79 lines
2.9 KiB
Python
79 lines
2.9 KiB
Python
# OpenAI Sync Example
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#
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# We are going to create a simple chatbot that creates stories based on a prompt. The chatbot will use the gpt-4o-mini LLM to generate the story using a user prompt.
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#
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# We will track the chatbot with AgentOps and see how it performs!
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# First let's install the required packages
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# # Install required dependencies
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# %pip install agentops
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# %pip install openai
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# %pip install python-dotenv
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# Then import them
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from openai import OpenAI
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import agentops
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import os
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from dotenv import load_dotenv
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# Next, we'll grab our API keys. You can use dotenv like below or however else you like to load environment variables
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load_dotenv()
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os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "your_openai_api_key_here")
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os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_api_key_here")
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# Next we initialize the AgentOps client.
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agentops.init(auto_start_session=True, trace_name="OpenAI Sync Example", tags=["openai", "sync", "agentops-example"])
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tracer = agentops.start_trace(
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trace_name="OpenAI Sync Example", tags=["openai-sync-example", "openai", "agentops-example"]
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)
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client = OpenAI()
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# And we are all set! Note the seesion url above. We will use it to track the chatbot.
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#
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# Let's create a simple chatbot that generates stories.
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system_prompt = """
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You are a master storyteller, with the ability to create vivid and engaging stories.
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You have experience in writing for children and adults alike.
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You are given a prompt and you need to generate a story based on the prompt.
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"""
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user_prompt = "Write a very short story about a cyber-warrior trapped in the imperial time period."
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=messages,
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)
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print(response.choices[0].message.content)
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# The response is a string that contains the story. We can track this with AgentOps by navigating to the trace url and viewing the run.
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# ## Streaming Version
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# We will demonstrate the streaming version of the API.
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stream = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=messages,
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stream=True,
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)
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for chunk in stream:
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if chunk.choices and len(chunk.choices) > 0:
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print(chunk.choices[0].delta.content or "", end="")
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agentops.end_trace(tracer, end_state="Success")
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# Let's check programmatically that spans were recorded in AgentOps
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print("\n" + "=" * 50)
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print("Now let's verify that our LLM calls were tracked properly...")
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try:
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result = agentops.validate_trace_spans(trace_context=tracer)
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agentops.print_validation_summary(result)
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except agentops.ValidationError as e:
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print(f"\n❌ Error validating spans: {e}")
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raise
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# Note that the response is a generator that yields chunks of the story. We can track this with AgentOps by navigating to the trace url and viewing the run.
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# All done!
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