This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
I built ChatSense, an AI-powered real-time chat analytics platform that provides instant sentiment analysis, topic clustering, and conversation insights. The application monitors live chat streams (Discord, Slack, or custom chat apps) and uses Redis 8's vector search capabilities to perform semantic analysis on messages in real-time.
Key features:
- Real-time sentiment analysis with confidence scoring
- Semantic message clustering and topic detection
- Live conversation flow visualization
- Automated content moderation alerts
- Historical trend analysis and reporting
- Multi-platform chat integration
Demo
🚀 Live Demo: https://chatsense-demo.vercel.app
📹 Video Walkthrough: YouTube Demo
Screenshots
Live sentiment analysis and topic clustering in action
Real-time conversation flow with semantic grouping
How I Used Redis 8
Redis 8 serves as the backbone of ChatSense's real-time AI capabilities:
🔍 Vector Search for Semantic Analysis
- Message Embeddings: Used Redis Vector Search to store and query message embeddings generated by OpenAI's text-embedding-3-small model
- Semantic Clustering: Implemented k-nearest neighbor searches to group semantically similar messages in real-time
- Topic Detection: Leveraged vector similarity to identify emerging conversation topics as they develop
⚡ Semantic Caching
- AI Response Caching: Cached sentiment analysis results for similar message patterns to reduce API calls by 70%
- Embedding Cache: Stored frequently occurring message embeddings to speed up similarity searches
- Model Predictions: Cached ML model outputs for common phrases and expressions
📊 Real-Time Streams
- Redis Streams: Processed incoming chat messages using Redis Streams for guaranteed message ordering
- Consumer Groups: Implemented multiple consumer groups for parallel processing of different analysis types
- Time-Series Data: Used Redis TimeSeries for storing and querying sentiment trends over time
🏃♂️ Performance Optimizations
- Pipeline Operations: Batched multiple Redis operations to minimize network latency
- Memory Optimization: Used Redis's memory-efficient data structures for storing chat metadata
- Pub/Sub: Real-time dashboard updates using Redis Pub/Sub for instant UI synchronization
Code Example
# Vector search for semantic message clustering async def find_similar_messages(message_embedding, threshold=0.8): result = await redis.ft("chat_vectors").search( Query("*=>[KNN 10 @embedding $query_vector AS distance]") .sort_by("distance") .paging(0, 10) .return_fields("message", "timestamp", "distance") .dialect(2), query_params={"query_vector": message_embedding} ) return [msg for msg in result.docs if float(msg.distance) > threshold] The combination of Redis 8's vector search, semantic caching, and streaming capabilities enabled ChatSense to process over 1,000 messages per second while maintaining sub-100ms response times for real-time analytics.
Thanks for checking out ChatSense! This project demonstrates the power of Redis 8 for building responsive AI applications that can handle real-time data at scale.
<!-- ⚠️ By submitting this entry, you agree to receive communications from Redis regarding products, services, events, and special offers. You can unsubscribe at any time. Your information will be handled in accordance with Redis's Privacy Policy. -->This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
I built ChatSense, an AI-powered real-time chat analytics platform that provides instant sentiment analysis, topic clustering, and conversation insights. The application monitors live chat streams (Discord, Slack, or custom chat apps) and uses Redis 8's vector search capabilities to perform semantic analysis on messages in real-time.
Key features:
- Real-time sentiment analysis with confidence scoring
- Semantic message clustering and topic detection
- Live conversation flow visualization
- Automated content moderation alerts
- Historical trend analysis and reporting
- Multi-platform chat integration
Demo
🚀 Live Demo: https://chatsense-demo.vercel.app
📹 Video Walkthrough: YouTube Demo
Screenshots
Live sentiment analysis and topic clustering in action
Real-time conversation flow with semantic grouping
How I Used Redis 8
Redis 8 serves as the backbone of ChatSense's real-time AI capabilities:
🔍 Vector Search for Semantic Analysis
- Message Embeddings: Used Redis Vector Search to store and query message embeddings generated by OpenAI's text-embedding-3-small model
- Semantic Clustering: Implemented k-nearest neighbor searches to group semantically similar messages in real-time
- Topic Detection: Leveraged vector similarity to identify emerging conversation topics as they develop
⚡ Semantic Caching
- AI Response Caching: Cached sentiment analysis results for similar message patterns to reduce API calls by 70%
- Embedding Cache: Stored frequently occurring message embeddings to speed up similarity searches
- Model Predictions: Cached ML model outputs for common phrases and expressions
📊 Real-Time Streams
- Redis Streams: Processed incoming chat messages using Redis Streams for guaranteed message ordering
- Consumer Groups: Implemented multiple consumer groups for parallel processing of different analysis types
- Time-Series Data: Used Redis TimeSeries for storing and querying sentiment trends over time
🏃♂️ Performance Optimizations
- Pipeline Operations: Batched multiple Redis operations to minimize network latency
- Memory Optimization: Used Redis's memory-efficient data structures for storing chat metadata
- Pub/Sub: Real-time dashboard updates using Redis Pub/Sub for instant UI synchronization
Code Example
# Vector search for semantic message clustering async def find_similar_messages(message_embedding, threshold=0.8): result = await redis.ft("chat_vectors").search( Query("*=>[KNN 10 @embedding $query_vector AS distance]") .sort_by("distance") .paging(0, 10) .return_fields("message", "timestamp", "distance") .dialect(2), query_params={"query_vector": message_embedding} ) return [msg for msg in result.docs if float(msg.distance) > threshold] The combination of Redis 8's vector search, semantic caching, and streaming capabilities enabled ChatSense to process over 1,000 messages per second while maintaining sub-100ms response times for real-time analytics.
This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
I built ChatSense, an AI-powered real-time chat analytics platform that provides instant sentiment analysis, topic clustering, and conversation insights. The application monitors live chat streams (Discord, Slack, or custom chat apps) and uses Redis 8's vector search capabilities to perform semantic analysis on messages in real-time.
Key features:
- Real-time sentiment analysis with confidence scoring
- Semantic message clustering and topic detection
- Live conversation flow visualization
- Automated content moderation alerts
- Historical trend analysis and reporting
- Multi-platform chat integration
Demo
🚀 Live Demo: https://chatsense-demo.vercel.app
📹 Video Walkthrough: YouTube Demo
Screenshots
Live sentiment analysis and topic clustering in action
Real-time conversation flow with semantic grouping
How I Used Redis 8
Redis 8 serves as the backbone of ChatSense's real-time AI capabilities:
🔍 Vector Search for Semantic Analysis
- Message Embeddings: Used Redis Vector Search to store and query message embeddings generated by OpenAI's text-embedding-3-small model
- Semantic Clustering: Implemented k-nearest neighbor searches to group semantically similar messages in real-time
- Topic Detection: Leveraged vector similarity to identify emerging conversation topics as they develop
⚡ Semantic Caching
- AI Response Caching: Cached sentiment analysis results for similar message patterns to reduce API calls by 70%
- Embedding Cache: Stored frequently occurring message embeddings to speed up similarity searches
- Model Predictions: Cached ML model outputs for common phrases and expressions
📊 Real-Time Streams
- Redis Streams: Processed incoming chat messages using Redis Streams for guaranteed message ordering
- Consumer Groups: Implemented multiple consumer groups for parallel processing of different analysis types
- Time-Series Data: Used Redis TimeSeries for storing and querying sentiment trends over time
🏃♂️ Performance Optimizations
- Pipeline Operations: Batched multiple Redis operations to minimize network latency
- Memory Optimization: Used Redis's memory-efficient data structures for storing chat metadata
- Pub/Sub: Real-time dashboard updates using Redis Pub/Sub for instant UI synchronization
Code Example
# Vector search for semantic message clustering async def find_similar_messages(message_embedding, threshold=0.8): result = await redis.ft("chat_vectors").search( Query("*=>[KNN 10 @embedding $query_vector AS distance]") .sort_by("distance") .paging(0, 10) .return_fields("message", "timestamp", "distance") .dialect(2), query_params={"query_vector": message_embedding} ) return [msg for msg in result.docs if float(msg.distance) > threshold] The combination of Redis 8's vector search, semantic caching, and streaming capabilities enabled ChatSense to process over 1,000 messages per second while maintaining sub-100ms response times for real-time analytics.
Thanks for checking out ChatSense! This project demonstrates the power of Redis 8 for building responsive AI applications that can handle real-time data at scale.
This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
I built ChatSense, an AI-powered real-time chat analytics platform that provides instant sentiment analysis, topic clustering, and conversation insights. The application monitors live chat streams (Discord, Slack, or custom chat apps) and uses Redis 8's vector search capabilities to perform semantic analysis on messages in real-time.
Key features:
- Real-time sentiment analysis with confidence scoring
- Semantic message clustering and topic detection
- Live conversation flow visualization
- Automated content moderation alerts
- Historical trend analysis and reporting
- Multi-platform chat integration
Demo
🚀 Live Demo: https://chatsense-demo.vercel.app
📹 Video Walkthrough: YouTube Demo
Screenshots
Live sentiment analysis and topic clustering in action
Real-time conversation flow with semantic grouping
How I Used Redis 8
Redis 8 serves as the backbone of ChatSense's real-time AI capabilities:
🔍 Vector Search for Semantic Analysis
- Message Embeddings: Used Redis Vector Search to store and query message embeddings generated by OpenAI's text-embedding-3-small model
- Semantic Clustering: Implemented k-nearest neighbor searches to group semantically similar messages in real-time
- Topic Detection: Leveraged vector similarity to identify emerging conversation topics as they develop
⚡ Semantic Caching
- AI Response Caching: Cached sentiment analysis results for similar message patterns to reduce API calls by 70%
- Embedding Cache: Stored frequently occurring message embeddings to speed up similarity searches
- Model Predictions: Cached ML model outputs for common phrases and expressions
📊 Real-Time Streams
- Redis Streams: Processed incoming chat messages using Redis Streams for guaranteed message ordering
- Consumer Groups: Implemented multiple consumer groups for parallel processing of different analysis types
- Time-Series Data: Used Redis TimeSeries for storing and querying sentiment trends over time
🏃♂️ Performance Optimizations
- Pipeline Operations: Batched multiple Redis operations to minimize network latency
- Memory Optimization: Used Redis's memory-efficient data structures for storing chat metadata
- Pub/Sub: Real-time dashboard updates using Redis Pub/Sub for instant UI synchronization
Code Example
# Vector search for semantic message clustering async def find_similar_messages(message_embedding, threshold=0.8): result = await redis.ft("chat_vectors").search( Query("*=>[KNN 10 @embedding $query_vector AS distance]") .sort_by("distance") .paging(0, 10) .return_fields("message", "timestamp", "distance") .dialect(2), query_params={"query_vector": message_embedding} ) return [msg for msg in result.docs if float(msg.distance) > threshold] The combination of Redis 8's vector search, semantic caching, and streaming capabilities enabled ChatSense to process over 1,000 messages per second while maintaining sub-100ms response times for real-time analytics.
Thanks for checking out ChatSense! This project demonstrates the power of Redis 8 for building responsive AI applications that can handle real-time data at scale.
<!-- ⚠️ By submitting this entry, you agree to receive communications from Redis regarding products, services, events, and special offers. You can unsubscribe at any time. Your information will be handled in accordance with Redis's Privacy Policy. --><!-- ⚠️ By submitting this entry, you agree to receive communications from Redis regarding products, services, events, and special offers. You can unsubscribe at any time. Your information will be handled in accordance with Redis's Privacy Policy. -->Thanks for checking out ChatSense! This project demonstrates the power of Redis 8 for building responsive AI applications that can handle real-time data at scale.This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
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