langwatch

import os import langwatch from langwatch.instrumentors import OpenAIInstrumentor from openai import OpenAI # Ensure your API key is set as an environment variable or pass it directly # os.environ["LANGWATCH_API_KEY"] = "YOUR_LANGWATCH_API_KEY" api_key = os.environ.get("LANGWATCH_API_KEY", "") if not api_key: print("Warning: LANGWATCH_API_KEY environment variable not set. Traces will not be sent.") # For demonstration, we'll proceed but you should set your key. # In a real application, you might raise an error or configure LangWatch to only log locally. # Initialize LangWatch early in your application # Automatically instruments OpenAI calls within the decorated functions langwatch.setup( api_key=api_key, instrumentors=[OpenAIInstrumentor()] ) client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "")) # Assume OpenAI API key is also set @langwatch.trace(name="UserInteraction") async def handle_user_query(query: str): print(f"Processing query: {query}") try: response = client.chat.completions.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": query} ] ) result = response.choices[0].message.content print(f"Assistant's response: {result}") return result except Exception as e: langwatch.get_current_trace().error(str(e)) # Record error if something goes wrong print(f"An error occurred: {e}") raise async def main(): await handle_user_query("Tell me a fun fact about Python.") # Ensure traces are flushed before the application exits await langwatch.shutdown() import asyncio asyncio.run(main())