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

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 ✅

   • 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 ✅

   • Implements pgai Vectorizer for efficient embedding creation
   • Uses vectorizer for semantic code analysis
   • Optimizes embedding storage and retrieval

3. All the Extensions! ✅

   • 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