adk-rag-agent

name adk-rag-agent description 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 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