Class: Google::Cloud::AIPlatform::V1::RagVectorDbConfig

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/cloud/aiplatform/v1/vertex_rag_data.rb

Overview

Config for the Vector DB to use for RAG.

Defined Under Namespace

Classes: Pinecone, RagManagedDb, VertexVectorSearch

Instance Attribute Summary collapse

Instance Attribute Details

#api_auth ⇒ ::Google::Cloud::AIPlatform::V1::ApiAuth

Returns Authentication config for the chosen Vector DB.

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/vertex_rag_data.rb', line 82

class RagVectorDbConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The config for the default RAG-managed Vector DB.
  # @!attribute [rw] knn
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::KNN]
  #     Performs a KNN search on RagCorpus.
  #     Default choice if not specified.
  #
  #     Note: The following fields are mutually exclusive: `knn`, `ann`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  # @!attribute [rw] ann
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::ANN]
  #     Performs an ANN search on RagCorpus. Use this if you have a lot of
  #     files (> 10K) in your RagCorpus and want to reduce the search latency.
  #
  #     Note: The following fields are mutually exclusive: `ann`, `knn`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  class RagManagedDb
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Config for KNN search.
    class KNN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Config for ANN search.
    #
    # RagManagedDb uses a tree-based structure to partition data and
    # facilitate faster searches. As a tradeoff, it requires longer indexing
    # time and manual triggering of index rebuild via the ImportRagFiles and
    # UpdateRagCorpus API.
    # @!attribute [rw] tree_depth
    #   @return [::Integer]
    #     The depth of the tree-based structure. Only depth values of 2 and 3 are
    #     supported.
    #
    #     Recommended value is 2 if you have if you have O(10K) files in the
    #     RagCorpus and set this to 3 if more than that.
    #
    #     Default value is 2.
    # @!attribute [rw] leaf_count
    #   @return [::Integer]
    #     Number of leaf nodes in the tree-based structure. Each leaf node
    #     contains groups of closely related vectors along with their
    #     corresponding centroid.
    #
    #     Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus).
    #
    #     Default value is 500.
    class ANN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end
  end

  # The config for the Pinecone.
  # @!attribute [rw] index_name
  #   @return [::String]
  #     Pinecone index name.
  #     This value cannot be changed after it's set.
  class Pinecone
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # The config for the Vertex Vector Search.
  # @!attribute [rw] index_endpoint
  #   @return [::String]
  #     The resource name of the Index Endpoint.
  #     Format:
  #     `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}`
  # @!attribute [rw] index
  #   @return [::String]
  #     The resource name of the Index.
  #     Format:
  #     `projects/{project}/locations/{location}/indexes/{index}`
  class VertexVectorSearch
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#pinecone ⇒ ::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::Pinecone

Returns The config for the Pinecone.

Note: The following fields are mutually exclusive: pinecone, rag_managed_db, vertex_vector_search. If a field in that set is populated, all other fields in the set will automatically be cleared.

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/vertex_rag_data.rb', line 82

class RagVectorDbConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The config for the default RAG-managed Vector DB.
  # @!attribute [rw] knn
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::KNN]
  #     Performs a KNN search on RagCorpus.
  #     Default choice if not specified.
  #
  #     Note: The following fields are mutually exclusive: `knn`, `ann`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  # @!attribute [rw] ann
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::ANN]
  #     Performs an ANN search on RagCorpus. Use this if you have a lot of
  #     files (> 10K) in your RagCorpus and want to reduce the search latency.
  #
  #     Note: The following fields are mutually exclusive: `ann`, `knn`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  class RagManagedDb
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Config for KNN search.
    class KNN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Config for ANN search.
    #
    # RagManagedDb uses a tree-based structure to partition data and
    # facilitate faster searches. As a tradeoff, it requires longer indexing
    # time and manual triggering of index rebuild via the ImportRagFiles and
    # UpdateRagCorpus API.
    # @!attribute [rw] tree_depth
    #   @return [::Integer]
    #     The depth of the tree-based structure. Only depth values of 2 and 3 are
    #     supported.
    #
    #     Recommended value is 2 if you have if you have O(10K) files in the
    #     RagCorpus and set this to 3 if more than that.
    #
    #     Default value is 2.
    # @!attribute [rw] leaf_count
    #   @return [::Integer]
    #     Number of leaf nodes in the tree-based structure. Each leaf node
    #     contains groups of closely related vectors along with their
    #     corresponding centroid.
    #
    #     Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus).
    #
    #     Default value is 500.
    class ANN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end
  end

  # The config for the Pinecone.
  # @!attribute [rw] index_name
  #   @return [::String]
  #     Pinecone index name.
  #     This value cannot be changed after it's set.
  class Pinecone
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # The config for the Vertex Vector Search.
  # @!attribute [rw] index_endpoint
  #   @return [::String]
  #     The resource name of the Index Endpoint.
  #     Format:
  #     `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}`
  # @!attribute [rw] index
  #   @return [::String]
  #     The resource name of the Index.
  #     Format:
  #     `projects/{project}/locations/{location}/indexes/{index}`
  class VertexVectorSearch
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#rag_embedding_model_config ⇒ ::Google::Cloud::AIPlatform::V1::RagEmbeddingModelConfig

Returns Optional. Immutable. The embedding model config of the Vector DB.

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/vertex_rag_data.rb', line 82

class RagVectorDbConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The config for the default RAG-managed Vector DB.
  # @!attribute [rw] knn
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::KNN]
  #     Performs a KNN search on RagCorpus.
  #     Default choice if not specified.
  #
  #     Note: The following fields are mutually exclusive: `knn`, `ann`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  # @!attribute [rw] ann
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::ANN]
  #     Performs an ANN search on RagCorpus. Use this if you have a lot of
  #     files (> 10K) in your RagCorpus and want to reduce the search latency.
  #
  #     Note: The following fields are mutually exclusive: `ann`, `knn`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  class RagManagedDb
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Config for KNN search.
    class KNN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Config for ANN search.
    #
    # RagManagedDb uses a tree-based structure to partition data and
    # facilitate faster searches. As a tradeoff, it requires longer indexing
    # time and manual triggering of index rebuild via the ImportRagFiles and
    # UpdateRagCorpus API.
    # @!attribute [rw] tree_depth
    #   @return [::Integer]
    #     The depth of the tree-based structure. Only depth values of 2 and 3 are
    #     supported.
    #
    #     Recommended value is 2 if you have if you have O(10K) files in the
    #     RagCorpus and set this to 3 if more than that.
    #
    #     Default value is 2.
    # @!attribute [rw] leaf_count
    #   @return [::Integer]
    #     Number of leaf nodes in the tree-based structure. Each leaf node
    #     contains groups of closely related vectors along with their
    #     corresponding centroid.
    #
    #     Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus).
    #
    #     Default value is 500.
    class ANN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end
  end

  # The config for the Pinecone.
  # @!attribute [rw] index_name
  #   @return [::String]
  #     Pinecone index name.
  #     This value cannot be changed after it's set.
  class Pinecone
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # The config for the Vertex Vector Search.
  # @!attribute [rw] index_endpoint
  #   @return [::String]
  #     The resource name of the Index Endpoint.
  #     Format:
  #     `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}`
  # @!attribute [rw] index
  #   @return [::String]
  #     The resource name of the Index.
  #     Format:
  #     `projects/{project}/locations/{location}/indexes/{index}`
  class VertexVectorSearch
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#rag_managed_db ⇒ ::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb

Returns The config for the RAG-managed Vector DB.

Note: The following fields are mutually exclusive: rag_managed_db, pinecone, vertex_vector_search. If a field in that set is populated, all other fields in the set will automatically be cleared.

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/vertex_rag_data.rb', line 82

class RagVectorDbConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The config for the default RAG-managed Vector DB.
  # @!attribute [rw] knn
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::KNN]
  #     Performs a KNN search on RagCorpus.
  #     Default choice if not specified.
  #
  #     Note: The following fields are mutually exclusive: `knn`, `ann`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  # @!attribute [rw] ann
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::ANN]
  #     Performs an ANN search on RagCorpus. Use this if you have a lot of
  #     files (> 10K) in your RagCorpus and want to reduce the search latency.
  #
  #     Note: The following fields are mutually exclusive: `ann`, `knn`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  class RagManagedDb
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Config for KNN search.
    class KNN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Config for ANN search.
    #
    # RagManagedDb uses a tree-based structure to partition data and
    # facilitate faster searches. As a tradeoff, it requires longer indexing
    # time and manual triggering of index rebuild via the ImportRagFiles and
    # UpdateRagCorpus API.
    # @!attribute [rw] tree_depth
    #   @return [::Integer]
    #     The depth of the tree-based structure. Only depth values of 2 and 3 are
    #     supported.
    #
    #     Recommended value is 2 if you have if you have O(10K) files in the
    #     RagCorpus and set this to 3 if more than that.
    #
    #     Default value is 2.
    # @!attribute [rw] leaf_count
    #   @return [::Integer]
    #     Number of leaf nodes in the tree-based structure. Each leaf node
    #     contains groups of closely related vectors along with their
    #     corresponding centroid.
    #
    #     Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus).
    #
    #     Default value is 500.
    class ANN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end
  end

  # The config for the Pinecone.
  # @!attribute [rw] index_name
  #   @return [::String]
  #     Pinecone index name.
  #     This value cannot be changed after it's set.
  class Pinecone
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # The config for the Vertex Vector Search.
  # @!attribute [rw] index_endpoint
  #   @return [::String]
  #     The resource name of the Index Endpoint.
  #     Format:
  #     `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}`
  # @!attribute [rw] index
  #   @return [::String]
  #     The resource name of the Index.
  #     Format:
  #     `projects/{project}/locations/{location}/indexes/{index}`
  class VertexVectorSearch
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end

#vertex_vector_search ⇒ ::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::VertexVectorSearch

Returns The config for the Vertex Vector Search.

Note: The following fields are mutually exclusive: vertex_vector_search, rag_managed_db, pinecone. If a field in that set is populated, all other fields in the set will automatically be cleared.

Returns:



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# File 'proto_docs/google/cloud/aiplatform/v1/vertex_rag_data.rb', line 82

class RagVectorDbConfig
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The config for the default RAG-managed Vector DB.
  # @!attribute [rw] knn
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::KNN]
  #     Performs a KNN search on RagCorpus.
  #     Default choice if not specified.
  #
  #     Note: The following fields are mutually exclusive: `knn`, `ann`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  # @!attribute [rw] ann
  #   @return [::Google::Cloud::AIPlatform::V1::RagVectorDbConfig::RagManagedDb::ANN]
  #     Performs an ANN search on RagCorpus. Use this if you have a lot of
  #     files (> 10K) in your RagCorpus and want to reduce the search latency.
  #
  #     Note: The following fields are mutually exclusive: `ann`, `knn`. If a field in that set is populated, all other fields in the set will automatically be cleared.
  class RagManagedDb
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods

    # Config for KNN search.
    class KNN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end

    # Config for ANN search.
    #
    # RagManagedDb uses a tree-based structure to partition data and
    # facilitate faster searches. As a tradeoff, it requires longer indexing
    # time and manual triggering of index rebuild via the ImportRagFiles and
    # UpdateRagCorpus API.
    # @!attribute [rw] tree_depth
    #   @return [::Integer]
    #     The depth of the tree-based structure. Only depth values of 2 and 3 are
    #     supported.
    #
    #     Recommended value is 2 if you have if you have O(10K) files in the
    #     RagCorpus and set this to 3 if more than that.
    #
    #     Default value is 2.
    # @!attribute [rw] leaf_count
    #   @return [::Integer]
    #     Number of leaf nodes in the tree-based structure. Each leaf node
    #     contains groups of closely related vectors along with their
    #     corresponding centroid.
    #
    #     Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus).
    #
    #     Default value is 500.
    class ANN
      include ::Google::Protobuf::MessageExts
      extend ::Google::Protobuf::MessageExts::ClassMethods
    end
  end

  # The config for the Pinecone.
  # @!attribute [rw] index_name
  #   @return [::String]
  #     Pinecone index name.
  #     This value cannot be changed after it's set.
  class Pinecone
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end

  # The config for the Vertex Vector Search.
  # @!attribute [rw] index_endpoint
  #   @return [::String]
  #     The resource name of the Index Endpoint.
  #     Format:
  #     `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}`
  # @!attribute [rw] index
  #   @return [::String]
  #     The resource name of the Index.
  #     Format:
  #     `projects/{project}/locations/{location}/indexes/{index}`
  class VertexVectorSearch
    include ::Google::Protobuf::MessageExts
    extend ::Google::Protobuf::MessageExts::ClassMethods
  end
end