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https://github.com/antflydb/knowledge-graphs-workshop\ncd knowledge-graphs-workshop\nmake up # → http://127.0.0.1:8000 (Ctrl-C stops everything)"}]}]]}]]}],["$","p",null,{"className":"!text-[clamp(0.9rem,1.4vw,1.15rem)]","children":["Prerequisites: ",["$","code",null,{"children":"uv"}]," and Antfly v0.3.0-rc.1 on your PATH (set ",["$","code",null,{"children":"ANTFLY_BIN"}]," if an older ",["$","code",null,{"children":"antfly"}]," is installed)."]}],["$","p",null,{"className":"!text-[clamp(0.9rem,1.4vw,1.15rem)]","children":"The first run pulls GLiNER2.5 and EmbeddingGemma (~2 GB) and the prebuilt database (~350 MB); after that it starts in seconds."}]]}],["$","div",null,{"className":"shrink-0","children":["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/qr-handout.svg","alt":"QR code for antfly.io/learn/workshops/building-knowledge-graphs","className":"!w-32"}]}]]}]] 44:["$","p",null,{"children":["Slides and setup: antfly.io/learn/workshops/building-knowledge-graphs. ",["$","code",null,{"children":"make"}]," on its own lists every target."]}] 45:["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/text-to-graph.webp","alt":"The Text to graph tab: sentences on the left, the graph extracted from them on the right"}] 46:["$","p",null,{"children":"What we want: pass in the types of things you want to extract and the relationships between them, and get a graph back."}] 47:["$","h2",null,{"children":"Entity extraction from text"}] 48:["$","$1","0",{"children":["$","table",null,{"children":[["$","thead",null,{"children":["$","tr",null,{"children":[["$","th",null,{"children":"Entity types"}],["$","th",null,{"children":"Relationship vocabulary"}]]}]}],["$","tbody",null,{"children":[["$","tr",null,{"children":[["$","td",null,{"children":"character"}],["$","td",null,{"children":"parent_of"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":"house"}],["$","td",null,{"children":"member_of"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":"kingdom"}],["$","td",null,{"children":"rules"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":"city"}],["$","td",null,{"children":"located_in"}]]}]]}]]}]}] 63:T5fe, 49:["$","$1","1",{"children":["$","figure",null,{"className":"deck-diagram not-prose","dangerouslySetInnerHTML":{"__html":"$63"}}]}] 4a:["$","p",null,{"children":"You supply the vocabulary and the relationship definitions. These are the configured types and predicates from the ASoIaF dataset's config.yaml. They are a control knob, not a guarantee of correctness: note that Winterfell came out as a kingdom in one passage and a city in another."}] 4b:["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/graph-jon-snow.webp","alt":"Graph search for Jon Snow: his relationships fanned out around him, with evidence passages on the right"}] 4c:[["$","h2",null,{"children":"Entity resolving"}],"\n",["$","div",null,{"className":"max-w-3xl","children":["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/entity-resolution.webp","alt":"Arnold S. and Schwarzenegger resolve to the same entity, who was Governor of California and starred in Predator"}]}]] 4d:["$","p",null,{"children":"The next step beyond this workshop's pipeline. The workshop graph groups names by type and spelling (\"Tyrion's\" joins \"Tyrion\"), but \"Tyrion\" and \"Tyrion Lannister\" stay separate: it does not resolve aliases, surnames or pronouns."}] 4e:[["$","h2",null,{"children":"Pass through a decision model"}],"\n",["$","p",null,{"children":"Try to weed out weak or untrue relations."}],"\n",["$","table",null,{"children":[["$","thead",null,{"children":["$","tr",null,{"children":[["$","th",null,{"children":"Ordered pair"}],["$","th",null,{"children":"Saved decision"}],["$","th",null,{}]]}]}],["$","tbody",null,{"children":[["$","tr",null,{"children":[["$","td",null,{"children":"Eddard \"Ned\" Stark → Winterfell"}],["$","td",null,{"children":["$","strong",null,{"children":"rules"}]}],["$","td",null,{"children":"0.575"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":"\"is the Lord of Winterfell\""}],["$","td",null,{"children":"no_relation"}],["$","td",null,{"children":"0.241"}]]}],["$","tr",null,{"children":[["$","td",null,{}],["$","td",null,{"children":"unclear"}],["$","td",null,{"children":"0.184"}]]}]]}]]}]] 4f:["$","p",null,{"children":"Walk through the ordered pair, the source wording, the three candidate labels, and the recorded choice. The decision model is told to respect direction, negation, uncertainty and mixed continuity, and to use the supplied passage rather than outside knowledge. The score is a model output, not a calibrated probability that the relationship is true."}] 50:["$","h2",null,{"children":"Change the config and rerun"}] 51:["$","$1","0",{"children":["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/config-relations.webp","alt":"The ASoIaF config.yaml: entity types and relation definitions with head and tail types"}]}] 52:["$","$1","1",{"children":["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/config-decision.webp","alt":"More of config.yaml: member_of, rules and located_in, and the relation decision instruction"}]}] 53:["$","p",null,{"children":["Turn the errors you saw into small configuration experiments, and test one change at a time against saved examples. A schema edit is a hypothesis, not an automatic fix. After editing a dataset's config.yaml, ",["$","code",null,{"children":"make build"}]," rebuilds it."]}] 54:[["$","h2",null,{"children":"Things to try"}],"\n",["$","p",null,{"children":"Search the ASoIaF graph:"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Why does Tyrion kill Tywin?"}],"\n",["$","li",null,{"children":"Jon Snow mother"}],"\n",["$","li",null,{"children":"Dorne"}],"\n"]}],"\n",["$","p",null,{"children":"Then pick one relationship:"}],"\n",["$","ol",null,{"children":["\n",["$","li",null,{"children":"Open its supporting passage."}],"\n",["$","li",null,{"children":"Does the passage support the relationship's type and direction?"}],"\n",["$","li",null,{"children":"Change one choice in config.yaml, rebuild, and compare."}],"\n"]}]] 55:["$","h2",null,{"children":"Why Not Just Ask an Agent?"}] 56:["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/claude-code-prompt.webp","alt":"Claude Code with the prompt: please make me a knowledge graph of all the characters in here. make no mistakes"}] 57:["$","p",null,{"children":"Why go through the trouble of building this when I could just ask an agent for the answer?"}] 58:["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/claude-code-plan.webp","alt":"Claude Code planning: 56 Wikipedia articles, a config of relationship types, chunks for parallel extraction"}] 59:["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/claude-code-agents.webp","alt":"Claude Code running 18 extraction subagents in parallel"}] 5a:["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/citadel-ledger.webp","alt":"Citadel Ledger, the character graph Claude built: 708 characters and 3,643 relations, with Jon Snow selected"}] 5b:[["$","h2",null,{"children":"What Claude did"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Used a bunch of knowledge outside the text corpus"}],"\n",["$","li",null,{"children":"Used 18 subagents and $62 of API credits"}],"\n",["$","li",null,{"children":"Merged entities \"by hand\""}],"\n",["$","li",null,{"children":"Its agents used varying conventions"}],"\n"]}],"\n",["$","p",null,{"children":"Agents can totally build knowledge graphs, but you should give them the right tools to do it."}]] 5c:[["$","h2",null,{"children":"Why not just ask an agent?"}],"\n",["$","ul",null,{"children":["\n",["$","li",null,{"children":"Keep data on-prem"}],"\n",["$","li",null,{"children":"More reproducible, scalable, and deterministic"}],"\n",["$","li",null,{"children":"Control terminology"}],"\n",["$","li",null,{"children":"Run it yourself, own it yourself"}],"\n",["$","li",null,{"children":"An agent won't know your data. Hallucinations are definitely still a thing"}],"\n"]}]] 5d:[["$","h2",null,{"children":["Why else? ",["$","em",null,{"children":"Enterprise"}]]}],"\n",["$","div",null,{"className":"relative w-fit px-[88px] py-[64px]","children":[["$","span",null,{"aria-hidden":"true","className":"absolute left-0 top-0 font-display text-[72px] leading-none text-primary","children":"“"}],["$","blockquote",null,{"className":"m-0 border-0 p-0 not-italic","children":[["$","p",null,{"children":"Each of our systems is internally coherent and externally isolated. Atlas, our telemetry platform, knows applications and their time series. Radar, our alerting system, knows thresholds and firing history. incident.io knows severities and responders. PagerDuty knows rotations. Spinnaker knows what shipped and when. And our Slack history knows, in a form no query engine can reach, which engineer solved something very similar last quarter and what they found."}],["$","p",null,{"className":"mt-[1.5em]","children":"The difficulty is that they do not agree on what they are talking about."}]]}],["$","span",null,{"aria-hidden":"true","className":"absolute right-0 bottom-0 font-display text-[72px] leading-none text-primary","children":"”"}]]}],"\n",["$","p",null,{"children":[["$","a",null,{"href":"https://netflixtechblog.com/ontology-driven-observability-building-an-end-to-end-knowledge-graph-at-netflix-scale-421392cf5adf","children":"Ontology-Driven Observability: Building an End-to-End Knowledge Graph at Netflix Scale"}]," ",["$","i",null,{"children":"netflixtechblog.com"}]]}]] 5e:[["$","h2",null,{"children":"Other tools"}],"\n",["$","table",null,{"children":[["$","thead",null,{"children":["$","tr",null,{"children":[["$","th",null,{"children":"Tool"}],["$","th",null,{"children":"What it's for"}]]}]}],["$","tbody",null,{"children":[["$","tr",null,{"children":[["$","td",null,{"children":["$","a",null,{"href":"https://github.com/getzep/graphiti","children":"Graphiti"}]}],["$","td",null,{"children":"Real-time knowledge graphs for agent memory"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":["$","a",null,{"href":"https://github.com/microsoft/graphrag","children":"GraphRAG"}]}],["$","td",null,{"children":"Graph-based retrieval-augmented generation, from Microsoft"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":["$","a",null,{"href":"https://github.com/HKUDS/LightRAG","children":"LightRAG"}]}],["$","td",null,{"children":"Simple, fast graph-based RAG"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":["$","a",null,{"href":"https://github.com/neo4j-labs/llm-graph-builder","children":"LLM Graph Builder"}]}],["$","td",null,{"children":"Neo4j graphs from unstructured documents"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":["$","a",null,{"href":"https://github.com/topoteretes/cognee","children":"Cognee"}]}],["$","td",null,{"children":"Open-source memory platform for agents"}]]}],["$","tr",null,{"children":[["$","td",null,{"children":["$","a",null,{"href":"https://github.com/mem0ai/mem0","children":"Mem0"}]}],["$","td",null,{"children":"A memory layer for agents and apps"}]]}]]}]]}]] 5f:["$","p",null,{"children":"Descriptions are paraphrased from each project's repository."}] 60:[["$","h2",null,{"children":"Apollo 13"}],"\n",["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/apollo13-timeline.webp","alt":"The Apollo 13 dataset in the workshop's graph search: the air-to-ground transcript on the left, one message's entities as a graph in the middle, and Review Board passages that share them on the right"}]] 61:[["$","h2",null,{"children":"Thank you"}],"\n",["$","img",null,{"src":"/art/workshops/building-knowledge-graphs/qr-handout.svg","alt":"QR code for antfly.io/learn/workshops/building-knowledge-graphs","className":"!w-40"}],"\n",["$","p",null,{"children":[["$","a",null,{"href":"https://antfly.io/learn/workshops/building-knowledge-graphs","children":"antfly.io/learn/workshops/building-knowledge-graphs"}]," · ",["$","a",null,{"href":"https://github.com/antflydb/knowledge-graphs-workshop","children":"github.com/antflydb/knowledge-graphs-workshop"}]]}]] 64:I[773835,["/_next/static/chunks/03tkozj46d1lk.js","/_next/static/chunks/25t113z064dua.js","/_next/static/chunks/2wm0x3rvqmfm-.js","/_next/static/chunks/2cfudnmkyb484.js","/_next/static/chunks/2l8y03_8mnj1z.js","/_next/static/chunks/1ar8njleeyr65.js"],"IconMark"] 3a:null 3e:[["$","title","0",{"children":"Building Knowledge Graphs · Deck · Antfly"}],["$","meta","1",{"name":"description","content":"From a pile of documents to entities and relationships you can query, computed on your own machine"}],["$","link","2",{"rel":"manifest","href":"/site.webmanifest","crossOrigin":"$undefined"}],["$","meta","3",{"name":"robots","content":"noindex"}],["$","link","4",{"rel":"canonical","href":"https://antfly.io/learn/workshops/building-knowledge-graphs"}],["$","link","5",{"rel":"icon","href":"/icon.png?icon.3d39xw099mfow.png","sizes":"96x96","type":"image/png"}],["$","link","6",{"rel":"apple-touch-icon","href":"/apple-icon.png?apple-icon.01p-gddkeum1j.png","sizes":"180x180","type":"image/png"}],["$","$L64","7",{}]]