Multi-Mind
Abstract
Multi-Mind is a collaborative AI aggregation platform that allows users to interact with multiple large language and image generation models — including OpenAI’s GPT, SDXL (self-hosted), and Meta’s LLaMA. The platform emphasizes high availability, interoperability, and scalability, enabling users to seamlessly switch between AI models, upload files, visualize outputs, and share sessions in real time. Additionally, Multi-Mind integrates with external services such as void-tech.cn, a community-driven technical site operated by the author. This integration allows AI workflows to be embedded in discussions and generated content to be shared seamlessly.
Author
Peiyan Lu
s4933335
Functionality
If fully developed, Multi-Mind will:
- Allow users to access multiple AI models (LLMs and image generators) through a single platform
- Provide session-based chat interfaces for GPT (via OpenAI API), SDXL (image generation), and LLaMA (hosted locally or remotely)
- Enable file upload and AI-driven interaction with uploaded content (PDFs, text, images)
- Offer side-by-side visual comparison of model outputs
- Support prompt collaboration, history saving, and session sharing (e.g., sharable links)
- Integrate with the external community platform void-tech.cn:
- Allow posting of AI-generated content (text, code, images) directly into user posts or shared sections
- Enable discussion threads to trigger AI-generated responses from within the site
- Embed extensibility via plugin-style API interface for future models or tools
- Authenticate users and manage per-user conversation history and file storage
Scope
The MVP of Multi-Mind will support:
- User login and session management
- Unified chat interface supporting:
- GPT (via OpenAI API)
- SDXL (image generation on private server)
- LLaMA (local/remote inference)
- File upload (PDF or plain text) and basic prompt interaction with uploaded content
- Side-by-side output viewing from at least two different models
- Basic history management (chat logs and file references)
- Integration with void-tech.cn to:
- Post generated content into blog-like posts or user areas
- Support prompt submissions from the platform via simple webhook or plugin
- Public cloud/VPS hosting of the platform for external access
Quality Attributes
Interoperability
Multi-Mind’s core function is to serve as a unified interface for disparate AI systems. Each backend (OpenAI, LLaMA, SDXL) uses different APIs and response formats. Multi-Mind defines a shared adapter interface to interact with these models and normalize their outputs for consistent UI display.
It also integrates with an external user-driven community platform — void-tech.cn — supporting bi-directional interaction. AI-generated content can be shared to discussion threads, and site content can act as triggers for AI responses.
- Measured by: Successful support of ≥3 AI backends and 1 third-party platform
- Tested via: Adapter compatibility tests, integration simulations, webhook/API call verification
Availability
As a collaborative tool, Multi-Mind should be reliably available and responsive, even under failure of specific APIs or heavy load. Non-blocking calls, retry strategies, and asynchronous processing are critical.
- Measured by: 95%+ uptime over continuous 48-hour periods, success rate of prompt execution
- Tested via: Simulated API failures, retry timeout tests, uptime monitoring tools
Scalability
The platform will support simultaneous users submitting prompts to different backends. Since calls are stateless, horizontal scaling is possible. Chat history and uploaded files will be persisted and retrieved on demand.
- Measured by: Stable performance (≤1s response time) under 50+ concurrent requests
- Tested via: Load testing using Locust or JMeter, resource profiling under simulated stress
Evaluation
The project will be evaluated via the following methods:
- Unit Testing & Mocks: Adapters for GPT, SDXL, and LLaMA will be tested with mock payloads to ensure compatibility and response formatting
- Integration Testing: System-wide workflows (prompt → model → response → external sharing) will be tested in automated and manual cases
- Load Testing: Use Locust to simulate 10, 50, and 100 concurrent prompt submissions across models
- Availability Testing: Monitor server and endpoint uptime using UptimeRobot; simulate API failures and test fallback responses
- Community Integration Testing: Submit and retrieve content from void-tech.cn using real or mocked API/webhook interactions
- Response Consistency: Compare outputs from multiple models using the same prompt and log differences
- Usability Feedback: Internal testers will use the platform to complete AI tasks and report on ease of use, integration success, and response quality