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Qdrant
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Qdrant
@qdrant_engine
High-performance Rust-based vector search engine. discord.com/invite/qdrant
qdrant.tech
Joined December 2020
112
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13.3K
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  • Pinned
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    Qdrant
    @qdrant_engine
    Mar 12
    Most vector databases treat retrieval as a single operation. That's the wrong abstraction. Storing embeddings and returning nearest neighbors is a solved problem. The hard problem is what happens next. We solve it through composable vector search, built in Rust. Today, led by
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    Qdrant
    @qdrant_engine
    Nov 3, 2023
    We are proud to share that this new X AI feature just announced by @elonmusk is powered by the Qdrant Vector Database. Congrats to the team! In Rust we Trust! 🦀✊😎
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    Elon Musk
    X
    @elonmusk
    Nov 3, 2023
    AI-based “See similar” posts feature is rolling out now
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    Qdrant
    @qdrant_engine
    Nov 5, 2023
    How does Grok by @xai access real-time knowledge of the world via the 𝕏 platform? Right, using a Vector Database, powered by Qdrant. In Rust, we trust! 🦀✊ Stay tuned.
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    Elon Musk
    X
    @elonmusk
    Nov 5, 2023
    Example of Grok vs typical GPT, where Grok has current information, but other doesn’t
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    Readers added context
    Grok received two segmented prompts. The typical GPT received a single prompt. Smaller chained prompts may result in a large language model providing more consistent answers. promptengineering.org/getting-starte…
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    Qdrant
    @qdrant_engine
    Jul 3, 2024
    For 40 years, BM25 has been the standard for search engines. However, it falls short for modern RAG applications. Say hello to BM42: The combination of semantic and keyword search
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    Qdrant
    @qdrant_engine
    Oct 31, 2024
    📘 𝐉𝐮𝐬𝐭 𝐎𝐮𝐭: 𝐓𝐡𝐞 𝐋𝐋𝐌 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫’𝐬 𝐇𝐚𝐧𝐝𝐛𝐨𝐨𝐤! For those building scalable LLM and RAG systems, 𝐓𝐡𝐞 𝐋𝐋𝐌 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫’𝐬 𝐇𝐚𝐧𝐝𝐛𝐨𝐨𝐤 has just launched, providing a complete, end-to-end guide to deploying real-world applications with
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    Qdrant
    @qdrant_engine
    Oct 6, 2024
    PDFs can be tricky to pull clean text or images from. PyMuPDF4LLM is a new library designed to simplify that process. In his blog post, Benito Martin demonstrates how it works by: 📄 Extracting text in Markdown format 🖼️ Handling image extraction and embedding 🏷️ Chunking
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    Qdrant
    @qdrant_engine
    Apr 17, 2025
    ⚕️ Multi-Agent Medical AI Assistant A full-stack system that combines agents, computer vision, hybrid RAG, and voice I/O into a unified pipeline for diagnosis, research, and patient interaction. The architecture includes: • LangGraph by @langchain for agent orchestration •
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    Qdrant
    @qdrant_engine
    Aug 14, 2023
    We solved *Semantic Search-as-you-Type* ⌨️ It is faster than embedding inference ⚡ It is Open-Source 👐 It is in Rust 🦀 We use it for our docs 📜 Read more how 👇
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    Qdrant
    @qdrant_engine
    Aug 3, 2025
    🚀 Improving RAG Accuracy with Hierarchical Reranking Merging internal and external retrieval in a single pass often lets noise from one source weaken the other, lowering relevance and increasing hallucinations. In his latest blog, @pavan_mantha1 explains a two-stage reranking
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    Qdrant
    @qdrant_engine
    Nov 12, 2023
    It looks like @elonmusk understands pretty well how vector similarity-powered AI recommendations work. A guest candidate for our Vector Space Talks podcast series.😀
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    38K
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    Qdrant
    @qdrant_engine
    May 26, 2025
    🎥 GraphRAG from video with Qdrant and @neo4j Build a multimodal retrieval pipeline that turns raw video into a knowledge graph. This project combines object detection, vision-to-text grounding, temporal tracking, and storage in both graphs and vectors to enable retrieval that
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    Qdrant
    @qdrant_engine
    Jun 6, 2025
    🧠📚 DecipherIt – AI-Powered Research Assistant An open-source NotebookLM alternative built with multi-agent orchestration, semantic search, and real-time web access. Upload documents, paste URLs, or type a topic. DecipherIt turns it into a full research workspace with
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    Qdrant
    @qdrant_engine
    Jun 8, 2025
    📚🤖 Agentic Retrieval System for Local Libraries E-Library-Agent by @itsclelia is a self-hosted AI agent that ingests, indexes, and queries your personal book or paper collections. It’s built on top of ingest-anything and powered by @llama_index, Qdrant, and @Linkup_platform.
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    Qdrant
    @qdrant_engine
    Apr 2, 2025
    🧠 Chunking changes everything in RAG. This benchmark post evaluated Fixed, Semantic, Agentic, and Recursive chunking in Agentic RAG. Built with @AgnoAgi, @qdrant_engine, @ragas_io, and @llama_index. And measured with relevant metrics: Context Recall, Faithfulness, Factual
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