Echo Intellect

中文

A voice-first personal knowledge assistant powered by RAG. Upload your documents, ask questions via voice or text, and get answers grounded in your own knowledge base.

Features

• Voice & Text Chat — Continuous voice conversation with interrupt support, plus streaming text chat with real-time Markdown rendering
• RAG Pipeline — Query optimization, parallel retrieval, RRF merging, optional reranking, and relevance filtering
• Knowledge Base — Upload .txt, .md, .pdf files directly from chat; background processing with status tracking
• Multi-LLM — Configure multiple providers (OpenAI, DeepSeek, etc.) and switch models on the fly
• References — Each response shows which knowledge chunks were used, with relevance scores
• i18n — English and Chinese UI

Architecture

┌────────────────────────────────────┐
│            React + Vite            │
│  Zustand · Tailwind · Streamdown  │
└──────────────┬─────────────────────┘
               │ HTTP / SSE
┌──────────────▼─────────────────────┐
│          FastAPI (uvicorn)         │
│  RAG Chain · STT/TTS · Ingestion  │
└──┬──────────┬──────────┬──────────┘
   │          │          │
 Qdrant    MongoDB     Redis
 vectors   metadata    cache

Tech Stack

Layer	Technologies
Backend	Python 3.11+, FastAPI, LangChain, OpenAI, Qdrant, MongoDB, Redis
Frontend	React 19, TypeScript, Vite, Tailwind CSS 4, Zustand, Streamdown
Speech	OpenAI Whisper (STT), OpenAI TTS
Infra	Docker Compose, Nginx

Getting Started

Prerequisites

• Docker & Docker Compose
• uv (Python package manager)
• Node.js 20+ & pnpm
• An OpenAI API key (or compatible provider)

1. Start infrastructure

cd deploy
docker compose up -d

This brings up MongoDB, Qdrant, and Redis.

2. Configure

Copy and edit the local config:

cp config.yaml config.local.yaml

Set your API keys and model preferences in config.local.yaml. This file is gitignored.

3. Run backend

uv sync
uv run python main.py

Backend starts at http://localhost:8000.

4. Run frontend

cd web
pnpm install
pnpm dev

Frontend starts at http://localhost:5173.

Project Structure

echo-intellect/
├── app/
│   ├── api/v1/          # REST endpoints (chat, import, models, speech)
│   ├── core/            # App factory, DI container, initialization
│   ├── ingestion/       # File readers, chunking, import service
│   ├── llms/            # Reranker
│   ├── models/          # Pydantic data models
│   ├── rag/             # RAG pipeline (retriever, filter, prompts, memory)
│   └── stores/          # Qdrant, MongoDB, Redis adapters
├── config/              # Settings, logging
├── deploy/              # Docker Compose, Dockerfile
├── tests/               # Unit & integration tests
├── web/                 # React frontend
│   ├── src/features/    # Feature modules (chat, voice, knowledge)
│   ├── src/i18n/        # Internationalization
│   └── deploy/          # Frontend Dockerfile + Nginx
├── config.yaml          # Default configuration
├── main.py              # Entry point
└── pyproject.toml       # Python dependencies

Configuration

All configuration is in config.yaml, overridable by config.local.yaml.

Key	Description
llm_providers	List of LLM providers with api_key, api_base, models
default_llm	Default model ID
openai.embedding_model	Embedding model for vector search
qdrant	Qdrant connection and collection settings
mongodb	MongoDB URI and database name
redis	Redis URI

License

Apache-2.0