tratum / searchlight

Searchlight is a Powerful and Efficient Text Processing API for PDF's

Searchlight

Searchlight is a powerful and efficient Text Processing API for PDF's developed with Python. It processes Documents to highlight specified search words and includes various features like word search, unique words count, highlighting search word and integration with MongoDB and AWS S3 bucket.

Features

• Word Search: Search for specific words in a PDF.
• Unique Words Count: Count the number of unique words in a PDF.
• Highlighting: Highlights the Search Word in the PDF.
• MongoDB Integration: Store data and results in MongoDB.
• AWS S3 Integration: Upload and retrieve PDFs from an AWS S3 bucket.

Installation

1. Clone the repository

   git clone https://github.com/tratum/Searchlight.git

2. Navigate to the project directory

   cd Searchlight

3. Create and activate a virtual environment

   python -m venv .venv
   source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`   

4. Install the required dependencies

   pip install -r requirements.txt

   
   

Configuration

1. Create a .env file in the root directory

   cd Searchlight
   touch .env

2. Navigate to the .env file and Configure your MongoDB and AWS S3 Settings

   ATLAS_URI= your_mongodb_uri
   DB_NAME= your_db_name
   COLLECTION_NAME= your_collection_name
   RAW_COLLECTION_NAME= your_collection_name
   USER_COLLECTION_NAME=tbl_users
   AWS_ACCESS_KEY ='your_aws_access_key'
   AWS_SECRET_KEY='your_aws_secret_access_key'
   BUCKET_NAME='your_s3_bucket_name'

Usage

1. Start the API Server

   python -m uvicorn main:app --reload

2. Use the following endpoint to upload a PDF and perform Text Processing

   http://127.0.0.1:8000/searchlight/upload

   Mandatory Parameters are:
   

   • keyword: The word to search and highlight in the PDF.
   • pdf : The PDF file to process.

Example

Here is an example of how to use the API with cURL:

  curl -X POST "http://127.0.0.1:8000/searchlight/upload" -F "keyword=example" -F "pdf=@/path/to/your/document.pdf"

Contribution

Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.

License

This project is licensed under the MIT License. See the LICENSE file for details

Acknowledgements

• This project is built with FastAPI