Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+
+---
+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
+
+```python
+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:
+
+```bash
+python dataset.py
+```
+
+### Using with RAG Systems
+
+The dataset is designed to work with the CSE Course RAG system:
+
+```python
+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
+
+```python
+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:
+
+```python
+# 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:
+
+```bibtex
+@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
+
+---
+
+**Note**: This dataset is intended for research and educational purposes. Please respect the original course materials' copyright and use appropriately.
+
+