src/pipeline.py

# """
# Complete Multi-Stage MCP Pipeline
# Orchestrates: Router → Executor → Compiler → Translator
# """

# import time
# from typing import Dict, Any
# from openai import OpenAI

# from .router import QueryRouter
# from .executor import MCPExecutor, MCP_SERVER_REGISTRY
# from .compiler import ResponseCompiler
# from .translator import FarmerTranslator


# class FarmerChatPipeline:
#     """Complete multi-stage MCP pipeline"""

#     def __init__(self, openai_client: OpenAI, location: Dict[str, Any]):
#         self.location = location
#         self.router = QueryRouter(openai_client, MCP_SERVER_REGISTRY)
#         self.executor = MCPExecutor()
#         self.compiler = ResponseCompiler()
#         self.translator = FarmerTranslator(openai_client)

#     async def process_query(self, query: str, verbose: bool = False) -> Dict[str, Any]:
#         """
#         Process farmer query through complete pipeline
        
#         Returns:
#             {
#                 "query": str,
#                 "routing": dict,
#                 "compiled_data": dict,
#                 "advice": str,
#                 "pipeline_time_seconds": float
#             }
#         """
#         if verbose:
#             print(f"\n🌾 Processing: {query}")
#             print(f"📍 Location: {self.location['name']}")

#         pipeline_start = time.time()

#         # STAGE 1: Query Routing
#         if verbose:
#             print("🎯 Stage 1: Routing...")
        
#         routing = self.router.route(query, self.location)
        
#         if verbose:
#             print(f"   → Servers: {', '.join(routing['required_servers'])}")

#         # STAGE 2: MCP Execution (Parallel)
#         if verbose:
#             print("⚙️ Stage 2: Executing MCP servers...")
        
#         raw_results = await self.executor.execute_parallel(
#             routing['required_servers'],
#             self.location['lat'],
#             self.location['lon']
#         )
        
#         if verbose:
#             print(f"   → Completed in {raw_results['execution_time_seconds']}s")

#         # STAGE 3: Response Compilation
#         if verbose:
#             print("🔗 Stage 3: Compiling results...")
        
#         compiled = self.compiler.compile(raw_results)
        
#         if verbose:
#             print(f"   → {compiled['completeness']}")

#         # STAGE 4: Farmer Translation
#         if verbose:
#             print("🌾 Stage 4: Generating advice...")
        
#         farmer_advice = self.translator.translate(query, compiled, self.location)

#         pipeline_time = time.time() - pipeline_start

#         if verbose:
#             print(f"✅ Complete! Total: {pipeline_time:.2f}s\n")

#         return {
#             "query": query,
#             "routing": routing,
#             "compiled_data": compiled,
#             "advice": farmer_advice,
#             "pipeline_time_seconds": round(pipeline_time, 2)
#         }

"""
Farmer.chat Pipeline - Main Orchestrator
Coordinates Router → Executor → Compiler stages for alert generation
"""

from typing import Dict, Any, Optional
from .router import QueryRouter
from .executor import MCPExecutor
from .compiler import ResponseCompiler


class FarmerChatPipeline:
    """
    Main pipeline orchestrating the complete alert generation workflow.
    
    Architecture:
    Stage 1: QueryRouter - Determines which MCP servers to query with priorities
    Stage 2: MCPExecutor - Executes parallel calls to selected MCP servers
    Stage 3: ResponseCompiler - Compiles results into actionable alert summary
    """
    
    def __init__(self, servers: Dict[str, Any], location: Dict[str, float]):
        """
        Initialize pipeline with MCP servers and default location.
        
        Args:
            servers: Dict mapping server names to initialized server instances
            location: Default location dict with 'latitude' and 'longitude' keys
        """
        self.servers = servers
        self.location = location
        
        # Initialize pipeline stages
        self.router = QueryRouter()
        self.executor = MCPExecutor(servers)
        self.compiler = ResponseCompiler()
        
        print(f"✓ Pipeline initialized for location: {location['latitude']:.4f}°N, {location['longitude']:.4f}°E")
    
    def generate_alert(
        self, 
        location: Optional[Dict[str, float]] = None,
        location_name: str = ""
    ) -> Dict[str, Any]:
        """
        Generate comprehensive alert summary for a location.
        
        Always queries ALL MCP servers. The compiler extracts only
        the alerting/concerning information from the comprehensive data.
        
        Args:
            location: Optional location override. Uses default if not provided.
            location_name: Human-readable location name for context
            
        Returns:
            Dict containing:
                - alert_summary: Compiled alert text (only concerning info)
                - location: Location coordinates used
                - mcp_results: Raw results from each MCP server
        """
        
        # Use provided location or default
        query_location = location or self.location
        
        print(f"\n{'='*60}")
        print(f"Generating Alert Summary for {location_name or 'Location'}")
        print(f"Coordinates: {query_location['latitude']:.4f}°N, {query_location['longitude']:.4f}°E")
        print(f"{'='*60}\n")
        
        # Stage 1: Route - Always query all servers for alerts
        print("Stage 1: Routing to all MCP servers...")
        routing = self.router.route_alert_query(query_location)
        print(f"✓ Routing complete: All 5 servers will be queried")
        
        # Stage 2: Execute - Query all MCP servers in parallel
        print("\nStage 2: Executing parallel MCP server calls...")
        mcp_results = self.executor.execute_parallel(routing, query_location)
        
        success_count = sum(1 for r in mcp_results.values() if r.get("status") == "success")
        print(f"✓ Execution complete: {success_count}/{len(mcp_results)} servers responded successfully")
        
        # Stage 3: Compile - Extract ONLY alerting information
        print("\nStage 3: Compiling alert summary (extracting concerning info only)...")
        alert_summary = self.compiler.compile_alert_summary(
            mcp_results, 
            query_location,
            location_name
        )
        print("✓ Alert summary generated (focusing on alerts/concerns only)")
        
        print(f"\n{'='*60}\n")
        
        return {
            "alert_summary": alert_summary,
            "location": query_location,
            "location_name": location_name,
            "mcp_results": mcp_results
        }
    
    def process_query(
        self, 
        query: str, 
        location: Optional[Dict[str, float]] = None
    ) -> Dict[str, Any]:
        """
        Process a specific farmer query through the pipeline.
        
        For now, also queries all servers to ensure comprehensive data.
        The compiler extracts query-relevant information.
        
        Args:
            query: Farmer's question or request
            location: Optional location override
            
        Returns:
            Dict containing:
                - response: Compiled response text
                - location: Location coordinates used
                - mcp_results: Raw results from MCP servers
        """
        
        # Use provided location or default
        query_location = location or self.location
        
        print(f"\n{'='*60}")
        print(f"Processing Query: {query}")
        print(f"Location: {query_location['latitude']:.4f}°N, {query_location['longitude']:.4f}°E")
        print(f"{'='*60}\n")
        
        # Stage 1: Route - For now, query all servers
        print("Stage 1: Routing to all MCP servers...")
        routing = self.router.route_query(query, query_location)
        print(f"✓ Routing complete: All servers will be queried")
        
        # Stage 2: Execute MCP calls
        print("\nStage 2: Executing MCP server calls...")
        mcp_results = self.executor.execute_parallel(routing, query_location)
        
        success_count = sum(1 for r in mcp_results.values() if r.get("status") == "success")
        print(f"✓ Execution complete: {success_count}/{len(mcp_results)} servers responded")
        
        # Stage 3: Compile response
        print("\nStage 3: Compiling response...")
        response = self.compiler.compile_response(query, mcp_results, query_location)
        print("✓ Response compiled")
        
        print(f"\n{'='*60}\n")
        
        return {
            "response": response,
            "location": query_location,
            "mcp_results": mcp_results
        }
    
    def update_location(self, location: Dict[str, float]):
        """
        Update default location for pipeline.
        
        Args:
            location: New default location dict with 'latitude' and 'longitude' keys
        """
        self.location = location
        print(f"✓ Default location updated to: {location['latitude']:.4f}°N, {location['longitude']:.4f}°E")
    
    def get_server_status(self) -> Dict[str, str]:
        """
        Get status of all MCP servers.
        
        Returns:
            Dict mapping server names to "available" or "unavailable"
        """
        status = {}
        for server_name, server in self.servers.items():
            try:
                # Try to check if server is responsive
                if hasattr(server, 'health_check'):
                    status[server_name] = "available" if server.health_check() else "unavailable"
                else:
                    status[server_name] = "available"  # Assume available if no health check
            except:
                status[server_name] = "unavailable"
        
        return status