# rahulnyk/knowledge_graph

Convert any text to a graph of knowledge. This can be used for Graph Augmented Generation or Knowledge Graph based QnA

Repository: https://github.com/rahulnyk/knowledge_graph
Canonical: https://ross.abutalabs.com/products/knowledge_graph
Language: Jupyter Notebook
License: MIT
License Family: permissive
Last push: 2026-08-15T04:41:44+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 74
- inputs: {"age_days": 1046, "days_push": 18, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3713, forks 591 (observed 2026-08-28T04:08:15.277109+00:00)

## What it is
A Python/Jupyter Notebook project that converts any text corpus into a knowledge graph by extracting concepts with an LLM and linking co-occurring concepts. It supports Graph Augmented Generation (GRAG) for QnA and includes graph visualization.

## Use cases
- build a knowledge graph from a pdf document
- chat with my documents using graph rag
- extract concepts and relationships from text
- visualize a text corpus as a network graph
- run graph algorithms to find important concepts in a document
- create a knowledge graph without using openai api

## When to avoid
- you need production-grade entity extraction with NER models
- you need a scalable graph database solution for enterprise data
- you need a polished application rather than notebook-based code

## Facets
- artifact type: library
- maturity: active
- function: nlp, rag, llm-inference, data-visualization, parser
- domain: artificial-intelligence, data-visualization, developer-tools
- platform: python, cross-platform
- tags: knowledge-graph, graph-rag, concept-extraction, graph-visualization, jupyter-notebook, local-llm, natural-language-processing, retrieval-augmented-generation

## Member repositories
- rahulnyk/knowledge_graph (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.277109+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:30:53.486366+00:00, confidence not recorded.
  - readme: https://github.com/rahulnyk/knowledge_graph (fetched 2026-08-28T04:08:15.277109+00:00, sha e93f9bd03c43)
- Data as of 2026-08-30T08:39:29.467469+00:00.