Echo AI Local Digital Twin & Document Knowledge Engine

Echo AI is a local-first, GPU-accelerated digital twin platform combining Retrieval-Augmented Generation (RAG) with local language models. Built to deliver absolute data privacy, Echo AI indexes local files and personal knowledge bases to provide conversational intelligence without external API dependencies.

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Executive Overview

Echo AI functions as a personal research assistant and digital twin. By embedding local documentation, codebases, and personal notes into a vector database, users can query complex personal archives in natural language with source attribution and instant retrieval speeds.

• Zero Data Leakage: All document processing, vector embedding, and model inference occur locally on user hardware.
• Multi-Format Document Ingestion: Native support for PDF, DOCX, Markdown, Source Code, and audio transcriptions.
• High-Fidelity Retrieval: Utilizes hybrid dense-sparse vector search to eliminate hallucinations and locate exact references.

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Core Technical Capabilities

RAG & Memory System

• Vector Indexing: Qdrant / LanceDB local vector database with sentence-transformer embeddings.
• Context Chunking: Smart semantic chunking preserving code block context and document section hierarchy.
• Local Model Support: Seamless integration with Gemma 3 4B, Llama 3, and Qwen 2.5 local models.

Key Technology Stack

• Frontend & Desktop Shell: Next.js, React, Tauri, Rust
• AI Core: Python, LangChain, Ollama API, PyTorch
• Vector Search: LanceDB, Qdrant Local