hatakekksheeshh/CSE_course_RAG

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license: mit task_categories: - question-answering - information-retrieval - text-generation language: - en tags: - rag - retrieval-augmented-generation - education - course-materials - faiss - embeddings - cse - computer-science size_categories: - 1K<n<10K

CSE Course RAG Dataset

A comprehensive dataset for Retrieval-Augmented Generation (RAG) systems containing processed Computer Science and Engineering (CSE) course materials from Ho Chi Minh City University of Technology (HCMUT). This dataset includes pre-built FAISS indices, processed course documents, raw PDFs, and converted images, ready for use in educational RAG applications.

Dataset Description

This dataset provides a complete pipeline-ready dataset for building RAG systems on educational course materials. It includes:

• Pre-built FAISS indices for fast semantic search
• Processed course data in structured JSON format
• Raw PDF documents (original course materials)
• Converted images (OCR-ready page images)
• Metadata and embeddings for retrieval and generation tasks

The dataset is designed to support research and development in educational AI systems, particularly for question-answering and information retrieval applications.

Dataset Structure

CSE_course_RAG/
├── indices/          # Pre-built FAISS indices for semantic search
├── processed/        # Processed course data (JSON format)
├── raw/             # Raw PDF documents
├── converted/       # Converted page images (OCR-ready)
├── data/            # Additional processed data
└── scratch/         # Temporary processing files

Supported Tasks

• Question Answering: Answer questions about course content using retrieved context
• Information Retrieval: Semantic search over course materials
• Text Generation: Generate answers based on retrieved course content

Dataset Details

Dataset Size

• Total Courses: Multiple CSE courses
• Documents: Syllabus and material documents per course
• Chunks: Pre-processed text chunks with embeddings
• Indices: FAISS indices for fast retrieval

Data Processing

The dataset has been processed through the following pipeline:

1. Conversion: PDFs/Office docs → page images
2. OCR: PaddleOCR text extraction
3. Parsing: Structured JSON extraction (syllabus and material parsers)
4. Chunking: Text chunking with overlap
5. Embedding: Sentence-transformer embeddings
6. Indexing: FAISS index construction

Data Fields

Processed Data (JSON):

• course: Course name
• course_id: Course code
• schema_version: Data schema version
• slides: Array of slide objects with:
  • page_index: Page number
  • chapter_num: Chapter number
  • source_file: Source file path
  • metadata: Processing metadata
  • raw_text: Extracted OCR text

FAISS Indices:

• Vector embeddings for semantic search
• Metadata mappings for chunk retrieval
• Course-specific indices

Usage

Download the Dataset

from huggingface_hub import snapshot_download

# Download the entire dataset
dataset_path = snapshot_download(
    repo_id="hatakekksheeshh/CSE_course_RAG",
    repo_type="dataset",
    local_dir="./data"
)

Or use the provided download script:

python dataset.py

Using with RAG Systems

The dataset is designed to work with the CSE Course RAG system:

from rag.query_pipeline import QueryPipeline

# Initialize pipeline with pre-built indices
pipeline = QueryPipeline(
    index_dir="./data/indices",
    embedding_model="sentence-transformers/all-MiniLM-L6-v2"
)

# Query the system
result = pipeline.answer(
    query="What is the grading policy?",
    course="Introduction_to_Computing"
)

Loading FAISS Indices

import faiss
import pickle

# Load FAISS index
index = faiss.read_index("./data/indices/course_name.index")

# Load metadata
with open("./data/indices/course_name_metadata.pkl", "rb") as f:
    metadata = pickle.load(f)

Processing Raw Data

If you need to reprocess the data:

# Load processed course data
import json

with open("./data/processed/course_name/course_name.json", "r") as f:
    course_data = json.load(f)

Dataset Statistics

The dataset includes:

• Multiple CSE courses covering various computer science topics
• Structured syllabus data with course information, grading policies, prerequisites
• Course materials including lecture slides and chapter content
• Pre-computed embeddings using sentence-transformers models
• FAISS indices optimized for fast similarity search

Evaluation

The dataset has been evaluated with the following metrics:

• Answer Faithfulness: +21.1% improvement with query rewriting
• Top Chunk Score: +80.9% improvement in reranker confidence
• Query-Answer Similarity: Semantic alignment between queries and answers
• Retrieval Performance: Query-Chunk similarity and reranker scores

Limitations

• The dataset contains course materials from HCMUT and may be specific to that institution's curriculum
• OCR quality depends on source document quality
• Some courses may have incomplete or missing materials
• The dataset is primarily in English

Citation

If you use this dataset in your research, please cite:

@dataset{cse_course_rag_2025,
  title={CSE Course RAG Dataset},
  author={Nguyen Quoc Hieu},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/hatakekksheeshh/CSE_course_RAG}
}

License

This dataset is released under the MIT License. See the LICENSE file for details.

Copyright: © 2025 Nguyen Quoc Hieu, Ho Chi Minh City University of Technology

Acknowledgments

• Ho Chi Minh City University of Technology (HCMUT) for providing course materials
• HuggingFace for hosting the dataset
• PaddleOCR for OCR capabilities
• sentence-transformers for embedding models
• FAISS for efficient similarity search

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Note: This dataset is intended for research and educational purposes. Please respect the original course materials' copyright and use appropriately.