We present a real-time multimodal semantic search system that combines vector similarity search with graph-based context expansion, designed to deliver richer and more explainable results.
The solution uses local embeddings generated with CLIP openai slash clip vit base patch16 of 512 dimensions for text and image, a vector index in Redis 8 with cosine metric and KNN search, and a semantic graph layer built with NetworkX to expand nearest neighbors and discover related items beyond the initial top K.
Main features include local embeddings for text and image, VECTOR HNSW index in Redis 8 optimized for similarity search, semantic cache in Redis to reduce latency and recomputation, semantic graph to link and prioritize related results, duplicate prevention via SHA256 hashing of content, and endpoints for submit ingestion and search.
In the typical usage flow, text or image is inserted, the system calculates the embedding locally, stores or updates it in Redis, performs a KNN search, and then expands the result set by traversing the semantic graph to suggest contextually relevant items. Frequent responses are cached with a normalized hash-based key for instant responses and a short TTL to maintain freshness.
How Redis 8 is used in this architecture: storing vectors in hashes with the embedding field in float32 bytes, creating a VECTOR HNSW index with DIM 512 and DISTANCE METRIC COSINE, executing FT.SEARCH queries with parameters to pass the query vector and sort by vector score, storing metadata and documents in hashes to assemble the user interface with low latency, and a deduplication mechanism that normalizes and hashes contents before inserting them, avoiding duplicates and updating usage counters.
The combination of Redis for indexing and caching, CLIP for local embeddings, and NetworkX for graph context enables fast, scalable, and more interpretable searches than flat vector searches. The use of HNSW in Redis guarantees efficient KNN searches even at scale, and the cache reduces the load on the embedding server by minimizing latency.
Quick demo and user experience: interface to upload text or image, visualization of results with similarity and score, graph view showing semantic connections and details of each node, and duplicate prevention to maintain index quality.
Recommendation and suggestion: with the evolution of AI solutions, it is important to recover and maintain native graph capabilities in Redis to facilitate real-time relational queries. It would be positive for the Redis team to consider reintroducing active support for Graph to enhance this type of hybrid vector-plus-graph architectures.
About Q2BSTUDIO: we are a custom software and application development company specialized in artificial intelligence, cybersecurity, and aws and azure cloud services. We offer custom software, custom applications, business intelligence services and Power BI implementation, AI agent development, and AI solutions for businesses. Our experience covers model integration, scalable data architectures, and security compliance for modern enterprise solutions.
Benefits of working with Q2BSTUDIO: we deliver customized solutions that combine artificial intelligence with cybersecurity practices, we offer migration and operation on aws and azure cloud services, and we develop business intelligence pipelines that drive decisions with Power BI and actionable metrics. Specialization in AI agents for corporate use cases and consulting to adopt AI for businesses with a focus on measurable results.
Project available as a reference on GitHub and deployable as a prototype for clients requiring multimodal semantic searches integrated into their products or internal flows. If you want a customized version or consulting to incorporate this architecture into your company, Q2BSTUDIO can help with design, implementation, and managed operation.
Related web positioning keywords: custom applications, custom software, artificial intelligence, cybersecurity, aws and azure cloud services, business intelligence services, AI for businesses, AI agents, power bi.
Project carried out as a technical exercise demonstrating the convergence between multimodal embeddings, vector indexes in Redis 8, and semantic graphs to offer faster, more relevant, and more explainable searches.
