Claude-skill-registry adk-rag-agent

Build RAG (Retrieval-Augmented Generation) agents with Google ADK and Vertex AI RAG Engine. Use when implementing document Q&A, knowledge base search, or citation-backed responses. Covers VertexAiRagRetrieval tool, corpus setup, and citation formatting.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/adk-rag-agent" ~/.claude/skills/majiayu000-claude-skill-registry-adk-rag-agent && rm -rf "$T"

manifest: skills/data/adk-rag-agent/SKILL.md

Google ADK RAG Agent

Build agents that answer questions from document corpora using Vertex AI RAG Engine.

Requirements

Vertex AI backend (not Gemini API)
Google Cloud project with Vertex AI enabled
RAG corpus created in Vertex AI

Environment Variables

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}

Core Implementation

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],
)

Instruction Prompt Pattern

INSTRUCTION_PROMPT = """
You are an AI assistant with access to a specialized document corpus.

RETRIEVAL:
- Use retrieve_docs for specific knowledge questions
- Skip retrieval for casual conversation
- Ask clarifying questions when intent is unclear

SCOPE:
- Only answer questions related to the corpus
- Say "I don't have information about that" for out-of-scope queries

CITATIONS:
- Always cite sources at the end of responses
- Format: [Title](url) or [Document Section](url)
- Consolidate multiple citations from the same source
"""

Corpus Setup

Create corpus via Vertex AI Console or SDK:

from vertexai.preview import rag

# Create corpus
corpus = rag.create_corpus(display_name="my-corpus")

# Import documents (PDF, TXT, HTML)
rag.import_files(
    corpus_name=corpus.name,
    paths=["gs://bucket/doc.pdf"],  # or local files
    chunk_size=512,
    chunk_overlap=100,
)

Key Parameters

Parameter	Description	Default
`similarity_top_k`	Max chunks to retrieve	10
`vector_distance_threshold`	Min similarity (0-1, lower=stricter)	0.6
`chunk_size`	Tokens per chunk at import	512
`chunk_overlap`	Overlap between chunks	100

Citation Best Practices

Single source → single citation at end
Multiple sources → list all citations
Same document, multiple chunks → consolidate into one citation
Never expose internal chunk IDs to users