🔍 SearChat

AI-powered conversational search engine

Multi-model integration | Real-time conversational search | Deep Research support

🌟 Project Overview

SearChat is a modern AI-powered conversational search engine built with Turborepo monorepo architecture, integrating Node.js + Koa backend and Vue 3 + TypeScript frontend.

🎯 Key Features:

🤖 Multi-model Support - Compatible with OpenAI, Anthropic, Gemini APIs
🔍 Multiple Search Engines - Support for Bing, Google, SearXNG and more
💬 Conversational Search - Multi-turn chat-based search experience
⏰ Chat History - Conversation history cached in browser (IndexedDB/LocalStorage)
🧠 Deep Research Mode - Refactoring deep research functionality
🔌 MCP Support - (TODO) Support for external MCP services
🖼️ Image Search - (TODO) Support for image and video search
📂 File Parsing - (TODO) Support for document upload and content extraction

✨ Core Features

🧠 Deep Research

Intelligent Research Mode - Deep research functionality
Iterative Exploration - Workflow orchestration based on LangChain + LangGraph
Comprehensive Report Generation - Automatically generate structured research reports

🤖 AI Model Support

Important

To achieve the best results, the model must support Tool Call (Function Calling).

OpenAI API compatible
Google Gemini API compatible
Anthropic API compatible
Google Vertex AI compatible

🔍 Multi-Search Engine Integration

SearXNG - Open source aggregated search, no API key required
Bing Search - Microsoft Bing web search API
Google Search - Google web search API
Tavily - Tavily web search API
Exa - Exa.ai web search API
Bocha - BochaAI web search API
ChatGLM Web Search - Zhipu AI free search plugin

🎨 Modern Interface Experience

Responsive Design - Perfect adaptation for desktop and mobile
Dark/Light Theme - Support for automatic system theme switching
Internationalization - Multi-language interface (i18n)
Real-time Streaming - Typewriter effect answer display
Contextual Conversation - Support for multi-turn dialogue and history

🔬 Deep Research Mode

Deep Research mode uses AI-driven iterative search and analysis to generate comprehensive and in-depth research reports on any topic.

Key Features:

🔄 Iterative Research - Automatically identifies knowledge gaps and performs follow-up searches
📊 Structured Reports - Generates well-organized research reports with citations
🔗 Citation Support - Includes source references with configurable formats ([[citation:1]] or clickable URLs)
🎯 Multi-Engine Search - Leverages multiple search engines for comprehensive results

📹 Feature Demo

Demo

📦 Standalone Usage

If you want to integrate Deep Research capabilities into your own Node.js project:

npm install deepsearcher

Quick Example:

import { DeepResearch } from 'deepsearcher';

const deepResearch = new DeepResearch({
  searcher: async ({ query }) => {
    // Your search implementation
    return searchResults;
  },
  options: {
    type: 'openai',
    apiKey: 'your-api-key',
    enableCitationUrl: false, // Use [[citation:1]] format
  },
});

const agent = await deepResearch.compile();
const result = await agent.invoke({
  messages: [{ role: 'user', content: 'Your research question' }],
});

Citation Format Options:

enableCitationUrl: true (default) - Outputs <sup>[[1](url)]</sup> format with clickable links
enableCitationUrl: false - Outputs [[citation:1]] simple format

Documentation: DeepResearch NPM Package

🐳 Quick Deployment (Recommended Docker)

📋 Prerequisites

Install Docker and Docker Compose
Prepare AI model API keys (configure in model.json)
Optional: Configure search engine API keys (in docker-compose.yaml)
Ensure network access to required services (SearXNG needs Google access)

🚀 One-Click Deployment

1. Create docker-compose.yaml file

Please refer to the deploy/docker-compose.yaml file.

2. Configure Environment Variables

Edit the docker-compose.yaml file and modify the corresponding environment variables in the search_chat service:

services:
  search_chat:
    container_name: search_chat
    image: docker.cnb.cool/aigc/aisearch:v1.2.0-alpha
    environment:
      # Server Configuration
      - PORT=3000

      # Search Engine API Keys (configure as needed)
      - BING_SEARCH_KEY=your_bing_key
      - GOOGLE_SEARCH_KEY=your_google_key
      - GOOGLE_SEARCH_ID=your_google_cse_id
      - TAVILY_KEY=your_tavily_key
      - ZHIPU_KEY=your_zhipu_key
      - EXA_KEY=your_exa_key
      - BOCHA_KEY=your_bocha_key

      # Web Content Extraction (optional)
      - JINA_KEY=your_jina_key

      # SearXNG Configuration (included by default, ready to use)
      - SEARXNG_HOSTNAME=http://searxng:8080
      - SEARXNG_SAFE=0
      - SEARXNG_LANGUAGE=en
      - SEARXNG_ENGINES=bing,google
      - SEARXNG_IMAGES_ENGINES=bing,google

      # DeepResearch Configuration
      - DEEP_MAX_RESEARCH_LOOPS=3
      - DEEP_NUMBER_OF_INITIAL_QUERIES=3

      # Domain Whitelist (optional)
      - WHITELIST_DOMAINS=
    volumes:
      - ./model.json:/app/apps/server/dist/model.json
    ports:
      - "3000:3000"
    restart: always

3. Configure AI Models (Required)

Create and edit the model.json file in the same directory as docker-compose.yaml to configure AI models and API keys:

[
  {
    "provider": "openai",
    "type": "openai",
    "baseURL": "https://api.openai.com/v1",
    "apiKey": "sk-your-openai-api-key",
    "apiMode": "openai-responses",
    "models": [
      {
        "name": "gpt-4o-mini",
        "alias": "GPT-4o Mini",
        "description": "OpenAI GPT-4o Mini model",
        "maxTokens": 262144,
        "intentAnalysis": true
      },
      {
        "name": "gpt-4o",
        "alias": "GPT-4o",
        "description": "OpenAI GPT-4o model",
        "maxTokens": 262144
      }
    ]
  },
  {
    "provider": "anthropic",
    "type": "anthropic",
    "baseURL": "https://api.anthropic.com/v1",
    "apiKey": "sk-your-anthropic-api-key",
    "models": [
      {
        "name": "claude-sonnet-4-5",
        "alias": "Claude Sonnet 4.5",
        "description": "Anthropic Claude Sonnet 4.5",
        "maxTokens": 131072
      }
    ]
  }
]

Models with intentAnalysis: true will be used for search intent analysis and query rewriting. It's recommended to set smaller models here to improve response speed.

Configuration Description:

provider: Model provider name
type: API type (openai/anthropic/google etc.)
baseURL: API base URL
apiKey: Your API key
apiMode: Optional for OpenAI-compatible providers. Use openai-completions (default) for Chat Completions, or openai-responses for the OpenAI Responses API. Leave it unset for OpenAI-compatible endpoints that do not support the Responses API.
models: Model list with name, alias, description and max tokens

When apiMode is set on an OpenAI provider, the main chat response, search intent analysis, and DeepResearch flows use the same mode.

4. Start Services

docker compose up -d

5. Access Application

Open your browser and visit: http://localhost:3000

🔄 Update Deployment

# Stop services
docker compose down

# Pull latest image
docker pull docker.cnb.cool/aigc/searchchat:latest

# Restart
docker compose up -d

🔍 Search Engine Configuration

The project supports multiple search engines. Choose the appropriate search source based on your needs. SearXNG is recommended.

🆓 SearXNG (Recommended - Free & Open Source)

Advantages: Completely free, no API key required, aggregates multiple search sources, protects privacy

SearXNG is an open-source metasearch engine that aggregates results from multiple search services without tracking users. Built into Docker deployment, ready to use out of the box.

Configuration Options:

SEARXNG_ENGINES: Set search engines (default: bing,google)
SEARXNG_LANGUAGE: Search language (zh=Chinese, en-US=English, all=all)
SEARXNG_SAFE: Safe search level (0=off, 1=moderate, 2=strict)

[!IMPORTANT]

Make sure to activate the json format to use the API. This can be done by adding the following line to the searxng/settings.yml file:

search:
    formats:
        - html
        - json

💻 Local Development

📋 Requirements

Node.js >= 20
Package Manager yarn@3.5.1
Build Tool Turborepo

🏗️ Project Architecture

search_with_ai/
├── apps/
│   ├── server/          # Backend service (Koa + TypeScript)
│   │   ├── src/
│   │   │   ├── app.ts           # Application entry
│   │   │   ├── controller.ts    # Route controllers
│   │   │   ├── interface.ts     # Type definitions
│   │   │   └── model.json       # Model configuration
│   │   └── package.json
│   └── web/             # Frontend application (Vue 3 + TypeScript)
│       ├── src/
│       │   ├── pages/           # Page components
│       │   ├── stores/          # Pinia state management
│       │   └── components/      # Common components
│       └── package.json
├── deploy/              # Deployment configuration
│   ├── docker-compose.yaml
│   ├── .env.docker
│   └── model.json
└── package.json         # Root configuration

🚀 Development Workflow

1. Install Dependencies

# Clone project
git clone https://github.com/sear-chat/SearChat.git
cd SearChat

# Install dependencies (run in root, will install all sub-project dependencies)
yarn install

2. Configure Environment

Copy and edit server environment configuration:

# Copy environment configuration template
cp apps/server/.env apps/server/.env.local

# Edit configuration file
vim apps/server/.env.local

3. Start Development Services

# Start both frontend and backend development servers
yarn dev

# Or use Turborepo command
turbo dev

Access URLs:

Frontend: http://localhost:5173
Backend: http://localhost:3000

4. Build Production Version

# Build all applications
yarn build

# Or
turbo build

🔧 Development Tools

Backend Tech Stack

Framework: Koa.js + TypeScript
AI Integration: LangChain + LangGraph
Search Engines: Multi-engine adapter pattern

Frontend Tech Stack

Framework: Vue 3 + Composition API
Build: Vite + TypeScript
UI Library: TDesign Vue Next
State Management: Pinia + persistence
Styling: Tailwind CSS + Less

🤝 Contributing

Welcome to contribute to the project! Please follow these steps:

Fork the project to your GitHub account
Create a feature branch git checkout -b feature/amazing-feature
Commit your changes git commit -m 'Add amazing feature'
Push the branch git push origin feature/amazing-feature
Create a Pull Request

🐛 Issue Reporting

GitHub Issues - Report bugs or feature requests
GitHub Discussions - Technical discussions and Q&A

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

SearXNG - Open source search engine
LangChain - AI application development framework
Tencent EdgeOne - CDN acceleration support

⭐ If this project helps you, please give it a Star!

🚀 Back to top

Name		Name	Last commit message	Last commit date
Latest commit History 488 Commits
.devops		.devops
.github		.github
.vscode		.vscode
apps		apps
assets		assets
deploy		deploy
packages/deepresearch		packages/deepresearch
.cnb.yml		.cnb.yml
.dockerignore		.dockerignore
.editorconfig		.editorconfig
.gitattributes		.gitattributes
.gitignore		.gitignore
.yarnrc.yml		.yarnrc.yml
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
CONTRIBUTION.md		CONTRIBUTION.md
LICENSE		LICENSE
README.md		README.md
README_JP.md		README_JP.md
README_ZH_CN.md		README_ZH_CN.md
package.json		package.json
turbo.json		turbo.json
wechat.jpg		wechat.jpg
yarn.lock		yarn.lock

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🔍 SearChat

推荐开源AI项目 / Recommended Open Source AI Project

WeClaws

🌟 Project Overview

✨ Core Features

🧠 Deep Research

🤖 AI Model Support

🔍 Multi-Search Engine Integration

🎨 Modern Interface Experience

🔬 Deep Research Mode

📹 Feature Demo

📦 Standalone Usage

🐳 Quick Deployment (Recommended Docker)

📋 Prerequisites

🚀 One-Click Deployment

1. Create docker-compose.yaml file

2. Configure Environment Variables

3. Configure AI Models (Required)

4. Start Services

5. Access Application

🔄 Update Deployment

🔍 Search Engine Configuration

🆓 SearXNG (Recommended - Free & Open Source)

💻 Local Development

📋 Requirements

🏗️ Project Architecture

🚀 Development Workflow

1. Install Dependencies

2. Configure Environment

3. Start Development Services

4. Build Production Version

🔧 Development Tools

Backend Tech Stack

Frontend Tech Stack

🤝 Contributing

🐛 Issue Reporting

📄 License

🙏 Acknowledgments

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