This example shows how to add content from a Google Cloud Storage (GCS) bucket to your knowledge base. This allows you to process documents stored in Google Cloud without downloading them locally.

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Code

gcs.py
"""This cookbook shows how to add content from a GCS bucket to the knowledge base.
1. Run: `python cookbook/agent_concepts/knowledge/12_from_gcs.py` to run the cookbook
"""

import asyncio

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.remote_content.remote_content import GCSContent
from agno.vectordb.pgvector import PgVector

contents_db = PostgresDb(
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    knowledge_table="knowledge_contents",
)

# Create Knowledge Instance
knowledge = Knowledge(
    name="Basic SDK Knowledge Base",
    description="Agno 2.0 Knowledge Implementation",
    contents_db=contents_db,
    vector_db=PgVector(
        table_name="vectors", db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"
    ),
)

# Add from GCS
asyncio.run(
    knowledge.add_content_async(
        name="GCS PDF",
        remote_content=GCSContent(
            bucket_name="thai-recepies", blob_name="ThaiRecipes.pdf"
        ),
        metadata={"remote_content": "GCS"},
    )
)


agent = Agent(
    name="My Agent",
    description="Agno 2.0 Agent Implementation",
    knowledge=knowledge,
    search_knowledge=True,
    debug_mode=True,
)

agent.print_response(
    "What is the best way to make a Thai curry?",
    markdown=True,
)

​
Usage

1

Install libraries

pip install -U agno sqlalchemy psycopg pgvector google-cloud-storage
2

Configure Google Cloud credentials

Set up your GCS credentials using one of these methods:
  • Service Account Key: Set GOOGLE_APPLICATION_CREDENTIALS environment variable
  • gcloud CLI: gcloud auth application-default login
  • Workload Identity (if running on Google Cloud)
3

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agno/pgvector:16
4

Run the example

python cookbook/knowledge/basic_operations/07_from_gcs.py

​
Params

ParameterTypeDefaultDescription
bucket_namestr-Name of the GCS bucket containing the file.
blob_namestr-Name of the blob (file) within the bucket.
prefixOptional[str]-Path prefix for organizing files in the bucket.
bucketOptional[Any]-Pre-configured GCS bucket object.