Tutorial Lengkap Qdrant: Vector Database untuk Aplikasi AI

Qdrant adalah vector database performa tinggi yang dirancang untuk similarity search dan aplikasi AI. Library ini menyediakan penyimpanan dan retrieval vector embeddings yang efisien, menjadikannya ideal untuk membangun sistem rekomendasi, semantic search, dan aplikasi RAG.

Mengapa Qdrant?

• Performa tinggi: Engine berbasis Rust untuk kecepatan
• Rich filtering: Kombinasikan vector search dengan filter metadata
• Scalable: Mode terdistribusi untuk dataset besar
• Mudah digunakan: REST dan gRPC APIs
• Cloud native: Siap Docker dan Kubernetes

• Semantic search
• Sistem rekomendasi
• RAG (Retrieval-Augmented Generation)
• Image similarity search
• Anomaly detection

Instalasi

# Python client
pip install qdrant-client

Jalankan Qdrant dengan Docker
docker run -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrantstorage:/qdrant/storage:z \

    qdrant/qdrant

Verify
python -c "from qdrantclient import QdrantClient; print('Qdrant client ready')"

Quick Start

1. Koneksi ke Qdrant

from qdrantclient import QdrantClient

Instance lokal
client = QdrantClient("localhost", port=6333)

In-memory (untuk testing)
client = QdrantClient(":memory:")

Qdrant Cloud
client = QdrantClient(
    url="https://xxx-xxx.us-east-1-0.aws.cloud.qdrant.io",
    apikey="your-api-key"
)

Cek koneksi
print(client.getcollections())

from qdrantclient import QdrantClient
from qdrantclient.models import Distance, VectorParams


client = QdrantClient("localhost", port=6333)

Buat collection
client.createcollection(
    collectionname="mycollection",
    vectorsconfig=VectorParams(size=384, distance=Distance.COSINE)

)

List collections
collections = client.getcollections()
print(collections)

3. Insert Vectors

from qdrantclient.models import PointStruct

Insert points
client.upsert(
    collectionname="mycollection",

    points=[
        PointStruct(
            id=1,
            vector=[0.1, 0.2, 0.3, ...],  # vektor 384-dim
            payload={"title": "Dokumen 1", "category": "tech"}
        ),
        PointStruct(
            id=2,
            vector=[0.4, 0.5, 0.6, ...],
            payload={"title": "Dokumen 2", "category": "science"}
        )
    ]
)

# Search
results = client.search(
    collectionname="mycollection",

    queryvector=[0.1, 0.2, 0.3, ...],
    limit=5
)

for result in results:
    print(f"ID: {result.id}, Score: {result.score}")
    print(f"Payload: {result.payload}")

Bekerja dengan Embeddings

1. Menggunakan Sentence Transformers

from qdrantclient import QdrantClient
from qdrantclient.models import Distance, VectorParams, PointStruct
from sentencetransformers import SentenceTransformer


Initialize
client = QdrantClient("localhost", port=6333)
model = SentenceTransformer("all-MiniLM-L6-v2")

Buat collection
client.recreatecollection(
    collectionname="documents",

    vectorsconfig=VectorParams(size=384, distance=Distance.COSINE)
)

Siapkan dokumen
documents = [
    {"id": 1, "text": "Machine learning sangat menarik", "category": "tech"},
    {"id": 2, "text": "Natural language processing", "category": "tech"},
    {"id": 3, "text": "Resep masakan untuk pemula", "category": "food"},
]

Generate embeddings dan insert
points = []
for doc in documents:
    embedding = model.encode(doc["text"]).tolist()
    points.append(PointStruct(
        id=doc["id"],
        vector=embedding,
        payload={"text": doc["text"], "category": doc["category"]}
    ))

client.upsert(collectionname="documents", points=points)