Update Render build configuration: fix deps, force py3.11, add build script

backend/services/intelligence/agents/specialized/base.py

@@ -0,0 +1,78 @@
+"""
+Base class for SIF specialized agents.
+"""
+import traceback
+import json
+import asyncio
+import re
+from collections import Counter
+from typing import List, Dict, Any, Optional
+from datetime import datetime
+from loguru import logger
+from services.intelligence.txtai_service import TxtaiIntelligenceService
+from services.intelligence.agents.core_agent_framework import BaseALwrityAgent, AgentAction, TaskProposal
+from services.intelligence.sif_agents import SharedLLMWrapper, LocalLLMWrapper
+
+try:
+    # Try importing from pipeline first (standard location)
+    from txtai.pipeline import Agent, LLM
+    TXTAI_AVAILABLE = True
+except ImportError:
+    try:
+        # Fallback to top-level import
+        from txtai import Agent, LLM
+        TXTAI_AVAILABLE = True
+    except ImportError:
+        TXTAI_AVAILABLE = False
+        Agent = None
+        LLM = None
+        logger.warning("txtai not available, using fallback implementation")
+
+class SIFBaseAgent(BaseALwrityAgent):
+    def __init__(self, intelligence_service: TxtaiIntelligenceService, user_id: str, agent_type: str = "sif_agent", model_name: str = "Qwen/Qwen2.5-3B-Instruct", llm: Any = None, **kwargs):
+        # Hybrid LLM Strategy:
+        # 1. Shared LLM for external/high-quality generation
+        self.shared_llm = SharedLLMWrapper(user_id)
+        
+        # 2. Local LLM for internal agent work (default for SIF agents)
+        if llm is None:
+            if not TXTAI_AVAILABLE:
+                raise RuntimeError("txtai is required for SIF specialized agents but is not available")
+            
+            # Explicitly force task='language-generation' (txtai internal name) which maps to 'text-generation'
+            # Using 'text-generation' directly fails because txtai mapping.get() defaults to 'text2text-generation'
+            task_to_use = "language-generation"
+            if any(x in model_name for x in ["Qwen", "Instruct", "GPT", "Llama"]):
+                task_to_use = "language-generation"
+            
+            logger.info(f"[{self.__class__.__name__}] Initializing LocalLLMWrapper with model={model_name}, task={task_to_use}")
+            llm = LocalLLMWrapper(model_name, task=task_to_use)
+                
+        self.intelligence = intelligence_service
+        super().__init__(user_id, agent_type, model_name, llm, **kwargs)
+        
+    def _log_agent_operation(self, operation: str, **kwargs):
+        """Standardized logging for agent operations."""
+        logger.info(f"[{self.__class__.__name__}] {operation}")
+        if kwargs:
+            logger.debug(f"[{self.__class__.__name__}] Parameters: {kwargs}")
+
+    def _create_txtai_agent(self):
+        """
+        SIF agents use the intelligence service directly, but we can expose
+        capabilities via a standard agent interface if needed.
+        """
+        if not TXTAI_AVAILABLE or Agent is None:
+            logger.warning(f"[{self.__class__.__name__}] txtai Agent not available (TXTAI_AVAILABLE={TXTAI_AVAILABLE}, Agent={Agent})")
+            raise RuntimeError(f"[{self.__class__.__name__}] txtai Agent not available")
+        
+        # Return a simple agent that can use the LLM
+        try:
+            _llm_for_agent = self.llm
+            for _ in range(3):
+                _llm_for_agent = getattr(_llm_for_agent, "llm", _llm_for_agent)
+            return Agent(llm=_llm_for_agent, tools=[])
+        except Exception as e:
+            logger.error(f"Failed to create txtai Agent for {self.__class__.__name__}: {e}")
+            # Fail fast: Re-raise the exception instead of returning None
+            raise e