Krishna C.

The Realtime Document Search project is aimed at building a centralized platform for storing, organizing, and retrieving all types of documents which are used in construction domain like approvals, designs and plans for a construction project. The system is built using Java as the primary programming language and utilizes Elasticsearch as the search engine, Grafana for monitoring, and Kafka for real-time data processing and messaging. The key features of the system include: Document Upload: Users can upload a wide range of documents, including text, images, CAD files, 3D digarams and PDFs, to the system, which will automatically categorize and store them in a structured manner. Search and Retrieval: The system's powerful search functionality, powered by Elasticsearch, will allow users to quickly search and find the documents they need. The search is siloed based on the user permissions and projects. Also provides rich search filters for the users to query information faster Monitoring: Grafana is used for providing real-time monitoring of the system's performance, ensuring that the system runs smoothly and efficiently. Real-time Processing: Kafka enables real-time processing of document-related data and messaging, allowing for instant updates and notifications to be sent to users. This helps the search to ingest the documents and make it available for the users in almost realtime. User Management: The system will include a user management module that allows administrators to add, edit, and delete users, as well as control access to documents. This Document Management System will provide an efficient and user-friendly solution for managing documents within an organization, streamlining document-related processes and improving overall productivity.

The Translation Management System project is designed to automate the translation process and streamline the workflow of managing translated content. The system is built using Python as the primary programming language and will integrate Elasticsearch for search and retrieval, and RabbitMQ for message management. The key features of the system include: Content Translation: Users can upload content for translation, and the system will automatically allocate the task to available translators and track its progress. Translation Memory: The system will maintain a translation memory database, which will store previously translated content and suggest matches for new translations, reducing time and effort. Search and Retrieval: Elasticsearch will provide powerful search functionality, allowing users to easily search for and retrieve translated content. Message Management: RabbitMQ will manage the message exchange between various components of the system, ensuring efficient and reliable communication. Translation Workflow: The system will include a robust translation workflow, which will allow users to manage multiple projects, set priorities, and monitor progress. This Translation Management System will provide a comprehensive solution for managing the translation process, improving efficiency, and reducing the time and effort required to translate content.