💡 Summary
FastGPT-plugin enables modular development of AI-driven question-answering systems with minimal setup.
🎯 Target Audience
🤖 AI Roast: “Powerful, but the setup might scare off the impatient.”
Risk: Medium. Review: outbound network access (SSRF, data egress); API keys/tokens handling and storage. Run with least privilege and audit before enabling in production.
FastGPT-plugin
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.
The system tools previously utilized in FastGPT have been migrated to this repository, and future development of new tools will also be conducted within this repository.
Deeply modularize FastGPT to achieve maximum extensibility.
Expansion Modules
- [x] System Tools
- [x] App templates
- [ ] RAG Algorithm
- [ ] Agent Strategy
- [ ] Third-party Integration
System Tool Features
- [x] Independent tool execution
- [ ] Hot-swappable plugins
- [ ] Secure and elegant secret configuration
- [ ] Visual debugging support
- [ ] Reverse invocation of FastGPT
- [ ] Plugin version management
- [ ] SSE streaming response support
- [ ] Enhanced security policies
Documentation & Development Guides
Pros
- Modular architecture allows for easy extensibility.
- Supports a variety of AI capabilities.
- Minimal setup required for deployment.
Cons
- Some features are still in development.
- Documentation may lack depth in certain areas.
Related Skills
pytorch
S“It's the Swiss Army knife of deep learning, but good luck figuring out which of the 47 installation methods is the one that won't break your system.”
agno
S“It promises to be the Kubernetes for agents, but let's see if developers have the patience to learn yet another orchestration layer.”
nuxt-skills
S“It's essentially a well-organized cheat sheet that turns your AI assistant into a Nuxt framework parrot.”
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Copyright belongs to the original author labring.