Deprecated Components - Rasa Documentation

SingleStepLLMCommandGenerator

The SingleStepLLMCommandGenerator is deprecated and no longer recommended for use in Rasa 3.x. It will be removed in Rasa 4.0.0.

To interpret the user’s message in context, the current implementation of the SingleStepLLMCommandGenerator uses in-context learning, information about the current state of the conversation, and flows defined in your assistant. Descriptions and slot definitions of each flow are included in the prompt as relevant information. However, to scale to a large number of flows, the LLM-based command generator includes only the flows that are relevant to the current state of the conversation, see flow retrieval.

Prompt Template

The default prompt template serves as a dynamic framework enabling the SingleStepLLMCommandGenerator to render prompts. The template consists of a static component, as well as dynamic components that get filled in when rendering a prompt:

Current state of the conversation - This part of the template captures the ongoing dialogue.
Defined flows and slots - This part of the template provides the context and structure for the conversation. It outlines the overarching theme, guiding the model’s understanding of the conversation’s purpose.
Active flow and slot - Active elements within the conversation that require the model’s attention.

The prompt template for the SingleStepLLMCommandGenerator is as follows:

Your task is to analyze the current conversation context and generate a list of actions to start new business processes that we call flows, to extract slots, or respond to small talk and knowledge requests.

These are the flows that can be started, with their description and slots:
{% for flow in available_flows %}
{{ flow.name }}: {{ flow.description }}
    {% for slot in flow.slots -%}
    slot: {{ slot.name }}{% if slot.description %} ({{ slot.description }}){% endif %}{% if slot.allowed_values %}, allowed values: {{ slot.allowed_values }}{% endif %}
    {% endfor %}
{%- endfor %}

===
Here is what happened previously in the conversation:
{{ current_conversation }}

===
{% if current_flow != None %}
You are currently in the flow "{{ current_flow }}".
You have just asked the user for the slot "{{ current_slot }}"{% if current_slot_description %} ({{ current_slot_description }}){% endif %}.

{% if flow_slots|length > 0 %}
Here are the slots of the currently active flow:
{% for slot in flow_slots -%}
- name: {{ slot.name }}, value: {{ slot.value }}, type: {{ slot.type }}, description: {{ slot.description}}{% if slot.allowed_values %}, allowed values: {{ slot.allowed_values }}{% endif %}
{% endfor %}
{% endif %}
{% else %}
You are currently not in any flow and so there are no active slots.
This means you can only set a slot if you first start a flow that requires that slot.
{% endif %}
If you start a flow, first start the flow and then optionally fill that flow's slots with information the user provided in their message.

The user just said """{{ user_message }}""".

===
Based on this information generate a list of actions you want to take. Your job is to start flows and to fill slots where appropriate. Any logic of what happens afterwards is handled by the flow engine. These are your available actions:
* Slot setting, described by "SetSlot(slot_name, slot_value)". An example would be "SetSlot(recipient, Freddy)"
* Starting another flow, described by "StartFlow(flow_name)". An example would be "StartFlow(transfer_money)"
* Cancelling the current flow, described by "CancelFlow()"
* Clarifying which flow should be started. An example would be Clarify(list_contacts, add_contact, remove_contact) if the user just wrote "contacts" and there are multiple potential candidates. It also works with a single flow name to confirm you understood correctly, as in Clarify(transfer_money).
* Intercepting and handle user messages with the intent to bypass the current step in the flow, described by "SkipQuestion()". Examples of user skip phrases are: "Go to the next question", "Ask me something else".
* Responding to knowledge-oriented user messages, described by "SearchAndReply()"
* Responding to a casual, non-task-oriented user message, described by "ChitChat()".
* Handing off to a human, in case the user seems frustrated or explicitly asks to speak to one, described by "HumanHandoff()".
{% if is_repeat_command_enabled %}
* Repeat the last bot messages, described by "RepeatLastBotMessages()". This is useful when the user asks to repeat the last bot messages.
{% endif %}

===
Write out the actions you want to take, one per line, in the order they should take place.
Do not fill slots with abstract values or placeholders.
Only use information provided by the user.
Only start a flow if it's completely clear what the user wants. Imagine you were a person reading this message. If it's not 100% clear, clarify the next step.
Don't be overly confident. Take a conservative approach and clarify before proceeding.
If the user asks for two things which seem contradictory, clarify before starting a flow.
If it's not clear whether the user wants to skip the step or to cancel the flow, cancel the flow.
Strictly adhere to the provided action types listed above.
Focus on the last message and take it one step at a time.
Use the previous conversation steps only to aid understanding.

Your action list:

Customization

You can customize the SingleStepLLMCommandGenerator as much as you wish. General customization options that are available for both LLMCommandGenerators are listed in the section General Customizations.

Customizing the Prompt Template

If you cannot get something to work via editing the flow and slot descriptions (see section customizing the prompt), you can go one level deeper and customise the prompt template used to drive the SingleStepLLMCommandGenerator. To do this, write your own prompt as a jinja2 template and provide it to the component as a file:

config.yml

pipeline:
  - name: SingleStepLLMCommandGenerator
    prompt_template: prompts/command-generator.jinja2

Deprecation WarningThe former LLMCommandGenerator’s prompt configuration is replaced by SingleStepLLMCommandGenerator’s prompt_template in version 3.9.0. The prompt configuration variable is now deprecated and will be removed in version 4.0.0.

The prompt template also allows the utilization of variables to incorporate dynamic information. You can access the comprehensive list of available variables here to use in your custom prompt template.

Variable	Type	Description
`available_flows`	List[Dict[str, Any]]	A list of all flows available in the assistant.
`current_conversation`	str	A readable representation of the current conversation. a simple example: `USER: hello\nAI: hi\nUSER: What is my transfer limit?`
`current_flow`	str	ID of the current active flow. example: `check_transfer_limit`
`current_slot`	str	Name of the currently asked slot. example: `transfer_limit_type`
`current_slot_description`	str	Description of the currently asked collect step. example: `Daily or monthly transfer limit.`
`current_slot_type`	str	Type of slot currently asked in the collect step. example: `categorical`
`current_slot_allowed_values`	str	Allowed values of the slot in the currently asked collect step. example: `["daily", "monthly"]`
`flow_slots`	List[Dict[str, Any]]	A list of slots from the current active flow.
`user_message`	str	The latest message sent by the user. example: `What is my transfer limit?`

Iterating over the flow_slots variable can be useful to create a prompt that lists all the slots of the current active flow,

{% for slot in flow_slots -%}
- name: {{ slot.name }}, value: {{ slot.value }}, type: {{ slot.type }}, description: {{ slot.description}}{% if slot.allowed_values %}, allowed values: {{ slot.allowed_values }}{% endif %}
{% endfor %}

Variable	Type	Description
`slot.name`	str	Name of the slot. example: `transfer_money_has_sufficient_funds`
`slot.description`	str	Description of the slot. example: `Checks if there is sufficient balance`
`slot.value`	str	Value of the slot. example: `True`
`slot.type`	str	Type of the slot. example: `bool`
`slot.allowed_values`	List[str]	List of allowed values for the slot. example: `[True, False]`

Iterating over the available_flows variable can be useful to create a prompt that lists all the flows,

{% for flow in available_flows %}
{{ flow.name }}: {{ flow.description }}
    {% for slot in flow.slots -%}
    slot: {{ slot.name }}{% if slot.description %} ({{ slot.description }}){% endif %}{% if slot.allowed_values %}, allowed values: {{ slot.allowed_values }}{% endif %}
    {% endfor %}
{%- endfor %}

Variable	Type	Description
`flow.name`	str	Name of the flow. example: `transfer_money`
`flow.description`	str	Description of the flow. example: `This flow lets users send money.`
`flow.slots`	List[Dict[str, Any]]	A list of slots from the flow.

MultiStepLLMCommandGenerator

The MultiStepLLMCommandGenerator is deprecated and no longer recommended for use in Rasa 3.x. It will be removed in Rasa 4.0.0.

The MultiStepLLMCommandGenerator also uses in-context learning to interpret the user’s message in context, but breaks down the task into several steps to make the job of the LLM easier. The component was designed to enable cheaper and smaller LLMs, such as gpt-3.5-turbo, as viable alternatives to costlier but more powerful models such as gpt-4-0613. The steps are:

handling the flows (starting, ending, etc.) and
filling out the slots

Accordingly, instead of just a single prompt that handles everything, the MultiStepLLMCommandGenerator has two prompts:

handle_flows and
fill_slots.

The following diagram shows which prompt is used when:

Prompt selection flow for the MultiStepLLMCommandGenerator

If no flow is currently active, the handle_flows prompt is used to start or clarify flows. If a flow is started, next the fill_slots prompt is executed to fill any slots of the newly started flow. If a flow is currently active, the fill_slots prompt is called to fill any new slots of the currently active flow. If the user message (also) indicates that, for example, a new flow should be started or the active flow should be canceled, a ChangeFlow command is triggered. This results in calling the handle_flows prompt to start, cancel, or clarify flows. If that prompt leads to starting a flow, the fill_slots prompt is executed again to fill any slots of that new flow.

Prompt Templates

The default prompt templates serve as a dynamic framework enabling the MultiStepLLMCommandGenerator to render prompts. The templates consists of a static component, as well as dynamic components that get filled in when rendering a prompt.

handle_flows
fill_slots

Current state of the conversation: This part of the template captures the ongoing dialogue.
Defined flows: This part of the template provides the context and structure for the conversation. It outlines the overarching theme, guiding the model’s understanding of the conversation’s purpose.
Active flow: Displays the name of the current active flow (if any). Used within conditional statements to customize the message about the flow’s status.

Customization

You can customize the MultiStepLLMCommandGenerator as much as you wish. General customization options that are available for both LLMCommandGenerators are listed in the section General Customizations.

Customizing The Prompt Template

If you cannot get something to work via editing your flow and slot descriptions (see section customizing the prompt), you can go one level deeper and customise the prompt templates used to drive the MultiStepLLMCommandGenerator. To do this, write your own prompt as a jinja2 template and provide it to the component as a file:

config.yml

pipeline:
  - name: MultiStepLLMCommandGenerator
    prompt_templates:
      handle_flows:
        file_path: prompts/handle_flows_template.jinja2
      fill_slots:
        file_path: prompts/fill_slots_template.jinja2

You can customize both prompts using the example configuration above, or you can choose to customize only a specific prompt. The prompt template also allows the utilization of variables to incorporate dynamic information.

handle_flows

Here is a comprehensive list of available variables to use in your custom handle_flows prompt template:

Variable	Type	Description
`available_flows`	List[Dict[str, Any]]	A list of all flows available in the assistant.
`current_conversation`	str	A readable representation of the current conversation. a simple example: `USER: hello\nAI: hi\nUSER: I need to send money`
`current_flow`	str	ID of the current active flow. example: `transfer_money`
`last_user_message`	str	The latest message sent by the user. example: `I want to transfer money`

Iterating over the available_flows variable can be useful to create a prompt that lists all the flows,

{% for flow in available_flows %}
- {{ flow.name }}: {{ flow.description }}
{%- endfor %}

Variable	Type	Description
`flow.name`	str	Name of the flow. example: `transfer_money`
`flow.description`	str	Description of the flow. example: `This flow lets users send money.`

By default following commands can be predicted in this step: StartFlow, Clarify, CancelFlow, CannotHandle.

fill_slots

Here is a comprehensive list of available variables to use in your custom fill_slots prompt template:

Variable	Type	Description
`available_flows`	List[Dict[str, Any]]	A list of all flows available in the assistant.
`current_conversation`	str	A readable representation of the current conversation. a simple example: `USER: hello\nAI: hi\nUSER: I need to send money`
`current_flow`	str	ID of the current active flow. example: `transfer_money`
`current_slot`	str	Name of the currently asked slot. example: `transfer_money_recipient`
`current_slot_description`	str	Description of the currently asked collect step. example: `the name of the person`
`current_slot_type`	str	The type of the current slot. example: `the name of the person`
`current_slot_allowed_values`	str	The allowed values or options for the slot currently being asked for within the active flow. example of allowed values for a slot: `Size: [Small, Medium, Large]`
`flow_slots`	List[Dict[str, Any]]	A list of slots from the current active flow.
`last_user_message`	str	The most recent message sent by the user. example: `I want to transfer money`
`top_flow_is_pattern`	bool	Indicates whether the current active flow is a pattern.
`top_user_flow`	Flow	The top user flow object in the internal dialog stack.
`top_user_flow_slots`	List[Dict[str, Any]]	The slots associated with the top user flow.
`flow_active`	bool	Indicates whether a flow is currently active.

Iterating over the flow_slots or the top_user_flow_slots variable can be useful to create a prompt that lists all the slots of the current active flow (can be a pattern) or the top user flow,

{% for slot in flow_slots -%}
- name: {{ slot.name }}, value: {{ slot.value }}, type: {{ slot.type }}, description: {{ slot.description}}{% if slot.allowed_values %}, allowed values: {{ slot.allowed_values }}{% endif %}
{% endfor %}

Variable	Type	Description
`slot.name`	str	Name of the slot. example: `transfer_money_has_sufficient_funds`
`slot.description`	str	Description of the slot. example: `Checks if there is sufficient balance`
`slot.value`	str	Value of the slot. example: `True`
`slot.type`	str	Type of the slot. example: `bool`
`slot.allowed_values`	List[str]	List of allowed values for the slot. example: `[True, False]`

Iterating over the available_flows variable can be useful to create a prompt that lists all the flows,

{% for flow in available_flows %}
- {{ flow.name }}: {{ flow.description }}
{%- endfor %}

Variable	Type	Description
`flow.name`	str	Name of the flow. example: `transfer_money`
`flow.description`	str	Description of the flow. example: `This flow lets users send money.`

By default only SetSlot commands can be predicted in this step.

Prompt Tuning

Apart from the prompt customization proposed in the section on Customizing The Prompt, the MultiStepLLMCommandGenerator might benefit from some additional prompt tuning.

Few-shot learning

Few-shot learning is a machine learning approach in which an AI model learns to make accurate predictions by being trained on a very small number of labeled examples. Our internal experiments showed that adding some examples of user message to command pairs to the prompt template handle_flows helped to improve the performance of the LLM, especially for the Clarify command. To do so, curate a small list of user message - action list pairs of examples that are specific to the domain of your assistant. Next, you can add them to the prompt template handle_flows after line 22. Here is an example:

===
Here are some examples of user messages -> action list:
1. "book" -> Clarify(book_hotel, book_restaurant, book_flight)
2. "I want to book a hotel in Paris." -> StartFlow(book_hotel)
3. "Can you help me with my booking?" -> Clarify(book_hotel, book_restaurant, book_flight)
4. "Nevermind, stop it." -> CancelFlow()

Language of the prompt

In our internal experiments, we have found that smaller LLMs benefit from having the complete prompt in a single language. Hence, if your assistant is built to understand and respond to a user in a language other than English and the flows descriptions as well as the collect step descriptions are in that same language, the LLM might benefit from translating the prompt template to that language as well. For example, assume users are talking in German to your assistant and the flow descriptions and collect step descriptions are also in German. To simplify the job of the LLM it might help to translate the complete prompt template to German as well. You can use strong LLMs, such as gpt-4-0613, for translating. However, we recommend to manually check the prompt templates after translating it automatically.

Formatting

In both prompt templates, handle_flows and fill_slots, we iterate over the flows and/or the slots. Depending on the descriptions you wrote for those, it might make sense to update the for loop in the prompt templates. For example, if your slot descriptions are rather long and contain a list of bullet points, the slot list in the final prompt might look like this:

The current user message started the process "transfer_money".
Here are its slots:
- recipient (text): - name of the person to send money to
- should be a proper name
- amount (float): - the amount of money to send
- do not extract currency values
- amount should be a float

As you see it is quite hard to distinguish between the description and the actual slots. To help the LLM to clearly distinguish between the slot and the description, we recommend to update the for loop in the jinja2 prompt template as follows:

{% for slot in flow_slots %}
{{loop.index}}. {{ slot.name }} ({{slot.type}}){% if slot.description %}: {{ slot.description}} {% endif %}{% if slot.allowed_values %}(allowed values: {{ slot.allowed_values }}){% endif %}
{%- endfor %}

This results in the following:

The current user message started the process "transfer_money".
Here are its slots:
1. recipient (text): - name of the person to send money to
- should be a proper name
2. amount (float): - the amount of money to send
- do not extract currency values
- amount should be a float

Current Limitations

The MultiStepLLMCommandGenerator will be able to generate the following commands:

in handle_flows step: StartFlow, Clarify, CancelFlow, CannotHandle.
in fill_slots step: SetSlot. The CannotHandle command will be triggered from the handle_flows prompt when the scope of the user message is beyond starting, canceling, or clarifying a flow. The command will trigger the cannot handle pattern to indicate that the user message can not be treated as expected.

​SingleStepLLMCommandGenerator

​Prompt Template

​Customization

Customizing the Prompt Template

​MultiStepLLMCommandGenerator

​Prompt Templates

​Customization

Customizing The Prompt Template

handle_flows

fill_slots

Prompt Tuning

Few-shot learning

Language of the prompt

Formatting

​Current Limitations

SingleStepLLMCommandGenerator

Prompt Template

Customization

MultiStepLLMCommandGenerator

Prompt Templates

Customization

Current Limitations