Chat Operator

Overview

The Chat LOP operator facilitates the creation, editing, and management of conversations with AI models within TouchDesigner. It allows users to define the roles (user, assistant, system) and content of messages, control the flow of the conversation, and integrate with other LOPs for advanced AI workflows. This operator is particularly useful for prototyping conversational AI agents, setting up example conversations, and enforcing specific patterns for the LLM to follow.

Requirements

• Requires the ChatTD LOP to be present in the network, as it handles the actual API calls to the AI model.
• No specific Python dependencies beyond those required by TouchDesigner and the ChatTD LOP.

Input/Output

Inputs

• Input Table (DAT, optional): A table DAT containing pre-existing conversation data with columns for role, message, id, and timestamp. Allows loading conversations from external sources.

Outputs

• Conversation DAT: A table DAT named conversation_dat that stores the current state of the conversation, including roles, messages, IDs, and timestamps.

Parameters

Messages Page

Active (Active) op('chat').par.Active Toggle

Default:

Off

Call Assistant (Callassistant) op('chat').par.Callassistant Pulse

Default:

None

Call User (Calluser) op('chat').par.Calluser Pulse

Default:

None

Input Handling (Inputhandling) op('chat').par.Inputhandling Menu

Default:

prepend

Options:

prepend, append, index, none

Insert Index (Insertindex) op('chat').par.Insertindex Integer

Default:

1

Message (Message) op('chat').par.Message Sequence

Default:

None

Role (Msg 0) (Message0role) op('chat').par.Message0role Menu

Default:

system

Options:

user, assistant, system

Text (Msg 0) (Message0text) op('chat').par.Message0text String

Default:

None

Role (Msg 1) (Message1role) op('chat').par.Message1role Menu

Default:

user

Options:

user, assistant, system

Text (Msg 1) (Message1text) op('chat').par.Message1text String

Default:

None

Model Page

Understanding Model Selection

Operators utilizing LLMs (LOPs) offer flexible ways to configure the AI model used:

• ChatTD Model (Default): By default, LOPs inherit model settings (API Server and Model) from the central ChatTD component. You can configure ChatTD via the "Controls" section in the Operator Create Dialog or its parameter page.
• Custom Model: Select this option in "Use Model From" to override the ChatTD settings and specify the API Server and AI Model directly within this operator.
• Controller Model: Choose this to have the LOP inherit its API Server and AI Model parameters from another operator (like a different Agent or any LOP with model parameters) specified in the Controller [ Model ] parameter. This allows centralizing model control.

The Search toggle filters the AI Model dropdown based on keywords entered in Model Search. The Show Model Info toggle (if available) displays detailed information about the selected model directly in the operator's viewer, including cost and token limits.

Available LLM Models + Providers Resources

The following links point to API key pages or documentation for the supported providers. For a complete and up-to-date list, see the LiteLLM provider docs.

• openrouter
• openai
• groq
• gemini
• ollama
• lmstudio
• custom

Output Settings Header

Max Tokens (Maxtokens) op('chat').par.Maxtokens Int

The maximum number of tokens the model should generate.

Default:

256

Temperature (Temperature) op('chat').par.Temperature Float

Controls randomness in the response. Lower values are more deterministic.

Default:

0

Model Selection Header

Use Model From (Modelselection) op('chat').par.Modelselection Menu

Choose where the model configuration comes from.

Default:

custom_model

Options:

chattd_model, custom_model, controller_model

Controller [ Model ] (Modelcontroller) op('chat').par.Modelcontroller OP

Operator providing model settings when 'Use Model From' is set to controller_model.

Default:

None

Select API Server (Apiserver) op('chat').par.Apiserver StrMenu

Select the LiteLLM provider (API server).

Default:

groq

Menu Options:

• openrouter (openrouter)
• openai (openai)
• groq (groq)
• gemini (gemini)
• ollama (ollama)
• lmstudio (lmstudio)
• custom (custom)

AI Model (Model) op('chat').par.Model StrMenu

Specific model to request. Available options depend on the selected provider.

Default:

qwen-2.5-32b

Menu Options:

• qwen-2.5-32b (qwen-2.5-32b)

Search (Search) op('chat').par.Search Toggle

Enable dynamic model search based on a pattern.

Default:

off

Options:

off, on

Model Search (Modelsearch) op('chat').par.Modelsearch Str

Pattern to filter models when Search is enabled.

Default:

"" (Empty String)

Conversation Page

Use System Message (Usesystemmessage) op('chat').par.Usesystemmessage Toggle

Default:

On

System Message (Systemmessage) op('chat').par.Systemmessage String

Default:

helpful TD assistant

Use User Prompt (Useuserprompt) op('chat').par.Useuserprompt Toggle

Default:

On

User Prompt (Userprompt) op('chat').par.Userprompt String

Default:

pretend to be the user

Clear Conversation (Clearconversation) op('chat').par.Clearconversation Pulse

Default:

None

Load from Input (Loadfrominput) op('chat').par.Loadfrominput Pulse

Default:

None

Conversation ID (Conversationid) op('chat').par.Conversationid String

Default:

chat1

Callbacks Page

Callbacks Header

Callback DAT (Callbackdat) op('chat').par.Callbackdat DAT

Default:

None

Edit Callbacks (Editcallbacksscript) op('chat').par.Editcallbacksscript Pulse

Default:

None

Create Callbacks (Createpulse) op('chat').par.Createpulse Pulse

Default:

None

onTaskStart (Ontaskstart) op('chat').par.Ontaskstart Toggle

Default:

Off

onTaskComplete (Ontaskcomplete) op('chat').par.Ontaskcomplete Toggle

Default:

Off

Textport Debug Callbacks (Debugcallbacks) op('chat').par.Debugcallbacks Menu

Default:

None

Options:

None, Basic, Detailed

About Page

Show Built In Pars (Showbuiltin) op('chat').par.Showbuiltin Toggle

Default:

Off

Bypass (Bypass) op('chat').par.Bypass Toggle

Default:

Off

Chattd (Chattd) op('chat').par.Chattd OP

Default:

/dot_lops/ChatTD

Version (Version) op('chat').par.Version String

Default:

1.0.0

Last Updated (Lastupdated) op('chat').par.Lastupdated String

Default:

2024-11-06

Creator (Creator) op('chat').par.Creator String

Default:

dotsimulate

Website (Website) op('chat').par.Website String

Default:

https://dotsimulate.com

Callbacks

Available Callbacks:

• onTaskStart
• onTaskComplete

Example Callback Structure:

def onTaskStart(info):
# Called when a call to the assistant or user begins
# info dictionary contains details like op, callType
pass

def onTaskComplete(info):
# Called when the AI response is received and processed
# info dictionary contains details like op, result, conversationID
pass

Performance Considerations

• Long conversations (many message blocks) can increase processing time.
• Higher temperature settings might slightly increase AI response time.
• Ensure the linked ChatTD operator is configured correctly.

Usage Examples

Basic Conversation

# Get the operator
chat_op = op('chat1')

# Configure messages (assuming default 5 blocks)
chat_op.par.Message0role = 'system'
chat_op.par.Message0text = 'You are a pirate.'
chat_op.par.Message1role = 'user'
chat_op.par.Message1text = 'Hello there!'
chat_op.par.Message2role = 'assistant'
chat_op.par.Message2text = '' # Assistant will fill this

# Call the assistant
chat_op.par.Callassistant.pulse()

# Check the output DAT
conversation_log = chat_op.op('conversation_dat')
print(conversation_log.rows()[-1]) # Print the last message (assistant's response)

Loading from Input Table

# Assume 'conv_table' DAT exists with role, message columns

# Connect table
chat_op = op('chat1')
chat_op.inputConnectors[0].connect(op('conv_table'))

# Load data
chat_op.par.Inputhandling = 'none' # Important if you want exact load
chat_op.par.Loadfrominput.pulse()

Common Use Cases

• Prototyping conversational AI agents.
• Building example conversations for training or demos.
• Enforcing specific response patterns or personas.
• Integrating chat interfaces into installations.
• Creating dynamic user experiences.