LLM Block

Select the underlying model to use for generating text. The default model is gpt-4o. Available models include:

• claude-3-5-sonnet@20240620
• deepseek-chat
• gemini-1.0-pro
• gemini-1.0-pro-001
• gemini-1.0-pro-002
• gemini-1.0-pro-vision-001
• gemini-1.5-flash-001
• gemini-1.5-flash-002
• gemini-1.5-pro-001
• gemini-1.5-pro-002
• gpt-3.5-turbo
• gpt-3.5-turbo-0125
• gpt-3.5-turbo-1106
• gpt-4
• gpt-4-0125-preview
• gpt-4-1106-preview
• gpt-4-turbo
• gpt-4-turbo-2024-04-09
• gpt-4o
• gpt-4o-2024-08-06
• gpt-4o-mini
• llama-v3-70b-instruct
• mixtral-8x7b-instruct

Controls the randomness of the output. The default value is 0.7. The range is from 0 to 2 with a step of 0.01.

The maximum number of tokens to generate. The default value is 300. The minimum value is 100.

The format of the response. The default is text. Options include:

• text: For plain text outputs.
• json_object: For structured JSON outputs.

Messages to be sent to the model. This input supports Jinja templating for dynamic message construction.

Each message is defined with a role (such as system, user, or assistant) and content. Below are examples of how to structure the input:

• Example 1: Basic Chat Interaction
  This is a simple conversation where the user asks a question.

  1
  [
  2
    {
  3
      "role": "system",
  4
      "content": "You are a helpful assistant."
  5
    },
  6
    {
  7
      "role": "user",
  8
      "content": "Can you explain the laws of gravity in simple terms?"
  9
    }
  10
  ]

• Example 2: Using Jinja Templating for Dynamic Inputs
  This example demonstrates how you can use variables to create dynamic prompts. The model will receive the final rendered text after Jinja processes the template.

  1
  [
  2
    {
  3
      "role": "system",
  4
      "content": "You are an AI tutor specialized in {{ topic }}."
  5
    },
  6
    {
  7
      "role": "user",
  8
      "content": "Explain {{ concept }} with examples."
  9
    }
  10
  ]

• Example 3: List of Messages Basic Multi-Step Conversation
  This example shows multiple back-and-forth messages, which can be passed into List of Messages to build on context of a conversation. Notice how the conversation context builds on each message, which can make subsequent responses from the LLM more relevant and refined:

  List of Messages Example

  1
  [
  2
    {
  3
      "role": "user",
  4
      "content": "I'm having trouble logging into my account."
  5
    },
  6
    {
  7
      "role": "assistant",
  8
      "content": "Can you confirm if you're seeing an error message?"
  9
    },
  10
    {
  11
      "role": "user",
  12
      "content": "Yes, it says 'Incorrect password'."
  13
    },
  14
    {
  15
      "role": "assistant",
  16
      "content": "Please try resetting your password using the 'Forgot Password' link on the login page."
  17
    }
  18
  ]