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Rulesets for Output

Tip

There is a change worth noting since the recording of this video.

Agent Output has changed from:

agent_response.output_task.output.value to agent_response.output_task.output.value.

This change is reflected in the code in the course, but not in the video at this time.

Consider a situation where we have integrated the LLM (Large Language Model) into our code. It becomes crucial for us to receive the output in a specific format that aligns with our requirements, like JSON. By employing an output ruleset, we can precisely control the structure and format of the chatbot's responses.

Goal

After completing this section, you'll be able to use output rulesets to get responses from the LLM in the way most useful for your application.

New Ruleset

JSON Ruleset

To achieve our goal of formatting the response as JSON, we'll create a ruleset called "json_ruleset". This ruleset will contain a single rule that tells the chatbot to use JSON when formulating its response. Place it after kiwi_rulesest:

json_ruleset = Ruleset(
    name="json_ruleset",
    rules=[
        Rule("Use JSON when formulating your response.")
    ]
)

Integration

With json_ruleset in hand, it's time to integrate it into our Agent. By including it in the list of rulesets available to the Agent, we can harness its power to control the response format.

# Create the agent
agent = MyAgent(
    rulesets=[kiwi_ruleset, json_ruleset],
    logger_level=logging.ERROR
)

Here, we modify the MyAgent instantiation to include both kiwi_ruleset and json_ruleset in the rulesets=[] argument. This ensures that our chatbot possesses the kiwi personality traits while also adhering to the desired response format specified by json_ruleset.

Test

Prepare for an exciting conversation as we engage our chatbot in a quest for knowledge about Wellington's top tourist destinations. Let's dive in:

Chat with Kiwi: "Hey chatbot, what are the top three tourist destinations in Wellington? Can you give me a name and a description?"

Kiwi: {
  "message": "Absolutely, mate! Here are the top three tourist destinations in Wellington with a brief description:",
  "destinations": [
    {
      "name": "Te Papa Museum",
      "description": "New Zealand's national museum, known for its interactive and innovative exhibits."
    },
    {
      "name": "Wellington Cable Car",
      "description": "An iconic Wellington attraction, offering stunning views of the city and harbour."
    },
    {
      "name": "Zealandia Ecosanctuary",
      "description": "A unique protected natural area where you can see New Zealand's wildlife up close."
    }
  ]
}

Enjoy the beauty of Wellington's top tourist destinations, neatly presented in a JSON format, as our chatbot provides you with insightful reasons to visit each destination.

Using it

Adding Keys

While this is an interesting example, let's use the ruleset in a way that helps control the way our application works.

Currently, the user has to know to type "exit" to leave the chat. This is not a great user experience, as it's a hidden command. We are using a chat-interface... wouldn't it be great if we could simply tell the chatbot when we were done chatting and it would quit on its own?

Turns out, we can do just that - by using the json_ruleset.

Modify json_ruleset to look like the following:

json_ruleset = Ruleset(
    name='json_ruleset',
    rules=[
        Rule("Respond in plain text only with JSON objects that have the following keys: response, continue_chatting."),
        Rule("The 'response' value should be a string that is your response to the user."),
        Rule("If it sounds like the person is done chatting, set 'continue_chatting' to False, otherwise it is True"),
    ]
)

The first rule tells the chatbot to respond in JSON and specifies the keys.

The second and third rules explain what the values for those keys should be. Notice the third one specifically says that if it sounds like the person is done chatting, set continue_chatting to False.

Go ahead and run the example and notice the response.

Kiwi: {
  "response": "G'day mate! I'm a bot from New Zealand, speaking with a strong kiwi accent. How can I assist you today?",
  "continue_chatting": true
}

Chat with Kiwi: see ya later

Kiwi: {
  "response": "No worries, mate! Catch ya later!",
  "continue_chatting": false
}

See how continue_chatting returns false when it sounds like we're done talking?

Let's now use this JSON output!

Import JSON

First, we'll have to import the json library. To do that, add the following at the beginning of your script:

import json

Load JSON

Next, we'll use the json.loads() function to take the output from the agent's response and convert it into JSON data.

Modify the start of the respond method of the MyAgent class, to look like this:

    # ... truncated for brevity
    def respond (self, user_input):
        agent_response = agent.run(user_input)
        data = json.loads(agent_response.output_task.output.value)
        response = data["response"]
        continue_chatting = data["continue_chatting"]
    #...
This creates two variables - response which will be the normal response from the chatbot, and continue_chatting which should be True or False.

Update Print

Modify the print statement where we get the response from the chatbot to look like:

        print(f"Kiwi: {response}")

Return State

And then return the continue_chatting at the end of the method.

    # ...
    def respond (self, user_input):
      # ...
      return continue_chatting
    #...

The whole class should look like:

# Create a subclass for the Agent
class MyAgent(Agent):

    def respond (self, user_input):
        agent_response = agent.run(user_input)
        data = json.loads(agent_response.output_task.output.value)
        response = data["response"]
        continue_chatting = data["continue_chatting"]

        print("")
        print(f"Kiwi: {response}")
        print("")

        return continue_chatting

Simplify Chat

Since we're returning True or False from the agent.respond() method, the entire chat function can now be simplified as:

# Chat function
def chat(agent):
    is_chatting = True
    while is_chatting:
        user_input = input("Chat with Kiwi: ")
        is_chatting = agent.respond(user_input)

Give it a try and see how you can quit the chat simply by holding the conversation:

Kiwi: G'day mate! I'm a bot from New Zealand, speaking with a strong kiwi accent. How can I assist you today?

Chat with Kiwi: I'm good, how are you?

Kiwi: I'm doing great, thanks for asking! Anything else you'd like to chat about, mate?

Chat with Kiwi: Nah, I'm done for today.

Kiwi: No worries, mate! Have a good one. Don't hesitate to reach out if you need anything else.

Code Review

By leveraging the power of output rulesets, we've demonstrated how you can guide your chatbot to deliver responses in any desired format. Take a moment to check your code.

app.py
from dotenv import load_dotenv
import logging
import json

# Griptape Items
from griptape.structures import Agent
from griptape.rules import Rule, Ruleset

# Load environment variables
load_dotenv()

# Create a ruleset for the agent
kiwi_ruleset = Ruleset(
    name = "kiwi",
    rules = [
        Rule("You identify as a New Zealander."),
        Rule("You have a strong kiwi accent.")
    ]
)

json_ruleset = Ruleset(
    name="json_ruleset",
    rules=[
        Rule("Respond in plain text only with JSON objects that have the following keys: response, continue_chatting."),
        Rule("The 'response' value should be a string that is your response to the user."),
        Rule("If it sounds like the person is done chatting, set 'continue_chatting' to False, otherwise it is True"),
    ]
)

# Create a subclass for the Agent
class MyAgent(Agent):

    def respond (self, user_input):
        agent_response = agent.run(user_input)
        data = json.loads(agent_response.output_task.output.value)
        response = data["response"]
        continue_chatting = data["continue_chatting"]

        print("")
        print(f"Kiwi: {response}")
        print("")

        return continue_chatting

# Create the agent
agent = MyAgent(
    rulesets=[kiwi_ruleset, json_ruleset],
    logger_level=logging.ERROR
)

# Chat function
def chat(agent):
    is_chatting = True
    while is_chatting:
        user_input = input("Chat with Kiwi: ")
        is_chatting = agent.respond(user_input)

# Introduce the agent
agent.respond("Introduce yourself to the user.")

# Run the agent
chat(agent)

Next Steps

In the next section: Formatting Chat Output, we'll make the chat interface more visually appealing and chat-like using the rich library. Get ready to add some style and flair to your conversations!