LLM
Caller 2
The Caller 2 node is designed to handle commands and responses based on both user and system prompts. Below is a breakdown of its key parameters:
Label: The name given to the node. You can rename this to something meaningful for your workflow.
Model: This allows you to select the language model that will generate responses. You can choose from various models depending on your needs.
User Prompt: This field captures the user’s input (or query) that will be sent to the model for processing. It’s the starting point for generating a dynamic response.
System Prompt: Here, you can set a predefined set of instructions for the model to follow. This can guide the AI on how to behave during the interaction (e.g., responding in a specific tone, or avoiding certain information).
Commands: You can define a list of commands for the node to execute. These commands are triggered based on user inputs or specific conditions within the workflow. Command names must be single words, without spaces.
Fallback Outputs: This field allows you to define a backup or fallback response in case the model call doesn’t return a valid output or fails. It's helpful to maintain flow even in the event of an error.
Results: This section is where the final response generated by the model or command execution is displayed. It's what will be sent back to the user as the output.
This node is typically used when you need to combine user input with system-level commands and provide a response that either calls the model or processes information based on set parameters.
Get Chat History
The Get Chat History node is used to retrieve past messages from the conversation history. This node is essential when you need to maintain context or reference previous interactions within a workflow. Here's a breakdown of the key elements:
Label: The name of the node. You can rename this to make it more descriptive based on the specific use case.
Limit: This field determines the number of past messages you want to retrieve. Setting a limit helps to control how many messages are brought back into the current workflow context. The minimum limit is 1, meaning it will retrieve at least one message if available.
Messages (object[]): This is an output object that contains the retrieved chat history, which can be used by other nodes or functions within the workflow. The output will be in the form of an array of messages.
Fallback Outputs: This field allows you to define an alternative or backup action in case no valid chat history is found or if the retrieval fails. This ensures the workflow can continue even if there's an issue with retrieving the chat history.
Results: The actual output from retrieving the chat history will appear here. It can then be passed to other nodes or used for further processing.
Usage
This node is typically used when you need to retrieve past messages to inform future actions or responses. For example, it might be used to maintain conversation context or pull relevant details from earlier in the chat to improve response accuracy.
LLM
The LLM node is a streamlined way to interact with a language model (like GPT-3.5) to generate responses based on user input. This node simplifies the configuration process while still offering control over essential parameters. Here’s how each component functions:
Label: The name of the node. By default, it's labeled LLM. You can rename it to something more specific based on your workflow.
Model: Choose model from the drop down.
Pricing details for input and output are also provided to help you manage costs based on usage.
User Prompt: This is a required field where you input the user's question or message that the model will process to generate a response.
System Prompt: This optional field allows you to set instructions or guidelines that define how the model should behave while responding. For example, you can instruct the model to respond formally or focus on a specific topic.
Temperature: This parameter controls the randomness or creativity of the model's responses. A higher value (closer to 2) produces more diverse and creative answers, while a lower value (closer to 0) makes the output more deterministic and focused.
Presence Penalty: This setting discourages the model from repeating the same content, ensuring more varied responses. Higher values encourage the model to avoid repetition and generate new content.
Max Tokens: This field sets the maximum number of tokens (words or parts of words) the model can use to generate a response. Limiting this helps control the length of the generated text.
Top P: This is a sampling method that controls how broad the model’s responses can be. A lower value restricts the model to consider only the most likely tokens, while a higher value allows more creative and varied responses.
Usage
The LLM node is ideal for straightforward text generation where you need quick, efficient interactions without a lot of complex settings. It's a simplified interface that still gives you control over key parameters like temperature, token length, and response variability. This makes it perfect for scenarios where simplicity and speed are more important than fine-tuning.
We’d love to hear from you! Reach out to documentation@integrail.ai
Last updated