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Based Crash Course

Welcome to the Based Crash Course! This guide introduces you to Based, a powerful, domain-specific programming language designed to build platform agnostic conversational agents. Deploy conversational workflows on chat, voice, email, SMS, and more with ease, enabling seamless data exchange and a unified user experience across platforms.

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What Is Based?

Based is a high-level AI instruction language crafted to design dynamic conversational agents that operate flawlessly across multiple communication channels. It provides developers with an elegant, high-level syntax to build interactive workflows quickly and reliably. Key features of Based include:
  • Intuitive and Expressive Syntax: Develop complex conversational logic with clarity and brevity.
  • Specialized Constructs: Utilize built-in keywords like talk, loop, until, and ask to manage conversation flow and state effortlessly.
  • Cross-Platform Flexibility: Create agents deployable on chat, voice, email, SMS, and more—all while sharing data seamlessly across channels.

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Core Conversation Flow Constructs

Based scripts use a trio of keywords to build interactive conversations:
  1. talk: Sends a message or prompt to the user and waits for a response (or specify False as the second argument to wait for the user to send a message first).
  2. loop: Begins a conversational block that allows for repeated prompting.
  3. until: Specifies the condition under which the loop should end.
In practice, the talk keyword is not used in isolation. It is usually enclosed in a loop/until structure. This pattern keeps the conversation repeating until valid input is obtained. Example Usage: 
loop:
    # Send a prompt to the user asking for their preferred contact method.
    response = talk(
        "Hi there! What's your preferred contact method (email, phone, or SMS)?",
        True,
        {"preferred_contact": "email"} // Example default value
    )
until "User provides a valid contact method":
    contactInfo = response.ask(
        question="Extract and validate the contact method from the response.",
        example={"preferred_contact": "email"}
    )
    # Validate the contact method; if invalid, the prompt repeats.
    if contactInfo["preferred_contact"] not in ["email", "phone", "SMS"]:
        print("Invalid contact method provided. Re-prompting...")
    else:
        print("Valid contact method received!")

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Core Language Constructs

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The ask Method

The ask method is used to extract structured data from a response. By providing an example object, you can ensure that the output is formatted predictably for further processing. Example: 
# Extract structured details (full name and email) from a user response.
userDetails = response.ask(
    question="Return the user's full name and email address.",
    example={"full_name": "John Doe", "email": "[email protected]"}
)

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Tool Schema Until Conditions

Beyond string conditions, Based supports tool schema until conditions that extract structured parameters when a condition matches: 
# Define a tool schema
get_appointment = {
    "name": "get_appointment",
    "description": "Book an appointment when user provides date and time",
    "parameters": {
        "type": "object",
        "properties": {
            "date": {"type": "string", "description": "Appointment date"},
            "time": {"type": "string", "description": "Appointment time"}
        },
        "required": ["date", "time"]
    }
}

# Use with parameter binding
loop:
    response = talk("When would you like to schedule your appointment?", True)
until "user wants to cancel":
    say("No problem, cancelled.")
until get_appointment as appointment:
    # appointment contains: {"date": "...", "time": "..."}
    say(f"Booked for {appointment['date']} at {appointment['time']}!", exact=True)
For full details, see Tool Schema Until Conditions.

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State Persistence

Variables persist across conversation turns using the state dictionary: 
state = {}

# Store data during conversation
state["user_name"] = "John"
state["order_items"] = []

# Access later in the flow
say(f"Hello again, {state['user_name']}!", exact=True)

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Built-in Utilities

Use print() for debugging—outputs appear in the session trace: 
print("Processing order...")
print(f"Order data: {order_data}")

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Sending SMS

Send SMS messages programmatically with send_sms: 
result = await send_sms(
    to="+12025551234",
    content="Your order is confirmed!",
    from_number="+15551234567"  # From your phone library
)

if result.success:
    say("Confirmation sent!", exact=True)

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HTTP Requests

Use the standard requests module for API calls: 
response = requests.get(
    url="https://api.example.com/users/123",
    headers={"Authorization": "Bearer token"}
)

if response.ok:
    user = response.json()
    say(f"Found user: {user['name']}", exact=True)

# POST requests
response = requests.post(
    url="https://api.example.com/orders",
    headers={"Content-Type": "application/json"},
    json={"items": order_items}
)

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Full Example: An Interactive Workflow

Below is a complete example that combines talk, loop, until, and ask to build a seamless interactive conversation: 
state = {}
meta_prompt = "You're an assistant that helps the user book shifts."
res = say("Hello, I'm a chatbot. Let's start by gathering your details.")

loop:
    # Initiate conversation to collect user details.
    phone_response = talk(
        f"{meta_prompt} Please provide your phone number, full name, and facility name. Confirm these details once provided.",
        True,
        {"phone_number": "+16179011508", "full_name": "John Doe", "facility_name": "Facility A"}
    )
until "User has provided phone number, full name, and facility name and confirmed them":
    # Extract the user details.
    info = phone_response.ask(
        question="Extract the user's phone number, full name, and facility name.",
        example={"phone_number": "+16179011508", "full_name": "John Doe", "facility_name": "Facility A"}
    )
    
    # Verify details via API
    response = requests.get(
        url='https://apigateway.example.com/api/facilities',
        headers={
            'authorization': 'Bearer 123',
            'cbh-facility-user-phone-number-auth': info["phone_number"]
        }
    )
    answer_ = response.json().ask(
        question="Verify the provided details. If successful, return the user's name, facility name, and email; otherwise, return 'ERROR'.",
        example={"name": "John Doe", "facility_name": "Facility A", "email": "[email protected]", "success": true}
    )
    print("Answer:", answer_)

    if not answer_["success"]:
        loop:
            phone_response = talk("Sorry, we couldn't verify your details. Please re-enter your information.", True)
        until "User confirms corrected details":
            info = phone_response.ask(
                question="Return the user's phone number, full name, and facility name.",
                example={"phone_number": "+16179011508", "full_name": "John Doe", "facility_name": "Facility A"}
            )
            state["info"] = info
    else:
        print("Verification successful:", answer_)

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Voice Deployment Functions

When deploying Based agents for voice conversations, you have access to special functions for call control:

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transfer(phone_number, options?)

Transfers the current call to another phone number, with optional extension dialing support. 
# Basic transfer
transfer("+1-800-123-4567")

# Transfer with extension (dials extension after call connects)
transfer("5302321272", "123")

# Transfer with extension and custom pause time
transfer("5302321272", {"extension": "123", "pauseSeconds": 2})

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end_call()

Ends the current call gracefully. 
say("Thank you for calling. Goodbye!", exact=True)
end_call()
For more details on voice functions and their parameters, see the Language Fundamentals guide.

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Additional Notes

  • Based’s design abstracts away complexity while providing powerful control over conversational flows.
  • The constructs loop, talk, until, and ask empower you to design robust, interactive workflows.
  • Tool schema until conditions enable structured parameter extraction directly from user intents.
  • State persistence automatically maintains variables across conversation turns.
  • Print line debugging helps troubleshoot flows via session traces.
  • HTTP requests use the standard requests module for full API compatibility.
  • SMS sending is available via send_sms() for notifications and confirmations.
  • For voice deployments, use transfer() and end_call() to control call flow.
For comprehensive documentation on all constructs, see the Language Fundamentals guide.

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Conclusion

Armed with these constructs, you can build dynamic, platform-agnostic conversational agents using Based. This guide has provided enhanced examples that showcase how to structure interactive conversations effectively. Happy coding!