Triepod Agentic Retrieval-Augmented Generation (RAG) System

Project Overview

The Triepod system is a sophisticated platform designed to provide a modular, extensible Retrieval-Augmented Generation (RAG) solution with a platform-plugin architecture. The project consists of two primary components:

1. Triepod Platform: Core infrastructure providing:

   • Vector search capabilities
   • Workflow engine
   • API services
   • Modular architectural components

2. Triepod Plugin: A business process plugin implementing specific workflows such as:

   • Architectural Assessment (AA)
   • Statement of Work (SOW)

Core Architectural Principles

1. Platform-Plugin Architecture

The system is built on a platform-plugin architecture to achieve:

• Clear separation between core RAG functionality and business-specific implementations
• Ability to develop multiple plugins on a single platform
• Flexible deployment options

2. Modular Design

Key design goals include:

• Easy component interchangeability
• High scalability
• Simplified maintenance and testing
• Clear component abstractions
• Pluggable components, especially for prompts

Recommended Directory Structure

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Key Development Guidelines

1. Environment & Configuration Management

API Key Security

• Use `[object Object]` for secure API key management
• Never hardcode sensitive credentials

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Dependency Management

• Maintain `[object Object]` with all project dependencies
• Use `[object Object]` to update requirements automatically
• Verify `[object Object]` before running tests or executing code

2. Error Handling & Resilience

Graceful Failure Handling

• Implement comprehensive try-except blocks
• Ensure system operability during component failures
• Provide meaningful error messages
• Include fallback mechanisms

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3. Code Documentation

Docstring Best Practices

• Provide clear, concise docstrings for all functions and classes
• Include purpose, parameter types, return value description, and potential exceptions

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4. Logging Guidelines

Structured Logging

• Use JSON-formatted logs in production
• Include appropriate severity levels
• Enable color-coded terminal output

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5. Testing Strategies

Unit Testing

• Use pytest for all component tests
• Cover edge cases and failure scenarios
• Match test file names with production code
• Store tests in `[object Object]`

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6. Workflow System Implementation

• Respect platform-plugin separation
• Store phase-specific instructions in the plugin directory
• Implement proper error handling and recovery
• Log workflow transitions for debugging

7. Docker Containerization

• Each component will have its own max-w-7xl mx-auto px-4
• Services organized in `[object Object]`
• Use "triepod-network" network name
• Support hot-reloading in development
• Use multi-stage builds for production

Recommended Next Steps

1. Complete platform-plugin separation
2. Implement missing utility components
3. Enhance test coverage
4. Standardize error handling
5. Develop comprehensive module documentation

Additional Resources

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