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knowledge (23), the (22), graph (22), dev (17), #knowledgegraph (17), comments (16), follow (16), min (15), read (15), and (14), add (14), comment (14), rag (13), search (12), entity (11), graphrag (10), with (9), from (9), building (9), tae (9), kim (9), your (8), for (8), wikidata (7), what (7), research (7), jun (7), cross (6), work (6), not (6), one (6), graphs (6), how (6), eyanpen (6), saravanan (6), jaichandaran (6), joseph (6), anady (6), community (5), that (5), why (5), claude (5), may (5), built (4), code (4), model (4), adding (4), memory (4), agent (4), gemini (4), reactions (4), create (3), software (3), keep (3), official (3), partner (3), you (3), need (3), evolution (3), understanding (3), lingual (3), resolution (3), trade (3), 534 (3), aliases (3), 883 (3), nodes (3), real (3), extraction (3), fifth (3), layer (3), forming (3), six (3), tool (3), authors (3), wrote (3), spec (3), wikipedia (3), engineering (3), mapped (3), langchain (3), core (3), here (3), structure (3), reveals (3), biggest (3), pitfall (3), seven (3), identities (3), temporary (3), learning (3), loop (3), should (3), separating (3), facts (3), interpretations (3), memorygraph (3), git (3), capture (3), dead (3), ends (3), vector (3), domain (3), ontology (3), 2asy (3), reads (3), tariff (3), news (3), japan (3), labs (3), are (3), systems (3), actually (3), western (3), teams (3), across (3), repositories (3), static (3), analysis (3), part (3), posts (3), apr (3), sunjun (3), daniel (3), yarmoluk (3), altea (3), vane (3), laurent (3), picard (3), ryosuke (3), tsuji (3), menu (3), account (2), log (2), database (2), about (2), algolia (2), google (2), diamond (2), sponsors (2), matter (2), system (2), agents (2), post (2), ability (2), sort (2), top (2), latest (2), relevant (2), sign (2), llm (2), machinelearning (2), reaction (2), opensource (2), schema (2), seo (2), tier (2), authority (2), sameas (2), threading (2), getting (2), wrong (2), place, where, coders, share, stay, date, grow, their, careers, made, love, 2016, 2026, ruby, rails, powers, other, inclusive, communities, open, source, forem, terms, use, privacy, policy, conduct, mlh, shop, free, postgres, contact, showcase, organization, accounts, advertise, help, education, tracks, videos, challenges, home, space, discuss, development, manage, career, neon, platform, thank, our, supporting, doesn, remember, more, than, think, embeddings, operator, demo, logseq, drives, enterprise, mbse, when, documents, broke, teaching, coding, hooks, really, doing, deep, dive, into, microsoft, blog, beyond, replaced, similarity, traversal, context, trending, guides, resources, retrievalaugmentedgeneration, entitytyping, architecture, python, dataextraction, aiagents, typescript, staticanalysis, hidden, connections, often, misses, programming, right, left, hide, close, powered, navigation, skip, content,

Text of the page (random words):
knowledgegraph dev community skip to content navigation menu search powered by algolia search log in create account dev community close knowledgegraph follow hide create post posts left menu sign in for the ability to sort posts by relevant latest or top right menu how japan s research labs are building rag systems that actually work and what western teams keep getting wrong xu xu xu xu xu xu follow jun 20 how japan s research labs are building rag systems that actually work and what western teams keep getting wrong ai programming knowledgegraph rag 1 reaction comments add comment 4 min read your ai model is temporary your learning loop should not be saravanan jaichandaran saravanan jaichandaran saravanan jaichandaran follow jun 16 your ai model is temporary your learning loop should not be knowledgegraph claude memory ai comments add comment 5 min read building one knowledge graph across 46 repositories with static analysis part 1 hidden connections ai often misses ryosuke tsuji ryosuke tsuji ryosuke tsuji follow jun 22 building one knowledge graph across 46 repositories with static analysis part 1 ai knowledgegraph staticanalysis typescript 20 reactions comments 9 comments 12 min read the real work in graph rag is not extraction tae kim tae kim tae kim follow may 29 the real work in graph rag is not extraction graphrag rag knowledgegraph ai comments add comment 3 min read wikidata wikipedia and knowledge graph entity engineering joseph anady joseph anady joseph anady follow may 24 wikidata wikipedia and knowledge graph entity engineering seo wikidata knowledgegraph schema comments add comment 20 min read tier 4 entity and authority wikidata kg sameas threading joseph anady joseph anady joseph anady follow may 23 tier 4 entity and authority wikidata kg sameas threading seo schema knowledgegraph wikidata comments add comment 14 min read the fifth layer is forming six memory tool authors wrote a claude code spec saravanan jaichandaran saravanan jaichandaran saravanan jaichandaran follow jun 5 the fifth layer is forming six memory tool authors wrote a claude code spec opensource claude knowledgegraph aiagents 3 reactions comments add comment 8 min read building knowledge graphs with gemini laurent picard laurent picard laurent picard follow for google ai jun 12 building knowledge graphs with gemini ai gemini knowledgegraph dataextraction 8 reactions comments add comment 39 min read memorygraph git for knowledge built to capture research dead ends altea vane altea vane altea vane follow may 8 memorygraph git for knowledge built to capture research dead ends python opensource research knowledgegraph comments add comment 2 min read cross lingual entity resolution in a trade knowledge graph adding 39 534 aliases to 6 883 nodes tae kim tae kim tae kim follow jun 7 cross lingual entity resolution in a trade knowledge graph adding 39 534 aliases to 6 883 nodes ai machinelearning rag knowledgegraph comments add comment 6 min read i mapped langchain core as a knowledge graph here s what the structure reveals daniel yarmoluk daniel yarmoluk daniel yarmoluk follow may 1 i mapped langchain core as a knowledge graph here s what the structure reveals ai llm knowledgegraph agents 1 reaction comments add comment 2 min read from vector search to a cross domain ontology graph how 2asy ai reads tariff news tae kim tae kim tae kim follow jun 2 from vector search to a cross domain ontology graph how 2asy ai reads tariff news ai machinelearning rag knowledgegraph 2 reactions comments add comment 4 min read separating facts from interpretations in agent knowledge graphs sunjun sunjun sunjun follow apr 26 separating facts from interpretations in agent knowledge graphs ai llm knowledgegraph architecture comments add comment 12 min read the biggest pitfall in graphrag one entity seven identities eyanpen eyanpen eyanpen follow apr 24 the biggest pitfall in graphrag one entity seven identities graphrag entitytyping knowledgegraph rag comments add comment 7 min read why do we need graphrag the evolution from search to understanding eyanpen eyanpen eyanpen follow apr 24 why do we need graphrag the evolution from search to understanding graphrag rag knowledgegraph retrievalaugmentedgeneration comments add comment 5 min read sign in for the ability to sort posts by relevant latest or top trending guides resources building knowledge graphs with gemini building one knowledge graph across 46 repositories with static analysis part 1 how japan s research labs are building rag systems that actually work and what western teams ke beyond rag why i replaced similarity search with graph traversal for ai agent context what is graphrag really doing a deep dive into microsoft s blog post from vector search to a cross domain ontology graph how 2asy ai reads tariff news memorygraph git for knowledge built to capture research dead ends separating facts from interpretations in agent knowledge graphs your ai model is temporary your learning loop should not be the biggest pitfall in graphrag one entity seven identities i mapped langchain core as a knowledge graph here s what the structure reveals teaching ai coding agents with knowledge graph hooks wikidata wikipedia and knowledge graph entity engineering when adding documents broke my search system the fifth layer is forming six memory tool authors wrote a claude code spec the real work in graph rag is not extraction system operator demo a logseq knowledge graph drives enterprise mbse search why graph embeddings matter cross lingual entity resolution in a trade knowledge graph adding 39 534 aliases to 6 883 nodes why do we need graphrag the evolution from search to understanding your ai doesn t remember you that s about to matter more than you think dev diamond sponsors thank you to our diamond sponsors for supporting the dev community google ai is the official ai model and platform partner of dev neon is the official database partner of dev algolia is the 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