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# intent_recognition.py
from llm_router import LLMRouter
class ChainOfThoughtIntentRecognizer:
def __init__(self, llm_router: LLMRouter):
self.llm_router = llm_router
self.cot_templates = self._load_cot_templates()
async def recognize_intent(self, user_input: str, context: dict) -> dict:
"""
Multi-step reasoning for intent recognition
"""
# Step 1: Initial classification
initial_analysis = await self._step1_initial_classification(user_input)
# Step 2: Contextual refinement
refined_analysis = await self._step2_contextual_refinement(
user_input, initial_analysis, context
)
# Step 3: Confidence calibration
final_intent = await self._step3_confidence_calibration(refined_analysis)
return self._format_intent_output(final_intent)
async def _step1_initial_classification(self, user_input: str) -> dict:
cot_prompt = f"""
Let's think step by step about the user's intent:
User input: "{user_input}"
Step 1: Identify key entities and actions mentioned
Step 2: Map to common intent categories
Step 3: Estimate confidence for each category
Categories: [information_request, task_execution, creative_generation,
analysis_research, casual_conversation, troubleshooting]
"""
return await self.llm_router.route_inference(
"intent_classification", cot_prompt
)
async def _step2_contextual_refinement(self, user_input: str, initial_analysis: dict, context: dict) -> dict:
"""
Refine intent classification based on conversation context
"""
# TODO: Implement contextual refinement using conversation history
return initial_analysis
async def _step3_confidence_calibration(self, analysis: dict) -> dict:
"""
Calibrate confidence scores for final intent decision
"""
# TODO: Implement confidence calibration logic
return analysis
def _format_intent_output(self, intent_data: dict) -> dict:
"""
Format intent recognition output with confidence scores
"""
# TODO: Implement output formatting
return {
"intent": intent_data.get("intent", "unknown"),
"confidence": intent_data.get("confidence", 0.0),
"reasoning_steps": intent_data.get("reasoning_steps", [])
}
def _load_cot_templates(self) -> dict:
"""
Load Chain of Thought templates for different intent types
"""
return {
"information_request": """Let's analyze: {user_input}
Step 1: What information is the user seeking?
Step 2: Is it factual, procedural, or explanatory?
Step 3: What level of detail is appropriate?""",
"task_execution": """Let's analyze: {user_input}
Step 1: What action does the user want to perform?
Step 2: What are the required parameters?
Step 3: Are there any constraints or preferences?""",
"creative_generation": """Let's analyze: {user_input}
Step 1: What type of creative content is needed?
Step 2: What style, tone, and format?
Step 3: What constraints or guidelines apply?"""
}
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