Getting Started with Qdrant

[@python360]

This is valuable knowledge, thank you for sharing. I like that Qdrant is written in Rust as well!

[@sriramananthakrishnan138]

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]

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]

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]

could you update for method qdrant client -> method recommend -> argument: query_vector seem be changed?

[@Tripp111]

Hell yeah. Demos from the bathroom!

[@code_guy5067]

i was amazing with your speaking speed lol

[@rodrigomaldonado5280]

Thank you very useful!

[@JakubSK]

Dash not underscore

[@LouiseBelle-e7b]

Rudolph Extension

[@TonyAbner-j2q]

Buckridge Oval

[@BartonBaird-l2m]

Hackett Manors

[@sierrabrowneda3822]

Sheridan Cliff

[@ConnieJudith-e4m]

Maxine Forest

[@RiponSorkere-f2r]

Rodrick Plaza

[@DewarLucy-d2w]

Predovic Forge

[@EliotValentine-e7h]

Lenna Throughway

[@RebeccaHoag-z4k]

Ullrich Lane

[@GalsworthyVita-i5p]

Jenkins Way

[@MichaelSara-h2z]

Bode Prairie

[@MicheleFigueroa-w3q]

Mariane Mountain

[@EldaAkles-m5i]

O'Reilly Ports

[@BensonAgnes-n4l]

Monserrat Spur

[@BrendaSorrell-x8b]

Sally Plaza

[@RonnyOwensby-d9s]

Reynolds Drive

[@RegineShefte-y9l]

Trever Burgs

[@ThomsonGustave-i9z]

Seth Ford

[@AgnesVivien-g5h]

Christop Lake

[@MaudNick-b5y]

Mateo Cliff

[@SonmerfieldRobin-t7r]

Beahan Meadow

[@savire.ergheiz]

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.

[@BriceKoterba-r6j]

Kyra River

[@PamelaHelm-u8b]

Leif Heights

[@WildPeter-x2h]

Helene Shoal

[@SadieByrd-b3o]

Sven Cape

[@MatthewTownsend-s8z]

Mohamed Plains

[@DawneJosselyn-e3g]

Bailee Ramp

[@PhuongLary-r6t]

Beahan Mall

[@KelsenMaltz-w7l]

Yundt Course

[@IgnaciaLunde-v7y]

Jerde Path

[@darogajee3286]

very compliicated tutorial ever from 1:00 time you are writitng everything in docker

[@yash_renaissance_athlete]

actually, the tutorial couldn't have been simpler. It was very well put together.

[@연찐두빵]

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]..?