metadata

title: SmartManuals-AI
emoji: 🧠
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.30.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - RAG
  - LLM
  - Chroma
  - Gradio
  - OCR
  - HuggingFace
  - PDF
  - Word
  - SemanticSearch
  - SmartManualsAI

✅ SmartManuals-AI for Hugging Face Spaces

SmartManuals-AI is a local-first document QA system that uses retrieval-augmented generation (RAG), OCR, and semantic embeddings to answer technical questions from equipment manuals, service guides, and parts catalogs.

This app is optimized for Hugging Face Spaces and requires no user upload — just preload your manuals in a Manuals/ folder.

🔧 Features

🧠 Ask natural-language questions against your own manuals
📄 Supports both PDF and Word (.docx) documents
🔍 Uses sentence-transformers for semantic search
🗃️ Indexes chunks in ChromaDB (stored locally)
💬 Generates answers via Hugging Face models (default: Meta LLaMA 3.1 8B Instruct)
🖥️ Clean Gradio interface for querying

📁 Folder Structure

SmartManuals-AI/
├── app.py                 # Main Hugging Face app
├── Manuals/               # Place your PDF and DOCX manuals here
│   ├── OM_Treadmill.pdf
│   └── Parts_Bike.docx
├── chroma_store/         # Vector database (auto-generated)
├── requirements.txt      # Dependencies
└── README.md             # This file

🚀 Usage on Hugging Face Spaces

🔐 Environment Variable

Add this secret in your Space settings:

Name	Value
`HF_TOKEN`	Your Hugging Face token

Note: You must accept model licenses on Hugging Face Hub before using gated models like Llama-3.1-8B-Instruct.

📤 Uploading Manuals

Upload your PDF and Word documents directly to the Manuals/ folder in your Space repository.
No need for file uploads via the interface.

🧠 How It Works

On app startup:
- Text is extracted from PDFs (with OCR fallback) and .docx Word files
- Sentences are cleaned, chunked, and embedded with all-MiniLM-L6-v2
- Chunks are stored in a local ChromaDB vector database
At query time:
- Your question is embedded and semantically compared against chunks
- The most relevant chunks are passed to the LLM
- The LLM (LLaMA 3.1) generates a focused answer from context only

🤖 Default Model

This app uses: meta-llama/Llama-3.1-8B-Instruct
More models are supported behind-the-scenes (e.g. Mistral, Gemma)
No need to manually pick models, doc types, or categories

🧩 Supported File Types

✅ PDF (.pdf) with OCR fallback using Tesseract
✅ Word Documents (.docx)

🧪 Local Development

Clone and run locally:

git clone https://github.com/damoojeje/SmartManuals-AI.git
cd SmartManuals-AI
pip install -r requirements.txt
python app.py

📁 Place your manuals inside the Manuals/ directory before running.

👨🏽‍💻 Created By

Damilare Eniolabi
📧 damilareeniolabi@gmail.com
🔗 GitHub: @damoojeje

🔖 Tags

RAG · LLM · Gradio · ChromaDB · OCR · SemanticSearch · PDF · Word · SmartManualsAI · EquipmentQA