Mar 17 · 18 min read · When I last wrote about this project, I was benchmarking enterprise AI inference tooling against a local alternative on cutting-edge GPU hardware — and discovering that enterprise frameworks are not a
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Mar 13 · 14 min read · Why the database layer matters In a semantic search system, the database schema isn’t just storage. It defines how embeddings are stored, indexed, and queried. Many tutorials treat the database as a d
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Mar 2 · 6 min read · 💡 👉 Full Cookbook here: https://github.com/farzad528/azure-ai-search-python-playground/blob/main/azure-ai-search-perplexity-contextualized-embeddings.ipynb Perplexity just launched new SOTA embedd
Join discussionFeb 20 · 35 min read · You’ve built a RAG system. It works great. You add more documents to make it better. Answers get worse. Not slightly worse — noticeably worse. Your top-k results show “high similarity” scores but feel
Join discussionFeb 19 · 7 min read · Tag: Learning In Public I have been diving deep into the world of Large Language Models (LLMs) recently. It is easy to get distracted by the flashy new models releasing every week, but to really unde
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Feb 14 · 5 min read · We’re excited to announce the release of VectorChord 1.1 as we kick off the new year of horse. VectorChord 1.0 was a milestone for Postgres-native vector search: it made large-scale indexing fast enough to feel like iteration, not an outage. In 1.1, ...
Join discussionFeb 13 · 4 min read · Modern AI apps are not just about calling an LLM. They combine: LLMs Embeddings Vector databases Semantic search Retrieval-Augmented Generation (RAG) What is Spring AI? Spring AI is an abstraction layer provided by the Spring ecosystem that a...
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