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[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(2)
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p
|
Otherwise, read on to learn more about the demo and how it works!
|
[
"articles",
"articles/food-discovery-demo"
] | 1,700 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
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p
|
In general, our application consists of three parts: a FastAPI backend, a React frontend, and
|
[
"articles",
"articles/food-discovery-demo"
] | 1,701 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
|
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p
|
a Qdrant instance.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,702 |
[
"Food Discovery Demo - Qdrant",
"General architecture"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3)
|
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p
|
The architecture diagram below shows how these components interact with each other:
|
[
"articles",
"articles/food-discovery-demo"
] | 1,703 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(2)
|
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h2
|
Why did we use a CLIP model?
|
[
"articles",
"articles/food-discovery-demo"
] | 1,704 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
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p
|
CLIP is a neural network that can be used to encode both images and texts into vectors.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,705 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
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p
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And more importantly, both images and texts are vectorized into the same
|
[
"articles",
"articles/food-discovery-demo"
] | 1,706 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
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p
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latent space, so we can compare them directly.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,707 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
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] |
p
|
This lets you perform semantic search on images using text queries and the other way around.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,708 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
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p
|
For example, if
|
[
"articles",
"articles/food-discovery-demo"
] | 1,709 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
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p
|
you search for “flat bread with toppings”, you will get images of pizza.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,710 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
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p
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Or if you search for “pizza”, you will get images of some flat bread with toppings, even
|
[
"articles",
"articles/food-discovery-demo"
] | 1,711 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
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p
|
if they were not labeled as “pizza”.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,712 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
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p
|
This is because CLIP embeddings capture the semantics of the images and texts and can find the similarities between them
|
[
"articles",
"articles/food-discovery-demo"
] | 1,713 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(5)
|
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p
|
no matter the wording.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,714 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7)
|
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p
|
CLIP is available in many different ways.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,715 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7)
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p
|
We used the pretrained clip-ViT-B-32 model available in the Sentence-Transformers
|
[
"articles",
"articles/food-discovery-demo"
] | 1,716 |
[
"Food Discovery Demo - Qdrant",
"Why did we use a CLIP model?"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7)
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p
|
library, as this is the easiest way to get started.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,717 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(3)
|
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h2
|
The dataset
|
[
"articles",
"articles/food-discovery-demo"
] | 1,718 |
[
"Food Discovery Demo - Qdrant",
"The dataset"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
|
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p
|
The demo is based on the Wolt dataset.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,719 |
[
"Food Discovery Demo - Qdrant",
"The dataset"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
|
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p
|
It contains over 2M images of dishes from different restaurants along with some additional metadata.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,720 |
[
"Food Discovery Demo - Qdrant",
"The dataset"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8)
|
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] |
p
|
This is how a payload for a single dish looks like:
|
[
"articles",
"articles/food-discovery-demo"
] | 1,721 |
[
"Food Discovery Demo - Qdrant",
"The dataset"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
|
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p
|
Processing this amount of records takes some time, so we precomputed the CLIP embeddings, stored them in a Qdrant collection and exported the collection as
|
[
"articles",
"articles/food-discovery-demo"
] | 1,722 |
[
"Food Discovery Demo - Qdrant",
"The dataset"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
|
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p
|
a snapshot.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,723 |
[
"Food Discovery Demo - Qdrant",
"The dataset"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9)
|
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p
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You may download it here.
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[
"articles",
"articles/food-discovery-demo"
] | 1,724 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(4)
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h2
|
Different search modes
|
[
"articles",
"articles/food-discovery-demo"
] | 1,725 |
[
"Food Discovery Demo - Qdrant",
"Different search modes"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10)
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p
|
The FastAPI backend exposes just a single endpoint,
|
[
"articles",
"articles/food-discovery-demo"
] | 1,726 |
[
"Food Discovery Demo - Qdrant",
"Different search modes"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10)
|
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p
|
however it handles multiple scenarios.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,727 |
[
"Food Discovery Demo - Qdrant",
"Different search modes"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10)
|
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p
|
Let’s dive into them one by one and understand why they are needed.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,728 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(1)
|
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h3
|
Cold start
|
[
"articles",
"articles/food-discovery-demo"
] | 1,729 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
|
Recommendation systems struggle with a cold start problem.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,730 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
|
When a new user joins the system, there is no data about their preferences, so it’s hard to recommend
|
[
"articles",
"articles/food-discovery-demo"
] | 1,731 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
|
anything.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,732 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
|
The same applies to our demo.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,733 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
|
When you open it, you will see a random selection of dishes, and it changes every time you refresh the page.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,734 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
|
Internally,
|
[
"articles",
"articles/food-discovery-demo"
] | 1,735 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
|
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p
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the demo chooses some random points in the
|
[
"articles",
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] | 1,736 |
[
"Food Discovery Demo - Qdrant",
"Cold start"
] |
/articles/food-discovery-demo/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11)
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p
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vector space.
|
[
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[
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"Cold start"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13)
|
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p
|
That procedure should result in returning diverse results, so we have a higher chance of showing something interesting to the user.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,738 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(2)
|
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] |
h3
|
Textual search
|
[
"articles",
"articles/food-discovery-demo"
] | 1,739 |
[
"Food Discovery Demo - Qdrant",
"Textual search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
Since the demo suffers from the cold start problem, we implemented a textual search mode that is useful to start exploring the data.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,740 |
[
"Food Discovery Demo - Qdrant",
"Textual search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
|
The demo will use the CLIP model to encode the query into a vector and then search for the nearest neighbors
|
[
"articles",
"articles/food-discovery-demo"
] | 1,743 |
[
"Food Discovery Demo - Qdrant",
"Textual search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14)
|
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p
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in the vector space.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,744 |
[
"Food Discovery Demo - Qdrant",
"Textual search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
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p
|
This is implemented as a group search query to Qdrant.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,745 |
[
"Food Discovery Demo - Qdrant",
"Textual search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
|
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p
|
We didn’t use a simple search, but performed grouping by the restaurant to get more diverse results.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,746 |
[
"Food Discovery Demo - Qdrant",
"Textual search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
|
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p
|
Search groups
|
[
"articles",
"articles/food-discovery-demo"
] | 1,747 |
[
"Food Discovery Demo - Qdrant",
"Textual search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(16)
|
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] |
p
|
is a mechanism similar to GROUP BY clause in SQL, and it’s useful when you want to get a specific number of result per group (in our case just one).
|
[
"articles",
"articles/food-discovery-demo"
] | 1,748 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(3)
|
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h3
|
Exploring the results
|
[
"articles",
"articles/food-discovery-demo"
] | 1,749 |
[
"Food Discovery Demo - Qdrant",
"Exploring the results"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
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p
|
The main feature of the demo is the ability to explore the space of the dishes.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,750 |
[
"Food Discovery Demo - Qdrant",
"Exploring the results"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
|
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p
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[
"articles",
"articles/food-discovery-demo"
] | 1,751 |
[
"Food Discovery Demo - Qdrant",
"Exploring the results"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(17)
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p
|
and the demo will update the search results accordingly.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,752 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(1)
|
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h4
|
Negative feedback only
|
[
"articles",
"articles/food-discovery-demo"
] | 1,753 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
Qdrant Recommendation API needs at least one positive example to work.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,754 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
However, in our demo
|
[
"articles",
"articles/food-discovery-demo"
] | 1,755 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
we want to be able to provide only negative examples.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,756 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
This is because we want to be able to say “I don’t like this dish” without having to like anything first.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,757 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
To achieve this, we use a trick.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,758 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
|
We negate the vectors of the disliked dishes and use their mean as a query.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,759 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
|
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p
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This way, the disliked dishes will be pushed away
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[
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[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
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html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
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p
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from the search results.
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[
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[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(19)
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p
|
This works because the cosine distance is based on the angle between two vectors, and the angle between a vector and its negation is 180 degrees.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,762 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
Food Discovery Demo implements that trick
|
[
"articles",
"articles/food-discovery-demo"
] | 1,763 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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] |
p
|
by calling Qdrant twice.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,764 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
Initially, we use the Scroll API to find disliked items,
|
[
"articles",
"articles/food-discovery-demo"
] | 1,765 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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] |
p
|
and then calculate a negated mean of all their vectors.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,766 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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] |
p
|
That allows using the Search Groups API
|
[
"articles",
"articles/food-discovery-demo"
] | 1,767 |
[
"Food Discovery Demo - Qdrant",
"Negative feedback only"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(21)
|
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p
|
to find the nearest neighbors of the negated mean vector.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,768 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h4:nth-of-type(2)
|
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h4
|
Positive and negative feedback
|
[
"articles",
"articles/food-discovery-demo"
] | 1,769 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
Since the Recommendation API requires at least one positive example, we can use it only when
|
[
"articles",
"articles/food-discovery-demo"
] | 1,770 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
the user has liked at least one dish.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,771 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
We could theoretically use the same trick as above and negate the disliked dishes, but it would be a bit weird, as Qdrant has
|
[
"articles",
"articles/food-discovery-demo"
] | 1,772 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
that feature already built-in, and we can call it just once to do the job.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,773 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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] |
p
|
It’s always better to perform the search server-side.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,774 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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] |
p
|
Thus, in this case we just call
|
[
"articles",
"articles/food-discovery-demo"
] | 1,775 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
the Qdrant server with a list of positive and negative examples,
|
[
"articles",
"articles/food-discovery-demo"
] | 1,776 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(22)
|
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p
|
so it can find some points which are close to the positive examples and far from the negative ones.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,777 |
[
"Food Discovery Demo - Qdrant",
"Positive and negative feedback"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(23)
|
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p
|
From the user perspective nothing changes comparing to the previous case.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,778 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h3:nth-of-type(4)
|
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h3
|
Location-based search
|
[
"articles",
"articles/food-discovery-demo"
] | 1,779 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
Last but not least, location plays an important role in the food discovery process.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,780 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
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You are definitely looking for something you can find nearby, not on the other
|
[
"articles",
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] | 1,781 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
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p
|
side of the globe.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,782 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
Therefore, your current location can be toggled as a filtering condition.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,783 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
You can enable it by clicking on “Find near me” icon
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[
"articles",
"articles/food-discovery-demo"
] | 1,784 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
in the top right.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,785 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
This way you can find the best pizza in your neighborhood, not in the whole world.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,786 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
Qdrant geo radius filter is a perfect choice for this.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,787 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
It lets you
|
[
"articles",
"articles/food-discovery-demo"
] | 1,788 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(24)
|
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p
|
filter the results by distance from a given point.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,789 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
Such a filter needs a payload index to work efficiently, and it was created on a collection
|
[
"articles",
"articles/food-discovery-demo"
] | 1,790 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
we used to create the snapshot.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,791 |
[
"Food Discovery Demo - Qdrant",
"Location-based search"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(25)
|
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p
|
When you import it into your instance, the index will be already there.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,792 |
[
"Food Discovery Demo - Qdrant"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(5)
|
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h2
|
Using the demo
|
[
"articles",
"articles/food-discovery-demo"
] | 1,793 |
[
"Food Discovery Demo - Qdrant",
"Using the demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26)
|
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p
|
The Food Discovery Demo is available online, but if you prefer to run it locally, you can do it with Docker.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,794 |
[
"Food Discovery Demo - Qdrant",
"Using the demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26)
|
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p
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The
|
[
"articles",
"articles/food-discovery-demo"
] | 1,795 |
[
"Food Discovery Demo - Qdrant",
"Using the demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(26)
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p
|
README describes all the steps more in detail, but here is a quick start:
|
[
"articles",
"articles/food-discovery-demo"
] | 1,796 |
[
"Food Discovery Demo - Qdrant",
"Using the demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
|
The demo will be available at http://localhost:8001, but you won’t be able to search anything until you import the snapshot into your Qdrant
|
[
"articles",
"articles/food-discovery-demo"
] | 1,797 |
[
"Food Discovery Demo - Qdrant",
"Using the demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
|
instance.
|
[
"articles",
"articles/food-discovery-demo"
] | 1,798 |
[
"Food Discovery Demo - Qdrant",
"Using the demo"
] |
/articles/food-discovery-demo/
|
html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(27)
|
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p
|
If you don’t want to bother with hosting a local one, you can use the Qdrant
|
[
"articles",
"articles/food-discovery-demo"
] | 1,799 |
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