Requirement Introduction

1. AI-based automatic generation of corporate HR split welcome messages

The system utilizes AI technology to automatically generate and send friendly and professional welcome messages for the opening of job seeker interactions, enhancing the job seeker experience and guiding them to the next step of interaction.

2. Building a data storage cluster based on Docker containerization technology

Build and configure a MySQL data storage cluster with Docker containerization technology to support the subsequent chat history persistence function. This includes choosing the appropriate MySQL version, implementing cluster deployment using container orchestration tools such as Docker Compose or Kubernetes, and performing basic configuration such as user rights management, database creation, and cluster monitoring.

3. Linear and non-linear persistence of chat history

Design and implement the persistent storage function of chat history, supporting the storage of conversation content in both chronological (linear) and branch jumping (non-linear) ways, to ensure that the conversation history can be safely preserved and easy to query and review.

4. Enterprise private knowledge base construction and management

Build and manage an enterprise private knowledge base to support the knowledge acquisition and answering functions of HR Intelligent Alter ego. The knowledge base will contain company-specific information such as company culture, job descriptions, hiring processes, FAQs, etc., and can be regularly updated and expanded. This private knowledge base will serve as the underlying data source for intelligent Q&A with big language models to ensure accuracy and consistency of answers.

5. Intelligent Q&A based on OpenAI and other big language models

Enhance Chatbot's intelligent Q&A capabilities with advanced large language models such as OpenAI to ensure that the system is able to analyze and understand job seekers' complex questions and provide accurate and personalized responses.

6. LLM Model Switching

Supports flexible switching between multiple LLM models, so that the most appropriate model can be selected for answering according to different dialog contents.

7. Model Configuration and Tuning

Fine-tune LLM models to make them more adaptable to recruitment scenarios and support dynamic adjustment of different model parameters to improve Q&A accuracy.

8. Multi-Round Dialogue Management

Implement the multi-round conversation management function to ensure that Chatbot can understand and process consecutive conversations, maintain context consistency, and recover to the previous state after a conversation is interrupted.

9. Job seeker information collection

During the dialog with job seekers, the system actively collects key information, such as name, contact information, work experience, skills and so on. With a preset scope of information collection, Chatbot is able to ask relevant questions at the right time and store the collected information in a structured database to support subsequent recruitment decisions and processes.

10. Candidate Information Summary and Email Push

The system automatically outlines and summarizes the collected candidate information and generates a concise candidate information report when the information is deemed complete. The system can automatically send the report to the HR of the organization via email, so that HR can quickly assess the qualifications of the applicant and further improve the recruitment efficiency.