README.md

metadata

license: mit
task_categories:
  - text-retrieval
  - question-answering
language:
  - en
tags:
  - building-defects
  - construction-quality
  - ncc
  - vba
  - rag
  - embeddings
  - bge
pretty_name: Building Defects RAG Index
size_categories:
  - 1K<n<10K

Building Defects RAG Index

This dataset contains pre-built retrieval artifacts for the Building Defects & Quality chatbot. It includes chunked documents, embeddings, and search indexes ready for deployment.

Purpose

This dataset enables Retrieval-Augmented Generation (RAG) for answering questions about building defects, construction tolerances, NCC Volume Two requirements, and VBA Guide to Standards and Tolerances.

Embedding Model

• Model: BAAI/bge-small-en-v1.5
• Dimension: 384
• Storage: float16 for 50% compression

Dataset Schema

chunks.parquet

Document chunks with metadata for retrieval.

Column	Type	Description
chunk_id	string	Unique identifier for the chunk
text	string	The chunk text content
heading_path	list[string]	Hierarchical heading context
source	string	Source PDF filename
page	int	Page number in source document
chunk_hash	string	SHA-256 hash (first 16 chars)

embeddings.parquet

Embedding vectors for semantic search.

Column	Type	Description
chunk_id	string	Matches chunk_id in chunks.parquet
chunk_hash	string	SHA-256 hash for deduplication
embedding	fixed_size_list[float16]	Embedding vector

faiss_index.bin

Serialized FAISS index for fast approximate nearest neighbor search. Uses IndexFlatIP (inner product) for cosine similarity search.

bm25_index.pkl

Serialized BM25 index for lexical/keyword search. Used in hybrid retrieval with reciprocal rank fusion.

source_manifest.json

Manifest of source files with hashes for change detection.

Field	Type	Description
sources	list[SourceFile]	Source file metadata
created_at	string	ISO 8601 creation timestamp
total_chunks	int	Total chunk count
total_embeddings	int	Total embedding count

index_version.txt

Single-line version identifier for cache invalidation. Format: {timestamp}_{hash} (e.g., 20240115_123456_abc123)

Statistics

• Total Chunks: 1,523
• Total Embeddings: 1,523
• Embedding Dimension: 384
• Source Documents: 0
• Storage Format: Parquet with Snappy compression

Usage

Loading with HuggingFace Datasets

from datasets import load_dataset

# Load chunks
chunks = load_dataset("sadickam/BuildingDefect_index", data_files="chunks.parquet")

# Load embeddings
embeddings = load_dataset("sadickam/BuildingDefect_index", data_files="embeddings.parquet")

Loading with huggingface_hub

from huggingface_hub import hf_hub_download

# Download FAISS index
faiss_path = hf_hub_download(
    repo_id="sadickam/BuildingDefect_index",
    filename="faiss_index.bin",
    repo_type="dataset",
)

# Download BM25 index
bm25_path = hf_hub_download(
    repo_id="sadickam/BuildingDefect_index",
    filename="bm25_index.pkl",
    repo_type="dataset",
)

Loading FAISS Index

import faiss

index = faiss.read_index(faiss_path)

License

This dataset is released under the MIT License.

Sources

This dataset is based on:

• NCC Volume Two (2022) - National Construction Code, Australian Building Codes Board
• VBA Guide to Standards and Tolerances - Victorian Building Authority