Documentation | Guava

Intent Helpers

Guava provides an intent classification helper for routing caller requests. IntentRecognizer classifies caller utterances into your predefined intents and returns matching actions for the dialog engine to handle.

IntentRecognizer

IntentRecognizer classifies a free-text caller utterance against your predefined intent labels and returns all plausible matches as SuggestedAction objects. Use it inside on_action_request() to map caller language to routing decisions — return the full list and let the dialog engine handle disambiguation automatically.

Import: from guava.helpers.llm import IntentRecognizer

PythonTypeScript

from guava.helpers.llm import IntentRecognizer

IntentRecognizer(intent_choices: list[str] | dict[str, str])
recognizer.classify(intent: str) -> list[SuggestedAction] | None

classify(intent: str) -> list[SuggestedAction] | None — Returns all choices from intent_choices that plausibly match intent, ordered by likelihood. Returns None if no choice matches. It is recommended to return the full list from on_action_request to let the dialog engine handle disambiguation automatically.

PythonTypeScript

support_agent.py

import guava
from guava import Agent, SuggestedAction
from guava.helpers.llm import IntentRecognizer

agent = Agent(
    name="Support",
    organization="Acme Corp",
    purpose="Help the caller with their support request.",
)

intent_recognizer = IntentRecognizer({
    'check order status': 'Caller wants to look up the status of an existing order.',
    'bill pay': 'Caller wants to make a payment or ask about their bill.',
    'anything else': 'Caller has a request that does not fit the above categories.',
})

@agent.on_action_request
def on_action_request(call: guava.Call, request: str) -> SuggestedAction | list[SuggestedAction] | None:
    return intent_recognizer.classify(request)

@agent.on_action("check order status")
def check_order_status(call: guava.Call):
    call.transfer("+15555555555", "Transfer the caller to the order status team.")

@agent.on_action("bill pay")
def bill_pay(call: guava.Call):
    call.transfer("+15555555555", "Transfer the caller to billing.")

@agent.on_action("anything else")
def anything_else(call: guava.Call):
    call.transfer("+15555555555", "Connect the caller with a live agent.")

IntentRecognizer (openai — deprecated)

Deprecated: IntentRecognizer from guava.helpers.openai is deprecated. Use the new IntentRecognizer from guava.helpers.llm above instead.

IntentRecognizer classifies a free-text caller utterance into one of your predefined intent labels. Use it inside on_action_request() to map vague caller language to clean routing decisions.

PythonTypeScript

signature

from guava.helpers.openai import IntentRecognizer

IntentRecognizer(intent_choices: list[str] | dict[str, str], client: openai.OpenAI | None = None)
recognizer.classify(intent: str) -> str | None

classify(intent: str) -> str | None — Returns the single choice string from intent_choices that best matches intent, or None if the model cannot match any choice. When intent_choices is a dict, the keys are the valid return values; values are used only as descriptions to guide the model.

PythonTypeScript

support_agent.py

import guava
from guava import Agent, SuggestedAction
from guava.helpers.openai import IntentRecognizer

agent = Agent(
    name="Support",
    organization="Acme Corp",
    purpose="Help the caller with their support request.",
)

intent_recognizer = IntentRecognizer(
    ['check order status', 'bill pay', 'anything else']
)

@agent.on_action_request
def on_action_request(call: guava.Call, request: str) -> SuggestedAction:
    return SuggestedAction(key=intent_recognizer.classify(request))

@agent.on_action("check order status")
def check_order_status(call: guava.Call):
    call.transfer("+15555555555", "Transfer the caller to the order status team.")

@agent.on_action("bill pay")
def bill_pay(call: guava.Call):
    call.transfer("+15555555555", "Transfer the caller to billing.")

@agent.on_action("anything else")
def anything_else(call: guava.Call):
    call.transfer("+15555555555", "Connect the caller with a live agent.")

IntentClarifier (deprecated)

Deprecated: IntentClarifier from guava.helpers.openai is deprecated. Use the new IntentRecognizer from guava.helpers.llm above instead — it returns all plausible matches by default, replacing the need for a separate clarifier.

IntentClarifier analyzes a caller's intent and returns the subset of choices that could plausibly match, ordered by likelihood. Use this when an intent may be ambiguous and you need to surface options for the caller to confirm.

PythonTypeScript

signature

from guava.helpers.openai import IntentClarifier

IntentClarifier(intent_choices: list[str] | dict[str, str], client: openai.OpenAI | None = None)
clarifier.propose_choices(intent: str) -> list[str]

propose_choices(intent: str) -> list[str] — Returns a list of choices that could match intent, ordered by likelihood:

• One element if the intent clearly maps to a single choice.
• Multiple elements if the intent is ambiguous.
• Empty list if the intent matches none of the provided choices.

An empty list means the caller's intent is out-of-scope — not that an error occurred. When intent_choices is a dict, only the keys appear in the returned list.

PythonTypeScript

scheduler_agent.py

import guava
from guava import Agent, SuggestedAction
from guava.helpers.openai import IntentClarifier

agent = Agent(
    name="Scheduler",
    organization="Acme Corp",
    purpose="Help callers manage their appointments.",
)

intent_clarifier = IntentClarifier(
    ['reschedule appointment', 'cancel appointment', 'check appointment time']
)

@agent.on_action_request
def on_action_request(call: guava.Call, request: str) -> SuggestedAction:
    matches = intent_clarifier.propose_choices(request)
    if len(matches) == 1:
        # Unambiguous — proceed directly
        return SuggestedAction(key=matches[0])
    elif len(matches) > 1:
        # Ambiguous — route to the most likely match; agent will confirm with caller
        return SuggestedAction(key=matches[0], description=f"Caller may have meant one of: {matches}")
    # len == 0: no match, return nothing so the agent keeps listening