graphrag-ts citation provenance

io.github.sadofriod由 GitHub 账号 sadofriod 发布v0.1.25更新于 Nov 3, 2026

针对 DOI 的分层来源撤稿与引用判定；证据不足时弃权。

AI 翻译由 AI 模型根据下方的原文描述翻译，可能有误。

原文描述

Source-tiered retraction and citation judgments for DOIs; abstains when evidence is thin.

已验证Streamable HTTP可网页运行网页搜索与抓取知识与记忆

概览

graphrag-ts citation provenance 的托管 MCP 服务（mcp.ashesborn.cloud）；可选 API 密钥；Streamable HTTP；3 个工具。

暂无 AI 概览：以下信息来自注册中心条目和服务本身。

端点
mcp.ashesborn.cloud
最近检查

安装

在 SourceWeft 中

  1. 打开 控制台中的 graphrag-ts citation provenance，将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable，通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体，或 SourceWeft 对话中。智能体会读取该服务器的安装指南，先向你展示发布者、端点和登录方式，经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.sadofriod%2Fgraphrag-ts/install.md，并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前，先告诉我它的发布者、端点和登录方式，等我确认。不要把 API 密钥写进命令或我会提交的文件；问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "graphrag-ts-citation-provenance": {
      "type": "http",
      "url": "https://mcp.ashesborn.cloud/mcp"
    }
  }
}

README

graphrag-ts

[Listed on mcpservers.org] [graphrag-ts MCP server]

Attribution is stored at write time, not asserted at read time.

graphrag-ts is a GraphRAG engine in TypeScript. It builds the knowledge graph in PostgreSQL, keeps source evidence on every node and edge, and answers with retrievable citations — or returns an explicit abstention when the coverage behind an answer is too thin to support it.

Benchmarks / 基准报告 English report · 中文报告

Hosted endpoint / 托管端点 https://mcp.ashesborn.cloud/mcp (anonymous tier: free integrity_paper_status, the two judgments answer with an x402 payment requirement; a keyed session unlocks them) · free DOI status: https://pay.ashesborn.cloud/api/v1/judge/paper?doi= · x402 wire format

[graphrag-ts-pg MCP connector – tool definition quality and endpoint health on Glama]

Try it in 60 seconds

No install, no API key, no model endpoint. Two ways in:

  • In a browser: Retraction Status — paste a DOI, get the source-by-source retraction status and the evidence tiers behind it, with no local model and no account.
  • From the command line:
bash
curl -s 'https://pay.ashesborn.cloud/api/v1/judge/paper?doi=10.1016/S0140-6736(97)11096-0'

The response is the paper plus every retraction record behind it, each with its source_tier and source_uri — the evidence the graph stores, not a model's recollection of it.

Point any Streamable HTTP MCP client at the hosted endpoint (public read tier, 20 requests/minute per IP):

json
{  "mcpServers": {    "graphrag-ts": { "url": "https://mcp.ashesborn.cloud/mcp" }  }}

A bearer API key unlocks the full tool set (graph queries, index jobs, memory writes). The two judgment tools also answer per call without a key via x402 payment ($0.02 / $0.10).

What it does differently

  • Postgres-native. Graph tables and pgvector live in the database you already operate — no Neo4j sidecar and no second store to keep in sync.
  • Incremental by construction. Rebuilds reuse unchanged community summaries, so the cost of a refresh tracks the delta rather than the corpus size.
  • Two retrieval shapes. Local: hybrid vector, keyword and graph retrieval over a community's evidence. Global: Map-Reduce over community summaries for corpus-wide questions.
  • Explicit abstention. Judgment verdicts carry source tiers and return an abstention when reference coverage is thin, instead of answering from a weak signal.
  • Evidence at chunk level. Entities and relationships keep the document and chunk they were extracted from, so an answer can be traced back to the text that produced it.

MCP tools

ToolAccessWhat it does
query_graphkeyedLocal hybrid retrieval against the latest published snapshot in a workspace
query_graph_globalkeyedCorpus-wide Map-Reduce retrieval over community summaries
submit_index_jobkeyedQueue Markdown from the configured input directory for indexing
remember_textkeyedQueue user-provided text explicitly; never reads chat history
get_index_jobkeyedStatus and safe error summary for an index job
integrity_paper_statuspublicRetraction status and identity for a paper
retraction_propagationpublic / x402Judge whether retraction evidence propagates to a paper
integrity_evidence_auditpublic / x402Audit the evidence path, source tiers and conflicts for a paper

Quick start — self-hosted

MCP with Docker Compose

The Compose stack starts PostgreSQL/pgvector and the MCP service. You must provide reachable chat and embedding endpoints; see the MCP guide for configuration, client setup, and workspace behavior.

bash
cp .env.example .env# Configure the database password and model endpoints in .env.docker compose up -d --buildcurl http://127.0.0.1:3000/healthz

Connect an MCP client to http://127.0.0.1:3000/mcp. For the optional local browser showcase, configure the models and run docker compose --profile showcase up -d --build --wait, then open http://127.0.0.1:8080. The showcase is a local demo, not an authentication layer; do not expose it to untrusted users.

Use the library

bash
pnpm installpnpm run db:generatecp .env.example .env# Configure DATABASE_URL and RAG_* model settings; see the guides below.pnpm run db:pushbun run examples/demo.ts

The library requires Bun, PostgreSQL with pgvector, and compatible chat and embedding endpoints. See the English guide or 中文指南 for environment setup, API examples, adapters, and tuning.

Overview

  • Markdown ingestion, entity/relationship extraction, and community detection
  • Full and incremental builds, with unchanged community summaries reused
  • Hybrid vector, keyword, and graph retrieval, plus corpus-wide Map-Reduce queries
  • MCP server with isolated workspaces and a local browser showcase

See the architecture guide for the pipeline and module details.

Benchmark

The reported in-corpus run reached 10/12 strict hits and 95.8% combined lexical recall on 12 questions from five indexed chapters. This is a small project benchmark, not human semantic grading or a cross-project comparison. The reports include methodology, per-query results, and limitations.

The English and Chinese reports are linked at the top of this page. To run the three-question Docker smoke benchmark, use pnpm benchmark:docker:quick with the model endpoints configured and loaded. See the MCP guide for prerequisites and the report for benchmark scope.

Listed on

Documentation

Contributing

Contributions are welcome through issues and pull requests. The project is organized around small, testable modules, so the most useful changes usually fit into one of these areas:

  • graph construction quality
  • retrieval quality and ranking
  • namespace isolation
  • model-loading robustness
  • documentation and examples

License

MIT

来源：README.md，提交 82fbaba

工具

3

integrity_paper_status

Look up one paper by DOI or OpenAlex id and return its identity (title, year, venue) plus every retraction record the graph holds for it, each with its source and source tier: A is an official registry, B an authoritative index, C an aggregator. Use it to check whether a paper is retracted and which sources assert it; use retraction_propagation to judge whether that retraction reaches a specific citing paper. Free in the public tier. Returns: paper identity and statuses[] with source, source_tier, is_retracted, retraction_date and source_uri; abstention_reason when the paper is not in the snapshot.

输入参数doipaper_id

retraction_propagation

Judge whether a retraction propagates to a paper through its citation graph: retraction evidence is weighted by source tier, the citing context is checked for an acknowledgement, and the tool returns CONTESTED, SUPPORTED or ABSTAINED with the evidence path behind the verdict. Use it after integrity_paper_status when the question is "does this retraction reach this paper". It abstains instead of guessing when reference coverage is thin. Returns: verdict, confidence, the evidence path that produced it, and abstention details when the verdict is ABSTAINED. Paid: $0.02 per call (x402 on Base, POST /api/v1/judge); a keyed MCP session also unlocks it.

输入参数doipaper_id

integrity_evidence_audit

Audit the evidence behind a paper's retraction picture: every source record with its tier and URI, the dates involved, conflicts between sources (an official registry and an aggregator disagreeing, or a citation acknowledging a retraction the graph does not propagate) and the coverage gaps that bound the conclusion. Use it when you must justify or challenge a verdict; for a one-line status use integrity_paper_status. Returns: evidence records with source tiers and URIs, detected conflict flags with codes and details, and the coverage that limits how strong the conclusion can be. Paid: $0.10 per call (x402 on Base, POST /api/v1/judge); a keyed MCP session also unlocks it.

输入参数doipaper_id

版本历史

2
  1. v0.1.25最新Nov 3, 2026
  2. v0.1.22Oct 29, 2026

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