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by jinruoxinchen • Analytics & Monitoring
A modular MCP-based FinTech question-and-answer chatbot that integrates with Dify to provide context-aware responses and automated business insights.
Maintain persistent conversational memory and low-hallucination context across user sessions using MCP memory services.
Integrate LLM-driven chat with transactional MySQL-backed business logic to handle accounts, transactions, and onboarding workflows.
Generate business insights and prioritize issues from user interactions via analytics pipelines and RAG-enabled knowledge stores.
This project implements a FinTech-focused conversational system built on the Model Context Protocol (MCP) and the Dify AI platform. It combines memory, sequential-thinking, and MySQL-backed MCP services to deliver low-hallucination, context-aware responses and transaction/account handling. The system also includes analytics pipelines to generate business insights, prioritize user issues, and support RAG-style knowledge retrieval for operational workflows.
Create multiple new entities in the knowledge graph
Create multiple new relations between entities in the knowledge graph. Relations should be in active voice
Add new observations to existing entities in the knowledge graph
Delete multiple entities and their associated relations from the knowledge graph
Delete specific observations from entities in the knowledge graph
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