Will we still need to learn query languages in the future? — Large language models have great potential to translate a natural language into a query language. For example, some people use GPT models to translate text to SQL, while others use GPT models to construct SPARQL queries. I prefer exploring how to translate natural language to Cypher query language. Context-Aware Knowledge Graph Chatbot With GPT-4 and Neo4j Learn how to implement a chatbot that bases its answers on the information retrieved from a graph database.medium.com

Avoid depending on external and ever changing APIs for your knowledge graph based chatbot — Large language models like ChatGPT have a knowledge cutoff date beyond which they are not aware of any events that happened later. Instead of fine-tuning models with later information, the trend is to provide additional external context to LLM at query time. I have written a couple of blog posts…

Learn how to develop a chatbot that can answer questions based on the information provided in your company’s documentation — Recently, I have been fascinated by the power of ChatGPT and its ability to construct various types of chatbots. I have tried and written about multiple approaches to implementing a chatbot that can access external information to improve its answers. I joined a few Discord channels during my chatbot coding…

Learn how to develop a LangChain agent that has multiple ways of interacting with the Neo4j database — ChatGPT inspired the world and started a new AI revolution. However, it seems that the latest trend is supplying ChatGPT with external information to increase its accuracy and give it the ability to answer questions where the answers are not present in public datasets. …

Learn how to implement a chatbot that bases its answers on the information retrieved from a graph database — Not so long ago, OpenAI added Chat API, which is optimized for generating conversations. The main difference between Chat and the older Completion APIs is that the Chat API allows specifying the conversation history. While the Completion API could be used for single-turn tasks like text generation, summarization, and similar…

Neo4j’s APOC plugin was split into two editions in the v5 version — Have you upgraded to or downloaded Neo4j v5, and now some of your favorite APOC procedures are not working anymore? You are not alone. As of Neo4j v5, APOC has been split into two editions. The key difference is that some of the procedures are officially supported and available in…

Use GPT-4 as a domain expert to help you extract knowledge from a video transcript. — A couple of days ago, I got access to GPT-4. The first thing that came to my mind was to test how well it performs as an information extraction model, where the task is to extract relevant entities and relationships from a given text. I have already played around with…

Leverage Cypher Aggregation feature to project in-memory graphs using all the flexibility and expressiveness of Cypher query language — Cypher Aggregation is a powerful feature of the Neo4j Graph Data Science library that allows users to project an in-memory graph using a flexible and expressive approach. While it was possible to use Cypher statements to project an in-memory graph for quite some time using Cypher Projection, it lacked some…

Use GraphSAGE algorithm in combination with OpenAI word embeddings to increase downstream document classification accuracy. — Natural Language Processing (NLP) has seen rapid advancements in recent years. One important aspect of this progress has been the use of embeddings, which are numerical representations of words or phrases that capture their meaning and relationships to other words in a language.