Primeiros passos com a integração do MongoDB Google ADK

mkdir google-adk-mongodb-projectcd google-adk-mongodb-projectpip install "google-adk[mongodb]" python-dotenv

GOOGLE_API_KEY="<gemini-api-key>"MONGODB_URI="<connection-string>"

import osimport timeimport asynciofrom dotenv import load_dotenvfrom pymongo import MongoClientfrom pymongo.operations import SearchIndexModelfrom google.adk.agents import Agentfrom google.adk.integrations.mongodb import (    MongoDbToolset,    MongoDbToolSettings,    MongoDbSessionService,)from google.adk.runners import Runnerfrom google.genai import types
load_dotenv()

client = MongoClient(os.environ["MONGODB_URI"])collection = client["google_adk_tutorial"]["penguins"]

collection.insert_many(    [        {            "title": "Emperor Penguins",            "embeddings": "Emperor penguins are the tallest and heaviest of all penguin species, standing up to 4 feet.",        },        {            "title": "Penguin Habitats",            "embeddings": "Penguins are flightless seabirds that live almost exclusively below the equator. Some island-dwellers can be found in warmer climates.",        },        {            "title": "Penguins of Patagonia",            "embeddings": "Patagonia is home to five penguin species: Magellanic, Humboldt, Gentoo, Southern Rockhopper, and King.",        },        {            "title": "Penguin Diving Depths",            "embeddings": "Emperor penguins can dive deeper than any other bird, reaching depths of over 500 meters.",        },        {            "title": "Penguin Nesting Habits",            "embeddings": "Some penguin species mate for life and return to the same nesting site every year.",        },    ])

search_index_model = SearchIndexModel(    definition={        "fields": [            {                "type": "autoEmbed",                "path": "embeddings",                "model": "voyage-4",                "modality": "text",            }        ]    },    name="autoembed_index",    type="vectorSearch",)
collection.create_search_index(model=search_index_model)
# Index build time varies with collection size and cluster typeprint("Waiting for the MongoDB Vector Search index to finish building...")time.sleep(60)
client.close()

toolset = MongoDbToolset(    connection_string=os.environ["MONGODB_URI"],    database_name="google_adk_tutorial",    settings=MongoDbToolSettings(        use_mongodb_auto_embedding=True,        default_embedding_field="embeddings",        default_vector_index_name="autoembed_index",    ),)

root_agent = Agent(    model="gemini-2.5-flash",    name="penguin_facts_agent",    description="Answers questions about penguins.",    instruction=(        "Search the penguins collection with your MongoDB"        " tools to answer questions about penguins. Remember"        " what the user has told you earlier in the"        " conversation."    ),    tools=[toolset],)

session_service = MongoDbSessionService(    connection_string=os.environ["MONGODB_URI"],    database_name="google_adk_tutorial",)
runner = Runner(    agent=root_agent,    app_name="google_adk_tutorial",    session_service=session_service,)

async def ask(runner, session_id, text):    content = types.Content(role="user", parts=[types.Part(text=text)])    async for event in runner.run_async(        user_id="tutorial_user",        session_id=session_id,        new_message=content,    ):        if event.is_final_response() and event.content:            print(f"\n[user]: {text}")            print(f"[agent]: {event.content.parts[0].text}")

async def main():    session = await session_service.create_session(        app_name="google_adk_tutorial",        user_id="tutorial_user",    )
    await ask(        runner, session.id, "How deep can emperor penguins dive?"    )    await ask(        runner, session.id, "What was the first thing I asked you?"    )

asyncio.run(main())