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--- |
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license: other |
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license_name: other-non-commercial |
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license_link: LICENSE |
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language: |
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- en |
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tags: |
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- aws |
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- amazon |
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- cloud |
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- cloud-computing |
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- documentation |
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- technical-documents |
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- embeddings |
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- llm |
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- vector-database |
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- faiss |
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- offline-embedding |
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- pdf |
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- chunking |
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- openai |
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- langchain |
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- cybersecurity |
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pretty_name: AWS All-in-One Docs Dataset |
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size_categories: |
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- 100K<n<1M |
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--- |
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# 📚 AWS PDF Chunk Dataset |
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This dataset consists of chunked text extracted from **all publicly available PDF documents on the Amazon Web Services (AWS) official website**. The data includes whitepapers, user guides, technical documentation, and best practice manuals—covering **virtually every AWS service, concept, and architecture** in depth. |
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It is designed to serve as a high-quality knowledge base for use in **embedding generation, vector databases, and retrieval-augmented generation (RAG)** systems. |
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--- |
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## 📦 Dataset Structure |
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The dataset is provided as a `.json` file. Each entry corresponds to a text chunk extracted from a PDF document using a sliding window with **800-token chunks and 100-token overlap**. This preserves semantic continuity and improves embedding-based retrieval performance. |
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```json |
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[ |
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{ |
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"id": "EC2Guide-001", |
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"source": "EC2Guide.pdf", |
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"chunk_id": 1, |
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"text": "Amazon EC2 allows scalable computing capacity in the AWS cloud..." |
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}, |
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... |
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] |
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``` |
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⚠️ Disclaimer |
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This dataset is not affiliated with or endorsed by Amazon Web Services. All content is extracted from publicly available PDF files hosted on aws.amazon.com and is provided strictly for educational and research purposes under fair use. |
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Commercial usage is not allowed. |
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--- |
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## 🔗 Author & Contact |
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- GitHub: [github.com/semihkalkandelen](https://github.com/semihkalkandelen) |
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- LinkedIn: [linkedin.com/in/semih-kalkandelen-6b1b47281/](https://www.linkedin.com/in/semih-kalkandelen-6b1b47281/) |
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