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Build and deploy knowledge-based AI Q&A systems with RAG and visual workflows.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "FastGPT" yet — see the docs or source repo.
Based on the uploaded product docs, FAQs, and internal process materials, design an internal knowledge assistant plan, including knowledge base structure, RAG retrieval flow, prompt strategy, and key metrics to validate before launch.
A practical enterprise Q&A assistant plan covering knowledge organization, retrieval pipeline, and evaluation criteria.
Design a visual AI workflow for customer support: receive user questions, retrieve the knowledge base, generate answers, detect low-confidence cases, and hand them off to humans. Explain each node’s role plus inputs and outputs.
A customer support automation workflow outline showing node logic, exception handling, and human handoff.
I already have a Q&A bot, but its answers are often inaccurate. Analyze possible issues in chunking, recall strategy, reranking, and prompting, then provide a troubleshooting checklist to optimize FastGPT retrieval quality.
A systematic checklist of troubleshooting and optimization recommendations for better answer quality.
Build, test, and evaluate AI agents with strong model and MCP support.
Deploy a production-ready FastMCP server with dynamically loaded tools and resources.
Adds reliable arithmetic and web fetching to improve LLM accuracy and access.
Build self-hosted visual AI workflows with agents, RAG, HITL, and observability.
Enable AI agents to ingest, manage, and semantically search temporal knowledge graphs.
Provides reusable prompts and workflows for OpenAI image generation and editing.