resolve conflict: take incoming notebook

config.py

@@ -3,10 +3,15 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-# ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
-# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
-# DEFAULT_MODEL = os.getenv("DEFAULT_MODEL", "claude-sonnet-4-20250514")
-# LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
-
 GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
-DEFAULT_MODEL = "gemini-2.5-flash"
+DEFAULT_MODEL  = "gemini-2.5-flash"
+
+# Redis
+REDIS_HOST       = os.getenv("REDIS_HOST", "localhost")
+REDIS_PORT       = int(os.getenv("REDIS_PORT", "6379"))
+REDIS_DB         = int(os.getenv("REDIS_DB", "0"))
+REDIS_PASSWORD   = os.getenv("REDIS_PASSWORD", None)
+REDIS_KEY_PREFIX = os.getenv("REDIS_KEY_PREFIX", "ai_agent")
+
+# Logging
+LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")

tools/base.py

@@ -4,24 +4,12 @@ Base components for the tools package: LLM dispatcher and central registry.
 
 import logging
 import json
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 from langchain_core.tools import Tool, StructuredTool
-from langchain_openai import ChatOpenAI
-from langchain_huggingface import HuggingFacePipeline
 from langchain_google_genai import ChatGoogleGenerativeAI
 try:
-    from ..config import (
-        QWEN_API_KEY, QWEN_BASE_URL, QWEN_MODEL, 
-        LOG_LEVEL, USE_LOCAL_LLM, LOCAL_MODEL_ID, 
-        GEMINI_API_KEY, OPENROUTER_API_KEY, OPENROUTER_BASE_URL, OPENROUTER_MODEL
-    )
+    from ..config import LOG_LEVEL, GEMINI_API_KEY
 except (ImportError, ValueError):
-    from config import (
-        QWEN_API_KEY, QWEN_BASE_URL, QWEN_MODEL, 
-        LOG_LEVEL, USE_LOCAL_LLM, LOCAL_MODEL_ID, 
-        GEMINI_API_KEY, OPENROUTER_API_KEY, OPENROUTER_BASE_URL, OPENROUTER_MODEL
-    )
+    from config import LOG_LEVEL, GEMINI_API_KEY
 
 logger = logging.getLogger(__name__)
 
@@ -43,63 +31,14 @@ def register_tool(name: str, description: str, parameters: list):
     return decorator
 
 def get_llm():
-    """
-    Initialize and return the LLM based on configuration (Gemini > Local > Cloud Qwen).
-    """
-    # 1. Prioritize OpenRouter
-    if OPENROUTER_API_KEY and not OPENROUTER_API_KEY.startswith("your-"):
-        logger.info(f"Initializing OpenRouter LLM ({OPENROUTER_MODEL})...")
-        return ChatOpenAI(
-            model=OPENROUTER_MODEL,
-            api_key=OPENROUTER_API_KEY,
-            base_url=OPENROUTER_BASE_URL,
-            temperature=0.1,
-            max_tokens=4096,
-        )
-
-    # 2. Fallback to Gemini
-    if GEMINI_API_KEY and not GEMINI_API_KEY.startswith("your-"):
-        logger.info("Initializing Google Gemini LLM...")
-        return ChatGoogleGenerativeAI(
-            model="gemini-2.0-flash", 
-            google_api_key=GEMINI_API_KEY,
-            temperature=0.1,
-        )
-
-    # 2. Local LLM
-    if USE_LOCAL_LLM:
-        logger.info(f"Loading Local LLM: {LOCAL_MODEL_ID}...")
-        try:
-            tokenizer = AutoTokenizer.from_pretrained(LOCAL_MODEL_ID)
-            model = AutoModelForCausalLM.from_pretrained(
-                LOCAL_MODEL_ID,
-                torch_dtype="auto",
-                device_map="auto"
-            )
-            
-            pipe = pipeline(
-                "text-generation",
-                model=model,
-                tokenizer=tokenizer,
-                max_new_tokens=512, # Reduce for speed
-                temperature=0.1,
-                do_sample=True,
-                repetition_penalty=1.1,
-                return_full_text=False, # Important: only return new tokens
-            )
-            
-            return HuggingFacePipeline(pipeline=pipe)
-        except Exception as e:
-            logger.error(f"Failed to load local model: {e}. Falling back to Cloud Qwen...")
-
-    if not QWEN_API_KEY:
-        raise ValueError("QWEN_API_KEY is not configured. Check your .env file.")
-        
-    return ChatOpenAI(
-        model=QWEN_MODEL or "qwen-plus",
+    """Initialize and return Gemini LLM."""
+    if not GEMINI_API_KEY:
+        raise ValueError("GEMINI_API_KEY is not configured. Check your .env file.")
+    logger.info("Initializing Google Gemini LLM...")
+    return ChatGoogleGenerativeAI(
+        model="gemini-2.5-flash",
+        google_api_key=GEMINI_API_KEY,
         temperature=0.1,
-        api_key=QWEN_API_KEY,
-        base_url=QWEN_BASE_URL,
     )
 
 def get_tool_schemas() -> list[dict]: