Nodes 2023 Create Graph Dashboards With Llm Powered Natural Language

TLDR Using Neod Dash, an open source project, with the text to Cipher translator plugin allows users to create interactive dashboards with visualizations and run natural language queries. The capability of the large language model from OpenAI to interpret vague requests was showcased, but its limitations in determining relationship direction accurately were also highlighted. The presentation emphasized the importance of using stronger models for increased accuracy and introduced a new vector index feature for various applications.

Key Insights

Setting Up Neod Dash and the Text to Cipher Translator Plugin

To begin using Neod Dash and the text to Cipher translator plugin, users need to set up the plugin within the web-based tool. This involves selecting a suitable model and generating cipher queries for different visualizations like tables, graphs, and pie charts. The demonstration showed the model's capability in interpreting ambiguous requests, while also pointing out its limitations in accurately determining relationship directions. It's important to familiarize oneself with the plugin setup process and the available models to effectively leverage natural language queries.

Leveraging Stronger Language Models for Complex Queries

The presentation emphasized the significance of using strong language models for improved accuracy in handling complex queries. It introduced a new vector index feature that enables users to store and utilize embeddings for various applications. This discussion highlighted the use of Cipher queries in Neo4j to construct a recipe recommender and explored the potential of leveraging different language model providers. Additionally, the limitations of GPT-3 for searching and the need to address duplicate recipe names were addressed, indicating the need for robust language models in such scenarios.

Understanding Query Optimization and Abstraction in Neo4j

The Q&A session shed light on the complexity of query optimization and the abstraction provided by Neo4j, acknowledging the necessity for fine-tuning in more intricate scenarios. This discussion underscored the importance of understanding the intricacies of query optimization within Neo4j to effectively harness the platform's capabilities. By grasping the nuances of query optimization and leveraging the abstraction provided by Neo4j, users can navigate complex scenarios while striving for optimal performance and efficiency in query execution.

Questions & Answers

What is Neod Dash and what are its main features?

Neod Dash is an open source project that allows users to create interactive dashboards with visualizations and is extendable. One of the main features of Neod Dash is the text to Cipher translator plugin, which translates natural language questions into Cipher queries.

How does the text to Cipher translator plugin work?

The plugin uses smart prompt engineering to convert human-readable questions into valid Cipher queries and visualize the results on the dashboard.

What was the dataset used in the demonstration of Neod Dash and the text to Cipher translator plugin?

The dataset used in the demonstration consisted of 2.2 million recipes and ingredients.

What limitations were highlighted in the conversation regarding the text to Cipher translator plugin?

The limitations included the model's difficulty in determining relationship direction accurately.

What new features and capabilities were discussed in the presentation?

The presentation discussed the introduction of a new vector index feature for storing and using embeddings for various applications. The speaker also demonstrated using Cipher queries in Neo4j to build a recipe recommender.

What challenges were acknowledged in the Q&A session?

The presentation acknowledged the complexity behind query optimization and the need for fine-tuning in more complex scenarios.

Summary of Timestamps

Neils presented on using llms to create NE forj dashboards with human-readable text using Neo Dash.

The conversation revolved around setting up and using the Neod Dash plugin to enable natural language queries with the large language model from OpenAI.

The presentation discussed using a plugin for complex queries, emphasizing the importance of using stronger models for increased accuracy.

A new vector index feature was introduced, allowing users to store and use embeddings for various applications.

In the Q&A session, they highlighted the complexity behind query optimization and the abstraction provided by Neo4j, while acknowledging the need for fine-tuning in more complex scenarios.