Automated Code Refactoring with Dependency Graph and Vector Database Context

[vjz3qz]

GitHub Issue Description

Title: Automated Code Refactoring with Dependency Graph and Vector Database Context

Description:

This issue outlines a plan to automate the refactoring of our codebase using a combination of project documentation, style guide generation, dependency graph analysis, and context retrieval from a vector database. The goal is to ensure cohesive and consistent refactoring across the entire codebase.

Step-by-Step Plan

1. Generate Documentation:

   • Use automated tools to create documentation from the codebase.

     # Example for Python using Sphinx
     sphinx-apidoc -o docs/ my_project/
     sphinx-build -b html docs/ docs/_build/

2. Generate Style Guide:

   • Use a tool or script to extract common coding conventions and generate a style guide from the existing codebase.

     pylint --generate-rcfile > pylintrc

3. Generate Dependency Graph:

   • Create a visual representation of the code dependencies.

     # Example for Python using pydeps
     pydeps my_project --output-format=json > my_project_dependencies.json

4. Iterate Through Nodes in Dependency Graph:

   • Start with nodes that have no dependencies.
   • Use the vector database to provide the most relevant context from similar files and dependent files.
   • Refactor each file based on the documentation and style guide.

Detailed Workflow

Step 1: Generate Documentation

Generate documentation automatically:

# Example for Python using Sphinx
sphinx-apidoc -o docs/ my_project/
sphinx-build -b html docs/ docs/_build/

Step 2: Generate Style Guide

Create or extract a style guide programmatically if possible, or use tools like pylint for Python:

pylint --generate-rcfile > pylintrc

Step 3: Generate Dependency Graph

Generate a dependency graph:

# Example for Python using pydeps
pydeps my_project --output-format=json > my_project_dependencies.json

Step 4: Refactor Nodes in Dependency Graph

1. Initialize the Vector Database:

   from vector_database import VectorDatabase
   
   vdb = VectorDatabase()

2. Retrieve Context from the Vector Database:

   def get_context(file_path):
      file_embedding = vdb.get_embedding(file_path)
      similar_files = vdb.query_similar(file_embedding)
      return [vdb.get_code(similar_file) for similar_file in similar_files]

3. Refactor with Retrieved Context:

   from refactor_ai import LLMRefactor
   
   def refactor_file_with_context(file_path):
      context = get_context(file_path)
      with open(file_path, 'r') as file:
          code = file.read()
      refactored_code = LLMRefactor.refactor(code, context=context)
      with open(file_path, 'w') as file:
          file.write(refactored_code)
   
   # Iterate through nodes in the dependency graph
   for file_path in dependency_order:
      refactor_file_with_context(file_path)

Example Dependency Order Processing

1. Parse the Dependency Graph:

   from pydeps import py2depgraph
   
   def get_dependency_order(project_path):
      dep_graph = py2depgraph.py2depgraph(project_path)
      nodes = dep_graph['nodes']
      edges = dep_graph['edges']
   
      # Implement topological sorting to get nodes with no dependencies first
      dependency_order = topological_sort(nodes, edges)
      return dependency_order

2. Topological Sorting Function:

   from collections import defaultdict, deque
   
   def topological_sort(nodes, edges):
      in_degree = defaultdict(int)
      graph = defaultdict(list)
   
      for node in nodes:
          in_degree[node] = 0
   
      for src, dst in edges:
          graph[src].append(dst)
          in_degree[dst] += 1
   
      queue = deque([node for node in nodes if in_degree[node] == 0])
      sorted_order = []
   
      while queue:
          node = queue.popleft()
          sorted_order.append(node)
   
          for neighbor in graph[node]:
              in_degree[neighbor] -= 1
              if in_degree[neighbor] == 0:
                  queue.append(neighbor)
   
      return sorted_order

3. Iterate and Refactor:

   project_path = "my_project"
   dependency_order = get_dependency_order(project_path)
   
   for file_path in dependency_order:
      refactor_file_with_context(file_path)

Summary

This process involves generating project documentation and a style guide, creating a dependency graph, and iteratively refactoring files starting from those with no dependencies. Using the vector database ensures that the refactoring maintains coherence and consistency by providing relevant context from similar and dependent files.

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Tasks:

1. Automate documentation generation.
2. Extract and create a style guide.
3. Generate and parse the dependency graph.
4. Implement the refactoring process using context from the vector database.