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    AnalysisGenAI Agents

    Self-Healing Codebase

    This code implements a workflow-based error detection and correction system that combines LangGraph, LLM capabilities, and vector database technology to detect runtime errors, generate fixes, and maintain a memory of bug patterns. The system takes function definitions and runtime arguments, processes them through a graph-based workflow, and maintains a hierarchical error management system enriched by vector-based similarity search.

    Several key factors motivate this implementation: 1. Automated Error Resolution - Manual debugging is time-consuming and error-prone - Automated fix generation streamlines the correction process - LLMs can provide context-aware code repairs 2. Pattern-Based Learning - Vector databases enable similarity-based bug pattern recognition - Previous fixes can inform future error resolution - Semantic search capabilities improve fix relevance 3. Structured Bug Knowledge - Vector embeddings capture semantic relationships between errors - ChromaDB enables efficient storage and retrieval of bug patterns - Hierarchical error categorization through vector spaces 4.…

    What you'll learn

    • 1
      State Management System:
    • 2
      Maintains workflow state using Pydantic models
    • 3
      Tracks function references, errors, and fixes
    • 4
      Ensures type safety and execution validation
    • 5
      LLM Integration:
    • 6
      Leverages LLM for code analysis and generation

    About this tutorial

    This hands-on Jupyter notebook is part of GenAI Agents, a free open-source repository by Nir Diamant covering ai agents techniques with runnable code examples and detailed explanations.

    Free and open-sourceRunnable Jupyter notebookActive community support
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