Project Plan

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Executive Summary

The CodeBase AI Assistant (CBAIA) is a sophisticated VS Code extension designed to overcome the limitations of existing AI coding assistants by processing entire codebases for comprehensive context, supporting multiple local and remote AI models, and providing production-grade performance and security. This project plan outlines the development of CBAIA as an autonomous AI-developed product with commercial potential while maintaining open-source compatibility.

1. Project Vision & Objectives

Vision Statement

To create the most comprehensive and flexible local AI coding assistant that understands entire codebases and delivers high-quality code suggestions across multiple model backends.

Primary Objectives

  • Process entire codebases (not just open files) to provide superior contextual understanding
  • Support multiple AI model providers (Ollama, LM Studio, custom endpoints)
  • Deliver a commercially viable product with production-grade quality
  • Create a solution that can be autonomously developed by an AI coding agent
  • Establish a flexible architecture that can accommodate future AI models

2. Detailed Requirements

2.1 Functional Requirements

Codebase Processing

  • FR1.1: Index and analyze the complete codebase on demand and incrementally
  • FR1.2: Create semantic understanding of project structure, dependencies, and relationships
  • FR1.3: Generate searchable symbol tables across the entire codebase
  • FR1.4: Track and update file changes in real-time to maintain index freshness
  • FR1.5: Support multiple programming languages and frameworks
  • FR1.6: Implement configurable file/directory exclusion patterns

Model Integration

  • FR2.1: Connect to Ollama models through the Ollama API
  • FR2.2: Integrate with LM Studio models via its local API
  • FR2.3: Support custom API endpoints for cloud-hosted or other local models
  • FR2.4: Allow configurable API keys and authentication for secure endpoints
  • FR2.5: Implement model switching without extension restart
  • FR2.6: Provide model parameter configuration (temperature, tokens, etc.)
  • FR2.7: Support streaming responses from models that provide them

Code Assistance Features

  • FR3.1: Provide inline code completions with configurable trigger mechanisms
  • FR3.2: Generate complete function implementations based on comments or function signatures
  • FR3.3: Offer code refactoring suggestions with preview capability
  • FR3.4: Implement code explanation functionality for selected blocks
  • FR3.5: Generate unit tests based on implementation code
  • FR3.6: Provide natural language query interface for codebase understanding
  • FR3.7: Detect and suggest fixes for potential bugs or performance issues

User Experience

  • FR4.1: Create intuitive configuration UI within VS Code settings
  • FR4.2: Implement status indicators for model status and current operations
  • FR4.3: Provide customizable keyboard shortcuts for all major features
  • FR4.4: Create visible progress indicators for long-running operations
  • FR4.5: Allow saving and sharing of extension configurations
  • FR4.6: Implement command palette integration for all major features

2.2 Non-Functional Requirements

Performance

  • NFR1.1: Startup time under 2 seconds for extension activation
  • NFR1.2: Code completion suggestion latency under 200ms
  • NFR1.3: Support codebases with up to 1 million lines of code
  • NFR1.4: Efficient memory usage (max 500MB during normal operation)
  • NFR1.5: Background indexing with minimal CPU impact
  • NFR1.6: Support for incremental updates to avoid full re-indexing

Security & Privacy

  • NFR2.1: No data transmission to external services unless explicitly configured
  • NFR2.2: Secure storage of API keys using VS Code's secret storage API
  • NFR2.3: Configurable exclusion of sensitive files from processing
  • NFR2.4: Option to anonymize code before sending to external endpoints
  • NFR2.5: Detailed logging of data handling operations with opt-out
  • NFR2.6: Compliance with data protection regulations (GDPR, CCPA)

Reliability

  • NFR3.1: Graceful error handling for all API connections
  • NFR3.2: Automatic recovery from model connection failures
  • NFR3.3: Extension stability during VS Code update cycles
  • NFR3.4: Persistence of configuration across VS Code restarts
  • NFR3.5: Full functionality in offline environments (with local models)
  • NFR3.6: Self-diagnostic capabilities with user-readable reports

Compatibility

  • NFR4.1: Support for VS Code 1.60+ and all recent stable releases
  • NFR4.2: Platform support for Windows 10+, macOS 10.15+, and popular Linux distributions
  • NFR4.3: Compatibility with standard VS Code extensions
  • NFR4.4: Support for remote development environments (SSH, containers)
  • NFR4.5: Compatibility with VS Codium and other VS Code derivatives

3. Technical Architecture

3.1 Extension Structure

  • VS Code extension core (TypeScript)
  • Model integration layer (TypeScript)
  • Code indexing engine (TypeScript/Rust via WASM)
  • Configuration management system
  • REST API client for model interactions

3.2 Components & Modules

Core Extension Module

  • Handles VS Code integration
  • Manages lifecycle and commands
  • Coordinates between components

Codebase Indexer

  • File system watcher
  • Abstract syntax tree (AST) generator
  • Symbol and reference extractor
  • Search index builder
  • Incremental update manager

Model Connector Framework

  • Abstract model interface
  • Concrete implementations for:
    • Ollama connector
    • LM Studio connector
    • Custom API endpoint connector
  • Model response formatter
  • Parameter validator

Context Builder

  • Relevant code selector
  • Context window optimizer
  • Language-specific context formatting
  • Prompt template manager

User Interface Components

  • Settings UI provider
  • Status bar indicators
  • Notification manager
  • Inline suggestion decorator
  • Configuration wizard

3.3 Data Flow

  1. User triggers code assistance feature
  2. Extension builds context from indexed codebase
  3. Context is packaged with appropriate prompt template
  4. Request is sent to configured model
  5. Response is processed and formatted
  6. Results are presented to user through appropriate UI

3.4 Configuration Structure

  • Model provider configurations
  • API endpoints and authentication
  • Code indexing preferences
  • Language-specific settings
  • User interface preferences
  • Performance tuning options

4. Development Phases & Milestones

Phase 1: Foundation (Weeks 1-3)

  • Milestone 1.1: Basic VS Code extension structure
  • Milestone 1.2: Configuration system implementation
  • Milestone 1.3: Model connector abstract layer
  • Milestone 1.4: Simple file processing capability
  • Deliverable: Functioning extension that can read configuration and connect to at least one model provider

Phase 2: Core Functionality (Weeks 4-8)

  • Milestone 2.1: Complete Ollama integration
  • Milestone 2.2: Complete LM Studio integration
  • Milestone 2.3: Custom endpoint integration
  • Milestone 2.4: Basic codebase indexing
  • Milestone 2.5: Code completion implementation
  • Deliverable: Extension that can provide basic code completions using any of the three model sources

Phase 3: Enhanced Codebase Understanding (Weeks 9-12)

  • Milestone 3.1: Advanced codebase indexing with symbols
  • Milestone 3.2: Relationship mapping between files and components
  • Milestone 3.3: Semantic code search implementation
  • Milestone 3.4: Context window optimization
  • Deliverable: Extension with deep codebase understanding and improved suggestions

Phase 4: Advanced Features (Weeks 13-16)

  • Milestone 4.1: Code explanation feature
  • Milestone 4.2: Unit test generation
  • Milestone 4.3: Refactoring suggestions
  • Milestone 4.4: Natural language query interface
  • Deliverable: Feature-complete extension with advanced capabilities

Phase 5: Performance & Polish (Weeks 17-20)

  • Milestone 5.1: Performance optimization
  • Milestone 5.2: UI/UX improvements
  • Milestone 5.3: Documentation completion
  • Milestone 5.4: Error handling refinement
  • Deliverable: Production-ready extension with optimized performance

Phase 6: Validation & Release (Weeks 21-24)

  • Milestone 6.1: Comprehensive testing completion
  • Milestone 6.2: Security audit completion
  • Milestone 6.3: Marketplace preparation
  • Milestone 6.4: Release management
  • Deliverable: Published extension on VS Code Marketplace

5. Testing Strategy

5.1 Testing Levels

  • Unit Testing: Individual components and utility functions
  • Integration Testing: Interactions between components
  • System Testing: End-to

Frequently Asked Questions

1. What is the CodeBase AI Assistant (CBAIA) project?

The CodeBase AI Assistant (CBAIA) project is a sophisticated VS Code extension designed to overcome the limitations of existing AI coding assistants by processing entire codebases for comprehensive context, supporting multiple local and remote AI models, and providing production-grade performance and security.

2. What are the primary objectives of the CBAIA project?

The primary objectives of the CBAIA project are to process entire codebases (not just open files) to provide superior contextual understanding, support multiple AI model providers (Ollama, LM Studio, custom endpoints), deliver a commercially viable product with production-grade quality, create a solution that can be autonomously developed by an AI coding agent, and establish a flexible architecture that can accommodate future AI models.

3. What are the key features of the CBAIA project?

The key features of the CBAIA project include:

  • Codebase processing: Index and analyze the complete codebase on demand and incrementally
  • Model integration: Connect to Ollama models through the Ollama API, integrate with LM Studio models via its local API, and support custom API endpoints for cloud-hosted or other local models
  • Code assistance features: Provide inline code completions with configurable trigger mechanisms, generate complete function implementations based on comments or function signatures, and offer code refactoring suggestions with preview capability
  • User experience: Create intuitive configuration UI within VS Code settings, implement status indicators for model status and current operations, and provide customizable keyboard shortcuts for all major features

4. What are the technical requirements for the CBAIA project?

The technical requirements for the CBAIA project include:

  • VS Code extension core (TypeScript)
  • Model integration layer (TypeScript)
  • Code indexing engine (TypeScript/Rust via WASM)
  • Configuration management system
  • REST API client for model interactions

5. What are the development phases and milestones for the CBAIA project?

The development phases and milestones for the CBAIA project include:

  • Phase 1: Foundation (Weeks 1-3)
  • Phase 2: Core Functionality (Weeks 4-8)
  • Phase 3: Enhanced Codebase Understanding (Weeks 9-12)
  • Phase 4: Advanced Features (Weeks 13-16)
  • Phase 5: Performance & Polish (Weeks 17-20)
  • Phase 6: Validation & Release (Weeks 21-24)

6. What is the testing strategy for the CBAIA project?

The testing strategy for the CBAIA project includes:

  • Unit testing: Individual components and utility functions
  • Integration testing: Interactions between components
  • System testing: End-to-end functionality
  • Performance testing: Under various load conditions and codebase sizes

7. What is the commercialization strategy for the CBAIA project?

The commercialization strategy for the CBAIA project includes:

  • Open-source model: MIT/Apache 2.0 license for core functionality
  • Monetization options: Premium features under commercial license, support subscriptions for enterprise users, custom model optimizations, and white-label options for businesses
  • Marketing strategy: Developer engagement, technical blog posts and tutorials, demo videos and feature highlights, and integration with popular frameworks and tools

8. What are the risks associated with the CBAIA project?

The risks associated with the CBAIA project include:

  • Technical risks: Large codebase indexing performance issues, model API changes breaking functionality, VS Code API changes affecting extension, memory leaks with large projects, and performance degradation over time
  • Business risks: Similar product released by major competitor, changes to VS Code extension policies, legal issues with commercial model providers, user adoption barriers, and sustainability concerns for free version

9. What is the implementation guide for AI agent developer?

The implementation guide for AI agent developer includes:

  • Development environment setup: VS Code Extension Development Tools, TypeScript and Node.js environment, testing frameworks and tools, and local model installations for development
  • Implementation approach: Start with minimal viable product (Phase 1), use test-driven development, implement features incrementally, and focus on core functionality before optimization
  • Code organization guidelines: Clear separation of concerns, well-defined interfaces between components, comprehensive documentation, and consistent code style and patterns
  • Deployment instructions: Packaging procedures, release checklist, documentation generation, and marketplace submission process

10. What is the release and maintenance plan for the CBAIA project?

The release and maintenance plan for the CBAIA project includes:

  • Release strategy: Pre-release to selected users for feedback, public beta with limited feature set, staged rollout of complete features, and full marketplace release
  • Release artifacts: VS Code extension package (.vsix), documentation website, quick-start guide, video tutorials, and sample configurations for popular models
  • Maintenance plan: Bi-weekly patch releases for first 3 months, monthly updates thereafter, major version updates quarterly, support for latest VS Code version within 2 weeks of release, and new model support based on popularity and demand