| --- |
| 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. |
|
|
| --- |
|
|
| ## 🔗 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 |
| ```python |
| import json |
| |
| with open("sample_topics.json") as f: |
| topics = json.load(f) |
| |
| print(topics.keys()) |
| ``` |
|
|
| --- |
|
|
| ### Load embeddings |
| ```python |
| 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) |
| ```python |
| 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 |
| ```python |
| 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. |
| ``` |
| |
| |
