Lokam Project Scaffolding System

To streamline the development of Proof of Concepts (POCs) and Minimum Viable Products (MVPs) for our clients at Lokam, we can establish a standardized, scalable, and modular project scaffolding system. This system will cater to the three primary use cases:

Use Cases

1. Regular Chatbot

   • Utilizing a Large Language Model (LLM) to respond to user inputs.

2. RAG-based Chatbot

   • Combining LLMs with Retrieval-Augmented Generation (RAG) techniques
   • Incorporating vector databases for context-rich responses

3. MCP AI Agent

   • An agent with Model Context Protocol (MCP) capabilities
   • Connecting to multiple data sources and systems to perform specific tasks

Implementation Guide

1. Standardized Tech Stack

Frontend

• React: For building user interfaces
• Tailwind CSS: For utility-first CSS styling
• Vite: As the build tool for faster development

Backend

• FastAPI: For building high-performance APIs
• PostgreSQL: As the primary relational database
• pgvector: An extension for PostgreSQL to handle vector embeddings

Additional Tools

• Docker: For containerization
• CI/CD Pipelines: Using GitHub Actions or GitLab CI

2. Modular Project Templates

a. Regular Chatbot Template

• Backend:
  • FastAPI setup with routes to handle user inputs
  • Integration with chosen LLMs (e.g., OpenAI, Claude, Gemini)
• Frontend:
  • React components for chat interface
  • Tailwind CSS for styling

b. RAG-based Chatbot Template

• Backend:
  • FastAPI setup with routes for document ingestion and querying
  • Integration with vector database using pgvector
  • Embedding generation using models like OpenAI's embeddings or SentenceTransformers
• Frontend:
  • React components for document upload and chat interface
  • Tailwind CSS for styling

c. MCP AI Agent Template

• Backend:
  • FastAPI setup with MCP server and client implementations
  • Connectors to various data sources and systems
• Frontend:
  • React components to monitor and interact with the agent
  • Tailwind CSS for styling

3. CLI Tool for Project Initialization

Create a Command Line Interface (CLI) tool (lokam-cli) to automate project setup:

Features

• Project type selection (Regular Chatbot, RAG-based Chatbot, MCP AI Agent)
• Project structure generation with pre-configured frontend and backend
• Git repository initialization and dependency installation
• Docker configuration setup
• CI/CD pipeline integration

Implementation

• Built using Python's click or argparse libraries
• Distributed internally via PyPI or as standalone executable

4. CI/CD Pipelines

Testing

• Backend testing with pytest
• Frontend testing with Jest

Deployment

• Docker containerization
• AWS deployment (ECS or EKS)
• Automated build and deployment via GitHub Actions or GitLab CI

5. Documentation and Training

• Maintain comprehensive documentation for templates and CLI tool
• Conduct development team training sessions

6. Feedback and Iteration

• Gather feedback from developers and clients
• Iterate on templates and tools to improve usability and performance# WAF

WAF