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Knowledge Graph and Next Steps

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Dhruv Parthasarathy edited this page Oct 8, 2024 · 1 revision

Executive Summary

In this phase we aim to transform Abstract Syntax Trees (ASTs) of a codebase into a comprehensive, queryable knowledge graph. This graph will serve as a foundation for advanced code analysis, test generation, and AI-assisted development. The project focuses on creating a rich, interconnected representation of code structures and relationships, optimized for consumption by Retrieval-Augmented Generation (RAG) systems and Large Language Models (LLMs).

Objectives

  1. Develop a robust knowledge graph generator from ASTs
  2. Create a flexible and extensible graph structure
  3. Implement an efficient query system for the knowledge graph
  4. Optimize the graph for RAG and LLM consumption

Knowledge Graph Generation

Graph Structure

The knowledge graph will consist of:

  1. Nodes:

    • Files
    • Functions
    • Classes
    • Methods
    • Variables
    • Modules/Packages

  2. Edges:

    • Function calls
    • Class inheritance
    • Method overrides
    • Variable usage
    • Import relationships
    • Data flow

Node Attributes

Each node will contain rich metadata:

  • Type (function, class, variable, etc.)
  • Name
  • File location
  • Line numbers
  • Docstrings/comments
  • Complexity metrics
  • Usage frequency

Edge Attributes

Edges will carry information about:

  • Type of relationship
  • Direction of relationship
  • Frequency of interaction
  • Data types involved (for variable usage)

Implementation Approach

  1. AST Traversal: Develop a robust AST traversal system that can extract relevant information for each node and edge type.

  2. Graph Construction: Use a graph database (e.g., Neo4j) or a custom graph structure to build the knowledge graph incrementally as ASTs are processed.

  3. Relationship Inference: Implement algorithms to infer complex relationships beyond direct AST representations (e.g., identifying design patterns, detecting code smells).

  4. Metadata Enrichment: Augment nodes and edges with additional metadata from static analysis and heuristics.

  5. Cross-File Analysis: Develop methods to link related entities across different files and modules.

Query System

The query system will serve as a bridge between the knowledge graph and higher-level applications, including RAG systems and LLMs.

Query Language

Design a flexible query language that allows for:

  • Complex graph traversals
  • Pattern matching
  • Aggregations and metrics computation
  • Natural language-like queries (for easier integration with LLMs)

Query Execution

Implement an efficient query execution engine that can:

  • Optimize query plans for large-scale graphs
  • Support parallel execution for complex queries
  • Provide real-time responses for common query patterns

Query Result Format

Design a structured output format that is:

  • Easy for machines to parse (JSON-based)
  • Rich in context (including relevant code snippets)
  • Hierarchical to represent complex relationships

RAG and LLM Integration

To facilitate seamless integration with RAG systems and LLMs:

  1. Embedding Generation: Create embeddings for nodes and subgraphs to enable semantic search and similarity comparisons.

  2. Context Preparation: Develop methods to extract relevant subgraphs and prepare them as context for LLM prompts.

  3. Query Translation: Implement a system to translate natural language queries into graph queries, and vice versa.

  4. Incremental Learning: Design the system to incorporate feedback from LLMs to refine the knowledge graph over time.

Phased Implementation

Phase 1: Core Knowledge Graph Generation

  • Implement basic AST to graph conversion
  • Establish fundamental node and edge types
  • Develop simple query capabilities

Phase 2: Advanced Relationship Inference

  • Implement cross-file analysis
  • Develop algorithms for complex relationship detection
  • Enhance metadata with static analysis results

Phase 3: Query System and Optimization

  • Design and implement the query language
  • Optimize graph storage and query execution
  • Develop the query result formatting system

Phase 4: RAG and LLM Integration

  • Implement embedding generation for nodes and subgraphs
  • Develop context preparation methods for LLM prompts
  • Create natural language query translation system

Expected Outcomes

  1. A comprehensive knowledge graph representing complex codebases
  2. An efficient query system for extracting insights from the graph
  3. A foundation for AI-assisted code analysis, test generation, and development

Future Directions

  • Real-time graph updates based on code changes
  • Integration with IDE plugins for immediate developer feedback
  • Expansion to support multiple programming languages
  • Development of specialized LLM models trained on the knowledge graph structure

By focusing on creating a rich, queryable knowledge graph from ASTs, CodeInsight will provide a powerful foundation for advanced code analysis and AI-assisted development. The graph's structure and query system are designed with RAG and LLM integration in mind, paving the way for sophisticated code understanding, test generation, and automated improvement suggestions.

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