Working with Chats

SDK methods such as llm.respond(), llm.applyPromptTemplate(), or llm.act() take in a chat parameter as an input. There are a few ways to represent a chat when using the SDK.

Option 1: Input a Single String

If your chat only has one single user message, you can use a single string to represent the chat. Here is an example with the .respond method.

prediction = llm.respond("What is the meaning of life?")

Option 2: Using the Chat Helper Class

For more complex tasks, it is recommended to use the Chat helper class. It provides various commonly used methods to manage the chat. Here is an example with the Chat class, where the initial system prompt is supplied when initializing the chat instance, and then the initial user message is added via the corresponding method call.

chat = Chat("You are a resident AI philosopher.")
chat.add_user_message("What is the meaning of life?")

prediction = llm.respond(chat)

You can also quickly construct a Chat object using the Chat.from_history method.

chat = Chat.from_history({"messages": [
  { "role": "system", "content": "You are a resident AI philosopher." },
  { "role": "user", "content": "What is the meaning of life?" },
]})

Option 3: Providing Chat History Data Directly

As the APIs that accept chat histories use Chat.from_history internally, they also accept the chat history data format as a regular dictionary:

prediction = llm.respond({"messages": [
  { "role": "system", "content": "You are a resident AI philosopher." },
  { "role": "user", "content": "What is the meaning of life?" },
]})