bhoomika19's picture
added frontend and gemini fallback
1af7ffc
"""
MCP (Model Context Protocol) service for web search fallback.
"""
import asyncio
import structlog
from typing import Dict, Any, Optional
import json
logger = structlog.get_logger()
class MCPService:
"""Service for MCP web search integration."""
def __init__(self):
"""Initialize MCP service."""
self.mcp_server_path = "pranavms13/web-search-mcp"
self.initialized = False
logger.info("MCP service initialized")
async def search_web(self, question: str) -> Dict[str, Any]:
"""
Search the web for math-related information using MCP.
Args:
question: The math question to search for
Returns:
Dictionary containing web search results
"""
try:
logger.info("Starting web search via MCP", question_length=len(question))
# TODO: Implement actual MCP integration
# For now, return a placeholder response
# Simulate web search delay
await asyncio.sleep(0.5)
# Mock response based on question type with realistic confidence scoring
confidence_score = 0.6 # Default confidence
if any(keyword in question.lower() for keyword in ['derivative', 'integral', 'calculus']):
answer = f"Based on web search: This appears to be a calculus problem. {question} involves applying standard calculus techniques. Consider using the fundamental theorem of calculus or integration by parts."
confidence_score = 0.75 # Higher confidence for calculus
elif any(keyword in question.lower() for keyword in ['algebra', 'equation', 'solve']):
answer = f"Based on web search: This is an algebraic problem. {question} can be solved using algebraic manipulation and equation solving techniques."
confidence_score = 0.7 # Good confidence for algebra
elif any(keyword in question.lower() for keyword in ['geometry', 'triangle', 'circle']):
answer = f"Based on web search: This is a geometry problem. {question} involves geometric principles and may require knowledge of shapes, areas, or angles."
confidence_score = 0.65 # Moderate confidence for geometry
elif any(keyword in question.lower() for keyword in ['statistics', 'probability', 'mean', 'standard deviation']):
answer = f"Based on web search: This is a statistics/probability problem. {question} requires understanding of statistical concepts and may involve data analysis."
confidence_score = 0.72 # Good confidence for stats
else:
answer = f"Based on web search: {question} is a mathematical problem that may require breaking down into smaller steps and applying relevant mathematical concepts."
confidence_score = 0.55 # Lower confidence for unknown types
# Adjust confidence based on question length and complexity
if len(question) > 100:
confidence_score += 0.05 # Slightly higher for detailed questions
if '=' in question and any(op in question for op in ['+', '-', '*', '/', '^']):
confidence_score += 0.1 # Higher for equations with operators
# Cap confidence to ensure it's below KB threshold for testing fallback
confidence_score = min(confidence_score, 0.79) # Always below 0.8 threshold
result = {
"answer": answer,
"source": "web_search",
"confidence": confidence_score,
"search_query": question,
"results_count": 1
}
logger.info("Web search completed via MCP",
answer_length=len(answer),
confidence=result["confidence"])
return result
except Exception as e:
logger.error("Web search via MCP failed", error=str(e))
raise Exception(f"MCP web search failed: {str(e)}")
async def initialize_mcp_connection(self):
"""Initialize connection to MCP server."""
try:
# TODO: Implement actual MCP server connection
# This would involve:
# 1. Spawning the MCP server process
# 2. Establishing JSON-RPC communication
# 3. Calling available tools like web_search
self.initialized = True
logger.info("MCP connection initialized successfully")
except Exception as e:
logger.error("Failed to initialize MCP connection", error=str(e))
raise
def is_available(self) -> bool:
"""Check if MCP service is available."""
return self.initialized