97 lines
3.4 KiB
Python
97 lines
3.4 KiB
Python
"""Lightweight intent classification using gpt-4o-mini."""
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import logging
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from functools import lru_cache
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from typing import Annotated, Literal
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from fastapi import Depends
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from openai import AsyncOpenAI
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from app.config import settings
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from app.memory.conversation import Message
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logger = logging.getLogger(__name__)
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Intent = Literal["codebase", "general", "clarification"]
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INTENT_PROMPT = """Classify this user message into one category:
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- "codebase": Questions about trading system code, architecture, files, methods, execution, strategies, exchanges, risk management, order handling, or technical implementation
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- "general": Greetings, meta-questions, off-topic ("How are you?", "What can you do?", "Hello")
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- "clarification": Follow-ups that rely on conversation context, not new retrieval ("Tell me more", "What did you mean?", "Can you explain that?")
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IMPORTANT: If the user is asking about specific code, files, classes, methods, or system behavior, classify as "codebase".
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Respond with ONLY the category name, nothing else."""
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class IntentClassifier:
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"""Lightweight intent classifier using gpt-4o-mini."""
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def __init__(self, api_key: str):
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"""Initialize the classifier.
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Args:
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api_key: OpenAI API key
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"""
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self.client = AsyncOpenAI(api_key=api_key)
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self.model = "gpt-4o-mini"
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async def classify(
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self,
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message: str,
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history: list[Message] | None = None,
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) -> Intent:
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"""Classify user message intent.
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Args:
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message: User's message
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history: Optional conversation history for context
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Returns:
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Classified intent: "codebase", "general", or "clarification"
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"""
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# Build context from history (last 2 turns)
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context = ""
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if history and len(history) >= 2:
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recent = history[-4:] # Last 2 exchanges
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context = "Recent conversation:\n"
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for msg in recent:
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role = "User" if msg.role == "user" else "Assistant"
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context += f"{role}: {msg.content[:100]}...\n" if len(msg.content) > 100 else f"{role}: {msg.content}\n"
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context += "\n"
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try:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": INTENT_PROMPT},
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{"role": "user", "content": f"{context}Current message: {message}"},
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],
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max_tokens=10,
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temperature=0,
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)
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raw_intent = response.choices[0].message.content.strip().lower()
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# Validate intent
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if raw_intent in ("codebase", "general", "clarification"):
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logger.debug(f"Classified intent: {raw_intent}")
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return raw_intent
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# Default to codebase for ambiguous cases (safer for RAG)
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logger.warning(f"Unexpected intent response: {raw_intent}, defaulting to codebase")
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return "codebase"
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except Exception as e:
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logger.warning(f"Intent classification failed: {e}, defaulting to codebase")
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return "codebase"
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@lru_cache()
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def get_intent_classifier() -> IntentClassifier:
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"""Get cached intent classifier instance."""
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return IntentClassifier(api_key=settings.openai_api_key)
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IntentClassifierDependency = Annotated[IntentClassifier, Depends(get_intent_classifier)]
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