README.md

metadata

dataset_name: FryAI Pie – AI Knowledge Graph Dataset (Preview)
pretty_name: FryAI Pie Knowledge Graph (Preview)
license: apache-2.0
tags:
  - ai
  - rag
  - knowledge-graph
  - embeddings
  - semantic-search
  - llm
  - machine-learning
  - dataset
  - education
size_categories:
  - n<1K
task_categories:
  - text-retrieval
  - feature-extraction
  - question-answering
task_ids:
  - document-retrieval
  - utterance-retrieval
homepage: https://automatekc.gumroad.com/l/lghqtv
repository: https://huggingface.co/datasets/Lucasautomatekc/fryai-pie-knowledge-graph
language:
  - en
annotations_creators:
  - machine-generated

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.

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🔗 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

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🔗 API Access (RapidAPI)

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

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📦 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

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🧠 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"])

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🏗️ 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

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🏷️ 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.

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🙌 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.