KubeChat

Overview:

The KubeChat is designed to provide an efficient, scalable, and secure environment for generating AI-powered content. The system integrates several services and components that work together to process and manage user inputs, contextual embeddings, and AI-generated responses.

Architecture:

1. User Interface (UI): The front-end interface through which users interact with the system. It is responsible for capturing user inputs and displaying the generated content.
2. Input Handler (IH): Acts as the central coordinator within the system, processing user inputs, managing data flow between components, and updating contexts based on historical data.
3. Memory Manager (MM): Stores and manages user inputs and contexts. It queries the Vector DB to retrieve related historical data to help generate relevant and contextual responses.
4. Embedding Services:
   • Embedding Queue (EQ): Prioritizes and manages embedding generation requests to ensure efficient processing.
   • Embedding Service (EM): Generates embeddings for the input data and retrieves related embeddings from the Vector DB. These embeddings help in understanding and matching the context of the user's input.
5. Prompt Service (PS): Manages and retrieves structured prompt templates from the database, which guide the AI in generating coherent and contextually relevant content.
6. Large Language Model (LLM) Services:
   • LLM Queue (LQM): Manages the processing queue for the LLM service, ensuring that each request is handled efficiently.
   • LLM Service (LLM): The core AI component that generates responses based on the given context and prompt templates.
7. Databases:
   • Vector DB (VDB): A specialized database designed to handle and retrieve vector embeddings. It supports efficient similarity searches to find related historical data.
   • Database (DB): A traditional database that stores and manages prompt templates and other necessary data.

Design Principles:

1. Scalability: Designed to handle an increasing load of user inputs and embedding generation requests without degradation in performance. This is achieved through efficient queuing systems and scalable architecture.
2. Efficiency: The system ensures prompt processing of inputs through optimized data flow, effective management of queues, and parallel processing where feasible.
3. Accuracy and Relevance: By leveraging historical data and contextual embeddings, the system ensures that the AI-generated content is accurate and contextually relevant.
4. Security and Privacy: Emphasizes protecting user data and generated content. The system can be integrated with secure storage solutions and encryption mechanisms to ensure data privacy.
5. Modularity: The system's architecture is modular, allowing for independent scaling and upgrading of each component as needed. This also facilitates easier maintenance and potential integration with other systems or services.
6. Performance Monitoring: Incorporates comprehensive logging and monitoring to track performance metrics, system health, and other critical indicators. This helps in proactive maintenance and performance optimization.

Deploy

pip3 -r requirements.txt

python3 src/prompt.py
python3 src/embedding.py
python3 src/llm.py

# Locate the file you want to the root of the project.
python3 src/flow.py

# input pdf filename -> input template2 -> wait for embedding -> input question

Contributor

Le Yao