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"""
Simplified agent implementations for Hugging Face Spaces demo.
"""
import asyncio
from typing import Any
class HRMAgent:
"""Hierarchical Reasoning Module - breaks down complex queries."""
def __init__(self, llm_client):
"""Initialize with an LLM client.
Args:
llm_client: LLM client (MockLLMClient or HuggingFaceClient)
"""
self.llm_client = llm_client
self.name = "HRM (Hierarchical Reasoning)"
async def process(self, query: str) -> dict[str, Any]:
"""Process query using hierarchical decomposition.
Args:
query: Input query to process
Returns:
Dictionary with response, confidence, and reasoning steps
"""
# Step 1: Decompose the query
decomposition_steps = await self._decompose_query(query)
# Step 2: Analyze each component
analysis_results = await self._analyze_components(decomposition_steps)
# Step 3: Synthesize hierarchical response
llm_result = await self.llm_client.generate(
prompt=f"Hierarchical analysis of: {query}", context=f"Components: {', '.join(decomposition_steps)}"
)
# Compile reasoning steps
reasoning_steps = [
f"1. Query decomposition: Identified {len(decomposition_steps)} key components",
f"2. Component analysis: {analysis_results}",
"3. Hierarchical synthesis: Combined insights from all levels",
f"4. Confidence assessment: {llm_result['confidence']:.1%} based on component clarity",
]
return {
"response": llm_result["response"],
"confidence": llm_result["confidence"],
"steps": reasoning_steps,
"components": decomposition_steps,
"tokens_used": llm_result.get("tokens_used", 0),
}
async def _decompose_query(self, query: str) -> list[str]:
"""Decompose query into hierarchical components."""
# Simulate decomposition based on query structure
await asyncio.sleep(0.05) # Simulate processing
# Simple heuristic decomposition
components = []
# Extract key phrases
query_lower = query.lower()
if "?" in query:
components.append("Question type: Analytical")
else:
components.append("Question type: Directive")
if "how" in query_lower:
components.append("Focus: Methodology/Process")
elif "what" in query_lower:
components.append("Focus: Definition/Identification")
elif "why" in query_lower:
components.append("Focus: Causation/Reasoning")
elif "should" in query_lower or "best" in query_lower:
components.append("Focus: Decision/Recommendation")
else:
components.append("Focus: General inquiry")
# Domain detection
if any(term in query_lower for term in ["database", "sql", "nosql", "storage"]):
components.append("Domain: Data Management")
elif any(term in query_lower for term in ["architecture", "design", "pattern"]):
components.append("Domain: System Architecture")
elif any(term in query_lower for term in ["performance", "optimization", "speed"]):
components.append("Domain: Performance Engineering")
elif any(term in query_lower for term in ["scale", "distributed", "cluster"]):
components.append("Domain: Distributed Systems")
else:
components.append("Domain: Software Engineering")
# Complexity assessment
word_count = len(query.split())
if word_count > 20:
components.append("Complexity: High (detailed query)")
elif word_count > 10:
components.append("Complexity: Medium")
else:
components.append("Complexity: Low (concise query)")
return components
async def _analyze_components(self, components: list[str]) -> str:
"""Analyze the decomposed components."""
await asyncio.sleep(0.03) # Simulate processing
# Generate analysis summary
analysis_parts = []
for component in components:
if "Focus:" in component:
focus = component.split(":")[1].strip()
analysis_parts.append(f"requires {focus.lower()} approach")
elif "Domain:" in component:
domain = component.split(":")[1].strip()
analysis_parts.append(f"applies to {domain}")
elif "Complexity:" in component:
complexity = component.split(":")[1].strip().split()[0]
analysis_parts.append(f"{complexity.lower()} complexity level")
return "; ".join(analysis_parts) if analysis_parts else "Standard analysis"
class TRMAgent:
"""Tree Reasoning Module - iterative refinement of responses."""
def __init__(self, llm_client):
"""Initialize with an LLM client.
Args:
llm_client: LLM client (MockLLMClient or HuggingFaceClient)
"""
self.llm_client = llm_client
self.name = "TRM (Iterative Refinement)"
self.max_iterations = 3
async def process(self, query: str) -> dict[str, Any]:
"""Process query using iterative refinement.
Args:
query: Input query to process
Returns:
Dictionary with response, confidence, and reasoning steps
"""
reasoning_steps = []
current_response = ""
current_confidence = 0.0
# Iterative refinement loop
for iteration in range(self.max_iterations):
step_num = iteration + 1
# Generate or refine response
if iteration == 0:
# Initial response
result = await self.llm_client.generate(prompt=query, context="")
current_response = result["response"]
current_confidence = result["confidence"]
reasoning_steps.append(
f"Iteration {step_num}: Initial response generated (confidence: {current_confidence:.1%})"
)
else:
# Refinement iteration
refinement_result = await self._refine_response(query, current_response, iteration)
current_response = refinement_result["response"]
# Confidence typically improves with refinement
confidence_improvement = min(0.1, (1 - current_confidence) * 0.3)
current_confidence = min(0.95, current_confidence + confidence_improvement)
reasoning_steps.append(
f"Iteration {step_num}: {refinement_result['refinement_type']} "
f"(confidence: {current_confidence:.1%})"
)
# Check if confidence is high enough to stop
if current_confidence > 0.85:
reasoning_steps.append(f"Early termination: High confidence ({current_confidence:.1%}) achieved")
break
# Final reasoning step
reasoning_steps.append(f"Final: Response refined through {len(reasoning_steps)} iterations")
return {
"response": current_response,
"confidence": round(current_confidence, 3),
"steps": reasoning_steps,
"iterations_used": min(iteration + 1, self.max_iterations),
"refinement_history": reasoning_steps,
}
async def _refine_response(self, query: str, current_response: str, iteration: int) -> dict[str, Any]:
"""Refine the current response."""
await asyncio.sleep(0.05) # Simulate refinement processing
# Different refinement strategies based on iteration
refinement_strategies = [
("Clarity enhancement", "improve clarity and precision"),
("Detail expansion", "add technical depth and specifics"),
("Validation check", "verify accuracy and completeness"),
]
strategy_name, strategy_action = refinement_strategies[iteration % len(refinement_strategies)]
# Generate refined response
refinement_prompt = f"""
Original query: {query}
Current response: {current_response}
Refinement task: {strategy_action}
"""
result = await self.llm_client.generate(
prompt=refinement_prompt, context=f"Refinement iteration {iteration + 1}"
)
# Enhance the response based on strategy
enhanced_response = current_response
if strategy_name == "Clarity enhancement":
enhanced_response = f"{current_response}. {result['response']}"
elif strategy_name == "Detail expansion":
enhanced_response = f"{current_response}. Furthermore, {result['response']}"
else: # Validation
enhanced_response = f"{current_response}. Validated: {result['response']}"
# Truncate if too long
if len(enhanced_response) > 300:
enhanced_response = enhanced_response[:297] + "..."
return {"response": enhanced_response, "refinement_type": strategy_name, "strategy_action": strategy_action}
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