Gemma 4 API Advanced Integration Guide: Hybrid Development with Gemini API
Advanced patterns for using Gemma 4 API alongside Gemini API. Covers Vertex AI deployment, fine-tuning, RAG pipelines, and cost optimization strategies.
Next.js 15 App Router × Gemini API: The Complete Full-Stack
Build production-grade full-stack AI applications with Next.js 15 App Router and the Gemini API. Covers Server Actions, Streaming, RAG pipelines, authentication, rate limiting, and deployment.
Building a Production RAG System with Gemini Embedding API and Pinecone
A step-by-step guide to building a production-ready RAG system using Gemini Embedding API and Pinecone. Covers index design, query optimization, chunking strategies, and cost management with practical Python code.
Multimodal RAG with Gemini API — Cross-Format Search over Images, PDFs, and Video
Build a production-grade multimodal RAG pipeline with Gemini 2.5 Pro: unified vector search across text, images, PDFs, and video with cost optimization and scaling patterns.
Building Production Semantic Search with Gemini Embeddings API — Design, Implementation, and Operations
A comprehensive guide to building production-grade semantic search with Gemini Embeddings API. Covers vector DB selection, reranking, recommendation engines, and cost optimization with practical code.
Applying TurboQuant to RAG and Vector Search — New Uses for KV Cache Compression
Google's TurboQuant compression technology extends beyond LLM inference to RAG pipeline vector databases. Learn how embedding vector compression can improve memory efficiency, search speed, and scalability for large-scale RAG systems.
Building RAG Agents with Gemini × LlamaIndex — From Document Search to Multi-Step Reasoning
A hands-on guide to building high-accuracy RAG agents with Gemini API and LlamaIndex — measured chunk-size comparisons, full hybrid-search and reranking implementations, an evaluation loop, and incremental sync that follows edits and deletions.
Gemini File Search API — Build AI Responses Grounded in Your Own Data Without RAG
Learn how to use Gemini File Search API to build AI responses grounded in your own documents without vector databases or RAG pipelines, with production-ready implementation patterns.
Gemini × LangChain Integration — Build RAG, Chains & Agents
Complete guide to using Gemini with LangChain. Covers ChatGoogleGenerativeAI setup, prompt chains, RAG pipelines (ChromaDB + Gemini embeddings), and ReAct agent construction.
Gemini 1M Token Long Context Strategies — Production Patterns for Large Document Processing
Master Gemini 2.5 Pro's 1M token context window for production workloads. Covers context caching, chunking strategies, RAG comparison, cost optimization, and real-world codebase + PDF corpus analysis.
Building Multimodal RAG Systems with Gemini: Processing Images, Video, and Text Together
Master multimodal retrieval-augmented generation with Gemini. Learn to process images, video frames, and text in unified RAG pipelines with production patterns.
Grounding with Google Search — Improve Gemini's Accuracy with Search
Learn how to use Gemini API's Grounding with Google Search to generate accurate, up-to-date responses. Covers Dynamic Retrieval, source citations, and cost management.