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Build RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine.
Passed 22 of 22 test cases with the skill, and 11 of 22 without it.
and 15 more in the full report
11 fixed12 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write Python import statements for Google ADK's core Agent class and Vertex AI RAG retrieval tool class without using legacy `google.genai` Client objects.
| Checked | Without skill | With skill |
|---|---|---|
| The python code imports Agent from google.adk. | Passed | Passed |
| The python code imports VertexAiRagRetrieval from google.adk.tools. | Missed | Passed |
The instructions your model reads when the skill loads.
Build agents that answer questions from document corpora using Vertex AI RAG Engine.
GOOGLE_GENAI_USE_VERTEXAI=1
GOOGLE_CLOUD_PROJECT=your-project-id
GOOGLE_CLOUD_LOCATION=us-central1
RAG_CORPUS=projects/{PROJECT_ID}/locations/{LOCATION}/ragCorpora/{CORPUS_ID}
from google.adk import Agent
from google.adk.tools import VertexAiRagRetrieval
# Configure RAG retrieval tool
rag_tool = VertexAiRagRetrieval(
name="retrieve_docs",
description="Retrieve relevant documentation for the question",
rag_corpus=os.environ["RAG_CORPUS"],
similarity_top_k=10,
vector_distance_threshold=0.6,
)
# Create agent with RAG tool
agent = Agent(
name="rag_agent",
model="gemini-2.0-flash-001",
instruction=INSTRUCTION_PROMPT,
tools=[rag_tool],
test cases passed, vs 11 / 22 without
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