AtlasBadSearch

MongoDB Text Search Implementation without using Atlas Search or $text

This is an educational implementation of a simple text search engine using MongoDB, FastAPI, and NLTK. It demonstrates basic concepts of text indexing and search but is not suitable for production use.

⚠️ Important Disclaimer

This project is for educational purposes only. It is not:

• A production-ready search solution
• Optimized for performance or scale
• Secure enough for real-world use
• A replacement for dedicated search technologies

Use only for learning about basic search concepts.

Features

• Basic document indexing with term frequency counting
• Simple search functionality
• Stemming and stopword removal using NLTK
• Bulk document indexing

Setup

1. Clone the repository:

git clone https://github.com/patw/atlasbadsearch.git
cd mongodb-text-search

2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file with your MongoDB connection string:

echo "MONGODB_URI=mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority" > .env
echo "MONGODB_DBNAME=search_db" >> .env

4. Run the FastAPI server:

uvicorn app.main:app --reload

API Endpoints

• POST /index - Index a single document
• POST /bulk_index - Index multiple documents at once
• GET /search - Search documents by query terms

Example Usage

Index a document:

curl -X POST "http://localhost:8000/index" \
-H "Content-Type: application/json" \
-d '{"text":"This is an example document to index"}'

Bulk index documents from file:

curl -X POST "http://localhost:8000/bulk_index" \
-H "Content-Type: application/json" \
-d @sample_bulk.txt

Search documents:

curl "http://localhost:8000/search?query=example document"

Educational Concepts Demonstrated

• Tokenization and stemming
• Stopword removal
• Term frequency counting
• Basic search scoring
• MongoDB document structure for search

License

MIT License - See LICENSE for details.