This feature aims to improve the Sunbird-Virtual Assistant by implementing a caching layer for GPT responses and associated documents. By caching responses and leveraging similarity search, the assistant can provide more personalized and efficient responses to user queries.
Goals & Mid-Point Milestone
Goals
[ ] [Goal 1]Implement caching layer for GPT responses and documents.
[ ] [Goal 2]Move system cache into a vector DB.
[ ] [Goal 3]Implement retrieval from cached documents using similarity search.
[ ] [Goal 4]Dockerize the service.
[ ] [Goals Achieved By Mid-point Milestone]
Setup/Installation
No specific setup or installation guide provided.
Expected Outcome
The final product should include a virtual assistant with enhanced performance and personalized responses. Cached GPT responses and documents should be utilized to improve response quality and efficiency.
Acceptance Criteria
GPT responses and associated documents are successfully cached.
System cache is moved into a vector DB.
Retrieval from cached documents using similarity search is implemented.
The service is Dockerized for containerization and deployment.
Implementation Details
Redis for building a caching layer.
Marqo DB as the primary vector DB for storing document vectors.
LLM-based vector similarity search for retrieval from cached documents.
Docker for containerization and deployment.
Ticket Contents
Description
This feature aims to improve the Sunbird-Virtual Assistant by implementing a caching layer for GPT responses and associated documents. By caching responses and leveraging similarity search, the assistant can provide more personalized and efficient responses to user queries.
Goals & Mid-Point Milestone
Goals
Setup/Installation
No specific setup or installation guide provided.
Expected Outcome
The final product should include a virtual assistant with enhanced performance and personalized responses. Cached GPT responses and documents should be utilized to improve response quality and efficiency.
Acceptance Criteria
GPT responses and associated documents are successfully cached. System cache is moved into a vector DB. Retrieval from cached documents using similarity search is implemented. The service is Dockerized for containerization and deployment.
Implementation Details
Redis for building a caching layer. Marqo DB as the primary vector DB for storing document vectors. LLM-based vector similarity search for retrieval from cached documents. Docker for containerization and deployment.
Mockups/Wireframes
No mockups or wireframes provided.
Product Name
Sunbird-Virtual Assistant
Organisation Name
Bandhu
Domain
Healthcare
Tech Skills Needed
Python
Mentor(s)
Vineet Upadhayay
Category
Backend