Ymn dev

nodes.py

@@ -3,7 +3,7 @@
 import yaml
 from pocketflow import Node, BatchNode
 from utils.crawl_github_files import crawl_github_files
-from utils.call_llm import call_llm
+from utils.call_llm_openai import call_llm
 from utils.crawl_local_files import crawl_local_files
 
 

utils/call_llm.py → utils/call_llm_gemini.py

@@ -233,3 +233,4 @@ def call_llm(prompt: str, use_cache: bool = True) -> str:
     print("Making call...")
     response1 = call_llm(test_prompt, use_cache=False)
     print(f"Response: {response1}")
+

utils/call_llm_openai.py

@@ -0,0 +1,96 @@
+import os
+import logging
+import json
+from datetime import datetime
+from openai import OpenAI
+
+# Configure logging
+def setup_logger():
+    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"
+    )
+
+    logger = logging.getLogger("llm_logger")
+    logger.setLevel(logging.INFO)
+    logger.propagate = False  # Don't propagate to root logger
+
+    file_handler = logging.FileHandler(log_file, encoding="utf-8")
+    file_handler.setFormatter(
+        logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
+    )
+    logger.addHandler(file_handler)
+    return logger
+
+logger = setup_logger()
+cache_file = os.getenv("LLM_CACHE_FILE", "llm_cache.json")
+
+# Wrapper for calling OpenAI o-series models with optional caching
+def call_llm(prompt: str, use_cache: bool = True) -> str:
+    """
+    Send a prompt to the OpenAI thinking model (default: o4-mini) with chain-of-thought enabled.
+    Caches responses in llm_cache.json by default.
+
+    Args:
+        prompt (str): The input prompt for the LLM.
+        use_cache (bool): Whether to read/write from cache.
+
+    Returns:
+        str: The model's response text.
+    """
+    logger.info(f"PROMPT: {prompt}")
+
+    # Load or check cache
+    if use_cache and os.path.exists(cache_file):
+        try:
+            with open(cache_file, "r", encoding="utf-8") as f:
+                cache = json.load(f)
+            if prompt in cache:
+                logger.info("CACHE HIT")
+                logger.info(f"RESPONSE: {cache[prompt]}")
+                return cache[prompt]
+        except Exception:
+            logger.warning("Failed to load cache; continuing without cache")
+
+    # Initialize OpenAI client
+    client = OpenAI(api_key=os.getenv("OPENAI_API_KEY", ""))
+    model_name = os.getenv("OPENAI_MODEL", "o4-mini")
+
+    # Send chat completion request
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=[{"role": "user", "content": prompt}],
+        max_completion_tokens=4000,
+        reasoning_effort="medium",
+        store=False
+    )
+    response_text = response.choices[0].message.content
+
+    logger.info(f"RESPONSE: {response_text}")
+
+    # Write to cache
+    if use_cache:
+        try:
+            cache = {}
+            if os.path.exists(cache_file):
+                with open(cache_file, "r", encoding="utf-8") as f:
+                    cache = json.load(f)
+        except Exception:
+            cache = {}
+
+        cache[prompt] = response_text
+        try:
+            with open(cache_file, "w", encoding="utf-8") as f:
+                json.dump(cache, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            logger.error(f"Failed to save cache: {e}")
+
+    return response_text
+
+if __name__ == "__main__":
+    test_prompt = "Hello, how are you?"
+    print("Making call...")
+    response = call_llm(test_prompt, use_cache=False)
+    print(f"Response: {response}")