Qdrant(@qdrant_engine)

Which German councils are discussing wind power? Poliscope answers in one search. 4,800 council ...

Qdrant (@qdrant_engine) — Mon, 19 Oct 2026 10:00:02 GMT

Which German councils are discussing wind power?

Poliscope answers in one search.  

4,800 council portals, 130M points, one self-hosted Qdrant node. 

Searches went from up to 20 s to 0.87 s on average with hybrid search, tenant filters, and TurboQuant.  

qdrant.tech/blog/case-stud…

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https://t.co/uXP12n7r2A

Qdrant (@qdrant_engine) — Wed, 14 Oct 2026 07:06:18 GMT

qdrant.tech/blog/pplx-embe…

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21× smaller vectors. 99.9% of baseline retrieval quality retained with rescoring. We tested Perplex...

Qdrant (@qdrant_engine) — Wed, 14 Oct 2026 07:05:47 GMT

21× smaller vectors. 99.9% of baseline retrieval quality retained with rescoring.

We tested Perplexity’s pplx-embed-v2-late with Qdrant’s TurboQuant across 4 ViDoRe v3 datasets.

856 KB → 40 KB per page, with a trade-off: rescoring requires the original vectors.

⁠Full benchmarks below 👇

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Read the benchmarks: https://t.co/ZlnUTTHGYw

Qdrant (@qdrant_engine) — Tue, 13 Oct 2026 09:31:10 GMT

Read the benchmarks: qdrant.tech/documentation/…

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780 ms during background optimization. 4.3 ms after it finished. Same Qdrant collection, very diffe...

Qdrant (@qdrant_engine) — Tue, 13 Oct 2026 09:29:25 GMT

780 ms during background optimization. 4.3 ms after it finished.

Same Qdrant collection, very different search latency.

We tested 13 optimizer configurations on 1.76M vectors to understand the trade-offs between indexing, recovery time, and query performance.

Here’s what we found 👇

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LangGraph docs: ⁠https://t.co/r7Wns5XOME Qdrant integration: https://t.co/6rxYzLD573

Qdrant (@qdrant_engine) — Fri, 09 Oct 2026 08:20:56 GMT

LangGraph docs: ⁠docs.langchain.com/oss/python/lan…

Qdrant integration: pypi.org/project/langgr…

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Your AI agents need memory that lasts beyond a single conversation. LangGraph supports long-term me...

Qdrant (@qdrant_engine) — Fri, 09 Oct 2026 08:20:49 GMT

Your AI agents need memory that lasts beyond a single conversation.

LangGraph supports long-term memory, allowing agents to store information and recall it across conversations. Now, you can use Qdrant as the storage layer with our new memory store integration.

Qdrant brings vector-native storage and semantic search to LangGraph’s memory system, so agents can retrieve relevant memories based on meaning, not just exact matches.

You can store user preferences, past interactions, and other useful context, then retrieve what matters when the agent needs it.

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Take the free course on @DeepLearningAI today, and learn how to give your robots real memory with #Q...

Qdrant (@qdrant_engine) — Fri, 09 Oct 2026 05:49:23 GMT

Take the free course on @DeepLearningAI today, and learn how to give your robots real memory with #QdrantEdge

DeepLearning.AI@DeepLearningAI

This assistant remembers where you left your keys. No network connection. No remote server. 

Build one yourself in a course built in partnership with Qdrant and taught by Dylan Couzon. 

👉 Enroll for free:hubs.la/Q04zGYrF0C
#AII#VectorSearchh#AIEngineeringg

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77× less vector RAM. 94.5% of baseline retrieval quality. We got early access to Google EmbeddingGe...

Qdrant (@qdrant_engine) — Wed, 07 Oct 2026 13:05:11 GMT

77× less vector RAM. 94.5% of baseline retrieval quality.

We got early access to Google EmbeddingGemma 2 and tested how far its Matryoshka-compatible embeddings could be compressed in Qdrant.

With shorter vectors + quantization + rescoring, we reduced the vector memory footprint by ~77× while retaining 94.5% of baseline retrieval quality.

Google also featured our work in their EmbeddingGemma 2 announcement.

Qdrant deep dive: qdrant.tech/blog/embedding…

Google’s announcement: blog.google/innovation-and…

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What happens when engineers are given a controversial topic and told to debate it? We’re bringing s...

Qdrant (@qdrant_engine) — Mon, 05 Oct 2026 13:30:01 GMT

What happens when engineers are given a controversial topic and told to debate it?

We’re bringing something a little different to San Francisco Tech Week.

Join Qdrant × @neo4j for Hard Negatives: Engineers Debate Night on October 9.

We’ll put engineers, AI researchers, and builders into 1v1 debates on spicy topics like:

“Open weights should be banned.”

The audience votes live on who wins each round.

Plus, your RSVP includes food and one drink.

October 9 | 6:30–9:00 PM PDT
Spots are limited and registration requires approval.

Link: luma.com/sf-meetup-oct26

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Lucy built a source-of-truth retrieval layer over 2.8M+ SEC and Korea DART filings. Four hybrid lan...

Qdrant (@qdrant_engine) — Sat, 03 Oct 2026 10:18:47 GMT

Lucy built a source-of-truth retrieval layer over 2.8M+ SEC and Korea DART filings.

Four hybrid lanes on Qdrant, payload filters applied inside the query, not after it.

94.7% Recall@10 on FinanceBench.

+0.160 answer accuracy from retrieval alone, with the answer model held fixed.

An open-weight model on Lucy RAG beat frontier models searching the web.

qdrant.tech/blog/case-stud…

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/5 Constella is still a research preview, so there’s plenty more to explore around the quality/late...

Qdrant (@qdrant_engine) — Tue, 29 Sep 2026 05:52:03 GMT

/5

Constella is still a research preview, so there’s plenty more to explore around the quality/latency trade-offs and real-world workloads.

qdrant.tech/blog/constella…

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/4 Nano and Zero are specifically trained to produce vectors compatible with Stella’s document embe...

Qdrant (@qdrant_engine) — Tue, 29 Sep 2026 05:51:41 GMT

/4

Nano and Zero are specifically trained to produce vectors compatible with Stella’s document embeddings. So you can switch between them depending on the trade-off you want between quality and query latency, without rebuilding the document index.

For example, Zero is extremely lightweight, while Nano is a small transformer that gets much closer to Stella’s retrieval quality.

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/3 Constella takes a different approach here: Documents → Stella → document vectors → Qdrant The...

Qdrant (@qdrant_engine) — Tue, 29 Sep 2026 05:51:28 GMT

/3 

Constella takes a different approach here:

Documents → Stella → document vectors → Qdrant

Then, for queries, you can use different models:

Query → Stella / Nano / Zero → query vector → Qdrant

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/2 Normally, your documents are embedded with a model and stored as vectors in Qdrant. When a user s...

Qdrant (@qdrant_engine) — Tue, 29 Sep 2026 05:50:53 GMT

/2
Normally, your documents are embedded with a model and stored as vectors in Qdrant. When a user searches, the query needs to be converted into a vector in the same vector space so Qdrant can compare it with the stored document vectors.

The problem is that changing the embedding model usually means re-embedding your documents too.

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What if you could change your query embedding model without re-embedding all your documents? That’s...

Qdrant (@qdrant_engine) — Tue, 29 Sep 2026 05:50:19 GMT

What if you could change your query embedding model without re-embedding all your documents?

That’s the idea behind Constella, a research preview from Qdrant.

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Thank you to the team at @BetterStackHQ for this video!

Qdrant (@qdrant_engine) — Fri, 25 Sep 2026 15:04:55 GMT

Thank you to the team at @BetterStackHQ for this video!

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Recently, we released an open-source embedding generation tool, Supernova, with a 10B dataset (Qdran...

Qdrant (@qdrant_engine) — Fri, 25 Sep 2026 15:03:30 GMT

Recently, we released an open-source embedding generation tool, Supernova, with a 10B dataset (Qdrant-FineWeb-10B) for free on @huggingface. These resources are excellent for evaluating, testing, and configuring your vector search infra.

ICYMI - @BetterStackHQ just posted a high-quality video explainer for these resources: youtube.com/watch?si=HA1qm…

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New course Launch: Building AI Assistants with On-Device Memory We’re excited to announce Building ...

Qdrant (@qdrant_engine) — Thu, 24 Sep 2026 15:51:44 GMT

New course Launch: Building AI Assistants with On-Device Memory

We’re excited to announce Building AI Assistants with On-Device Memory, a new course built in partnership with @DeepLearningAI and taught by @DylanCouzon, Developer Relations Lead at Qdrant.

In the course, you’ll build a local memory system that can store, retrieve, filter, and forget experiences using vector search. You’ll then extend it to work with text, voice, and images, and teach the assistant to recognize new objects from just a few examples, without retraining the model.

The course also covers how to run the assistant locally on Mac, Windows, and Linux, making it a practical introduction to building AI applications that can work offline and keep data on the device.

It’s an intermediate course with 8 lessons, 5 code examples, and a hands-on AI coding lab.

Enroll here:
deeplearning.ai/short-courses/…

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400K+ document vectors, migrated from Milvus to Qdrant. The team at Kaivid Labs recently migrated t...

Qdrant (@qdrant_engine) — Thu, 24 Sep 2026 09:36:41 GMT

400K+ document vectors, migrated from Milvus to Qdrant.

The team at Kaivid Labs recently migrated their vector database from Milvus to Qdrant and documented the entire process.

They cover how they handled the migration, verified that the data arrived intact, and then compared both systems across latency, throughput, and memory usage.

If you’re considering a similar migration, this is a useful real-world reference to see how they approached it and what they measured along the way.

Read the full write-up: kaivid.com/blogs/migrate-…

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Worth your time if you're interested in SPLADE and Neural Search!

Qdrant (@qdrant_engine) — Tue, 22 Sep 2026 13:24:08 GMT

Worth your time if you're interested in SPLADE and Neural Search!

Doug Turnbull@softwaredoug

Next up in Retrieval Augmented Gathering

This afternoon (1PM ET) we'll hear from @KShivendu_ of @qdrant_engine on doing neural search with no query-time inference

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How many candidates should your vector search retrieve? If you want 5 final results, retrieving 100...

Qdrant (@qdrant_engine) — Mon, 21 Sep 2026 13:00:01 GMT

How many candidates should your vector search retrieve?

If you want 5 final results, retrieving 100 candidates first gives a reranker or fusion stage more to work with.

We experimented with candidate depth across five public datasets and documented what we found.

Increasing limit from 10 → 500 improved the best possible nDCG@10 by up to 0.28, but the current score changed by at most 0.01.

Sometimes the documents are already retrieved, they just aren’t ranked highly enough.

qdrant.tech/articles/candi…

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And that’s a wrap of Vector Space Stream! 🎉 We spent 4+ hours going deep into vector search, from ...

Qdrant (@qdrant_engine) — Fri, 18 Sep 2026 10:16:49 GMT

And that’s a wrap of Vector Space Stream! 🎉

We spent 4+ hours going deep into vector search, from token-native storage and vector compression to robotics, new embedding geometries, dual encoders, adaptive hybrid search, billion-scale evaluation, and compute/storage separation.

And we loved having you with us throughout the entire stream.

Thank you to everyone who joined, asked questions, shared their thoughts, and stuck around for the whole thing.

If you missed it live, the recording is now available:youtube.com/live/PxGlBlqTx…m

And if you want to continue the conversations, ask questions, or dive deeper into any of the sessions, join our Discord community and let’s keep the discussion going,discord.gg/qdrant7

We’ll definitely try to do more streams like this.

Until then, thanks for spending 4+ hours with us! 🫶

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Vector Space Stream | Latest Research from Qdrant https://t.co/nSoc3dFVkN

Qdrant (@qdrant_engine) — Thu, 17 Sep 2026 14:00:07 GMT

Vector Space Stream | Latest Research from Qdrant x.com/i/broadcasts/1…

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When financial search needs both meaning and precision, hybrid search becomes interesting. Satyam S...

Qdrant (@qdrant_engine) — Tue, 15 Sep 2026 14:00:00 GMT

When financial search needs both meaning and precision, hybrid search becomes interesting.

Satyam Sahu explores this with Qdrant using dense embeddings, BM25, and SPLADE across 47 SEC filings from 10 tech companies.

A practical look at where different retrieval approaches work, and where they fall short.

aiadvances.org/rag-hybrid-sea…

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A research agent that gets better from its own run history. At n8n in the loop 2026, @itsclelia, De...

Qdrant (@qdrant_engine) — Tue, 15 Sep 2026 07:16:28 GMT

A research agent that gets better from its own run history.

At n8n in the loop 2026, @itsclelia, DevRel Engineer at Qdrant, will show how to build a self-healing research agent that learns from its own run history, without fine-tuning.

The setup is simple:
→ The agent runs research and produces an answer.
→ A Discord user gives it a human verdict.
→ That verdict is stored back in the same vector store the agent retrieves from.
→ On the next run, the agent retrieves both past successes and failures to understand what worked and what didn’t.

The same collection becomes the agent’s run history, evaluation set, and training signal, with the human in the loop acting as the writer.

Date: October 13–14, 2026

Get your ticket now: n8n.io/intheloop/

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A reminder that we’ve got an amazing series of events lined up for you all next week We’ve got thre...

Qdrant (@qdrant_engine) — Fri, 11 Sep 2026 15:00:00 GMT

A reminder that we’ve got an amazing series of events lined up for you all next week

We’ve got three community events happening across September 16-17:

→ Sep 16: Discord Office Hours, Virtual
An open discussion with the Qdrant team. No fixed agenda, bring your questions, share what you’re building, or just drop in and listen.

→ Sep 16: Bring Your Own Demo Night, In person in Berlin
Six community members, five minutes each, showing what they’ve built around vectors, embeddings, and agents. Pizza, beer, and networking with cognee.

→ Sep 17: Vector Space Stream, Virtual
4+ hours of technical talks covering token-native storage, vector compression, robotics, new embedding geometries, hybrid search, and extreme-scale evaluation.

All three are free to join.

Register here: qdrant.tech/events/

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We’ve just launched the Qdrant Beginner Course, built for people who want to understand vector searc...

Qdrant (@qdrant_engine) — Wed, 09 Sep 2026 16:34:28 GMT

We’ve just launched the Qdrant Beginner Course, built for people who want to understand vector search from the ground up, with 𝐧𝐨 𝐩𝐫𝐢𝐨𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝.

The course starts with the basics: why traditional search struggles, how embeddings represent meaning, and how similarity works. From there, you’ll build your first Qdrant collection, learn about vectors, payloads and HNSW, and understand the differences between dense, sparse, and hybrid search.

You’ll also learn how to design a complete vector search system, including filtering, RAG, and deployment considerations, before putting everything together in a hands-on multimodal supplier risk intelligence capstone project.

The core course takes under 2 hours, with the capstone taking around 3 more hours. It’s self-paced, hands-on, and you can earn a 𝐬𝐡𝐚𝐫𝐞𝐚𝐛𝐥𝐞 𝐜𝐞𝐫𝐭𝐢𝐟�qdrant.tech/course/beginne…lete it.

Start here: https://t.co/GEjzxAgMbB

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Your search can be working perfectly and still be badly tuned. Vector search has a lot of tuning kn...

Qdrant (@qdrant_engine) — Tue, 08 Sep 2026 08:51:15 GMT

Your search can be working perfectly and still be badly tuned.

Vector search has a lot of tuning knobs: `hnsw_ef`, candidate depth, RRF `k`, quantization, reranking, and more.

The problem is, when search quality isn't where you want it to be, which knob should you change first?

We tested this across five public datasets and found that the answer depends on where the problem actually is.

For example, increasing candidate depth from 10 → 500 improved the best achievable score by up to 0.28. But the final score improved by at most 0.01 because the relevant documents were already being retrieved, they just weren't ranking high enough.

So instead of changing settings until the score improves, first identify what's failing, then tune the knob connected to that problem.

That's what we explore in our latest article: how to systematically tune retrieval without guessing.

qdrant.tech/blog/tuning-re…

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Next week is packed at Qdrant. We’ve got 3 community events happening across Sept 16-17, covering e...

Qdrant (@qdrant_engine) — Mon, 07 Sep 2026 11:56:17 GMT

Next week is packed at Qdrant.

We’ve got 3 community events happening across Sept 16-17, covering everything from open discussions and community demos to the latest research in vector search.

Sept 16:
• Discord Office Hours: an open conversation with the Qdrant team. Bring your questions or talk about what you’re building.
• Bring Your Own Demo Night in Berlin with @cognee_: 6 community builders, 5 minutes each to demo what they’ve built with vectors, embeddings, or agents. Plus pizza, drinks & networking.

Sept 17:
• Vector Space Stream: 4+ hours of technical talks on vector compression, Qdrant Edge, embedding geometries, hybrid search, and extreme-scale vector evaluation.

No marketing pitches. Just technical talks, research, experiments & builders.

All 3 are free → qdrant.tech/events

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We just released Qdrant 1.19.1, a patch release with some serious performance improvements under the...

Qdrant (@qdrant_engine) — Fri, 04 Sep 2026 16:47:07 GMT

We just released Qdrant 1.19.1, a patch release with some serious performance improvements under the hood.

Quantized HNSW search is now ~1.5–2.5× faster, and payload-heavy shard transfers are 1.5× faster.

Plus improvements to TurboQuant SIMD scoring, batched HNSW search, deletion checks, and more.

Release here: github.com/qdrant/qdrant/…

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@huggingface 7/ Compute from @Vultr. Distribution with @skypilot_org. Corpus and hosting from @huggi...

Qdrant (@qdrant_engine) — Tue, 01 Sep 2026 13:09:37 GMT

@huggingface 7/ Compute from @Vultr. Distribution with @skypilot_org. Corpus and hosting from @huggingface.

The era of 1M-vector datasets and synthetic approximations is over. Real data, real ground truth, open tooling. All links in the first post.

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6/ Supernova, the open-source engine that we used, is fully open source and free. Four phases: embe...

Qdrant (@qdrant_engine) — Tue, 01 Sep 2026 13:09:07 GMT

6/ Supernova, the open-source engine that we used, is fully open source and free.

Four phases: embedding generation, GPU-native brute-force ground truth, cluster loading, evaluation. YAML-configured, scales across @awscloud, @googlecloud, @Azure, @kubernetesio, and Slurm via @skypilot_org.

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@huggingface 5/ Two companion datasets ship alongside it: PubMed-Multi-Vector: dense, sparse, and C...

Qdrant (@qdrant_engine) — Tue, 01 Sep 2026 13:08:19 GMT

@huggingface 5/ Two companion datasets ship alongside it:

PubMed-Multi-Vector: dense, sparse, and ColBERT-style multi-vector over one corpus. 8.37B multi-vector tokens.

Coyo-Vector-Embeddings: multimodal image-caption embeddings at 2,048 dimensions.

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@huggingface 4/ The 120,000 queries cover dense, sparse, and filtered search, so you can measure wha...

Qdrant (@qdrant_engine) — Tue, 01 Sep 2026 13:08:10 GMT

@huggingface 4/ The 120,000 queries cover dense, sparse, and filtered search, so you can measure what production runs: deep retrieval and filters against exact answers, not 90% recall@10 on a synthetic corpus.

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@huggingface 3/ Qdrant-FineWeb-10B, the largest open-source vector search benchmark dataset, is live...

Qdrant (@qdrant_engine) — Tue, 01 Sep 2026 13:08:04 GMT

@huggingface 3/ Qdrant-FineWeb-10B, the largest open-source vector search benchmark dataset, is live on Hugging Face:

10B FineWeb documents
Dense + sparse vectors from gte-multilingual-base
24.47 TB of vectors, 28.66 TB of text and metadata
Exact top-1,000 ground truth for 120,000 queries

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@huggingface 2/ Why was this unsolved? Exact ground truth at billion scale means comparing every que...

Qdrant (@qdrant_engine) — Tue, 01 Sep 2026 13:07:47 GMT

@huggingface 2/ Why was this unsolved? Exact ground truth at billion scale means comparing every query against every vector. At 10B documents, over a quadrillion distance computations.

We like hard problems. With @Vultr, we absorbed that cost so the community doesn't have to.

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1/ Production vector search: billions of vectors, thousands of RPS, tight tail latency. Public bench...

Qdrant (@qdrant_engine) — Tue, 01 Sep 2026 13:07:25 GMT

1/ Production vector search: billions of vectors, thousands of RPS, tight tail latency. Public benchmarks don't come close. So we built what was missing and gave it away.

Write-up: qdrant.tech/blog/qdrant-fi…
Dataset on @huggingface: huggingface.co/datasets/Qdran…
Supernova Code: github.com/qdrant-labs/su…

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Vector Space Stream - September 17 - Virtual Register: https://t.co/qlSQ3Tdtlo

Qdrant (@qdrant_engine) — Mon, 31 Aug 2026 17:47:11 GMT

Vector Space Stream - September 17 - Virtual

Register: luma.com/vector-space-s…

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(6/7) And there’s a session on hybrid search tuning. The work benchmarks a hybrid pipeline across 5...

Qdrant (@qdrant_engine) — Mon, 31 Aug 2026 17:46:44 GMT

(6/7)
And there’s a session on hybrid search tuning.

The work benchmarks a hybrid pipeline across 5 datasets to understand which tuning parameters actually move retrieval quality.

Not just what each knob does.

Which knobs actually matter on real data.

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(5/7) The research goes beyond traditional vector search too. We’ll look at: → Hyperbolic and spher...

Qdrant (@qdrant_engine) — Mon, 31 Aug 2026 17:46:30 GMT

(5/7)
The research goes beyond traditional vector search too.

We’ll look at:
→ Hyperbolic and spherical geometries
→ New approaches to embeddings
→ Asymmetric dual encoders
→ Smaller on-device query encoders
→ Efficient edge retrieval architectures

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(4/7) Then there’s robotics. Reachy + Qdrant Edge explores what it takes to give a robot memory tha...

Qdrant (@qdrant_engine) — Mon, 31 Aug 2026 17:46:11 GMT

(4/7)
Then there’s robotics.

Reachy + Qdrant Edge explores what it takes to give a robot memory that runs directly on its own hardware.

Embedded. In-process. No cloud.

The session looks at memory and recognition while staying fully offline.

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(3/7) Vector compression is another major topic. The stream covers the research behind TurboQuant a...

Qdrant (@qdrant_engine) — Mon, 31 Aug 2026 17:45:59 GMT

(3/7)
Vector compression is another major topic.

The stream covers the research behind TurboQuant and the turbo4 datatype, including how vector compression can be applied to multivector search while maintaining high recall.

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(2/7) One session explores token-native storage, with research showing: 2–3× compression 10–100× fa...

Qdrant (@qdrant_engine) — Mon, 31 Aug 2026 17:45:43 GMT

(2/7)
One session explores token-native storage, with research showing:

2–3× compression
10–100× faster reads
2–20× faster writes

for scaled workloads.

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A few hours of deep dives into what @qdrant_engine researchers and engineers are actually working on...

Qdrant (@qdrant_engine) — Mon, 31 Aug 2026 17:45:17 GMT

A few hours of deep dives into what @qdrant_engine researchers and engineers are actually working on.

We are hosting Vector Space Stream, that is happening on September 17.

Here’s what’s on the agenda. 🧵

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What if warehouse robots could search product catalogs without the network? Rahul Sharma shows how ...

Qdrant (@qdrant_engine) — Tue, 25 Aug 2026 08:07:23 GMT

What if warehouse robots could search product catalogs without the network?

Rahul Sharma shows how Qdrant Edge + MobileCLIP2 enables offline visual search, with multivectors helping recognize products across different views.

Read more: levelup.gitconnected.com/solving-the-mi…

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Only 2 days to go! We’re coming to New York for an AI debate night where you’ll defend the side you ...

Qdrant (@qdrant_engine) — Mon, 24 Aug 2026 18:00:00 GMT

Only 2 days to go! We’re coming to New York for an AI debate night where you’ll defend the side you didn’t choose.

Join us for Hard Negatives: AI Engineers Debate & Game Night.

Draw a controversial AI take at random, get 3 minutes to prepare, and 10 minutes to convince the room. The audience votes live, and the strongest arguments win prizes.

Time: 6:30 PM – 10:00 PM EDT

Limited spots. 21+. Registration approval required.

Register: luma.com/nyc-meetup-qdr…

Your browser does not support the video tag.

🔗 View on Twitter

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How to cut RAG token costs by 67% without adding another reranking service? @cappybaradeploy combin...

Qdrant (@qdrant_engine) — Mon, 24 Aug 2026 14:12:30 GMT

How to cut RAG token costs by 67% without adding another reranking service?

@cappybaradeploy combines Qdrant’s native ColBERT reranking, binary quantization, and sentence-level retrieval to send only the most relevant parts of a document to the LLM.

The benchmark achieved 67.1% fewer input tokens, while keeping reranking inside Qdrant instead of adding an external API.

A practical deep dive into building more efficient RAG pipelines.

Read the full article:
pub.towardsai.net/how-qdrant-red…

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What if your AI Agent didn’t need to answer the same question twice? Semantic caching can recognize...

Qdrant (@qdrant_engine) — Thu, 20 Aug 2026 15:30:00 GMT

What if your AI Agent didn’t need to answer the same question twice?

Semantic caching can recognize when two differently worded questions have the same meaning and reuse an existing response instead of calling the LLM again.

In this practical deep dive, Tina Sharma builds a semantic cache with Qdrant, benchmarks it, tunes similarity thresholds, and compares single-vector vs. multi-vector retrieval.

The results: 57.1% cache hit rate, 55.7% fewer tokens, and ~15ms response time for cache hits.

Read the full article and explore the implementation:

ai.gopubby.com/how-to-use-sem…

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Today’s the day! Let’s talk about Qdrant 1.19. Join us for our Qdrant Discord Office Hours as we wa...

Qdrant (@qdrant_engine) — Thu, 20 Aug 2026 12:06:58 GMT

Today’s the day! Let’s talk about Qdrant 1.19.

Join us for our Qdrant Discord Office Hours as we walk through the latest features and updates in 1.19, followed by an open Q&A and community discussion.

Time: 5:00 PM CEST
Join here: discord.gg/PGemY6ugf?even…

Have questions about 1.19? Bring them along, we’d love to hear what you’re working on.

More on Qdrant 1.19: qdrant.tech/blog/qdrant-1.…

See you there!

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