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    Use Vertex AI Vector Search with Vertex AI RAG Engine

    This page shows you how to connect your Vertex AI RAG Engine to Vertex AI Vector Search .

    You can also follow along using the Vertex AI RAG Engine with Vertex AI Vector Search notebook.

    Vertex AI Vector Search setup

    Vertex AI Vector Search is based on Vector Search technology developed by Google research. With Vector Search you can use the same infrastructure that provides a foundation for Google products such as Google Search, YouTube, and Google Play.

    To integrate with Vertex AI RAG Engine, an empty Vector Search index is required.

    Set up Vertex AI SDK

    To set up Vertex AI SDK, see Setup .

    Create Vector Search index

    To create a Vector Search index that's compatible with your RAG Corpus, the index has to meet the following criteria:

    1. IndexUpdateMethod must be STREAM_UPDATE , see Create stream index .

    2. Distance measure type must be explicitly set to one of the following:

      • DOT_PRODUCT_DISTANCE
      • COSINE_DISTANCE

    3. Dimension of the vector must be consistent with the embedding model you plan to use in the RAG corpus. Other parameters can be tuned based on your choices, which determine whether the additional parameters can be tuned.

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      def 
  
 vector_search_create_streaming_index 
 ( 
  
 project 
 : 
  
 str 
 , 
  
 location 
 : 
  
 str 
 , 
  
 display_name 
 : 
  
 str 
 , 
  
 gcs_uri 
 : 
  
 Optional 
 [ 
 str 
 ] 
  
 = 
  
 None 
 ) 
  
 - 
>  
 aiplatform 
 . 
 MatchingEngineIndex 
 : 
  
 """Create a vector search index. 
 Args: 
 project (str): Required. Project ID 
 location (str): Required. The region name 
 display_name (str): Required. The index display name 
 gcs_uri (str): Optional. The Google Cloud Storage uri for index content 
 Returns: 
 The created MatchingEngineIndex. 
 """ 
  
 # 
  
 Initialize 
  
 the 
  
 Vertex 
  
 AI 
  
 client 
  
 aiplatform 
 . 
 init 
 ( 
 project 
 = 
 project 
 , 
  
 location 
 = 
 location 
 ) 
  
 # 
  
 Create 
  
 Index 
  
 index 
  
 = 
  
 aiplatform 
 . 
 MatchingEngineIndex 
 . 
 create_tree_ah_index 
 ( 
  
 display_name 
 = 
 display_name 
 , 
  
 contents_delta_uri 
 = 
 gcs_uri 
 , 
  
 description 
 = 
 "Matching Engine Index" 
 , 
  
 dimensions 
 = 
 100 
 , 
  
 approximate_neighbors_count 
 = 
 150 
 , 
  
 leaf_node_embedding_count 
 = 
 500 
 , 
  
 leaf_nodes_to_search_percent 
 = 
 7 
 , 
  
 index_update_method 
 = 
 "STREAM_UPDATE" 
 , 
  
 # 
  
 Options 
 : 
  
 STREAM_UPDATE 
 , 
  
 BATCH_UPDATE 
  
 distance_measure_type 
 = 
 aiplatform 
 . 
 matching_engine 
 . 
 matching_engine_index_config 
 . 
 DistanceMeasureType 
 . 
 DOT_PRODUCT_DISTANCE 
 , 
  
 ) 
  
 return 
  
 index

    Create Vector Search index endpoint

    Public endpoints are supported by Vertex AI RAG Engine.

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      def 
  
 vector_search_create_index_endpoint 
 ( 
  
 project: 
  
 str 
 , 
  
 location: 
  
 str 
 , 
  
 display_name: 
  
 str 
 ) 
  
 - 
>  
 None: 
  
 """Create a vector search index endpoint. 
  
 Args: 
  
 project 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 Project 
  
 ID 
  
 location 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 The 
  
 region 
  
 name 
  
 display_name 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 The 
  
 index 
  
 endpoint 
  
 display 
  
 name 
  
 """ 
  
 # 
  
 Initialize 
  
 the 
  
 Vertex 
  
 AI 
  
 client 
  
 aiplatform 
 . 
 init 
 ( 
 project 
 = 
 project 
 , 
  
 location 
 = 
 location 
 ) 
  
 # 
  
 Create 
  
 Index 
  
 Endpoint 
  
 index_endpoint 
  
 = 
  
 aiplatform 
 . 
 MatchingEngineIndexEndpoint 
 . 
 create 
 ( 
  
 display_name 
 = 
 display_name 
 , 
  
 public_endpoint_enabled 
 = 
 True 
 , 
  
 description 
 = 
 "Matching Engine Index Endpoint" 
 , 
  
 ) 
  
 print 
 ( 
 index_endpoint 
 . 
 name 
 )

    Deploy an index to an index endpoint

    Before we do the nearest neighbor search, the index has to be deployed to an index endpoint.

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      def 
  
 vector_search_deploy_index 
 ( 
  
 project: 
  
 str 
 , 
  
 location: 
  
 str 
 , 
  
 index_name: 
  
 str 
 , 
  
 index_endpoint_name: 
  
 str 
 , 
  
 deployed_index_id: 
  
 str 
 , 
 ) 
  
 - 
>  
 None: 
  
 """Deploy a vector search index to a vector search index endpoint. 
  
 Args: 
  
 project 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 Project 
  
 ID 
  
 location 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 The 
  
 region 
  
 name 
  
 index_name 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 The 
  
 index 
  
 to 
  
 update 
 . 
  
 A 
  
 fully 
 - 
 qualified 
  
 index 
  
 resource 
  
 name 
  
 or 
  
 a 
  
 index 
  
 ID 
 . 
  
 Example: 
  
 "projects/123/locations/us-central1/indexes/my_index_id" 
  
 or 
  
 "my_index_id" 
 . 
  
 index_endpoint_name 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 Index 
  
 endpoint 
  
 to 
  
 deploy 
  
 the 
  
 index 
  
 to 
 . 
  
 deployed_index_id 
  
 ( 
 str 
 ) 
 : 
  
 Required 
 . 
  
 The 
  
 user 
  
 specified 
  
 ID 
  
 of 
  
 the 
  
 DeployedIndex 
 . 
  
 """ 
  
 # 
  
 Initialize 
  
 the 
  
 Vertex 
  
 AI 
  
 client 
  
 aiplatform 
 . 
 init 
 ( 
 project 
 = 
 project 
 , 
  
 location 
 = 
 location 
 ) 
  
 # 
  
 Create 
  
 the 
  
 index 
  
 instance 
  
 from 
  
 an 
  
 existing 
  
 index 
  
 index 
  
 = 
  
 aiplatform 
 . 
 MatchingEngineIndex 
 ( 
 index_name 
 = 
 index_name 
 ) 
  
 # 
  
 Create 
  
 the 
  
 index 
  
 endpoint 
  
 instance 
  
 from 
  
 an 
  
 existing 
  
 endpoint 
 . 
  
 index_endpoint 
  
 = 
  
 aiplatform 
 . 
 MatchingEngineIndexEndpoint 
 ( 
  
 index_endpoint_name 
 = 
 index_endpoint_name 
  
 ) 
  
 # 
  
 Deploy 
  
 Index 
  
 to 
  
 Endpoint 
  
 index_endpoint 
  
 = 
  
 index_endpoint 
 . 
 deploy_index 
 ( 
  
 index 
 = 
 index 
 , 
  
 deployed_index_id 
 = 
 deployed_index_id 
  
 ) 
  
 print 
 ( 
 index_endpoint 
 . 
 deployed_indexes 
 )

    If it's the first time that you're deploying an index to an index endpoint, it takes approximately 30 minutes to automatically build and initiate the backend before the index can be stored. After the first deployment, the index is ready in seconds. To see the status of the index deployment, open the Vector Search Console , select the Index endpointstab, and choose your index endpoint.

    Identify the resource name of your index and index endpoint, which have the following formats:

    • projects/${PROJECT_NUMBER}/locations/${LOCATION_ID}/indexes/${INDEX_ID}
    • projects/${PROJECT_NUMBER}/locations/${LOCATION_ID}/indexEndpoints/${INDEX_ENDPOINT_ID} .

    Use Vertex AI Vector Search in Vertex AI RAG Engine

    After the Vector Search instance is set up, follow the steps in this section to set the Vector Search instance as the vector database for the RAG application.

    Set the vector database to create a RAG corpus

    When you create the RAG corpus, specify only the full INDEX_ENDPOINT_NAME and INDEX_NAME . Make sure to use the numeric ID for both index and index endpoint resource names. The RAG corpus is created and automatically associated with the Vector Search index. Validations are performed on the criteria . If any of the requirements aren't met, the request is rejected.

    Python

    Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Python API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
  vertexai 
 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # vector_search_index_name = "projects/{PROJECT_ID}/locations/{LOCATION}/indexes/{INDEX_ID}" 
 # vector_search_index_endpoint_name = "projects/{PROJECT_ID}/locations/{LOCATION}/indexEndpoints/{INDEX_ENDPOINT_ID}" 
 # display_name = "test_corpus" 
 # description = "Corpus Description" 
 # Initialize Vertex AI API once per session 
  vertexai 
 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 # Configure embedding model (Optional) 
 embedding_model_config 
 = 
 rag 
 . 
 RagEmbeddingModelConfig 
 ( 
 vertex_prediction_endpoint 
 = 
 rag 
 . 
 VertexPredictionEndpoint 
 ( 
 publisher_model 
 = 
 "publishers/google/models/text-embedding-005" 
 ) 
 ) 
 # Configure Vector DB 
 vector_db 
 = 
 rag 
 . 
 VertexVectorSearch 
 ( 
 index 
 = 
 vector_search_index_name 
 , 
 index_endpoint 
 = 
 vector_search_index_endpoint_name 
 ) 
 corpus 
 = 
 rag 
 . 
 create_corpus 
 ( 
 display_name 
 = 
 display_name 
 , 
 description 
 = 
 description 
 , 
 backend_config 
 = 
 rag 
 . 
 RagVectorDbConfig 
 ( 
 rag_embedding_model_config 
 = 
 embedding_model_config 
 , 
 vector_db 
 = 
 vector_db 
 , 
 ), 
 ) 
 print 
 ( 
 corpus 
 ) 
 # Example response: 
 # RagCorpus(name='projects/1234567890/locations/us-central1/ragCorpora/1234567890', 
 # display_name='test_corpus', description='Corpus Description', embedding_model_config=... 
 # ...

    REST 

       
 # TODO(developer): Update and un-comment the following lines: 
  
 # CORPUS_DISPLAY_NAME = " YOUR_CORPUS_DISPLAY_NAME 
" 
  
 # Full index/indexEndpoint resource name 
  
 # Index: projects/${PROJECT_NUMBER}/locations/${LOCATION_ID}/indexes/${INDEX_ID} 
  
 # IndexEndpoint: projects/${PROJECT_NUMBER}/locations/${LOCATION_ID}/indexEndpoints/${INDEX_ENDPOINT_ID} 
  
 # INDEX_RESOURCE_NAME = " YOUR_INDEX_ENDPOINT_RESOURCE_NAME 
" 
  
 # INDEX_NAME = " YOUR_INDEX_RESOURCE_NAME 
" 
  
 # Call CreateRagCorpus API to create a new RagCorpus 
  
curl  
-X  
POST  
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
-H  
 "Content-Type: application/json" 
  
https:// ${ 
 LOCATION_ID 
 } 
-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_NUMBER 
 } 
/locations/ ${ 
 LOCATION_ID 
 } 
/ragCorpora  
-d  
 '{ 
 "display_name" : ' 
 \" 
 " 
 ${ 
 CORPUS_DISPLAY_NAME 
 } 
 " 
 \" 
 ', 
 "rag_vector_db_config" : { 
 "vertex_vector_search": { 
 "index":' 
 \" 
 " 
 ${ 
 INDEX_NAME 
 } 
 " 
 \" 
 ' 
 "index_endpoint":' 
 \" 
 " 
 ${ 
 INDEX_ENDPOINT_NAME 
 } 
 " 
 \" 
 ' 
 } 
 } 
 }' 
  
 # Call ListRagCorpora API to verify the RagCorpus is created successfully 
  
curl  
-sS  
-X  
GET  
 \ 
  
-H  
 "Content-Type: application/json" 
  
 \ 
  
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
  
 "https:// 
 ${ 
 LOCATION_ID 
 } 
 -aiplatform.googleapis.com/v1beta1/projects/ 
 ${ 
 PROJECT_NUMBER 
 } 
 /locations/ 
 ${ 
 LOCATION_ID 
 } 
 /ragCorpora"

    Import files using the RAG API

    Use the ImportRagFiles API to import files from Cloud Storage or Google Drive into the Vector Search index. The files are embedded and stored in the Vector Search index.

    REST

      # TODO(developer): Update and uncomment the following lines: 
 # RAG_CORPUS_ID = "your-rag-corpus-id" 
 # 
 # Google Cloud Storage bucket/file location. 
 # For example, "gs://rag-fos-test/" 
 # GCS_URIS= "your-gcs-uris" 
 # Call ImportRagFiles API to embed files and store in the BigQuery table 
curl  
-X  
POST  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
https://us-central1-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_NUMBER 
 } 
/locations/us-central1/ragCorpora/ ${ 
 RAG_CORPUS_ID 
 } 
/ragFiles:import  
 \ 
-d  
 '{ 
 "import_rag_files_config": { 
 "gcs_source": { 
 "uris": ' 
 \" 
 " 
 ${ 
 GCS_URIS 
 } 
 " 
 \" 
 ' 
 }, 
 "rag_file_chunking_config": { 
 "chunk_size": 512 
 } 
 } 
 }' 
 # Call ListRagFiles API to verify the files are imported successfully 
curl  
-X  
GET  
 \ 
-H  
 "Content-Type: application/json" 
  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https://us-central1-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_NUMBER 
 } 
/locations/us-central1/ragCorpora/ ${ 
 RAG_CORPUS_ID 
 } 
/ragFiles

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
 vertexai 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # corpus_name = "projects/{PROJECT_ID}/locations/us-central1/ragCorpora/{rag_corpus_id}" 
 # paths = ["https://drive.google.com/file/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" 
 ) 
 response 
 = 
 rag 
 . 
 import_files 
 ( 
 corpus_name 
 = 
 corpus_name 
 , 
 paths 
 = 
 paths 
 , 
 transformation_config 
 = 
 rag 
 . 
 TransformationConfig 
 ( 
 rag 
 . 
 ChunkingConfig 
 ( 
 chunk_size 
 = 
 512 
 , 
 chunk_overlap 
 = 
 100 
 ) 
 ), 
 import_result_sink 
 = 
 "gs://sample-existing-folder/sample_import_result_unique.ndjson" 
 , 
 # Optional, this has to be an existing storage bucket folder, and file name has to be unique (non-existent). 
 max_embedding_requests_per_min 
 = 
 900 
 , 
 # Optional 
 ) 
 print 
 ( 
 f 
 "Imported 
 { 
 response 
 . 
 imported_rag_files_count 
 } 
 files." 
 ) 
 # Example response: 
 # Imported 2 files.

    Retrieve relevant contexts using the RAG API

    After completion of the file imports, the relevant context can be retrieved from the Vector Search index by using the RetrieveContexts API.

    REST

      # TODO(developer): Update and uncomment the following lines: 
 # RETRIEVAL_QUERY="your-retrieval-query" 
 # 
 # Full RAG corpus resource name 
 # Format: 
 # "projects/${PROJECT_NUMBER}/locations/us-central1/ragCorpora/${RAG_CORPUS_ID}" 
 # RAG_CORPUS_RESOURCE="your-rag-corpus-resource" 
 # Call RetrieveContexts API to retrieve relevant contexts 
curl  
-X  
POST  
 \ 
  
-H  
 "Content-Type: application/json" 
  
 \ 
  
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
https://us-central1-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_NUMBER 
 } 
/locations/us-central1:retrieveContexts  
 \ 
  
-d  
 '{ 
 "vertex_rag_store": { 
 "rag_resources": { 
 "rag_corpus": ' 
 \" 
 " 
 ${ 
 RAG_CORPUS_RESOURCE 
 } 
 " 
 \" 
 ', 
 }, 
 }, 
 "query": { 
 "text": ' 
 \" 
 " 
 ${ 
 RETRIEVAL_QUERY 
 } 
 " 
 \" 
 ', 
 "similarity_top_k": 10 
 } 
 }'

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      from 
  
 vertexai 
  
 import 
 rag 
 import 
  
 vertexai 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # corpus_name = "projects/[PROJECT_ID]/locations/us-central1/ragCorpora/[rag_corpus_id]" 
 # Initialize Vertex AI API once per session 
 vertexai 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 response 
 = 
 rag 
 . 
 retrieval_query 
 ( 
 rag_resources 
 = 
 [ 
 rag 
 . 
 RagResource 
 ( 
 rag_corpus 
 = 
 corpus_name 
 , 
 # Optional: supply IDs from `rag.list_files()`. 
 # rag_file_ids=["rag-file-1", "rag-file-2", ...], 
 ) 
 ], 
 text 
 = 
 "Hello World!" 
 , 
 rag_retrieval_config 
 = 
 rag 
 . 
 RagRetrievalConfig 
 ( 
 top_k 
 = 
 10 
 , 
 filter 
 = 
 rag 
 . 
 utils 
 . 
 resources 
 . 
 Filter 
 ( 
 vector_distance_threshold 
 = 
 0.5 
 ), 
 ), 
 ) 
 print 
 ( 
 response 
 ) 
 # Example response: 
 # contexts { 
 #   contexts { 
 #     source_uri: "gs://your-bucket-name/file.txt" 
 #     text: ".... 
 #   ....

    Generate content using Vertex AI Gemini API

    To generate content using Gemini models, make a call to the Vertex AI GenerateContent API. By specifying the RAG_CORPUS_RESOURCE in the request, the API automatically retrieves data from the Vector Search index.

    REST

      # TODO(developer): Update and uncomment the following lines: 
 # MODEL_ID=gemini-2.0-flash 
 # GENERATE_CONTENT_PROMPT="your-generate-content-prompt" 
 # GenerateContent with contexts retrieved from the FeatureStoreOnline index 
curl  
-X  
POST  
 \ 
-H  
 "Authorization: Bearer 
 $( 
gcloud  
auth  
print-access-token ) 
 " 
  
 \ 
-H  
 "Content-Type: application/json" 
  
https://us-central1-aiplatform.googleapis.com/v1beta1/projects/ ${ 
 PROJECT_NUMBER 
 } 
/locations/us-central1/publishers/google/models/ ${ 
 MODEL_ID 
 } 
:generateContent  
 \ 
-d  
 '{ 
 "contents": { 
 "role": "user", 
 "parts": { 
 "text": ' 
 \" 
 " 
 ${ 
 GENERATE_CONTENT_PROMPT 
 } 
 " 
 \" 
 ' 
 } 
 }, 
 "tools": { 
 "retrieval": { 
 "vertex_rag_store": { 
 "rag_resources": { 
 "rag_corpus": ' 
 \" 
 " 
 ${ 
 RAG_CORPUS_RESOURCE 
 } 
 " 
 \" 
 ', 
 }, 
 "similarity_top_k": 8, 
 "vector_distance_threshold": 0.32 
 } 
 } 
 } 
 }'

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      from 
  
 vertexai 
  
 import 
 rag 
 from 
  
 vertexai.generative_models 
  
 import 
 GenerativeModel 
 , 
 Tool 
 import 
  
 vertexai 
 # TODO(developer): Update and un-comment below lines 
 # PROJECT_ID = "your-project-id" 
 # corpus_name = "projects/{PROJECT_ID}/locations/us-central1/ragCorpora/{rag_corpus_id}" 
 # Initialize Vertex AI API once per session 
 vertexai 
 . 
 init 
 ( 
 project 
 = 
 PROJECT_ID 
 , 
 location 
 = 
 "us-central1" 
 ) 
 rag_retrieval_tool 
 = 
 Tool 
 . 
 from_retrieval 
 ( 
 retrieval 
 = 
 rag 
 . 
 Retrieval 
 ( 
 source 
 = 
 rag 
 . 
 VertexRagStore 
 ( 
 rag_resources 
 = 
 [ 
 rag 
 . 
 RagResource 
 ( 
 rag_corpus 
 = 
 corpus_name 
 , 
 # Optional: supply IDs from `rag.list_files()`. 
 # rag_file_ids=["rag-file-1", "rag-file-2", ...], 
 ) 
 ], 
 rag_retrieval_config 
 = 
 rag 
 . 
 RagRetrievalConfig 
 ( 
 top_k 
 = 
 10 
 , 
 filter 
 = 
 rag 
 . 
 utils 
 . 
 resources 
 . 
 Filter 
 ( 
 vector_distance_threshold 
 = 
 0.5 
 ), 
 ), 
 ), 
 ) 
 ) 
 rag_model 
 = 
 GenerativeModel 
 ( 
 model_name 
 = 
 "gemini-2.0-flash-001" 
 , 
 tools 
 = 
 [ 
 rag_retrieval_tool 
 ] 
 ) 
 response 
 = 
 rag_model 
 . 
 generate_content 
 ( 
 "Why is the sky blue?" 
 ) 
 print 
 ( 
 response 
 . 
 text 
 ) 
 # Example response: 
 #   The sky appears blue due to a phenomenon called Rayleigh scattering. 
 #   Sunlight, which contains all colors of the rainbow, is scattered 
 #   by the tiny particles in the Earth's atmosphere.... 
 #   ...

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