RFW0107: Implementing an Efficient Chunking Mechanism for Monlam.ai Web App
Named Concepts
Monlam.ai Web App:
This is the website where you can find and use all the Monlam AI models.
Chunking Mechanism:
refers to a process in which a large piece of data or information is broken down into smaller, more manageable parts or "chunks."
Summary
The Monlam web app features AI models that currently have a text processing limit due to their constraints. Some users require the processing of large files, necessitating the implementation of a chunking mechanism for models like Speech-to-Text (STT), Text-to-Speech (TTS), Translation, and Optical Character Recognition (OCR). Additionally, a user-friendly interface with progress tracking is proposed to enhance the user experience in handling such situations.
Input
Existing web app that doesn't have an Efficient Chunking Mechanism for the AI models
Expected Output
The expected output involves a Web app feature that successfully processes the input data through the implemented chunking mechanism. For users dealing with large files, the output should reflect accurate results from models like STT, TTS, Translation, and OCR. Additionally, the proposed user interface should allow users to track the progress of these processes, ensuring a seamless and transparent experience.
Expected Timeline
You need to mention the expected timeline you want.
RFW0107: Implementing an Efficient Chunking Mechanism for Monlam.ai Web App
Named Concepts
Monlam.ai Web App: This is the website where you can find and use all the Monlam AI models.
Chunking Mechanism: refers to a process in which a large piece of data or information is broken down into smaller, more manageable parts or "chunks."
Summary
The Monlam web app features AI models that currently have a text processing limit due to their constraints. Some users require the processing of large files, necessitating the implementation of a chunking mechanism for models like Speech-to-Text (STT), Text-to-Speech (TTS), Translation, and Optical Character Recognition (OCR). Additionally, a user-friendly interface with progress tracking is proposed to enhance the user experience in handling such situations.
Input
Existing web app that doesn't have an Efficient Chunking Mechanism for the AI models
Expected Output
The expected output involves a Web app feature that successfully processes the input data through the implemented chunking mechanism. For users dealing with large files, the output should reflect accurate results from models like STT, TTS, Translation, and OCR. Additionally, the proposed user interface should allow users to track the progress of these processes, ensuring a seamless and transparent experience.
Expected Timeline
You need to mention the expected timeline you want.
References
Include all the relevent references.