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import json

from datetime import datetime

import numpy as np

from qdrant_client import QdrantClient

from qdrant_client.http import models

from testcontainers.qdrant import QdrantContainer

def basic_example():

with QdrantContainer() as qdrant:

# Get connection parameters

host = qdrant.get_container_host_ip()

port = qdrant.get_exposed_port(qdrant.port)

# Create Qdrant client

client = QdrantClient(host=host, port=port)

print("Connected to Qdrant")

# Create collection

collection_name = "test_collection"

vector_size = 128

client.create_collection(

collection_name=collection_name,

vectors_config=models.VectorParams(size=vector_size, distance=models.Distance.COSINE),

)

print(f"Created collection: {collection_name}")

# Generate test vectors and payloads

num_vectors = 5

vectors = np.random.rand(num_vectors, vector_size).tolist()

payloads = [

{

"text": "AI and machine learning are transforming industries",

"category": "Technology",

"tags": ["AI", "ML", "innovation"],

"timestamp": datetime.utcnow().isoformat(),

},

{

"text": "New study reveals benefits of meditation",

"category": "Health",

"tags": ["wellness", "mental health"],

"timestamp": datetime.utcnow().isoformat(),

},

{

"text": "Global warming reaches critical levels",

"category": "Environment",

"tags": ["climate", "sustainability"],

"timestamp": datetime.utcnow().isoformat(),

},

{

"text": "Stock market shows strong growth",

"category": "Finance",

"tags": ["investing", "markets"],

"timestamp": datetime.utcnow().isoformat(),

},

{

"text": "New restaurant opens in downtown",

"category": "Food",

"tags": ["dining", "local"],

"timestamp": datetime.utcnow().isoformat(),

},

]

# Upload vectors with payloads

client.upsert(

collection_name=collection_name,

points=models.Batch(ids=list(range(num_vectors)), vectors=vectors, payloads=payloads),

)

print("Uploaded vectors with payloads")

# Search vectors

search_result = client.search(collection_name=collection_name, query_vector=vectors[0], limit=3)

print("\nSearch results:")

for scored_point in search_result:

print(

json.dumps(

{"id": scored_point.id, "score": scored_point.score, "payload": scored_point.payload}, indent=2

)

)

# Filtered search

filter_result = client.search(

collection_name=collection_name,

query_vector=vectors[0],

query_filter=models.Filter(

must=[models.FieldCondition(key="category", match=models.MatchValue(value="Technology"))]

),

limit=2,

)

print("\nFiltered search results:")

for scored_point in filter_result:

print(

json.dumps(

{"id": scored_point.id, "score": scored_point.score, "payload": scored_point.payload}, indent=2

)

)

# Create payload index

client.create_payload_index(

collection_name=collection_name, field_name="category", field_schema=models.PayloadFieldSchema.KEYWORD

)

print("\nCreated payload index on category field")

# Create vector index

client.create_payload_index(

collection_name=collection_name, field_name="tags", field_schema=models.PayloadFieldSchema.KEYWORD

)

print("Created payload index on tags field")

# Scroll through collection

scroll_result = client.scroll(collection_name=collection_name, limit=10, with_payload=True, with_vectors=True)

print("\nScrolled through collection:")

for point in scroll_result[0]:

print(json.dumps({"id": point.id, "payload": point.payload}, indent=2))

# Get collection info

collection_info = client.get_collection(collection_name)

print("\nCollection info:")

print(

json.dumps(

{

"name": collection_info.name,

"vectors_count": collection_info.vectors_count,

"points_count": collection_info.points_count,

"status": collection_info.status,

},

indent=2,

)

)

# Update payload

client.set_payload(collection_name=collection_name, payload={"new_field": "updated value"}, points=[0, 1])

print("\nUpdated payload for points 0 and 1")

# Delete points

client.delete(collection_name=collection_name, points_selector=models.PointIdsList(points=[4]))

print("Deleted point with id 4")

# Clean up

client.delete_collection(collection_name)

print("\nDeleted collection")

if __name__ == "__main__":

basic_example()