TechHub

Mathieu JacomyAh, my latest tool, just out of the oven! Just in time for my Summer break... It's called *Vandolie*. It's for high school students, but it may work for you as well. I will let you discover it by yourself.👉 <a href="https://jacomyma.github.io/vandolie/en/" rel="nofollow noopener" translate="no" target="_blank">https://jacomyma.github.io/vandolie/en/</a>It's like a mini CorTexT for teenagers, if you know that tool. But it runs entirely in the browser.Entirely localized in Danish.Consider it a beta version. Usable, but feel free to file GitHub issues for feedback & bugs.<a href="https://mas.to/tags/CSSH" class="mention hashtag" rel="nofollow noopener" target="_blank">#CSSH</a> <a href="https://mas.to/tags/DistantReading" class="mention hashtag" rel="nofollow noopener" target="_blank">#DistantReading</a> <a href="https://mas.to/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a>

Christian Drumm 🇪🇺🧗🚵Playing with <a href="https://mastodon.social/@duckdb" class="u-url mention" rel="nofollow noopener" target="_blank">@duckdb</a>, <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> and <a href="https://mastodon.social/tags/skiplists" class="mention hashtag" rel="nofollow noopener" target="_blank">#skiplists</a>. Got exercise to understand how <a href="https://mastodon.social/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> works under the hood.

garywith ai now you set up a box and run deepseek local - 98% of frontier models 20x cheaper <a href="https://infosec.exchange/tags/token" class="mention hashtag" rel="nofollow noopener" target="_blank">#token</a> gen <a href="https://infosec.exchange/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://infosec.exchange/tags/rag" class="mention hashtag" rel="nofollow noopener" target="_blank">#rag</a> <a href="https://infosec.exchange/tags/license" class="mention hashtag" rel="nofollow noopener" target="_blank">#license</a> <a href="https://infosec.exchange/tags/innovation" class="mention hashtag" rel="nofollow noopener" target="_blank">#innovation</a> <a href="https://infosec.exchange/tags/open" class="mention hashtag" rel="nofollow noopener" target="_blank">#open</a> weights <a href="https://infosec.exchange/tags/comm" class="mention hashtag" rel="nofollow noopener" target="_blank">#comm</a> use ok

gary<a href="https://mastodon.social/@nickbearded" class="u-url mention" rel="nofollow noopener" target="_blank">@nickbearded</a> have to build it, there are a few layers here and there, kind of started out as generic portal quest...we will see if i can get anything built - i may have to just sell clusters first and then hire a coding genius or two. I do not think this or the 90 pt plan are particularly unique but just have to try and follow through - get a working prototype for the rag pipelines - make it a cohesive dashboard <a href="https://infosec.exchange/tags/osint" class="mention hashtag" rel="nofollow noopener" target="_blank">#osint</a> <a href="https://infosec.exchange/tags/comp" class="mention hashtag" rel="nofollow noopener" target="_blank">#comp</a> intel <a href="https://infosec.exchange/tags/realtime" class="mention hashtag" rel="nofollow noopener" target="_blank">#realtime</a> info <a href="https://infosec.exchange/tags/portal" class="mention hashtag" rel="nofollow noopener" target="_blank">#portal</a> <a href="https://infosec.exchange/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a>/token/s <a href="https://infosec.exchange/tags/federated" class="mention hashtag" rel="nofollow noopener" target="_blank">#federated</a> distributed inference <a href="https://infosec.exchange/tags/semantic" class="mention hashtag" rel="nofollow noopener" target="_blank">#semantic</a> search

➴➴➴Æ🜔Ɲ.Ƈꭚ⍴𝔥єɼ👩🏻‍💻Okay, Back of the napkin math: - There are probably 100 million sites and 1.5 billion pages worth indexing in a <a href="https://lgbtqia.space/tags/search" class="mention hashtag" rel="nofollow noopener" target="_blank">#search</a> engine - It takes about 1TB to <a href="https://lgbtqia.space/tags/index" class="mention hashtag" rel="nofollow noopener" target="_blank">#index</a> 30 million pages. - We only care about text on a page.I define a page as worth indexing if: - It is not a FAANG site - It has at least one referrer (no DD Web) - It's activeSo, this means we need 40TB of fast data to make a good index for the internet. That's not "runs locally" sized, but it is nonprofit sized.My size assumptions are basically as follows: - <a href="https://lgbtqia.space/tags/URL" class="mention hashtag" rel="nofollow noopener" target="_blank">#URL</a> - <a href="https://lgbtqia.space/tags/TFIDF" class="mention hashtag" rel="nofollow noopener" target="_blank">#TFIDF</a> information - Text <a href="https://lgbtqia.space/tags/Embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#Embeddings</a> - Snippet We can store an index for 30kb. So, for 40TB we can store an full internet index. That's about $500 in storage.Access time becomes a problem. TFIDF for the whole internet can easily fit in ram. Even with <a href="https://lgbtqia.space/tags/quantized" class="mention hashtag" rel="nofollow noopener" target="_blank">#quantized</a> embeddings, you can only fit 2 million per GB in ram. Assuming you had enough RAM it could be fast: TF-IDF to get 100 million candidated, <a href="https://lgbtqia.space/tags/FAISS" class="mention hashtag" rel="nofollow noopener" target="_blank">#FAISS</a> to sort those, load snippets dynamically, potentially modify rank by referers etc.6 128 MG <a href="https://lgbtqia.space/tags/Framework" class="mention hashtag" rel="nofollow noopener" target="_blank">#Framework</a> <a href="https://lgbtqia.space/tags/desktops" class="mention hashtag" rel="nofollow noopener" target="_blank">#desktops</a> each with 5tb HDs (plus one raspberry pi to sort the final condidates from the six machines) is enough to replace <a href="https://lgbtqia.space/tags/Google" class="mention hashtag" rel="nofollow noopener" target="_blank">#Google</a>. That's about $15k. In two to three years this will be doable on a single machine for around $3k.By the end of the decade it should be able to be run as an app on a powerful desktopThree years after that it can run on a <a href="https://lgbtqia.space/tags/laptop" class="mention hashtag" rel="nofollow noopener" target="_blank">#laptop</a>.Three years after that it can run on a <a href="https://lgbtqia.space/tags/cellphone" class="mention hashtag" rel="nofollow noopener" target="_blank">#cellphone</a>.By #2040 it's a background process on your cellphone.

Nebraska.CodeAdam Barney is 'Demystifying LLMs: How They Work and Why It Matters' July 24th at Nebraska.Code().<a href="https://nebraskacode.amegala.com/" rel="nofollow noopener" translate="no" target="_blank">https://nebraskacode.amegala.com/</a><a href="https://mastodon.social/tags/Travefy" class="mention hashtag" rel="nofollow noopener" target="_blank">#Travefy</a> <a href="https://mastodon.social/tags/LargeLanguageModels" class="mention hashtag" rel="nofollow noopener" target="_blank">#LargeLanguageModels</a> <a href="https://mastodon.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLM</a> <a href="https://mastodon.social/tags/languagemodels" class="mention hashtag" rel="nofollow noopener" target="_blank">#languagemodels</a> <a href="https://mastodon.social/tags/TechTalk" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechTalk</a> <a href="https://mastodon.social/tags/tokenization" class="mention hashtag" rel="nofollow noopener" target="_blank">#tokenization</a> <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://mastodon.social/tags/attentionmechanisms" class="mention hashtag" rel="nofollow noopener" target="_blank">#attentionmechanisms</a> <a href="https://mastodon.social/tags/finetuning" class="mention hashtag" rel="nofollow noopener" target="_blank">#finetuning</a> <a href="https://mastodon.social/tags/engineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#engineering</a> <a href="https://mastodon.social/tags/softwareengineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#softwareengineering</a> <a href="https://mastodon.social/tags/softwareengineer" class="mention hashtag" rel="nofollow noopener" target="_blank">#softwareengineer</a> <a href="https://mastodon.social/tags/Nebraska" class="mention hashtag" rel="nofollow noopener" target="_blank">#Nebraska</a> <a href="https://mastodon.social/tags/TechnologyConference" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechnologyConference</a> <a href="https://mastodon.social/tags/lincolnnebraska" class="mention hashtag" rel="nofollow noopener" target="_blank">#lincolnnebraska</a> <a href="https://mastodon.social/tags/devconference" class="mention hashtag" rel="nofollow noopener" target="_blank">#devconference</a>

N-gated Hacker News🚨 BREAKING NEWS: <a href="https://mastodon.social/tags/Embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#Embeddings</a>, that ancient artifact from the dusty corners of ML, are apparently the unsung heroes of technical writing! 🤯 Who knew that connecting texts like a glorified game of connect-the-dots could revolutionize your mundane documentation? 💡 Thank you, Captain Obvious, for this revelation! 😂 <a href="https://technicalwriting.dev/ml/embeddings/overview.html" rel="nofollow noopener" translate="no" target="_blank">https://technicalwriting.dev/ml/embeddings/overview.html</a> <a href="https://mastodon.social/tags/TechnicalWriting" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechnicalWriting</a> <a href="https://mastodon.social/tags/MLRevolution" class="mention hashtag" rel="nofollow noopener" target="_blank">#MLRevolution</a> <a href="https://mastodon.social/tags/ConnectTheDots" class="mention hashtag" rel="nofollow noopener" target="_blank">#ConnectTheDots</a> <a href="https://mastodon.social/tags/DocumentationInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#DocumentationInsights</a> <a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#HackerNews</a> <a href="https://mastodon.social/tags/ngated" class="mention hashtag" rel="nofollow noopener" target="_blank">#ngated</a>

Hacker NewsEmbeddings Are Underrated<a href="https://technicalwriting.dev/ml/embeddings/overview.html" rel="nofollow noopener" translate="no" target="_blank">https://technicalwriting.dev/ml/embeddings/overview.html</a><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#HackerNews</a> <a href="https://mastodon.social/tags/Embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#Embeddings</a> <a href="https://mastodon.social/tags/Underrated" class="mention hashtag" rel="nofollow noopener" target="_blank">#Underrated</a> <a href="https://mastodon.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://mastodon.social/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataScience</a> <a href="https://mastodon.social/tags/AIInsights" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIInsights</a>

Markus EiseleFrom Strings to Semantics: Comparing Text with Java, Quarkus, and Embeddings Learn how to build an AI-powered text similarity service using Quarkus, LangChain4j, and local embedding models. <a href="https://myfear.substack.com/p/java-quarkus-text-embeddings-similarity" rel="nofollow noopener" translate="no" target="_blank">https://myfear.substack.com/p/java-quarkus-text-embeddings-similarity</a> <a href="https://mastodon.online/tags/Java" class="mention hashtag" rel="nofollow noopener" target="_blank">#Java</a> <a href="https://mastodon.online/tags/Quarkus" class="mention hashtag" rel="nofollow noopener" target="_blank">#Quarkus</a> <a href="https://mastodon.online/tags/Embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#Embeddings</a> <a href="https://mastodon.online/tags/Ollama" class="mention hashtag" rel="nofollow noopener" target="_blank">#Ollama</a> <a href="https://mastodon.online/tags/LangChain4j" class="mention hashtag" rel="nofollow noopener" target="_blank">#LangChain4j</a>

JMLR'Variance-Aware Estimation of Kernel Mean Embedding', by Geoffrey Wolfer, Pierre Alquier.<a href="http://jmlr.org/papers/v26/23-0161.html" rel="nofollow noopener" translate="no" target="_blank">http://jmlr.org/papers/v26/23-0161.html</a> <a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://sigmoid.social/tags/embedding" class="mention hashtag" rel="nofollow noopener" target="_blank">#embedding</a> <a href="https://sigmoid.social/tags/empirical" class="mention hashtag" rel="nofollow noopener" target="_blank">#empirical</a>

Hacker NewsHNSW index for vector embeddings in approx 500 LOC<a href="https://github.com/dicroce/hnsw" rel="nofollow noopener" translate="no" target="_blank">https://github.com/dicroce/hnsw</a><a href="https://mastodon.social/tags/HackerNews" class="mention hashtag" rel="nofollow noopener" target="_blank">#HackerNews</a> <a href="https://mastodon.social/tags/HNSW" class="mention hashtag" rel="nofollow noopener" target="_blank">#HNSW</a> <a href="https://mastodon.social/tags/vector" class="mention hashtag" rel="nofollow noopener" target="_blank">#vector</a> <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://mastodon.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#machinelearning</a> <a href="https://mastodon.social/tags/GitHub" class="mention hashtag" rel="nofollow noopener" target="_blank">#GitHub</a> <a href="https://mastodon.social/tags/500LOC" class="mention hashtag" rel="nofollow noopener" target="_blank">#500LOC</a>

MSvanaBig update to my Embeddings Playground. I added support for the first free-to-use embedding model: "all-MiniLM-L6-v2" from Sentence transformers (<a href="https://www.sbert.net/" rel="nofollow noopener" translate="no" target="_blank">https://www.sbert.net/</a>).Try the Embeddings playground here: <a href="https://embeddings.svana.name" rel="nofollow noopener" translate="no" target="_blank">https://embeddings.svana.name</a><a href="https://mastodon.social/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ai</a> <a href="https://mastodon.social/tags/ml" class="mention hashtag" rel="nofollow noopener" target="_blank">#ml</a> <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://mastodon.social/tags/programming" class="mention hashtag" rel="nofollow noopener" target="_blank">#programming</a>

^.^Nomic Embed Code. <a href="https://mastodon.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> specifically for code from Nomic.<a href="https://www.nomic.ai/blog/posts/introducing-state-of-the-art-nomic-embed-code" rel="nofollow noopener" translate="no" target="_blank">https://www.nomic.ai/blog/posts/introducing-state-of-the-art-nomic-embed-code</a>

FIZ ISE Research GroupWe are very happy that our colleage <a href="https://sigmoid.social/@GenAsefa" class="u-url mention" rel="nofollow noopener" target="_blank">@GenAsefa</a> has contributed the chapter on "Neurosymbolic Methods for Dynamic Knowledge Graphs" for the newly published Handbook on Neurosymbolic AI and Knowledge Graphs together with Mehwish Alam and Pierre-Henri Paris.Handbook: <a href="https://ebooks.iospress.nl/doi/10.3233/FAIA400" rel="nofollow noopener" translate="no" target="_blank">https://ebooks.iospress.nl/doi/10.3233/FAIA400</a> our own chapter on arxive: <a href="https://arxiv.org/abs/2409.04572" rel="nofollow noopener" translate="no" target="_blank">https://arxiv.org/abs/2409.04572</a><a href="https://sigmoid.social/tags/neurosymbolicAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#neurosymbolicAI</a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://sigmoid.social/tags/generativeAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#generativeAI</a> <a href="https://sigmoid.social/tags/LLMs" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLMs</a> <a href="https://sigmoid.social/tags/knowledgegraphs" class="mention hashtag" rel="nofollow noopener" target="_blank">#knowledgegraphs</a> <a href="https://sigmoid.social/tags/semanticweb" class="mention hashtag" rel="nofollow noopener" target="_blank">#semanticweb</a> <a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://sigmoid.social/tags/graphembeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#graphembeddings</a>

Judith van StegerenShould you use OpenAI (or other closed-source) embeddings?1. Try the lightest embedding model first 2. If it doesn’t work, try a beefier model and do a blind comparison 3. If you are already using a relatively large model, only then try some blind test against a proprietary model. If you really find it that the closed-source model is better for your application, then go for it.Paraphrased from <a href="https://iamnotarobot.substack.com/p/should-you-use-openais-embeddings" rel="nofollow noopener" translate="no" target="_blank">https://iamnotarobot.substack.com/p/should-you-use-openais-embeddings</a><a href="https://fosstodon.org/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://fosstodon.org/tags/genai" class="mention hashtag" rel="nofollow noopener" target="_blank">#genai</a> <a href="https://fosstodon.org/tags/openai" class="mention hashtag" rel="nofollow noopener" target="_blank">#openai</a> <a href="https://fosstodon.org/tags/ada" class="mention hashtag" rel="nofollow noopener" target="_blank">#ada</a>

FIZ ISE Research GroupPoster from our colleague <a href="https://blog.epoz.org/" class="u-url mention" rel="nofollow noopener" target="_blank">@epoz</a> from UGent-IMEC Linked Data & Solid course. "Exploding Mittens - Getting to grips with huge SKOS datasets" on semantic embeddings enhanced SPARQL queries for ICONCLASS data. Congrats for the 'best poster' award ;-) poster: <a href="https://zenodo.org/records/14887544" rel="nofollow noopener" translate="no" target="_blank">https://zenodo.org/records/14887544</a> iconclass on GitHub: <a href="https://github.com/iconclass" rel="nofollow noopener" translate="no" target="_blank">https://github.com/iconclass</a><a href="https://sigmoid.social/tags/rdf2vec" class="mention hashtag" rel="nofollow noopener" target="_blank">#rdf2vec</a> <a href="https://sigmoid.social/tags/bert" class="mention hashtag" rel="nofollow noopener" target="_blank">#bert</a> <a href="https://sigmoid.social/tags/llm" class="mention hashtag" rel="nofollow noopener" target="_blank">#llm</a> <a href="https://sigmoid.social/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://sigmoid.social/tags/iconclass" class="mention hashtag" rel="nofollow noopener" target="_blank">#iconclass</a> <a href="https://sigmoid.social/tags/semanticweb" class="mention hashtag" rel="nofollow noopener" target="_blank">#semanticweb</a> <a href="https://sigmoid.social/tags/lod" class="mention hashtag" rel="nofollow noopener" target="_blank">#lod</a> <a href="https://sigmoid.social/tags/linkeddata" class="mention hashtag" rel="nofollow noopener" target="_blank">#linkeddata</a> <a href="https://sigmoid.social/tags/knowledgegraphs" class="mention hashtag" rel="nofollow noopener" target="_blank">#knowledgegraphs</a> <a href="https://sigmoid.social/tags/dh" class="mention hashtag" rel="nofollow noopener" target="_blank">#dh</a> <a href="https://nfdi.social/@nfdi4culture" class="u-url mention" rel="nofollow noopener" target="_blank">@nfdi4culture</a> <a href="https://wisskomm.social/@fiz_karlsruhe" class="u-url mention" rel="nofollow noopener" target="_blank">@fiz_karlsruhe</a> <a href="https://sigmoid.social/tags/iconclass" class="mention hashtag" rel="nofollow noopener" target="_blank">#iconclass</a>

lqdevNomic Embed Text V2: An Open Source, Multilingual, Mixture-of-Experts Embedding Model <a href="https://toot.lqdev.tech/tags/nomic" class="mention hashtag" rel="nofollow noopener" target="_blank">#nomic</a> <a href="https://toot.lqdev.tech/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ai</a> <a href="https://toot.lqdev.tech/tags/embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#embeddings</a> <a href="https://www.luisquintanilla.me/feed/nomic-embed-text-v2?utm_medium=feed" rel="nofollow noopener" translate="no" target="_blank">https://www.luisquintanilla.me/feed/nomic-embed-text-v2?utm_medium=feed</a>

PyData MadridTenemos cita el 20 de febrero 🔥 Nos vemos en BBVA AI Factory para hablar embeddings para contratación financiera y de redes neuronales de grafos. Estamos probando <a class="mention" href="https://bsky.app/profile/guild.host" rel="nofollow noopener" target="_blank">@guild.host</a>, ¡reserva tu plaza aquí! 👇 <a href="https://guild.host/events/embeddings-para-contratacin-306046050" rel="nofollow noopener" target="_blank">guild.host/events/embed...</a> <a class="hashtag" href="https://bsky.app/search?q=%23PyData" rel="nofollow noopener" target="_blank">#PyData</a> <a class="hashtag" href="https://bsky.app/search?q=%23PyDataMadrid" rel="nofollow noopener" target="_blank">#PyDataMadrid</a> <a class="hashtag" href="https://bsky.app/search?q=%23python" rel="nofollow noopener" target="_blank">#python</a> <a class="hashtag" href="https://bsky.app/search?q=%23embeddings" rel="nofollow noopener" target="_blank">#embeddings</a> <a class="hashtag" href="https://bsky.app/search?q=%23GraphNeuralNetworks" rel="nofollow noopener" target="_blank">#GraphNeuralNetworks</a> <a href="https://guild.host/events/embeddings-para-contratacin-306046050" rel="nofollow noopener" target="_blank">📄 Embeddings para contratación...</a>

Raphiki🚀 New Video Alert! Explore advanced image generation with Stable Diffusion in our latest "GenAI's Lamp" tutorial. Learn how to use <a href="https://mastodon.social/tags/Embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#Embeddings</a> and <a href="https://mastodon.social/tags/LoRAs" class="mention hashtag" rel="nofollow noopener" target="_blank">#LoRAs</a> to create stunning visuals. Watch now! 🎨 ✨ <a href="https://youtu.be/mZ6eVw8-MM8" rel="nofollow noopener" translate="no" target="_blank">https://youtu.be/mZ6eVw8-MM8</a><a href="https://mastodon.social/tags/StableDiffusion" class="mention hashtag" rel="nofollow noopener" target="_blank">#StableDiffusion</a> <a href="https://mastodon.social/tags/ComfyUI" class="mention hashtag" rel="nofollow noopener" target="_blank">#ComfyUI</a> <a href="https://mastodon.social/tags/TechAtWorldline" class="mention hashtag" rel="nofollow noopener" target="_blank">#TechAtWorldline</a>

Microsoft DevBlogsLeveraging embedding models for deep understanding was another breakthrough moment. OpenAI’s text_embedding_3 allowed us to grasp the nuances of user queries, going beyond mere keyword matches. This capability was instrumental in returning more relevant results and understanding user intent better. <a href="https://dotnet.social/tags/OpenAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenAI</a> <a href="https://dotnet.social/tags/Embeddings" class="mention hashtag" rel="nofollow noopener" target="_blank">#Embeddings</a>

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