundation",
        "description": "A mathematician develops a science to predict the future of humanity and works to save civilization from collapse.",
        "author": "Isaac Asimov",
        "year": 1951,
    },
    {
        "name": "Snow Crash",
        "description": "A futuristic world where the internet has evolved into a virtual reality metaverse.",
        "author": "Neal Stephenson",
        "year": 1992,
    },
    {
        "name": "Neuromancer",
        "description": "A hacker is hired to pull off a near-impossible hack and gets pulled into a web of intrigue.",
        "author": "William Gibson",
        "year": 1984,
    },
    {
        "name": "The War of the Worlds",
        "description": "A Martian invasion of Earth throws humanity into chaos.",
        "author": "H.G. Wells",
        "year": 1898,
    },
    {
        "name": "The Hunger Games",
        "description": "A dystopian society where teenagers are forced to fight to the death in a televised spectacle.",
        "author": "Suzanne Collins",
        "year": 2008,
    },
    {
        "name": "The Andromeda Strain",
        "description": "A deadly virus from outer space threatens to wipe out humanity.",
        "author": "Michael Crichton",
        "year": 1969,
    },
    {
        "name": "The Left Hand of Darkness",
        "description": "A human ambassador is sent to a planet where the inhabitants are genderless and can change gender at will.",
        "author": "Ursula K. Le Guin",
        "year": 1969,
    },
    {
        "name": "The Three-Body Problem",
        "description": "Humans encounter an alien civilization that lives in a dying system.",
        "author": "Liu Cixin",
        "year": 2008,
    },
]
# @block-end upload-data

# @block-start upload-points
EMBEDDING_MODEL="sentence-transformers/all-minilm-l6-v2"

client.upload_points(
    collection_name=COLLECTION_NAME,
    points=[
        models.PointStruct(
            id=idx,
            vector=models.Document(
                text=doc["description"],
                model=EMBEDDING_MODEL
            ),
            payload=doc
        )
        for idx, doc in enumerate(documents)
    ],
)
# @block-end upload-points

# @block-start query-engine
hits = client.query_points(
    collection_name=COLLECTION_NAME,
    query=models.Document(
        text="alien invasion",
        model=EMBEDDING_MODEL
    ),
    limit=3,
).points

for hit in hits:
    print(hit.payload, "score:", hit.score)
# @block-end query-engine

# @block-start create-payload-index
client.create_payload_index(
    collection_name=COLLECTION_NAME,
    field_name="year",
    field_schema=models.PayloadSchemaType.INTEGER,
)
# @block-end create-payload-index

# @block-start query-with-filter
hits = client.query_points(
    collection_name=COLLECTION_NAME,
    query=models.Document(
        text="alien invasion",
        model=EMBEDDING_MODEL
    ),
    query_filter=models.Filter(
        must=[models.FieldCondition(key="year", range=models.Range(gte=2000))]
    ),
    limit=1,
).points

for hit in hits:
    print(hit.payload, "score:", hit.score)
# @block-end query-with-filter