ericness · September 23, 2025 13:12 · ericness · Sep 23, 2025
diff --git a/vercel_structured_output.py b/vercel_structured_output.py
 #!/usr/bin/env python3
 """
 Standalone script to demonstrate structured output issue with Vercel AI Gateway.

 This script replicates the functionality of src/ai_news_agent/nodes/summarize_episode.py
 but uses Vercel AI Gateway with LangChain ChatOpenAI and structured output.

 Expected to demonstrate that .with_structured_output() doesn't work with Vercel AI Gateway.
 """

 import os
 from typing import Optional

 from dotenv import load_dotenv
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 from pydantic import BaseModel, Field, SecretStr

 load_dotenv()


 class EpisodeSummary(BaseModel):
    """Structured output model for episode summary."""
    
    title: str = Field(description="The episode title")
    summary: str = Field(description="A concise summary of the episode")
    key_topics: list[str] = Field(description="List of key topics discussed")
    duration: Optional[str] = Field(description="Episode duration", default=None)
    published_date: Optional[str] = Field(description="Publication date", default=None)


 def main():
    """Main function to test structured output with Vercel AI Gateway."""
    
    # Sample episode data (similar to what would come from RSS feed)
    sample_episode = {
        "title": "AI Daily Brief: Latest Developments in Large Language Models",
        "description": (
            "In today's episode, we discuss the latest breakthroughs in LLM technology, "
            "including new model releases from major AI labs, improvements in reasoning "
            "capabilities, and the growing importance of structured outputs in production "
            "systems. We also cover recent developments in AI safety and alignment research, "
            "the impact of multimodal AI systems, and emerging trends in enterprise AI adoption."
        ),
        "published_date": "2024-01-15",
        "duration": "15 minutes"
    }
    
    print("Testing Vercel AI Gateway with structured output...")
    print(f"Episode Title: {sample_episode['title']}")
    print(f"Episode Duration: {sample_episode['duration']}")
    print(f"Published: {sample_episode['published_date']}")
    print("-" * 80)
    
    # Configure ChatOpenAI to use Vercel AI Gateway
    vercel_token = os.getenv("VERCEL_AI_GATEWAY_TOKEN")
    if not vercel_token:
        raise ValueError("VERCEL_AI_GATEWAY_TOKEN environment variable is required")
    
    llm = ChatOpenAI(
        model="openai/gpt-4o-mini",
        temperature=0.3,
        base_url="https://ai-gateway.vercel.sh/v1",
        api_key=SecretStr(vercel_token),
        default_headers={
            "Authorization": f"Bearer {vercel_token}"
        }
    )
    
    # Create prompt template (similar to LangSmith prompt but simplified)
    prompt = ChatPromptTemplate.from_messages([
        ("system", (
            "You are an AI assistant that summarizes podcast episodes. "
            "Extract the key information and provide a structured summary with "
            "the title, a concise summary, key topics discussed, duration, and published date."
        )),
        ("user", (
            "Please summarize the following podcast episode:\n\n"
            "Title: {title}\n\n"
            "Description: {description}\n\n"
            "Duration: {duration}\n\n"
            "Published Date: {published_date}"
        ))
    ])
    
    print("Creating structured LLM with .with_structured_output()...")
    
    # This is where the issue likely occurs - using .with_structured_output() with Vercel Gateway
    structured_llm = llm.with_structured_output(EpisodeSummary)
    
    # Create the chain
    chain = prompt | structured_llm
    
    print("Invoking chain with structured output...")
    
    # Invoke the chain - this should either work or raise an exception
    result = chain.invoke({
        "title": sample_episode["title"],
        "description": sample_episode["description"],
        "duration": sample_episode["duration"],
        "published_date": sample_episode["published_date"]
    })
    
    # If we get here, structured output worked
    print("SUCCESS: Structured output worked!")
    print("Result type:", type(result))
    print("Result:")
    if isinstance(result, EpisodeSummary):
        print(f"  Title: {result.title}")
        print(f"  Summary: {result.summary}")
        print(f"  Key Topics: {result.key_topics}")
        print(f"  Duration: {result.duration}")
        print(f"  Published Date: {result.published_date}")
    else:
        print(f"  Raw result: {result}")
    
    return result


 if __name__ == "__main__":
    main()
	#!/usr/bin/env python3
	"""
	Standalone script to demonstrate structured output issue with Vercel AI Gateway.

	This script replicates the functionality of src/ai_news_agent/nodes/summarize_episode.py
	but uses Vercel AI Gateway with LangChain ChatOpenAI and structured output.

	Expected to demonstrate that .with_structured_output() doesn't work with Vercel AI Gateway.
	"""

	import os
	from typing import Optional

	from dotenv import load_dotenv
	from langchain_core.prompts import ChatPromptTemplate
	from langchain_openai import ChatOpenAI
	from pydantic import BaseModel, Field, SecretStr

	load_dotenv()


	class EpisodeSummary(BaseModel):
	"""Structured output model for episode summary."""

	title: str = Field(description="The episode title")
	summary: str = Field(description="A concise summary of the episode")
	key_topics: list[str] = Field(description="List of key topics discussed")
	duration: Optional[str] = Field(description="Episode duration", default=None)
	published_date: Optional[str] = Field(description="Publication date", default=None)


	def main():
	"""Main function to test structured output with Vercel AI Gateway."""

	# Sample episode data (similar to what would come from RSS feed)
	sample_episode = {
	"title": "AI Daily Brief: Latest Developments in Large Language Models",
	"description": (
	"In today's episode, we discuss the latest breakthroughs in LLM technology, "
	"including new model releases from major AI labs, improvements in reasoning "
	"capabilities, and the growing importance of structured outputs in production "
	"systems. We also cover recent developments in AI safety and alignment research, "
	"the impact of multimodal AI systems, and emerging trends in enterprise AI adoption."
	),
	"published_date": "2024-01-15",
	"duration": "15 minutes"
	}

	print("Testing Vercel AI Gateway with structured output...")
	print(f"Episode Title: {sample_episode['title']}")
	print(f"Episode Duration: {sample_episode['duration']}")
	print(f"Published: {sample_episode['published_date']}")
	print("-" * 80)

	# Configure ChatOpenAI to use Vercel AI Gateway
	vercel_token = os.getenv("VERCEL_AI_GATEWAY_TOKEN")
	if not vercel_token:
	raise ValueError("VERCEL_AI_GATEWAY_TOKEN environment variable is required")

	llm = ChatOpenAI(
	model="openai/gpt-4o-mini",
	temperature=0.3,
	base_url="https://ai-gateway.vercel.sh/v1",
	api_key=SecretStr(vercel_token),
	default_headers={
	"Authorization": f"Bearer {vercel_token}"
	}
	)

	# Create prompt template (similar to LangSmith prompt but simplified)
	prompt = ChatPromptTemplate.from_messages([
	("system", (
	"You are an AI assistant that summarizes podcast episodes. "
	"Extract the key information and provide a structured summary with "
	"the title, a concise summary, key topics discussed, duration, and published date."
	)),
	("user", (
	"Please summarize the following podcast episode:\n\n"
	"Title: {title}\n\n"
	"Description: {description}\n\n"
	"Duration: {duration}\n\n"
	"Published Date: {published_date}"
	))
	])

	print("Creating structured LLM with .with_structured_output()...")

	# This is where the issue likely occurs - using .with_structured_output() with Vercel Gateway
	structured_llm = llm.with_structured_output(EpisodeSummary)

	# Create the chain
	chain = prompt \| structured_llm

	print("Invoking chain with structured output...")

	# Invoke the chain - this should either work or raise an exception
	result = chain.invoke({
	"title": sample_episode["title"],
	"description": sample_episode["description"],
	"duration": sample_episode["duration"],
	"published_date": sample_episode["published_date"]
	})

	# If we get here, structured output worked
	print("SUCCESS: Structured output worked!")
	print("Result type:", type(result))
	print("Result:")
	if isinstance(result, EpisodeSummary):
	print(f" Title: {result.title}")
	print(f" Summary: {result.summary}")
	print(f" Key Topics: {result.key_topics}")
	print(f" Duration: {result.duration}")
	print(f" Published Date: {result.published_date}")
	else:
	print(f" Raw result: {result}")

	return result


	if __name__ == "__main__":
	main()
No results found