# InsightAI Dashboard: A Multi-Agent Driven AI Research Assistant and Report Generation Platform

> InsightAI Dashboard is a professional report generation platform based on multi-agent workflows. It provides users with an end-to-end AI-assisted research experience through an automated four-stage process (research, retrieval, analysis, writing) combined with RAG document Q&A, hybrid search, and structured output technologies.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-17T12:16:10.000Z
- 最近活动: 2026-05-17T12:25:38.689Z
- 热度: 159.8
- 关键词: 多智能体, RAG, 报告生成, AI研究, Next.js, PostgreSQL, 向量搜索, 工作流自动化
- 页面链接: https://www.zingnex.cn/en/forum/thread/insightai-dashboard-ai
- Canonical: https://www.zingnex.cn/forum/thread/insightai-dashboard-ai
- Markdown 来源: floors_fallback

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## Introduction: InsightAI Dashboard—A Multi-Agent Driven AI Research and Report Generation Platform

InsightAI Dashboard is a professional report generation platform based on multi-agent workflows. It addresses the efficiency issues faced by researchers amid information explosion through an automated four-stage process (research, retrieval, analysis, writing) combined with RAG document Q&A, hybrid search, and other technologies, providing an end-to-end AI-assisted research experience. The platform supports functions such as report generation, document Q&A, and project management. Built using a modern web technology stack, its code is open-sourced on GitHub (under MIT License).

## Background: Pain Points of Research Processes in the Information Explosion Era

In the era of information explosion, knowledge workers such as researchers and analysts face the challenge of converting massive amounts of information into structured professional reports. The traditional research process involves multiple time-consuming and labor-intensive steps like searching for materials, reading literature, extracting key points, organizing analysis, and writing, leading to low efficiency. InsightAI Dashboard aims to solve this problem through a fully automated process, improving research efficiency while ensuring report quality and traceability.

## Core Approach: Four-Stage Multi-Agent Workflow

The platform uses a lightweight state machine-driven four-stage workflow: Idle → Researching → Retrieving → Analyzing → Writing → Completed. Each stage is handled by a dedicated AI agent:
- **Research Agent**: Parallel queries via multiple search engines (DuckDuckGo, Baidu, etc.), with Cheerio extracting core web content;
- **Retrieval Agent**: Hybrid search (vector similarity + BM25) + RRF fusion + MMR reordering;
- **Analysis Agent**: Extracts insights and generates structured data for charts;
- **Writing Agent**: Generates structured reports with cited sources to ensure verifiability.

## Technical Architecture: Modern Web Stack and Flexible AI Integration

**Frontend**: Next.js 14 App Router + TypeScript + Tailwind CSS, supporting responsive design and SSE real-time progress push;
**Backend**: Prisma ORM + PostgreSQL + pgvector (vector storage);
**AI Integration**: Vercel AI SDK, compatible with OpenAI API format services (Alibaba Cloud DashScope recommended by default), supporting tool calls, structured output, and streaming responses. It can flexibly switch between models like GPT-4, Claude, and Tongyi Qianwen.

## RAG and Document Processing: Intelligent Retrieval and Q&A Capabilities

The platform provides document Q&A functionality, supporting uploads in formats like PDF/DOCX/TXT:
- **Intelligent Chunking**: Markdown-aware chunking (800-1500 characters), preserving title hierarchy and special structures;
- **Hybrid Retrieval**: Vector + BM25 + RRF fusion + MMR reordering, balancing relevance and diversity;
- **Multi-turn Dialogue**: Understands context through LLM query rewriting, supporting follow-up question association.

## Application Scenarios and Technical Highlights

**Application Scenarios**: Suitable for business analysis (market trends, competitor analysis), academic research (literature reviews), technical research (technology selection), investment research (company analysis), content creation (in-depth articles), etc.;
**Technical Highlights**: Lightweight state machine workflow, hybrid retrieval strategy, Zod-validated structured output, automatic citation tracking, multi-format export (Markdown/DOCX/PDF).

## Conclusion: The Future Collaborative Model of AI-Assisted Research

InsightAI Dashboard represents the development direction of AI-assisted research tools: building end-to-end intelligent workflows to realize a collaborative model between AI (information collection, preliminary analysis, draft generation) and humans (direction control, quality review, deep thinking). This model not only improves efficiency but also ensures output quality. As LLM and multi-agent technologies mature, such tools will help knowledge workers free themselves from tedious information processing and focus on creative thinking.

## Suggestions and Open Source: Participation and Usage Guide

**Subscription and Billing**: Three-tier plans are available (Free: basic functions + limited tokens; Pro: higher limits + priority response; Team: collaboration features + shared space), billed based on token usage;
**Deployment Options**: Supports local development, Docker deployment, and cloud platform (Vercel/Railway) deployment;
**Open Source Contribution**: The project code is open-sourced on GitHub under the MIT License. Developers are welcome to learn and contribute.