with_structured_output

Example:

from pydantic import BaseModel
from langchain_google_genai import ChatGoogleGenerativeAI

class Person(BaseModel):
    name: str
    age: int

model = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview")
structured_model = model.with_structured_output(
    Person,
    method="json_schema",
)

result = structured_model.invoke(
    "Tell me about a person named Alice, age 30"
)
print(result)  # Person(name="Alice", age=30)

from pydantic import BaseModel
from langchain_google_genai import ChatGoogleGenerativeAI

class Recipe(BaseModel):
    name: str
    ingredients: list[str]
    steps: list[str]

model = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview")
structured_model = model.with_structured_output(
    Recipe, method="json_schema"
)

# Emits fully-parsed Recipe objects, not incremental JSON strings
for chunk in structured_model.stream(
    "Give me a recipe for chocolate chip cookies"
):
    print(chunk)  # Recipe(name=..., ingredients=[...], steps=[...])

model = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview")

schema = {
    "type": "object",
    "properties": {
        "title": {"type": "string"},
        "priority": {"type": "integer"},
    },
    "required": ["title", "priority"],
}

structured_model = model.with_structured_output(
    schema, method="json_schema"
)
result = structured_model.invoke("Create a task: finish report, priority 1")
print(result)  # {"title": "finish report", "priority": 1}

structured_model = model.with_structured_output(
    Person, method="json_schema", include_raw=True
)

result = structured_model.invoke("Tell me about Bob, age 25")
print(result["parsed"])  # Person(name="Bob", age=25)
print(result["raw"])  # AIMessage with full model response