modelo do chatbot
This workflow builds an intelligent chatbot designed to quickly recommend suitable health insurance products based on users' personal information and needs. By combining OpenAI's language model with persistent chat memory, the chatbot can dynamically interpret user input to provide personalized services. Additionally, by integrating external APIs and knowledge bases, it further enriches the content of responses, enhances user interaction experience, and addresses the issues of slow response times and inaccurate matching commonly found in traditional customer service.
Workflow Name
modelo do chatbot
Key Features and Highlights
This workflow builds an intelligent chatbot that integrates multiple data sources and AI assistants to deliver personalized health insurance product recommendations. Its standout feature lies in combining OpenAI’s language model with persistent chat memory stored in a Postgres database, enabling dynamic parsing of users’ personal information and needs for precise product matching. Additionally, it incorporates external APIs and knowledge base queries to enrich responses and enhance the interactive experience.
Core Problems Addressed
Helps users quickly and intelligently obtain health insurance plan information tailored to their individual conditions, addressing issues such as slow traditional customer service response and inaccurate product matching. By automating the process, it reduces manual costs while improving user satisfaction and conversion rates.
Application Scenarios
- Health insurance product consultation and recommendation
- Customer service automation
- Intelligent shopping assistant for e-commerce insurance platforms
- Support tools for insurance brokers
- Any chatbot scenario requiring customized recommendations based on user attributes
Main Process Steps
- Chat Trigger: Listens for user-initiated chat requests and initializes the conversation.
- If Condition Node: Checks whether the user has provided necessary personal information (e.g., name, age, city).
- Edit Fields1: Generates personalized chat input based on user-provided lead data to assist AI in understanding the user’s background.
- OpenAI Node: Calls OpenAI’s language model to generate intelligent replies based on user input and context.
- Postgres Chat Memory: Stores chat content and context in a Postgres database to enable conversation memory and support continuous dialogue.
- Products in Database: Executes MySQL queries to filter health insurance products that match the user’s criteria as parsed by AI.
- External API: Calls external interfaces to verify or supplement user identity information.
- Knowledge Base: Requests insurance-related knowledge base APIs to enrich response content.
- Edit Fields2 and OpenAI2: Adjusts chat inputs and performs final AI reply processing before delivering output to the user.
Systems and Services Involved
- OpenAI (language model and intelligent assistant)
- Postgres (chat history storage and AI memory)
- MySQL (health insurance product database)
- External API interfaces (user identity verification)
- HTTP-requested knowledge base services
Target Users and Value Proposition
- Insurance companies and brokers: Enhance customer consultation efficiency and automate product matching
- Health insurance e-commerce platforms: Enable intelligent shopping guidance and customer self-service
- Customer service teams: Reduce repetitive tasks and focus on handling complex issues
- End users: Receive fast, personalized insurance recommendations, saving time and effort
By integrating multiple systems with AI technology, this workflow provides an intelligent, efficient, and scalable chatbot solution tailored for the health insurance sector.