gemini-embeddings

talk2pdf is an AI-powered application that enables seamless, multilingual voice and text interaction with your PDFs. It combines advanced retrieval-augmented generation (RAG), Gemini AI, and speech APIs to support natural, conversational, and voice-based queries in multiple languages, making document exploration simple and interactive.

A plug-and-play framework for a RAG (Retrieval-Augmented Generation) pipeline using Google's Gemini Embedding 2 model, the first fully multimodal embedding model to store and query embeddings across text, images, video, and audio.

VerbaVista is an LLM-powered Streamlit app that transforms any YouTube video into structured English notes and an interactive chatbot. It automatically fetches, translates, chunks, and embeds transcripts using Gemini + LangChain, enabling contextual Q&A through a RAG pipeline with Chroma.

An AI-driven learning tool using RAG to analyze web, YouTube, and text content. Features a 3D Three.js UI with glassmorphism, powered by Flask, FAISS, and Gemini API. Offers Q&A, summaries, mock tests, and visualizations like mind maps and flowcharts for an engaging educational experience.

A CV Scorerer built with React and FastAPI. Uses Gemini API and Gemini Embeddings.

An intelligent medical document analysis system that uses AI to help users understand and query their medical PDFs through natural language conversations.

Graphiti service for manufacturing assets.

🔍 100%-offline-capable multimodal semantic search for macOS. Find your files by meaning, not name — text, code, PDFs, images, audio. Ollama + Qwen3-VL local, Gemini/OpenAI optional. Power-user tool, macOS-only.

Multimodal semantic search for local photo libraries using Gemini Embedding 2 — search by text or image, no captions required.

Production-grade Natural Language to SQL (NL-to-SQL) assistant powered by FastAPI & React 19. Features a 5-stage self-healing pipeline, dual-tiered semantic cache (ChromaDB + Gemini), auto golden query generation, and chunked Doc-QA ingestion with exact page-level PDF deep-linking.

An advanced Retrieval-Augmented Generation (RAG) system for querying medical documents and clinical PDFs. Built with a FastAPI backend, Streamlit chat interface, Pinecone vector database, Gemini embeddings, and LangChain orchestrated via Groq LLMs.