Use the quickstart to get familiar with RAG

  from 
  
 vertexai 
  
 import 
 rag 
 from 
  
 vertexai.generative_models 
  
 import 
  GenerativeModel 
 
 , 
  Tool 
 
 import 
  
  vertexai 
 
 # Create a RAG Corpus, Import Files, and Generate a response 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # display_name = "test_corpus" 
 # paths = ["https://drive.google.com/file/d/123", "gs://my_bucket/my_files_dir"]  # Supports Google Cloud Storage and Google Drive Links 
 # Initialize Vertex AI API once per session 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 # Create RagCorpus 
 # Configure embedding model, for example "text-embedding-005". 
 embedding_model_config 
 = 
 rag 
 . 
 RagEmbeddingModelConfig 
 ( 
 vertex_prediction_endpoint 
 = 
 rag 
 . 
 VertexPredictionEndpoint 
 ( 
 publisher_model 
 = 
 "publishers/google/models/text-embedding-005" 
 ) 
 ) 
 rag_corpus 
 = 
 rag 
 . 
 create_corpus 
 ( 
 display_name 
 = 
 display_name 
 , 
 backend_config 
 = 
 rag 
 . 
 RagVectorDbConfig 
 ( 
 rag_embedding_model_config 
 = 
 embedding_model_config 
 ), 
 ) 
 # Import Files to the RagCorpus 
 rag 
 . 
 import_files 
 ( 
 rag_corpus 
 . 
 name 
 , 
 paths 
 , 
 # Optional 
 transformation_config 
 = 
 rag 
 . 
 TransformationConfig 
 ( 
 chunking_config 
 = 
 rag 
 . 
 ChunkingConfig 
 ( 
 chunk_size 
 = 
 512 
 , 
 chunk_overlap 
 = 
 100 
 , 
 ), 
 ), 
 max_embedding_requests_per_min 
 = 
 1000 
 , 
 # Optional 
 ) 
 # Direct context retrieval 
 rag_retrieval_config 
 = 
 rag 
 . 
 RagRetrievalConfig 
 ( 
 top_k 
 = 
 3 
 , 
 # Optional 
 filter 
 = 
 rag 
 . 
 Filter 
 ( 
 vector_distance_threshold 
 = 
 0.5 
 ), 
 # Optional 
 ) 
 response 
 = 
 rag 
 . 
 retrieval_query 
 ( 
 rag_resources 
 = 
 [ 
 rag 
 . 
 RagResource 
 ( 
 rag_corpus 
 = 
 rag_corpus 
 . 
 name 
 , 
 # Optional: supply IDs from `rag.list_files()`. 
 # rag_file_ids=["rag-file-1", "rag-file-2", ...], 
 ) 
 ], 
 text 
 = 
 "What is RAG and why it is helpful?" 
 , 
 rag_retrieval_config 
 = 
 rag_retrieval_config 
 , 
 ) 
 print 
 ( 
 response 
 ) 
 # Enhance generation 
 # Create a RAG retrieval tool 
 rag_retrieval_tool 
 = 
  Tool 
 
 . 
 from_retrieval 
 ( 
 retrieval 
 = 
 rag 
 . 
 Retrieval 
 ( 
 source 
 = 
 rag 
 . 
 VertexRagStore 
 ( 
 rag_resources 
 = 
 [ 
 rag 
 . 
 RagResource 
 ( 
 rag_corpus 
 = 
 rag_corpus 
 . 
 name 
 , 
 # Currently only 1 corpus is allowed. 
 # Optional: supply IDs from `rag.list_files()`. 
 # rag_file_ids=["rag-file-1", "rag-file-2", ...], 
 ) 
 ], 
 rag_retrieval_config 
 = 
 rag_retrieval_config 
 , 
 ), 
 ) 
 ) 
 # Create a Gemini model instance 
 rag_model 
 = 
 GenerativeModel 
 ( 
 model_name 
 = 
 "gemini-2.0-flash-001" 
 , 
 tools 
 = 
 [ 
 rag_retrieval_tool 
 ] 
 ) 
 # Generate response 
 response 
 = 
 rag_model 
 . 
  generate_content 
 
 ( 
 "What is RAG and why it is helpful?" 
 ) 
 print 
 ( 
 response 
 . 
 text 
 ) 
 # Example response: 
 #   RAG stands for Retrieval-Augmented Generation. 
 #   It's a technique used in AI to enhance the quality of responses 
 # ...