Amplify (ChattUTC)

Prompting Language Documentation

This documentation provides examples of how to format prompts for different output structures and validation schemas. The examples illustrate requests for information about the first four presidents of the United States and their ages when they assumed office. Below are different prompting formats along with their descriptions.

CSV Format

To request data in CSV format, use the csv command followed by the desired data schema enclosed in curly braces {}. Each property in the schema is represented by a key-value pair, where the key is the column name and the value is the data type as a string.

Example

csv({name:"string", age:"string"}): Who were the first four presidents of the US and how old were they when they became president

This prompt would generate a CSV output with two columns: name and age, both expected to contain string values.

JSON Object Format

To request data in a simple JSON format, use the json command with a similar data schema as used for the CSV format.

Example

json({name:"string", age:"string"}): Who were the first four presidents of the US and how old were they when they became president

This prompt would result in a JSON object output where the name and age keys are expected to have string values.

JSON Schema Validation Format

For a more advanced JSON format with validation, use the json! keyword. The JSON schema provided should adhere to standards such as the JSON Schema Draft 07 or other relevant drafts.

Example

json!({
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "name": {
      "type": "string"
    },
    "age": {
      "type": "integer",
      "minimum": 0
    }
  },
  "required": ["name", "age"]
}) Who were the first presidents of the US and the age they became president?

In this example, the JSON schema ensures that the output will be an object with the name as a string and the age as a non-negative integer. Additionally, both name and age fields are required in the output.

Notes

• The structure of the command and schema should match the intended output format.
• When using the json! format for schema validation, ensure that the schema provided is compatible with the relevant JSON Schema draft specification.
• The data types and constraints within the schema, such as type, minimum, or required, can be customized to fit the needs of the user's request.

Building

Docker

Build locally:

docker build -t dev-gen-ai-image .
docker run -p 3000:3000 dev-gen-ai-image
docker run --env-file ./.env.local  -p 3000:3000 dev-gen-ai-image to pull in env file for multiple azure variables needed

Pull from ghcr:

docker run -p 3000:3000 ghcr.io/mckaywrigley/chatbot-ui:main

Running Locally

1. Clone Repo

git clone https://github.com/mckaywrigley/chatbot-ui.git

2. Install Dependencies

npm i

3. Run App

npm run dev

4. Use It

You should be able to start chatting.

Configuration

When deploying the application, the following environment variables can be set:

Environment Variable	Default value	Description
OpenAI
OPENAI_API_HOST	https://api.openai.com	The base url, for Azure use https://<endpoint>.openai.azure.com
OPENAI_API_TYPE	openai	The API type, options are openai or azure
OPENAI_API_VERSION	2023-03-15-preview	Only applicable for Azure OpenAI
AZURE_DEPLOYMENT_ID		Needed when Azure OpenAI, Ref Azure OpenAI API
OPENAI_ORGANIZATION		Your OpenAI organization ID
DEFAULT_MODEL	gpt-3.5-turbo	The default model to use on new conversations, for Azure use gpt-35-turbo
NEXT_PUBLIC_DEFAULT_SYSTEM_PROMPT	see here	The default system prompt to use on new conversations
NEXT_PUBLIC_DEFAULT_TEMPERATURE	1	The default temperature to use on new conversations
GOOGLE_API_KEY		See Custom Search JSON API documentation
GOOGLE_CSE_ID		See Custom Search JSON API documentation

Contact

If you have any questions, feel free to reach out to Mckay on Twitter.