Kacper Łukawski kacperlukawski

## quaterion-wine-reviews.ipynb

      
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                kacperlukawski
                / quaterion-wine-reviews.ipynb
            
            
              Created
              August 16, 2022 11:52
            
          
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## batch_search_benchmark.py
import multiprocessing
import tempfile
import time
from pathlib import Path
from urllib.request import urlretrieve

import h5py
from qdrant_client import QdrantClient
from qdrant_client.conversions.common_types import VectorParams
from qdrant_client.http.models import Distance, SearchRequest

## create_qdrant_collection.py
from qdrant_client import QdrantClient
from qdrant_client.conversions.common_types import VectorParams

client = QdrantClient("localhost", 6333)
client.recreate_collection(
    collection_name="test_collection",
    vectors_config=VectorParams(size=4, distance=Distance.EUCLID),
)

## loading_qdrant_vectors.py
vectors = [
    [.1, .0, .0, .0],
    [.0, .1, .0, .0],
    [.0, .0, .1, .0],
    [.0, .0, .0, .1],
    [.1, .0, .1, .0],
    [.0, .1, .0, .1],
    [.1, .1, .0, .0],
    [.0, .0, .1, .1],
    [.1, .1, .1, .1],

## batch_search_single_request.py
results = client.search_batch(
    collection_name="test_collection",
    requests=[
        SearchRequest(
            vector=[0., 0., 2., 0.],
            limit=1,
        ),
        SearchRequest(
            vector=[0., 0., 0., 0.01],
            with_vector=True,

## qdrant-0.10-multiple-vectors.ipynb

      
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                kacperlukawski
                / qdrant-0.10-multiple-vectors.ipynb
            
            
              Last active
              April 24, 2024 18:22
            
              
                Using multiple vectors in Qdrant 0.10
              
          
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## qdrant-0.10-multiple-vectors-create-collection.py
from qdrant_client import QdrantClient
from qdrant_client.http.models import VectorParams, Distance

client = QdrantClient()
client.recreate_collection(
   collection_name="multiple_vectors",
   vectors_config={
       "title": VectorParams(
           size=100,
           distance=Distance.EUCLID,

## qdrant-0.10-multiple-vectors-single-vector.py
client.recreate_collection(
   collection_name="single_vector",
   vectors_config=VectorParams(
       size=100,
       distance=Distance.COSINE,
   )
)

## qdrant-0.10-multiple-vectors-load-dataset.py
from datasets import load_dataset

dataset = load_dataset("ChristophSchuhmann/MS_COCO_2017_URL_TEXT")

## qdrant-0.10-multiple-vectors-convert-to-dataframe.py
import pandas as pd

dataset_df = pd.DataFrame(dataset["train"])
	import multiprocessing
	import tempfile
	import time
	from pathlib import Path
	from urllib.request import urlretrieve

	import h5py
	from qdrant_client import QdrantClient
	from qdrant_client.conversions.common_types import VectorParams
	from qdrant_client.http.models import Distance, SearchRequest
	from qdrant_client import QdrantClient
	from qdrant_client.conversions.common_types import VectorParams

	client = QdrantClient("localhost", 6333)
	client.recreate_collection(
	collection_name="test_collection",
	vectors_config=VectorParams(size=4, distance=Distance.EUCLID),
	)
	vectors = [
	[.1, .0, .0, .0],
	[.0, .1, .0, .0],
	[.0, .0, .1, .0],
	[.0, .0, .0, .1],
	[.1, .0, .1, .0],
	[.0, .1, .0, .1],
	[.1, .1, .0, .0],
	[.0, .0, .1, .1],
	[.1, .1, .1, .1],
	results = client.search_batch(
	collection_name="test_collection",
	requests=[
	SearchRequest(
	vector=[0., 0., 2., 0.],
	limit=1,
	),
	SearchRequest(
	vector=[0., 0., 0., 0.01],
	with_vector=True,
	from qdrant_client import QdrantClient
	from qdrant_client.http.models import VectorParams, Distance

	client = QdrantClient()
	client.recreate_collection(
	collection_name="multiple_vectors",
	vectors_config={
	"title": VectorParams(
	size=100,
	distance=Distance.EUCLID,
	client.recreate_collection(
	collection_name="single_vector",
	vectors_config=VectorParams(
	size=100,
	distance=Distance.COSINE,
	)
	)
	from datasets import load_dataset

	dataset = load_dataset("ChristophSchuhmann/MS_COCO_2017_URL_TEXT")
	import pandas as pd

	dataset_df = pd.DataFrame(dataset["train"])