This issue proposes the integration of a client-side Small Language Model (SLM) for text generation and natural language understanding (NLU) within the user interface. This functionality aims to address limitations associated with large language models (LLMs) and complements the Decision Manager implementation (issue #25).
Background:
Issue #25 highlights challenges associated with using Hugging Face Transformers for LLMs, including performance limitations in the browser.
The Decision Manager (issue #27) enhances task routing efficiency.
Proposed Solution:
Implement a client-side SLM for text processing and user interaction.
SLM Functionalities:
Text Filtering and Noise Reduction: Apply NLU to filter incoming text, removing irrelevant or noisy content before sending it to the LLM server.
Small Talk and User Engagement: During LLM processing for complex tasks, the SLM can handle casual conversation and maintain user engagement through small talk.
Task Completion Notification: Upon task completion, the SLM can seamlessly interrupt the conversation and inform the user, facilitating a natural transition back to the primary task.
Benefits:
Improved responsiveness and user experience by handling basic interactions and filtering on the client-side.
Reduced load on the LLM server for better performance, especially during background task execution.
Enhanced user engagement by maintaining interaction during LLM processing.
Considerations:
Selection of an appropriate SLM library suitable for client-side execution (consider size, accuracy trade-offs).
Training or fine-tuning the SLM for specific use cases and desired functionalities.
Ensuring a smooth transition between SLM and LLM interactions for a natural user experience.
Tasks:
[ ] Research and Select Client-Side SLM Library:
Evaluate available SLM libraries for their suitability in the browser environment.
Consider factors like accuracy, model size, and ease of integration.
[ ] Develop SLM Functionality:
Implement text filtering and noise reduction using NLU techniques.
Design conversation flows for small talk and task completion notification.
Ensure a seamless handover between SLM and LLM interactions.
[ ] Integration and Testing:
Integrate the SLM into the user interface framework.
Develop unit and integration tests to verify functionalities and user experience.
[ ] Performance Optimization:
Monitor performance impact of the SLM and optimize for responsiveness.
[ ] Documentation:
Document the chosen SLM library, its functionalities, and integration details.
Relation to Issue #25:
This issue directly addresses limitations identified in issue #25 regarding LLM performance in the browser. By implementing a client-side SLM, we can reduce LLM workload and improve overall responsiveness, complementing the Decision Manager's role in task routing efficiency.
This issue proposes the integration of a client-side Small Language Model (SLM) for text generation and natural language understanding (NLU) within the user interface. This functionality aims to address limitations associated with large language models (LLMs) and complements the Decision Manager implementation (issue #25).
Background:
Proposed Solution:
SLM Functionalities:
Benefits:
Considerations:
Tasks:
[ ] Research and Select Client-Side SLM Library:
[ ] Develop SLM Functionality:
[ ] Integration and Testing:
[ ] Performance Optimization:
[ ] Documentation:
Relation to Issue #25:
This issue directly addresses limitations identified in issue #25 regarding LLM performance in the browser. By implementing a client-side SLM, we can reduce LLM workload and improve overall responsiveness, complementing the Decision Manager's role in task routing efficiency.