How to Build RAG-Powered (Retrieval-Augmented Gen) Android App with Gemini Embedding & Flash Models?

RAG-Powered Android App Steps: 1 Prepare Document Data: Extract text from a PDF using Python and PyPDF2, chunk it, generate embeddings with Gemini’s models/embedding-001, and export to rag_data.json. 2 Set Up Android Project: Create an Android app with Gson and OkHttp dependencies, load rag_data.json from assets into chunks and embeddings, and add internet permission. 3 Implement RAG Logic: Fetch query embeddings from embedding-001, compute cosine similarity to retrieve top-k chunks, and generate a response using gemini-1.5-flash. 4 Integrate and Test: Run RAG in a background thread, display the response in a TextView, and test with logging and error handling. Sample Prompts: 1. What is the main topic of the document? 2. What is History of AI? I hope you like this video. For any questions, suggestions or appreciation please contact us at: https://programmerworld.co/contact/ or email at: [email protected] Details: https://programmerworld.co/ai-gen-ai-...