1050df780d
- Intent classification with LLM structured output (single/multi/ambiguous) - Discount agent with apply_discount and generate_coupon tools - Interrupt manager with 30-min TTL auto-expiration and retry prompts - Webhook escalation module with exponential backoff retry (max 3) - Three vertical industry templates (e-commerce, SaaS, fintech) - Template loading in AgentRegistry - Enhanced supervisor prompt with dynamic agent descriptions - 153 tests passing, 90.18% coverage
119 lines
3.8 KiB
Python
119 lines
3.8 KiB
Python
"""Intent classification using LLM structured output."""
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from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING, Protocol
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from pydantic import BaseModel
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if TYPE_CHECKING:
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from langchain_core.language_models import BaseChatModel
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from app.registry import AgentConfig
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logger = logging.getLogger(__name__)
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CLASSIFICATION_PROMPT = (
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"You are an intent classifier for a customer support system.\n"
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"Given a user message, determine which agent(s) should handle it.\n\n"
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"Available agents:\n{agent_list}\n\n"
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"Rules:\n"
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"- If the message clearly maps to one agent, return a single intent.\n"
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"- If the message contains multiple distinct requests, return multiple intents "
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"in execution order.\n"
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"- If the message is vague or doesn't match any agent, set is_ambiguous=True "
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"and provide a clarification_question.\n"
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"- Never route to the fallback agent unless truly ambiguous.\n"
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"- confidence should be between 0.0 and 1.0.\n"
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)
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AMBIGUITY_THRESHOLD = 0.5
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class IntentTarget(BaseModel, frozen=True):
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"""A single classified intent targeting a specific agent."""
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agent_name: str
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confidence: float
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reasoning: str
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class ClassificationResult(BaseModel, frozen=True):
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"""Result of intent classification -- may contain multiple intents."""
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intents: tuple[IntentTarget, ...]
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is_ambiguous: bool = False
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clarification_question: str | None = None
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class IntentClassifier(Protocol):
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"""Protocol for intent classification implementations."""
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async def classify(
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self,
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message: str,
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available_agents: tuple[AgentConfig, ...],
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) -> ClassificationResult: ...
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def _build_agent_list(agents: tuple[AgentConfig, ...]) -> str:
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"""Format agent descriptions for the classification prompt."""
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lines = []
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for agent in agents:
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lines.append(f"- {agent.name}: {agent.description} (permission: {agent.permission})")
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return "\n".join(lines)
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class LLMIntentClassifier:
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"""Classifies user intent using LLM structured output."""
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def __init__(self, llm: BaseChatModel) -> None:
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self._llm = llm
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async def classify(
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self,
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message: str,
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available_agents: tuple[AgentConfig, ...],
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) -> ClassificationResult:
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"""Classify user message into one or more agent intents."""
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agent_list = _build_agent_list(available_agents)
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system_prompt = CLASSIFICATION_PROMPT.format(agent_list=agent_list)
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structured_llm = self._llm.with_structured_output(ClassificationResult)
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try:
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result = await structured_llm.ainvoke(
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[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": message},
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]
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)
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except Exception:
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logger.exception("Intent classification failed, returning ambiguous")
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return ClassificationResult(
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intents=(),
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is_ambiguous=True,
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clarification_question="I'm not sure I understood. Could you please rephrase?",
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)
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if not isinstance(result, ClassificationResult):
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return ClassificationResult(
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intents=(),
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is_ambiguous=True,
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clarification_question="I'm not sure I understood. Could you please rephrase?",
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)
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# Apply ambiguity threshold
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if result.intents and all(i.confidence < AMBIGUITY_THRESHOLD for i in result.intents):
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return ClassificationResult(
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intents=result.intents,
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is_ambiguous=True,
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clarification_question=(
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result.clarification_question
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or "I'm not sure I understood. Could you please rephrase?"
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),
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)
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return result
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