Lucasautomatekc/AI-Knowledge-Graph-Preview

FryAI Pie – AI Knowledge Graph Dataset (Preview)

Structured AI explanations, topic clusters, embeddings, and RAG‑ready data

This repository contains a free preview of the FryAI Pie Knowledge Graph Dataset — a structured, developer‑friendly dataset designed for:

• —RAG pipelines
• —Semantic search
• —AI education tools
• —Topic clustering
• —Knowledge graph exploration
• —Embedding‑based retrieval
• —LLM fine‑tuning and evaluation

The full dataset (500+ explanations, full graph, full embeddings, full RAG chunks) is available on Gumroad.

🔗 Full Dataset (Paid)

Get the complete dataset here: https://automatekc.gumroad.com/l/lghqtv

Includes:

• —500+ human‑readable explanations
• —Full topic/category/cluster metadata
• —Full knowledge graph (hundreds of nodes + edges)
• —Full RAG chunks
• —Full embeddings (26MB)
• —Clean JSON structure
• —Ready for production RAG systems

🔗 API Access (RapidAPI)

Prefer API access instead of downloading files? Use the hosted API version: https://rapidapi.com/lucasautomatekc/api/fryai-knowledge-graph-api

📦 What’s Included in This Preview

This HuggingFace repo includes small, safe sample files that demonstrate the dataset structure without revealing the full paid content.

Included (Free Preview)

• —sample_topics.json
• —sample_clusters.json
• —sample_embeddings.json
• —sample_graph.json
• —sample_rag_chunks.json

Not Included (Paid Only)

• —Full articles
• —Full topic/category metadata
• —Full clusters
• —Full knowledge graph
• —Full embeddings
• —Full RAG chunks
• —Full ZIP dataset

🧠 Dataset Structure (Preview)

Topics (sample)

{
  "ai-chips-memory-requirements": {
    "slug": "ai-chips-memory-requirements",
    "question": "How do AI chip memory requirements work?"
  },
  "agentic-ai": {
    "slug": "agentic-ai",
    "question": "How does agentic AI work?"
  }
}

Clusters (sample)

{
  "artificial-intelligence": {
    "slug": "artificial-intelligence",
    "name": "Artificial Intelligence Topic Cluster",
    "articles": ["what-is-ai-liability"]
  }
}

Knowledge Graph (sample)

[
  {
    "id": "topic:ai-chips-memory-requirements",
    "type": "topic",
    "slug": "ai-chips-memory-requirements",
    "edges": [
      { "target": "article:how-does-ai-chips-memory-requirements-work", "type": "explains" }
    ]
  }
]

Embeddings (sample)

[
  {
    "id": "topic:ai-chips-memory-requirements",
    "type": "topic",
    "slug": "ai-chips-memory-requirements",
    "vector": [0.0313, -0.0237, 0.0314, -0.0207, 0.0215]
  }
]

RAG Chunks (sample)

[
  {
    "id": "chunk:ai-chips-memory-requirements-1",
    "articleSlug": "how-does-ai-chips-memory-requirements-work",
    "chunkIndex": 0,
    "text": "Sample text explaining how AI chip memory requirements work...",
    "vector": [0.0123, -0.0044, 0.0099, 0.0021, -0.0033]
  }
]

🧪 Example Usage (Python)

Load topics

import json

with open("sample_topics.json") as f:
    topics = json.load(f)

print(topics.keys())

Load embeddings

import json
import numpy as np

with open("sample_embeddings.json") as f:
    data = json.load(f)

vec = np.array(data[0]["vector"])
print(vec.shape)

Simple semantic search (preview)

import numpy as np

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

query = np.array([0.01, -0.02, 0.03, -0.01, 0.02])
scores = []

for item in data:
    sim = cosine_similarity(query, np.array(item["vector"]))
    scores.append((item["slug"], sim))

sorted(scores, key=lambda x: x[1], reverse=True)

RAG Chunk Example

with open("sample_rag_chunks.json") as f:
    chunks = json.load(f)

for c in chunks:
    print(c["articleSlug"], c["text"])

🏗️ Intended Use Cases

• —RAG pipelines
• —Semantic search
• —AI education tools
• —Topic clustering
• —Knowledge graph exploration
• —Embedding‑based retrieval
• —LLM evaluation
• —Fine‑tuning datasets
• —AI explainability tools

🏷️ Tags

ai
rag
knowledge-graph
embeddings
semantic-search
llm
machine-learning
dataset
education

📄 License

Preview License: apache-2.0 Full dataset license included in the Gumroad package.

🙌 Support the Project

If you find this dataset useful, consider grabbing the full version or starring the repo. Your support helps expand the dataset with new topics, clusters, and explanations.