This is valuable knowledge, thank you for sharing. I like that Qdrant is written in Rust as well!
@sriramananthakrishnan138
Жыл бұрын
This is an excellent tutorial ! Just one suggestion, can you please, the next time, use the words from the payload to create the embeddings for the vectors and then pad it as well to create vectors of n=100 dimensions and then show the same example. It will help people like me understand easy to use next steps to embed complete documents.
@fabsync
Жыл бұрын
Love your teaching style! One question.. if I have millions of data points and I want to create and save some query results.. so that I can show my clients something while I am creating/retrieving the new data query results…how would you go about doing that ? Would you do that with Qdrant only or will you combine it with another db?
@greendsnow
Жыл бұрын
More human way of adding your vectors is like this: points = [] for i in range(len(vectors)) point = models.PointStruct( id=1, vector={ "image": [0.9, 0.1, 0.1, 0.2], "text": [0.4, 0.7, 0.1, 0.8, 0.1, 0.1, 0.9, 0.2], }, ), points.append(point) client.upsert( collection_name="{collection_name}", points=points )
@BuiHungHoward
5 ай бұрын
could you update for method qdrant client -> method recommend -> argument: query_vector seem be changed?
@rodrigomaldonado5280
9 ай бұрын
Thank you very useful!
@code_guy5067
2 күн бұрын
i was amazing with your speaking speed lol
@Tripp111
8 ай бұрын
Hell yeah. Demos from the bathroom!
@JakubSK
Жыл бұрын
Dash not underscore
@LouiseBelle-e7b
11 күн бұрын
Rudolph Extension
@EliotValentine-e7h
11 күн бұрын
Lenna Throughway
@AnnaRodriguez-j1g
13 күн бұрын
Delmer Meadow
@RebeccaHoag-z4k
10 күн бұрын
Ullrich Lane
@MichaelSara-h2z
9 күн бұрын
Bode Prairie
@YuonneConner-l2r
8 күн бұрын
Talon Overpass
@BrendaSorrell-x8b
9 күн бұрын
Sally Plaza
@MicheleFigueroa-w3q
11 күн бұрын
Mariane Mountain
@AgnesVivien-g5h
11 күн бұрын
Christop Lake
@RegineShefte-y9l
10 күн бұрын
Trever Burgs
@EldaAkles-m5i
8 күн бұрын
O'Reilly Ports
@MaudNick-b5y
8 күн бұрын
Mateo Cliff
@RonnyOwensby-d9s
8 күн бұрын
Reynolds Drive
@SonmerfieldRobin-t7r
10 күн бұрын
Beahan Meadow
@WildPeter-x2h
9 күн бұрын
Helene Shoal
@BriceKoterba-r6j
8 күн бұрын
Kyra River
@PhuongLary-r6t
8 күн бұрын
Beahan Mall
@PamelaHelm-u8b
13 күн бұрын
Leif Heights
@DawneJosselyn-e3g
8 күн бұрын
Bailee Ramp
@SadieByrd-b3o
12 күн бұрын
Sven Cape
@KelsenMaltz-w7l
12 күн бұрын
Yundt Course
@savire.ergheiz
16 күн бұрын
The dev don't care about his own channel 😂 Those are random numbers you can't really says that livin lavida loca is trully related with that australian song. At least next time use some embedding data so that it will makes more sense 😅 Using random data for a product demo intended for production is just simply lazy.
@MatthewTownsend-s8z
9 күн бұрын
Mohamed Plains
@darogajee3286
11 ай бұрын
very compliicated tutorial ever from 1:00 time you are writitng everything in docker
@yash_renaissance_athlete
2 ай бұрын
actually, the tutorial couldn't have been simpler. It was very well put together.
@연찐두빵
Жыл бұрын
from qdrant_client import QdrantClient from qdrant_client.http import models client.upsert( collection_name="point_example", points=models.Batch( ids=[1, 2, 3], vectors=[ [0.9, 0.1, 0.1,0.5,0.6], [0.1, 0.9, 0.1,0.2,0.4], [0.1, 0.1, 0.9,0.5,0.2], ] ), ) After run this code, collection_name = "insert_for_point_examle" vector_id = 3 # 특정 ID의 벡터 조회 client.retrieve( collection_name=collection_name, ids=[1], with_vectors = True ) i checked my data id=1, but result is [Record(id=1, payload={}, vector=[0.9878784, 0.10976427, 0.10976427])] Why not [0.9, 0.1, 0.1,0.5,0.6]..?
Пікірлер: 36