Good afternoon everyone! My name is Eduardo Nacime and the purpose of today’s presentation is to talk about a family of natural language interface tools based on ChatGPT and Language Chain.
In this article, I will introduce the concept of natural language interfaces and discuss some features of ChatGPT. I will also show some experiments and results using these tools. Finally, I will conclude with the potential benefits of using these tools in the development of natural language interfaces for databases.
Introduction:
The use of large language models, such as ChatGPT, has gained popularity in recent years. These models are capable of generating high-quality textual answers and can be used for various tasks. One such task is the development of natural language interfaces for databases.
Language Chain:
Language Chain is a framework for developing applications powered by language models. It provides a generic interface for creating chains of components, which can include other chains. This allows for the development of complex applications that leverage the power of language models.
ChatGPT and Language Chain:
Many people are already using ChatGPT in their web interfaces to perform specific tasks. However, there is an opportunity to further exploit these tools by making educated API calls to Language Chain. OpenAI provides the Chat Completions API, which takes a list of messages as input and returns the model-generated message as output.
In the figure below, we can see how to create a Chat Model using an application. We call the search completions API, passing the model, the temperature, and the prompt. The prompt is a list of messages that contains the structure or questions that the user gives ChatGPT to respond to. Each message in the prompt comprises a role (system, user, or assistant) and content.
The system role sets the behavior of the assistant. We can define the assistant’s behavior by providing specific instructions on how it should behave during the conversation. We can use role requests or commands to provide work for the assistant. The user role represents the user’s input, and the assistant role refers to the response from ChatGPT.
Translation into SQL:
One of the tasks that can be performed using Language Chain is the translation of natural language queries into SQL code. To do this, we configure the system behavior to generate SQL queries based on the user’s input. We can pass information about the database schema manually or extract it from the user’s input.
Keyword Extraction:
Another task that can be performed using Language Chain is keyword extraction from natural language sentences. This is similar to the translation of SQL queries. We configure the system behavior and pass the query in natural language. Language Chain extracts the keywords from the query and performs a keyword search in the database.
Experiments and Results:
In our experiments, we explored the use of ChatGPT and Language Chain to build natural language interfaces for databases. We identified two alternatives: the SQL alternative and the keyword search (KWS) alternative.
The SQL alternative translates natural language queries directly into SQL code. The KWS alternative extracts keywords from the query and uses a keyword search to find the answers.
We conducted experiments using the Mojo database and manually created a ground truth with sample queries and their expected keywords. We compared the performance of the different approaches.
Conclusion:
The experiments suggest that the KWS approach, which uses prompts with context information and examples to help extract keywords, is the best performing approach. Extracting keywords is far simpler than generating SQL code, but the extracted keywords might not always be accurate.
In conclusion, the use of ChatGPT and Language Chain can greatly simplify the development of natural language interfaces for databases. These tools provide a straightforward way to create interactive applications that leverage the power of language models. By using these tools, developers can reduce the learning curve and create creative and innovative natural language interfaces.
Thank you for listening!
