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Project Idea: Codebase Genius - An Agentic AI-Powered Documentation Generator#

This project envisions "Codebase Genius," an agentic system built with Jac that ingests a GitHub repository, performs a deep analysis of its structure using object-spatial programming, and leverages a 1 billion parameter MTLLM (via the by <llm> syntax) to generate a rich suite of Markdown documentation. This documentation will meticulously describe the codebase's architecture and design, incorporating Mermaid diagrams for enhanced visual understanding.

Core Concepts & Jac Implementation#

  1. Input Source:

    • A GitHub repository URL.

  2. Codebase Analysis with Object-Spatial Programming:

    • Graph Representation: The entire codebase (files, directories, modules, classes, functions, methods, data structures, comments, configuration files, etc.) will be parsed into a detailed object-spatial graph.
      • Nodes: Represent individual code entities. Properties could include raw code, parsed AST elements, docstrings, file paths, line numbers, and extracted metadata.
        • Examples: FileNode, DirectoryNode, ModuleNode, ClassNode, FunctionNode, MethodNode, VariableNode, CommentNode.
      • Edges: Represent the myriad relationships between these entities.
        • Examples: ImportsEdge, CallsEdge (function/method calls), InheritsFromEdge, ContainsEdge (class contains method, module contains class), DataFlowEdge, DependencyEdge.
    • Analytical Walkers: Specialized walkers will autonomously traverse and analyze this code graph:
      • RepoCloningWalker: Clones the specified GitHub repository locally.
      • CodeParsingWalker: Iterates through the codebase, potentially using Jac's parsing capabilities or integrating with existing language parsers, to construct the nodes and edges of the code graph.
      • ArchitectureAnalysisWalker: Traverses the graph to identify high-level architectural patterns (e.g., MVC, Microservices, Layered), key components, modules, entry points, and their interactions. This walker might use heuristics or invoke MTLLM abilities for complex pattern recognition.
      • DependencyAnalysisWalker: Maps out inter-module, inter-class, and inter-function dependencies to understand coupling, cohesion, and potential impact areas.
      • DiagramDataExtractionWalker: Identifies and extracts structured data from the graph specifically for generating various Mermaid diagrams (e.g., class hierarchies for class diagrams, call sequences for sequence diagrams, component dependencies for architecture diagrams).

  3. Documentation Generation with MTLLM (1B Parameter Model):

    • Content Strategy & Outline:
      • An MTLLM-powered ability, plan_documentation_structure(code_graph_summary: str, repo_metadata: dict) -> DocumentationOutline by <llm>(), would analyze a summary of the code graph and repository metadata to determine an optimal structure for the documentation.
      • DocumentationOutline would be a Jac object detailing the main sections, subsections, and the types of diagrams appropriate for each.
    • Markdown Section Generation:
      • For each section defined in the DocumentationOutline, an ability like generate_markdown_section(section_topic: str, relevant_graph_extracts: list[NodeInfo], diagram_specs: list[MermaidSpec]) -> str by <llm>() would:
        • Receive the specific topic (e.g., "User Authentication Service").
        • Be provided with relevant data extracted from the code graph by the analysis walkers (e.g., code snippets of relevant classes/functions, their relationships, and docstrings).
        • Receive specifications for any Mermaid diagrams identified for this section.
        • Generate descriptive text in Markdown, explaining the design, purpose, and interactions related to the topic, seamlessly embedding Mermaid diagram definitions.
    • Mermaid Diagram Generation:
      • A dedicated MTLLM ability, or a specialized part of generate_markdown_section, generate_mermaid_code(diagram_type: str, elements: list, relationships: list) -> str by <llm>(), would translate the structured data (extracted by DiagramDataExtractionWalker) into valid Mermaid syntax.
        • Example: Given a list of class nodes and their inheritance edges, it would generate the Mermaid code for a class diagram.

  4. Output:

    • A suite of interlinked Markdown files.
    • A main README.md or index.md would serve as the entry point, providing an overview and navigation to other generated documents.
    • Generated documentation will be stored in a user-specified output directory or a docs folder within the analyzed repository.

Why Jac is the Right Tool#

  • Rich Code Representation: Data spatial programming offers a natural and powerful way to model the complex, interconnected nature of codebases.
  • Autonomous Agents (Walkers): Walkers can intelligently navigate and analyze the code graph, performing tasks like pattern detection and data extraction for documentation.
  • Seamless AI Integration (MTLLM): The by <llm> syntax provides a clean and powerful way to delegate complex natural language processing, content generation, and reasoning tasks to a sophisticated 1B parameter model.
  • Structured Data Handling: Jac's ability to define custom objects (archetypes) allows for structured representation of documentation plans, diagram specifications, and extracted code information, which can then effectively guide the LLM.
  • Extensibility: The system can be extended with new walkers for deeper analysis or support for more languages/diagram types.

High-Level Project Steps#

  1. Environment Setup: Configure Jac with MTLLM and integrate a chosen 1B parameter LLM (e.g., via a local Ollama setup or an API).
  2. Define Core Jac Archetypes: Specify node types (FileNode, ClassNode, FunctionNode) and edge types (ImportsEdge, CallsEdge) for the code graph.
  3. GitHub Integration: Develop the RepoCloningWalker.
  4. Code Parsing & Graph Construction: Implement the CodeParsingWalker(s). This might involve creating Jac-native parsers for common languages or wrappers around existing parsing libraries.
  5. Analysis Walkers Development:
    • Create walkers for architectural pattern identification.
    • Build walkers for detailed dependency mapping.
    • Develop walkers for extracting data specifically for Mermaid diagrams (class, sequence, component, entity-relationship, etc.).
  6. MTLLM Abilities for Documentation:
    • Implement the plan_documentation_structure ability.
    • Develop the generate_markdown_section ability, including Mermaid integration.
    • Refine the generate_mermaid_code ability for various diagram types.
  7. Markdown Output System: Create walkers or abilities to write the generated Markdown content and Mermaid diagrams to a structured set of files.
  8. CLI and User Interface: Design a simple command-line interface to accept a GitHub URL and output directory.
  9. Testing & Iteration: Thoroughly test with a variety of GitHub repositories (different sizes, languages, complexities). Refine prompts, walker logic, graph schema, and LLM interactions based on output quality.

"Codebase Genius" would be a landmark project demonstrating Jac's prowess in creating sophisticated, AI-driven developer tools. The generated documentation could significantly reduce the time developers spend understanding unfamiliar codebases.