The Prompt class is a dataclass that represents the prompt to be sent to the LLM. It is designed to be passed to a Message to enable the Prompt Playground.

​
Attributes

template
str

The template of the prompt if not chat mode.

formatted
str

The formatted version of the template if not chat mode.

completion
str

The completion of the prompt.

template_format
str

The format of the template, defaults to “f-string”.

inputs
Dict[str, str]

The inputs (variables) to the prompt, represented as a dictionary.

messages
Optional[List[PromptMessage]]

The list of messages that form the prompt if in chat mode.

provider
str

The provider of the LLM, such as “openai-chat”.

settings
Dict[str, Any]

The settings the LLM provider was called with.

​
Usage in non chat mode

LLMs such as GPT3 or Llama2, which do not operate in chat mode, can generate completions based on a simple string prompt. In this context, you should use the template and formatted attributes of the Prompt class, as demonstrated below.

import chainlit as cl
from chainlit.prompt import Prompt
from chainlit.playground.providers import ChatOpenAI

import os
# If no OPENAI_API_KEY is available, the OpenAI provider won't be available in the prompt playground
os.environ["OPENAI_API_KEY"] = "sk-..."

template = """Hello, this is a template.
This is a variable1 {variable1}
And this is variable2 {variable2}
"""

inputs = {
    "variable1": "variable1 value",
    "variable2": "variable2 value",
}

settings = {
    "model": "gpt-3.5-turbo",
    "temperature": 0,
    # ... more settings
}

# Let's pretend we made the call to openai and this is the response
completion = "The openai completion"

prompt = Prompt(
    # provider, settings, inputs and completion are common attributes
    # shared both in chat and non chat mode
    provider=ChatOpenAI.id,
    completion=completion,
    settings=settings,
    inputs=inputs,
    # In non chat mode, we use template and formatted instead of messages
    template=template,
)


@cl.on_chat_start
async def start():
    await cl.Message(
        content="This is a message with a prompt",
        prompt=prompt,
    ).send()

A non chat mode Prompt displayed in the Prompt Playground

​
Usage in chat mode

LLMs such as GPT3.5 or GPT4, which do operate in chat mode, generate completions based of a list of messages (often representing the conversation history). In this context, you should use the messsages attribute of the Prompt class, as demonstrated below.

import chainlit as cl
from chainlit.prompt import Prompt, PromptMessage
from chainlit.playground.providers import ChatOpenAI

import os
# If no OPENAI_API_KEY is available, the ChatOpenAI provider won't be available in the prompt playground
os.environ["OPENAI_API_KEY"] = "sk-..."

template = """Hello, this is a template.
This is a variable1 {variable1}
And this is variable2 {variable2}
"""

inputs = {
    "variable1": "variable1 value",
    "variable2": "variable2 value",
}

settings = {
    "model": "gpt-3.5-turbo",
    "temperature": 0,
    # ... more settings
}

# Let's pretend we made the call to openai and this is the response
completion = "The openai completion"

prompt = Prompt(
    # provider, settings, inputs and completion are common attributes
    # shared both in chat and non chat mode
    provider=ChatOpenAI.id,
    completion=completion,
    inputs=inputs,
    settings=settings,
    # In chat mode, we use messages instead of template & formatted
    messages=[
        PromptMessage(template=template, role="system"),
    ],
)


@cl.on_chat_start
async def start():
    await cl.Message(
        content="This is a message with a chat prompt",
        prompt=prompt,
    ).send()

A chat mode Prompt displayed in the Prompt Playground

Was this page helpful?

AskUserActionAction