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Rishee Panchal
Rishee Panchal

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AI Content Moderator - Powered by Redis

#redischallenge #devchallenge #database #ai

This is a submission for the Redis AI Challenge: Real-Time AI Innovators.

What I Built

This isn't a massive production grade application but it’s a lightweight system built using just HTML, CSS, and JavaScript. Think of it as a proof of concept UI dashboard that mirrors how Redis could be used in real world AI content pipelines.

The goal? To showcase how Redis 8 can power a multi-model, real-time moderation system by combining:

  • Vector Similarity Search

  • Semantic Caching

  • Streaming Analytics

  • AI Inference Metadata Storage

The app takes user input (e.g., social media posts, messages, comments), runs moderation analysis (toxicity, spam, sentiment), and displays real time metrics and similar content based on vector similarity.

Demo

Below is the demo of my app.

You can view the code here. You can find more details about the project in the Readme!

How I Used Redis 8

Redis Vector Similarity Search
Redis 8 supports powerful vector similarity queries using HNSW indexing. In this simulation, a Map called similarityIndex is used to mimic how embeddings are stored and searched.

similarityIndex.set(content, { embedding: generateMockEmbedding(content), metadata: result, timestamp: new Date() });
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Semantic caching using Redis
Redis 8 shines at caching semantic results, especially for costly AI inference tasks.
In my code, a Map named cacheEntries acts as a Redis cache:

cacheEntries.set(key, { result: result, timestamp: new Date(), hits: 1 });
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Each content input is hashed (generateCacheKey) and checked before re-processing:

let cacheHit = checkSemanticCache(cacheKey); if (cacheHit) { displayResults(cacheHit.result, true); }
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Multi model AI Inference Metadata with RedisJSON
RedisJSON is ideal for storing structured inference outputs like:

  • Toxicity

  • Spam

  • Sentiment

  • Processing time

  • Confidence scores

Here, AI results are stored as JS objects in the processedContent array and cacheEntries:

{ toxicity: 72, spam: 15, sentiment: -80, flagged: true, confidence: 88, categories: ['Toxicity', 'Spam'], processingTime: 320 }
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Some Real World Applications

  • Social Media Platforms: Moderate user generated content at scale with real time flagging
  • E-commerce: Analyze product reviews and seller communications for fraud/spam
  • Gaming Communities: Monitor chat messages and user interactions for toxicity
  • Content Publishing: Automated compliance checking for articles and comments
  • Customer Support: Real time analysis of support tickets and chat conversations

Summary

This project may not connect to Redis directly, but it intelligently mimics Redis 8’s AI + data infra capabilities within a single page app

Thank you for reading this!
Contact me at GitHub, LinkedIn

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