How do I install the Adk RAG Agent skill?

Run npx skillmds@latest add diegosouzapw/adk-rag-agent in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Which AI agents work with Adk RAG Agent?

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Is Adk RAG Agent free to use?

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Who published Adk RAG Agent?

diegosouzapw (@diegosouzapw) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Adk RAG Agent by diegosouzapw — Agent Skill

Name: Adk RAG Agent
Rating: 4.2 (5 reviews)
Author: diegosouzapw

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.

diegosouzapw Updated Sep 8, 2026 54 repo stars

File contents

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

References

Corpus setup details
Sample repo

---name: adk-rag-agentdescription: 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.---# Google ADK RAG AgentBuild 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```bashGOOGLE_GENAI_USE_VERTEXAI=1GOOGLE_CLOUD_PROJECT=your-project-idGOOGLE_CLOUD_LOCATION=us-central1RAG_CORPUS=projects/{PROJECT_ID}/locations/{LOCATION}/ragCorpora/{CORPUS_ID}```## Core Implementation```pythonfrom google.adk import Agentfrom google.adk.tools import VertexAiRagRetrieval# Configure RAG retrieval toolrag_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 toolagent = Agent( name="rag_agent", model="gemini-2.0-flash-001", instruction=INSTRUCTION_PROMPT, tools=[rag_tool],)```## Instruction Prompt Pattern```pythonINSTRUCTION_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 unclearSCOPE:- Only answer questions related to the corpus- Say "I don't have information about that" for out-of-scope queriesCITATIONS:- Always cite sources at the end of responses- Format: [Title](url) or [Document Section](url)- Consolidate multiple citations from the same source"""```## Corpus SetupCreate corpus via Vertex AI Console or SDK:```pythonfrom vertexai.preview import rag# Create corpuscorpus = 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 Practices1. Single source → single citation at end2. Multiple sources → list all citations3. Same document, multiple chunks → consolidate into one citation4. Never expose internal chunk IDs to users## References- [Corpus setup details](references/corpus-setup.md)- [Sample repo](https://github.com/google/adk-samples/tree/main/python/agents/RAG)

Frequently asked questions