Use Cases

Retrieval

Retrieval-Augmented Generation (RAG) leverages a knowledge base outside of the training data before consulting the LLM to generate a response. The following GPTScript implements RAG:

name: rag
description: implements retrieval-augmented generation
args: prompt: a string
tools: query

First query the ${prompt} in the knowledge base, then construsts an answer to ${prompt} based on the result of the query.

---

name: query
description: queries the prompt in the knowledge base
args: prompt: a string

... (implementation of knowledge base query follows) ...

The idea behind RAG is simple. Its core logic can be implemented in one GPTScript statement: First query the ${prompt} in the knowledge base, then construsts an answer to ${prompt} based on the result of the query. The real work of building RAG lies in the tool that queries your knowledge base.

You construct the appropriate query tool based on the type of knowledge base you have.

Knowledge Base	Query Tool
A vector database storing common document types such as text, HTML, PDF, and Word	Integrate with LlamaIndex for vector database support [Link to example]
Use the public or private internet to supply up-to-date knowledge	Implement query tools using a search engine, as shown in search.gpt
A structured database supporting SQL such as sqlite, MySQL, PostgreSQL, and Oracle DB	Implement query tools using database command line tools such as sqlite and mysql [Link to example]
An ElasticSearch/OpenSearch database storing logs or other text files	Implement query tools using database command line tools [Link to example]
Other databases such as graph or time series databases	Implement query tools using database command line tools [Link to example]

Task Automation

Planning

Here is a GPTScript that produces a detailed travel itinerary based on inputs from a user: travel-agent.gpt

Web UI Automation

Here is a GPTScript that automates a web browser to browse and navigate website, extract information: coachella-browse.gpt

For additional examples, please explore here.

API Automation

Here are a few GPTScripts that interact with issues on GitHub:

• Create GitHub Issues
• Modify GitHub Issues
• Query GitHub Issues

Agents and Assistants

Agents and assistants are synonyms. They are software programs that leverage LLM to carry out tasks.

In GPTScript, agents and assistants are implemented using tools. Tools can use other tools. Tools can be implemented using natural language prompts or using traditional programming languages such as Python or JavaScript. You can therefore build arbitrarily complex agents and assistants in GPTScript. Here is an example of an assistant that leverages an HTTP client, MongoDB, and Python code generation to display Hacker News headlines: hacker-news-headlines.gpt

Data Analysis

Depending on the context window supported by the LLM, you can either send a large amount of data to the LLM to analyze in one shot or supply data in batches.

Summarization

Here is a GPTScript that sends a large document in batches to the LLM and produces a summary of the entire document. hamlet-summarizer

Tagging

Here is a GPTScript that performs sentiment analysis on the input text: Social Media Post Sentiment Analyzer

CSV Files

Here is a GPTScript that reads the content of a CSV file and make query using natural language: csv-reader.gpt

Understanding Code

Here is a GPTScript that summarizes the code stored under the current directory: describe-code.gpt

Vision, Image, and Audio

Vision

Here is an example of a web app that leverages GPTScript to recognize ingredients in a photo and suggest a recipe based on them: recipe-generator.

Image Generation

Here is a GPTScript that takes a story prompt and generates an illustrated children's book: story-book.gpt