This text discusses the implementation of a context-aware knowledge graph chatbot using GPT-4 and Neo4j. The chatbot utilizes the OpenAI Chat API to generate conversations and retrieve information from a graph database. By providing conversation history or context, the chatbot is able to generate more accurate and relevant responses to user queries. The text also highlights the importance of using a knowledge base like Neo4j to ensure the chatbot provides accurate information and avoids hallucinations. The implementation involves generating Cypher statements based on user prompts and querying the Neo4j database to fetch relevant information. The retrieved results are then used to generate natural language responses using GPT-3.5-turbo. The chatbot implementation includes features such as storing conversation history, handling user inputs, and providing recommendations based on user preferences. The text concludes by mentioning the multi-language capabilities of GPT-3.5-turbo and GPT-4, as well as the potential for worldwide content dissemination without language barriers.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Context-Aware Knowledge Graph Chatbot | Implementation of chatbot using knowledge graph | Improved conversational abilities and accuracy | Better user experience and more accurate responses |
| OpenAI’s Chat API | Shift from Completion APIs to Chat API | More accurate and context-aware responses | Improving conversational tasks and customer support |
| Knowledge Graph-Based Chatbot Design | Use of knowledge base for chatbot answers | Complete control over chatbot responses | Providing accurate and reliable information |
| English2Cypher with GPT-4 | Generating Cypher statements based on user input | Improved ability to generate valid Cypher statements | Enhancing the functionality of chatbot and accuracy of generated Cypher statements |
| Graph2text | Generating natural language text based on database results | More authentic and human-like answers | Improving the natural language generation capabilities of the chatbot |
| Chatbot Implementation Using Streamlit | Development of chatbot user interface | Improved user experience and interaction | Creating a user-friendly and visually appealing interface |
| Multi-Language Capabilities | Ability to understand and generate responses in multiple languages | Language translation and localization | Enhancing the chatbot’s usability and accessibility |