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import time
import logging
from typing import List, Dict, Optional, Union, Tuple
from src.llm.base_provider import LLMProvider
from src.llm.hf_provider import HuggingFaceProvider
from src.llm.ollama_provider import OllamaProvider
from core.session import session_manager
from utils.config import config
logger = logging.getLogger(__name__)
class MentorProvider(LLMProvider):
"""Mentor provider that uses HF as expert and Ollama as mentor/coach"""
def __init__(self, model_name: str, timeout: int = 120, max_retries: int = 2):
super().__init__(model_name, timeout, max_retries)
self.hf_provider = None
self.ollama_provider = None
self.conversation_analyzer = ConversationAnalyzer()
# Initialize providers
try:
if config.hf_token:
self.hf_provider = HuggingFaceProvider(
model_name="DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf",
timeout=120
)
except Exception as e:
logger.warning(f"Failed to initialize HF provider: {e}")
try:
if config.ollama_host:
self.ollama_provider = OllamaProvider(
model_name=config.local_model_name,
timeout=60
)
except Exception as e:
logger.warning(f"Failed to initialize Ollama provider: {e}")
def generate(self, prompt: str, conversation_history: List[Dict]) -> Optional[str]:
"""Generate response using mentor approach"""
try:
# Step 1: Get expert response from HF Endpoint
hf_response = self._get_expert_response(prompt, conversation_history)
if not hf_response:
raise Exception("HF Endpoint expert failed to provide response")
# Step 2: Get mentor analysis from Ollama
mentor_insights = self._get_mentor_analysis(
prompt,
hf_response,
conversation_history
)
# Step 3: Combine expert response with mentor insights
combined_response = self._combine_responses(hf_response, mentor_insights)
# Step 4: Store interaction for learning
self._store_interaction(prompt, hf_response, mentor_insights)
return combined_response
except Exception as e:
logger.error(f"Mentor generation failed: {e}")
# Fallback to HF only
if self.hf_provider:
try:
logger.info("Falling back to HF Endpoint only")
return self.hf_provider.generate(prompt, conversation_history)
except Exception as fallback_error:
logger.error(f"HF fallback also failed: {fallback_error}")
raise Exception(f"All providers failed: {str(e)}")
def stream_generate(self, prompt: str, conversation_history: List[Dict]) -> Optional[Union[str, List[str]]]:
"""Stream response using mentor approach"""
try:
# For streaming, we'll stream HF response and add mentor insights at the end
if self.hf_provider:
hf_stream = self.hf_provider.stream_generate(prompt, conversation_history)
return hf_stream
else:
raise Exception("No HF provider available for streaming")
except Exception as e:
logger.error(f"Mentor stream generation failed: {e}")
raise
def _get_expert_response(self, prompt: str, conversation_history: List[Dict]) -> Optional[str]:
"""Get expert response from HF Endpoint"""
if not self.hf_provider:
return None
try:
logger.info("🤖 Getting expert response from HF Endpoint...")
response = self.hf_provider.generate(prompt, conversation_history)
logger.info("✅ HF Endpoint expert response received")
return response
except Exception as e:
logger.error(f"HF Endpoint expert failed: {e}")
return None
def _get_mentor_analysis(self, user_prompt: str, hf_response: str, conversation_history: List[Dict]) -> Dict:
"""Get mentor analysis and suggestions from Ollama"""
if not self.ollama_provider:
return {}
try:
logger.info("🐱 Getting mentor analysis from Ollama...")
# Create mentor prompt for analysis
mentor_prompt = self._create_mentor_prompt(user_prompt, hf_response, conversation_history)
# Get mentor insights
mentor_response = self.ollama_provider.generate(mentor_prompt, [])
# Parse mentor response into structured insights
insights = self.conversation_analyzer.parse_mentor_response(mentor_response)
logger.info("✅ Ollama mentor analysis completed")
return insights
except Exception as e:
logger.warning(f"Ollama mentor analysis failed: {e}")
return {}
def _create_mentor_prompt(self, user_prompt: str, hf_response: str, conversation_history: List[Dict]) -> str:
"""Create prompt for Ollama mentor to analyze interaction"""
conversation_context = "\n".join([
f"{msg['role']}: {msg['content']}"
for msg in conversation_history[-5:] # Last 5 messages for context
])
prompt = f"""
You are an AI mentor and conversation analyst. Your job is to analyze the interaction between a user and an expert AI, then provide insightful guidance.
ANALYZE THIS INTERACTION:
User Question: "{user_prompt}"
Expert Response: "{hf_response}"
Recent Conversation Context:
{conversation_context}
PROVIDE YOUR ANALYSIS IN THIS FORMAT:
<thinking_analysis>
Analyze the expert's reasoning approach, depth of analysis, and problem-solving methodology.
</thinking_analysis>
<goal_progress>
Assess how well this response advances toward the user's likely goals based on conversation history.
</goal_progress>
<follow_up_suggestions>
Provide 2-3 thoughtful follow-up questions or research directions that would deepen understanding.
</follow_up_suggestions>
<data_gathering>
Suggest what additional information or data would be valuable to collect.
</data_gathering>
<critical_insights>
Highlight any key insights, potential blind spots, or areas needing further exploration.
</critical_insights>
Keep your analysis concise but insightful. Focus on helping the user achieve their goals through better questioning and information gathering.
"""
return prompt
def _combine_responses(self, hf_response: str, mentor_insights: Dict) -> str:
"""Combine expert response with mentor insights"""
if not mentor_insights:
return hf_response
# Format mentor insights nicely
insights_section = "\n\n--- 🎓 Mentor Insights ---\n"
if mentor_insights.get('thinking_analysis'):
insights_section += f"\n🧠 **Thinking Analysis**\n{mentor_insights['thinking_analysis']}"
if mentor_insights.get('goal_progress'):
insights_section += f"\n\n🎯 **Goal Progress**\n{mentor_insights['goal_progress']}"
if mentor_insights.get('follow_up_suggestions'):
insights_section += f"\n\n🤔 **Follow-up Suggestions**\n{mentor_insights['follow_up_suggestions']}"
if mentor_insights.get('data_gathering'):
insights_section += f"\n\n📋 **Data to Gather**\n{mentor_insights['data_gathering']}"
if mentor_insights.get('critical_insights'):
insights_section += f"\n\n💡 **Critical Insights**\n{mentor_insights['critical_insights']}"
return f"{hf_response}{insights_section}"
def _store_interaction(self, user_prompt: str, hf_response: str, mentor_insights: Dict):
"""Store interaction for learning and pattern recognition"""
try:
user_session = session_manager.get_session("default_user")
interaction_log = user_session.get("interaction_log", [])
# Create interaction record
interaction = {
"timestamp": time.time(),
"user_prompt": user_prompt,
"expert_response": hf_response,
"mentor_insights": mentor_insights,
"conversation_length": len(interaction_log)
}
# Keep last 20 interactions
interaction_log.append(interaction)
if len(interaction_log) > 20:
interaction_log = interaction_log[-20:]
user_session["interaction_log"] = interaction_log
session_manager.update_session("default_user", user_session)
except Exception as e:
logger.warning(f"Failed to store interaction: {e}")
class ConversationAnalyzer:
"""Analyzes conversation patterns and provides insights"""
def parse_mentor_response(self, mentor_response: str) -> Dict:
"""Parse mentor response into structured insights"""
if not mentor_response:
return {}
insights = {}
# Extract sections using simple parsing
sections = {
'thinking_analysis': self._extract_section(mentor_response, 'thinking_analysis'),
'goal_progress': self._extract_section(mentor_response, 'goal_progress'),
'follow_up_suggestions': self._extract_section(mentor_response, 'follow_up_suggestions'),
'data_gathering': self._extract_section(mentor_response, 'data_gathering'),
'critical_insights': self._extract_section(mentor_response, 'critical_insights')
}
# Clean up sections
for key, value in sections.items():
if value:
# Remove markdown and clean text
cleaned = value.strip()
if cleaned:
insights[key] = cleaned
return insights
def _extract_section(self, text: str, section_name: str) -> Optional[str]:
"""Extract specific section from mentor response"""
start_tag = f"<{section_name}>"
end_tag = f"</{section_name}>"
start_idx = text.find(start_tag)
if start_idx == -1:
return None
start_idx += len(start_tag)
end_idx = text.find(end_tag, start_idx)
if end_idx == -1:
return None
return text[start_idx:end_idx].strip()
# Global instance
mentor_provider = MentorProvider("mentor_model")
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