update readme examples

utils/call_llm.py

@@ -1,14 +1,87 @@
-from openai import OpenAI
+from google import genai
+import os
+import logging
+import json
+from datetime import datetime
 
-# Learn more about calling the LLM: https://the-pocket.github.io/PocketFlow/utility_function/llm.html
-def call_llm(prompt):    
-    client = OpenAI(api_key="YOUR_API_KEY_HERE")
-    r = client.chat.completions.create(
-        model="gpt-4o",
-        messages=[{"role": "user", "content": prompt}]
-    )
-    return r.choices[0].message.content
+# Configure logging
+log_directory = os.getenv("LOG_DIR", "logs")
+os.makedirs(log_directory, exist_ok=True)
+log_file = os.path.join(log_directory, f"llm_calls_{datetime.now().strftime('%Y%m%d')}.log")
+
+# Set up logger
+logger = logging.getLogger("llm_logger")
+logger.setLevel(logging.INFO)
+logger.propagate = False  # Prevent propagation to root logger
+file_handler = logging.FileHandler(log_file)
+file_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s'))
+logger.addHandler(file_handler)
+
+# Simple cache configuration
+cache_file = "llm_cache.json"
+
+def call_llm(prompt: str, use_cache: bool = True) -> str:
+    # Log the prompt
+    logger.info(f"PROMPT: {prompt}")
     
+    # Check cache if enabled
+    if use_cache:
+        # Load cache from disk
+        cache = {}
+        if os.path.exists(cache_file):
+            try:
+                with open(cache_file, 'r') as f:
+                    cache = json.load(f)
+            except:
+                logger.warning(f"Failed to load cache, starting with empty cache")
+        
+        # Return from cache if exists
+        if prompt in cache:
+            logger.info(f"RESPONSE: {cache[prompt]}")
+            return cache[prompt]
+    
+    # Call the LLM if not in cache or cache disabled
+    client = genai.Client(
+        vertexai=True, 
+        project=os.getenv("GEMINI_PROJECT_ID", "your-project-id"),
+        location=os.getenv("GEMINI_LOCATION", "us-central1")
+    )
+    model = os.getenv("GEMINI_MODEL", "gemini-2.5-pro-exp-03-25")
+    response = client.models.generate_content(
+        model=model,
+        contents=[prompt]
+    )
+    response_text = response.text
+    
+    # Log the response
+    logger.info(f"RESPONSE: {response_text}")
+    
+    # Update cache if enabled
+    if use_cache:
+        # Load cache again to avoid overwrites
+        cache = {}
+        if os.path.exists(cache_file):
+            try:
+                with open(cache_file, 'r') as f:
+                    cache = json.load(f)
+            except:
+                pass
+        
+        # Add to cache and save
+        cache[prompt] = response_text
+        try:
+            with open(cache_file, 'w') as f:
+                json.dump(cache, f)
+        except Exception as e:
+            logger.error(f"Failed to save cache: {e}")
+    
+    return response_text
+
 if __name__ == "__main__":
-    prompt = "What is the meaning of life?"
-    print(call_llm(prompt))
+    test_prompt = "Hello, how are you?"
+    
+    # First call - should hit the API
+    print("Making call...")
+    response1 = call_llm(test_prompt, use_cache=False)
+    print(f"Response: {response1}")
+