快照格式的常用数据集

{
    "title": "Nash Social Welfare for Indivisible Items under Separable, Piecewise-Linear Concave Utilities",
    "DOI": "1612.05191"
}

from InstructorEmbedding import INSTRUCTOR

model = INSTRUCTOR("hkunlp/instructor-xl")
sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
instruction = "Represent the Research Paper title for retrieval; Input:"
embeddings = model.encode([[instruction, sentence]])

PUT /collections/{collection_name}/snapshots/recover
{
  "location": "https://snapshots.qdrant.io/arxiv_titles-3083016565637815127-2023-05-29-13-56-22.snapshot"
}

{
    "abstract": "Recently Cole and Gkatzelis gave the first constant factor approximation\nalgorithm for the problem of allocating indivisible items to agents, under\nadditive valuations, so as to maximize the Nash Social Welfare. We give\nconstant factor algorithms for a substantial generalization of their problem --\nto the case of separable, piecewise-linear concave utility functions. We give\ntwo such algorithms, the first using market equilibria and the second using the\ntheory of stable polynomials.\n  In AGT, there is a paucity of methods for the design of mechanisms for the\nallocation of indivisible goods and the result of Cole and Gkatzelis seemed to\nbe taking a major step towards filling this gap. Our result can be seen as\nanother step in this direction.\n",
    "DOI": "1612.05191"
}

from InstructorEmbedding import INSTRUCTOR

model = INSTRUCTOR("hkunlp/instructor-xl")
sentence = "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train."
instruction = "Represent the Research Paper abstract for retrieval; Input:"
embeddings = model.encode([[instruction, sentence]])

PUT /collections/{collection_name}/snapshots/recover
{
  "location": "https://snapshots.qdrant.io/arxiv_abstracts-3083016565637815127-2023-06-02-07-26-29.snapshot"
}

{
    "cafe": {
        "address": "VGX7+6R2 Vecchia Napoli, Valletta",
        "categories": ["italian", "pasta", "pizza", "burgers", "mediterranean"],
        "location": {"lat": 35.8980154, "lon": 14.5145106},
        "menu_id": "610936a4ee8ea7a56f4a372a",
        "name": "Vecchia Napoli Is-Suq Tal-Belt",
        "rating": 9,
        "slug": "vecchia-napoli-skyparks-suq-tal-belt"
    },
    "description": "Tomato sauce, mozzarella fior di latte, crispy guanciale, Pecorino Romano cheese and a hint of chilli",
    "image": "https://wolt-menu-images-cdn.wolt.com/menu-images/610936a4ee8ea7a56f4a372a/005dfeb2-e734-11ec-b667-ced7a78a5abd_l_amatriciana_pizza_joel_gueller1.jpeg",
    "name": "L'Amatriciana"
}

from PIL import Image
from sentence_transformers import SentenceTransformer

image_path = "5dbfd216-5cce-11eb-8122-de94874ad1c8_ns_takeaway_seelachs_ei_baguette.jpeg"

model = SentenceTransformer("clip-ViT-B-32")
embedding = model.encode(Image.open(image_path))

PUT /collections/{collection_name}/snapshots/recover
{
  "location": "https://snapshots.qdrant.io/wolt-clip-ViT-B-32-2446808438011867-2023-12-14-15-55-26.snapshot"
}