Creating Index#
my_index = aiplatform.MatchingEngineIndex.create_tree_ah_index(
    display_name=DISPLAY_NAME,
    contents_delta_uri=EMBEDDING_DIR,
    dimensions=DIMENSIONS,
    approximate_neighbors_count=150,
    distance_measure_type="DOT_PRODUCT_DISTANCE"
)
Creating Endpoint#
my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(
    display_name=f"{DISPLAY_NAME}-endpoint",
    network=VPC_NETWORK_FULL,
)
Deploy Index#
my_index_endpoint = my_index_endpoint.deploy_index(
    index=my_index, 
    deployed_index_id=DEPLOYED_INDEX_ID
)
my_index_endpoint.deployed_indexes
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Milvus
 Contents
  
Create VectorStore from texts
Create Index and deploy it to an Endpoint
Imports, Constants and Configs
Using Tensorflow Universal Sentence Encoder as an Embedder
Inserting a test embedding
Creating Index
Creating Endpoint
Deploy Index
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/vectorstores/examples/matchingengine.html