AI/참고/neo4j-graphrag-python-main/examples/customize/llms/vertexai_llm_structured_output.py

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"""
Simple example comparing VertexAI LLM V1 (legacy) vs V2 (structured output).

This demonstrates how V2's structured output provides type-safe, validated responses
compared to V1's prompt-based JSON extraction.

Prerequisites:
- Google Cloud project with Vertex AI API enabled
- Either:
  - GOOGLE_APPLICATION_CREDENTIALS environment variable set, or
  - Running on GCP with appropriate service account
"""

from pydantic import BaseModel
from neo4j_graphrag.llm import VertexAILLM
from neo4j_graphrag.types import LLMMessage
from vertexai.generative_models import GenerationConfig


# Define a Pydantic model for structured output
class Movie(BaseModel):
    title: str
    year: int
    director: str
    genre: str


# =============================================================================
# V1 (Legacy): Manual JSON mode with prompt engineering
# =============================================================================
print("=" * 60)
print("V1 Legacy: Manual JSON extraction with prompt engineering")
print("=" * 60)

# V1: Use generation_config
llm_v1 = VertexAILLM(
    model_name="gemini-2.5-flash",
    generation_config=GenerationConfig(
        response_mime_type="application/json", temperature=0
    ),
)

# V1 requires string input
v1_prompt = """Extract movie information and respond in JSON format.
Include: title, year, director, genre.

Text: Inception was directed by Christopher Nolan in 2010. It's a science fiction thriller."""

response_v1 = llm_v1.invoke(v1_prompt)
print(f"Response: {response_v1.content}")


# =============================================================================
# V2 (New): Structured output with Pydantic model
# =============================================================================
print("\n" + "=" * 60)
print("V2: Structured output with Pydantic model")
print("=" * 60)

# V2: Use clean LLM without constructor params
llm_v2 = VertexAILLM(model_name="gemini-2.5-flash")

# V2 uses list of LLMMessage for input
messages = [
    LLMMessage(
        role="user",
        content="Inception was directed by Christopher Nolan in 2010. It's a science fiction thriller.",
    )
]

# Pass response_format and temperature directly to invoke()
response_v2 = llm_v2.invoke(messages, response_format=Movie, temperature=0)

# Parse and validate in one step
movie = Movie.model_validate_json(response_v2.content)
print(f"Response: {response_v2.content}")


# =============================================================================
# V2 Alternative: Using JSON Schema instead of Pydantic
# =============================================================================
print("\n" + "=" * 60)
print("V2 Alternative: Structured output with JSON Schema")
print("=" * 60)

# Define a JSON schema (equivalent to the Movie Pydantic model)
# Note: VertexAI accepts raw JSON schemas (no wrapping required like OpenAI)
movie_schema = {
    "type": "object",
    "properties": {
        "title": {"type": "string"},
        "year": {"type": "integer"},
        "director": {"type": "string"},
        "genre": {"type": "string"},
    },
    "required": ["title", "year", "director", "genre"],
    "additionalProperties": False,
}

# Pass JSON schema as response_format
response_v2_schema = llm_v2.invoke(
    messages, response_format=movie_schema, temperature=0
)

print(f"Response: {response_v2_schema.content}")