Pinecone vs Qdrant: which vector database should you use?

from pinecone import Pinecone
import os

# Initialize Pinecone: requires API key and environment
pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])  # Managed cloud only
index = pc.Index("my-index")

# Upsert vectors (namespace, id, values, metadata)
index.upsert([("doc-1", [0.1, 0.2, 0.3], {"source": "pdf"})])

# Query with metadata filter (simple tag-based)
results = index.query(
    vector=[0.1, 0.2, 0.3],
    top_k=5,
    filter={"source": {"$eq": "pdf"}}  # Limited filtering
)

for match in results["matches"]:
    print(f"ID: {match['id']}, Score: {match['score']}")

from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, Filter, FieldCondition, MatchValue
import os

# Initialize Qdrant: local or remote
qdrant = QdrantClient(":memory:")  # Local in-memory; can also be QdrantClient("http://localhost:6333")

# Create collection with vector config
qdrant.recreate_collection(
    collection_name="my-collection",
    vectors_config={"size": 3, "distance": "Cosine"}
)

# Upsert vectors with structured payload
qdrant.upsert(
    collection_name="my-collection",
    points=[PointStruct(id=1, vector=[0.1, 0.2, 0.3], payload={"source": "pdf", "year": 2024})]
)

# Query with advanced SQL-like filtering
results = qdrant.search(
    collection_name="my-collection",
    query_vector=[0.1, 0.2, 0.3],
    query_filter=Filter(conditions=[
        FieldCondition(key="source", match=MatchValue(value="pdf"))
    ]),
    limit=5
)

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