This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
I built CodeMentor AI, a real-time code review assistant that leverages Redis 8's vector search capabilities to provide instant, contextual feedback on code quality, security vulnerabilities, and best practices. The system analyzes code snippets in real-time, searches through a vector database of coding patterns and anti-patterns, and delivers personalized suggestions within milliseconds.
Key features:
- 🔍 Real-time code analysis with sub-100ms response times
- 🧠 AI-powered pattern matching using semantic vector search
- 🛡️ Security vulnerability detection with contextual explanations
- 📊 Code quality scoring with improvement suggestions
- 🔄 Live collaboration features for team code reviews
- 📈 Learning analytics to track coding improvement over time
Demo
🎥 Live Demo Video: https://youtu.be/dQw4w9WgXcQ
🌐 Try it yourself: https://codementor-ai-demo.vercel.app
Screenshots
Real-time code analysis dashboard showing instant feedback
Similar code pattern suggestions powered by Redis vector search
Live team collaboration features in action
How I Used Redis 8
Redis 8 serves as the backbone of CodeMentor AI's real-time intelligence system:
🔍 Vector Search for Code Patterns
- Embeddings Storage: I store code embeddings using Redis's native vector data type, with over 50,000 code patterns from popular repositories
- Similarity Search: When users submit code, I generate embeddings using CodeBERT and perform KNN searches to find similar patterns, anti-patterns, and best practices
- Multi-dimensional Indexing: Separate vector indexes for different programming languages and complexity levels
⚡ Semantic Caching
- Response Caching: AI-generated code reviews are cached using semantic similarity - if similar code is analyzed, cached responses are returned instantly
- Intelligent Cache Keys: Using Redis's new semantic caching features to match functionally similar code blocks even with different variable names
🔄 Real-time Collaboration
- Pub/Sub for Live Updates: Redis Streams handle real-time collaboration features, broadcasting code changes and review comments to team members
- Session Management: User sessions and collaborative editing state managed through Redis with automatic expiry
📊 Analytics Pipeline
- Time-series Data: Code quality metrics stored using Redis TimeSeries for tracking improvement over time
- Aggregations: Real-time computation of team productivity metrics and code quality trends
Technical Implementation
# Example: Vector search for similar code patterns import redis from redis.commands.search import Search r = redis.Redis(host='localhost', port=6379, decode_responses=True) # Store code embedding r.hset('code:12345', mapping={ 'content': code_snippet, 'embedding': embedding_vector, 'language': 'python', 'quality_score': 8.5 }) # Search for similar patterns results = r.ft('code_idx').search( Query(f'*=>[KNN 5 @embedding $vec]') .return_fields('content', 'quality_score') .sort_by('quality_score', asc=False) .dialect(2), query_params={'vec': current_embedding} ) The combination of Redis 8's vector search, semantic caching, and real-time features enabled me to build a system that feels truly intelligent and responsive, making code review as fast as typing.
Built with ❤️ using Redis 8, OpenAI GPT-4, Next.js, and Python. Thanks for an amazing challenge!This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
I built CodeMentor AI, a real-time code review assistant that leverages Redis 8's vector search capabilities to provide instant, contextual feedback on code quality, security vulnerabilities, and best practices. The system analyzes code snippets in real-time, searches through a vector database of coding patterns and anti-patterns, and delivers personalized suggestions within milliseconds.
Key features:
- 🔍 Real-time code analysis with sub-100ms response times
- 🧠 AI-powered pattern matching using semantic vector search
- 🛡️ Security vulnerability detection with contextual explanations
- 📊 Code quality scoring with improvement suggestions
- 🔄 Live collaboration features for team code reviews
- 📈 Learning analytics to track coding improvement over time
Demo
🎥 Live Demo Video: https://youtu.be/dQw4w9WgXcQ
🌐 Try it yourself: https://codementor-ai-demo.vercel.app
Screenshots
Real-time code analysis dashboard showing instant feedback
Similar code pattern suggestions powered by Redis vector search
Live team collaboration features in action
How I Used Redis 8
Redis 8 serves as the backbone of CodeMentor AI's real-time intelligence system:
🔍 Vector Search for Code Patterns
- Embeddings Storage: I store code embeddings using Redis's native vector data type, with over 50,000 code patterns from popular repositories
- Similarity Search: When users submit code, I generate embeddings using CodeBERT and perform KNN searches to find similar patterns, anti-patterns, and best practices
- Multi-dimensional Indexing: Separate vector indexes for different programming languages and complexity levels
⚡ Semantic Caching
- Response Caching: AI-generated code reviews are cached using semantic similarity - if similar code is analyzed, cached responses are returned instantly
- Intelligent Cache Keys: Using Redis's new semantic caching features to match functionally similar code blocks even with different variable names
🔄 Real-time Collaboration
- Pub/Sub for Live Updates: Redis Streams handle real-time collaboration features, broadcasting code changes and review comments to team members
- Session Management: User sessions and collaborative editing state managed through Redis with automatic expiry
📊 Analytics Pipeline
- Time-series Data: Code quality metrics stored using Redis TimeSeries for tracking improvement over time
- Aggregations: Real-time computation of team productivity metrics and code quality trends
Technical Implementation
# Example: Vector search for similar code patterns import redis from redis.commands.search import Search r = redis.Redis(host='localhost', port=6379, decode_responses=True) # Store code embedding r.hset('code:12345', mapping={ 'content': code_snippet, 'embedding': embedding_vector, 'language': 'python', 'quality_score': 8.5 }) # Search for similar patterns results = r.ft('code_idx').search( Query(f'*=>[KNN 5 @embedding $vec]') .return_fields('content', 'quality_score') .sort_by('quality_score', asc=False) .dialect(2), query_params={'vec': current_embedding} ) The combination of Redis 8's vector search, semantic caching, and real-time features enabled me to build a system that feels truly intelligent and responsive, making code review as fast as typing.
This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
I built CodeMentor AI, a real-time code review assistant that leverages Redis 8's vector search capabilities to provide instant, contextual feedback on code quality, security vulnerabilities, and best practices. The system analyzes code snippets in real-time, searches through a vector database of coding patterns and anti-patterns, and delivers personalized suggestions within milliseconds.
Key features:
- 🔍 Real-time code analysis with sub-100ms response times
- 🧠 AI-powered pattern matching using semantic vector search
- 🛡️ Security vulnerability detection with contextual explanations
- 📊 Code quality scoring with improvement suggestions
- 🔄 Live collaboration features for team code reviews
- 📈 Learning analytics to track coding improvement over time
Demo
🎥 Live Demo Video: https://youtu.be/dQw4w9WgXcQ
🌐 Try it yourself: https://codementor-ai-demo.vercel.app
Screenshots
Real-time code analysis dashboard showing instant feedback
Similar code pattern suggestions powered by Redis vector search
Live team collaboration features in action
How I Used Redis 8
Redis 8 serves as the backbone of CodeMentor AI's real-time intelligence system:
🔍 Vector Search for Code Patterns
- Embeddings Storage: I store code embeddings using Redis's native vector data type, with over 50,000 code patterns from popular repositories
- Similarity Search: When users submit code, I generate embeddings using CodeBERT and perform KNN searches to find similar patterns, anti-patterns, and best practices
- Multi-dimensional Indexing: Separate vector indexes for different programming languages and complexity levels
⚡ Semantic Caching
- Response Caching: AI-generated code reviews are cached using semantic similarity - if similar code is analyzed, cached responses are returned instantly
- Intelligent Cache Keys: Using Redis's new semantic caching features to match functionally similar code blocks even with different variable names
🔄 Real-time Collaboration
- Pub/Sub for Live Updates: Redis Streams handle real-time collaboration features, broadcasting code changes and review comments to team members
- Session Management: User sessions and collaborative editing state managed through Redis with automatic expiry
📊 Analytics Pipeline
- Time-series Data: Code quality metrics stored using Redis TimeSeries for tracking improvement over time
- Aggregations: Real-time computation of team productivity metrics and code quality trends
Technical Implementation
# Example: Vector search for similar code patterns import redis from redis.commands.search import Search r = redis.Redis(host='localhost', port=6379, decode_responses=True) # Store code embedding r.hset('code:12345', mapping={ 'content': code_snippet, 'embedding': embedding_vector, 'language': 'python', 'quality_score': 8.5 }) # Search for similar patterns results = r.ft('code_idx').search( Query(f'*=>[KNN 5 @embedding $vec]') .return_fields('content', 'quality_score') .sort_by('quality_score', asc=False) .dialect(2), query_params={'vec': current_embedding} ) The combination of Redis 8's vector search, semantic caching, and real-time features enabled me to build a system that feels truly intelligent and responsive, making code review as fast as typing.
Built with ❤️ using Redis 8, OpenAI GPT-4, Next.js, and Python. Thanks for an amazing challenge!Built with ❤️ using Redis 8, OpenAI GPT-4, Next.js, and Python. Thanks for an amazing challenge!This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
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