Scalable LLM Architectures with Redis & GCP Vertex AI

☁️ Generative AI with Google Vertex AI comes with a specialized in-console studio experience, a dedicated API for Gemini and easy-to-use Python SDK designed for deploying and managing instances of Google's powerful language models.

⚡ Redis Enterprise offers fast and scalable vector search, with an API for index creation, management, blazing-fast search, and hybrid filtering. When coupled with its versatile data structures - Redis Enterprise shines as the optimal solution for building high-quality Large Language Model (LLM) apps.

This repo serves as a foundational architecture for building LLM applications with Redis and GCP services.

Reference architecture

1. Primary Data Sources
2. Data Extraction and Loading
3. Large Language Models
   • text-embedding-gecko@003 for embeddings
   • gemini-1.5-flash-001 for LLM generation and chat
4. High-Performance Data Layer (Redis)
   • Semantic caching to improve LLM performance and associated costs
   • Vector search for context retrieval from knowledge base

RAG + Semantic Caching demo

Open the code tutorial using the Colab notebook to get your hands dirty with Redis and Vertex AI on GCP.

Additional resources

• Streamlit PDF chatbot example app
• Redis vector search documentation
• Get started with RedisVL
• Google VertexAI resources
• More Redis ai resources