SourceScore

Verified claim · AI-ML · 100% confidence

GraphRAG introduced in: Edge et al. 2024 — Microsoft Research knowledge-graph RAG.

Last verified 2026-05-16 · Methodology veritas-v0.1 · 58a9c41f05c73a22

Structured fields

Subject
GraphRAG
Predicate
introduced_in
Object
Edge et al. 2024 — Microsoft Research knowledge-graph RAG
Confidence
100%
Tags
graphrag · microsoft · rag · knowledge-graph · foundational · 2024 · introduced_in

Sources (2)

  1. [1] preprint · arXiv (Edge, Trinh, Cheng, Bradley, Chao, Mody, Truitt, Larson / Microsoft Research) · 2024-04-24

    From Local to Global: A Graph RAG Approach to Query-Focused Summarization
    We present a Graph RAG approach to question answering over private text corpora that scales with both the generality of user questions and the quantity of source text to be indexed.
  2. [2] github release · Microsoft Research · 2024-07-02

    GraphRAG — official Microsoft Research repository

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GraphRAG introduced in: Edge et al. 2024 — Microsoft Research knowledge-graph RAG. — SourceScore Claim 58a9c41f05c73a22 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/58a9c41f05c73a22.json

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JavaScript / TypeScript

const r = await fetch("https://sourcescore.org/api/v1/claims/58a9c41f05c73a22.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "GraphRAG introduced in: Edge et al. 2024 — Microsoft Research knowledge-graph RAG."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/58a9c41f05c73a22.json") envelope = r.json() print(envelope["claim"]["statement"]) # "GraphRAG introduced in: Edge et al. 2024 — Microsoft Research knowledge-graph RAG."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_graphrag_fact() -> dict: """Fetch the verified SourceScore claim for GraphRAG.""" r = httpx.get("https://sourcescore.org/api/v1/claims/58a9c41f05c73a22.json") return r.json()