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Code Review Assistant using Open Source AI Challenge with pgai and Ollama

This is a submission for the Open Source AI Challenge with pgai and Ollama

What I Built

Code Review Assistant

Code Review Assistant is an AI-powered tool that helps developers improve their code quality through automated code analysis, similarity checking, and best practice recommendations. The application leverages PostgreSQL's AI capabilities and open-source models to provide intelligent code review suggestions.

  • Code storage in PostgreSQL with pgvector extension
  • Code embeddings generation using Ollama's CodeLlama
  • Similarity search using vector comparison
  • Code analysis and suggestions using CodeLlama
  • API endpoint for code review requests

Demo

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Code Review Assistant

What I Built

Code Review Assistant is an AI-powered tool that helps developers improve their code quality through automated code analysis, similarity checking, and best practice recommendations. The application leverages PostgreSQL's AI capabilities and open-source models to provide intelligent code review suggestions.

Key Features

  • πŸ” Automated code analysis using CodeLlama
  • πŸ’‘ Smart suggestions for code improvements
  • πŸ”„ Similar code pattern detection
  • πŸ“ Automatic code documentation generation
  • πŸ—„οΈ Efficient vector storage and similarity search
  • πŸš€ Real-time analysis and feedback

Tools Used

PostgreSQL Extensions

  1. pgvector

    • Used for storing and querying code embeddings
    • Implements efficient similarity search using vector operations
    • Powers the similar code pattern detection feature
  2. pgvectorscale

    • Enhances vector search performance with IVFFlat indexing
    • Optimizes query performance for large code repositories
    • Enables efficient scaling of the vector database
  3. pgai

    • Integrates AI capabilities directly within PostgreSQL
    • Facilitates real-time code analysis
    • Enhances vector operations performance
  4. pgai Vectorizer

    • Generates embeddings for code…

Tools Used

PostgreSQL Extensions

  1. pgvector

    • Used for storing and querying code embeddings
    • Implements efficient similarity search using vector operations
    • Powers the similar code pattern detection feature
  2. pgvectorscale

    • Enhances vector search performance with IVFFlat indexing
    • Optimizes query performance for large code repositories
    • Enables efficient scaling of the vector database
  3. pgai

    • Integrates AI capabilities directly within PostgreSQL
    • Facilitates real-time code analysis
    • Enhances vector operations performance
  4. pgai Vectorizer

    • Generates embeddings for code snippets
    • Provides efficient vector representation of code
    • Enables semantic similarity comparison

Other Tools

  • Node.js & Express: Backend server implementation
  • Ollama: Open-source model hosting and inference
  • CodeLlama: Code analysis and suggestion generation
  • Docker: Containerization (optional)

Prize Category Qualification

  1. Open-source Models from Ollama βœ…

    • Utilizes CodeLlama through Ollama for code analysis
    • Implements embedding generation using open-source models
    • Leverages Ollama's API for real-time inference
  2. Vectorizer Vibe βœ…

    • Implements pgai Vectorizer for efficient embedding creation
    • Uses vectorizer for semantic code analysis
    • Optimizes embedding storage and retrieval
  3. All the Extensions! βœ…

    • Implements all three PostgreSQL extensions:
      • pgvector for vector operations
      • pgvectorscale for performance optimization
      • pgai for AI integration

Developer Experience and Final Thoughts

Building Experience

  • Successfully integrated multiple PostgreSQL extensions
  • Leveraged open-source models for code analysis
  • Created an efficient vector-based similarity search system
  • Implemented real-time code analysis and suggestion generation

Technical Achievements

  • Efficient vector storage and retrieval system
  • Scalable architecture for large code repositories
  • Real-time code analysis and suggestion generation
  • Seamless integration of multiple AI capabilities

Future Improvements

  • Add support for more programming languages
  • Implement batch processing for large codebases
  • Add user authentication and project management
  • Enhance documentation generation capabilities

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