libtextclassifier/actions/actions_model.fbs

//
// Copyright (C) 2018 The Android Open Source Project
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//

include "annotator/model.fbs";
include "utils/codepoint-range.fbs";
include "utils/flatbuffers.fbs";
include "utils/intents/intent-config.fbs";
include "utils/resources.fbs";
include "utils/tokenizer.fbs";
include "utils/zlib/buffer.fbs";

file_identifier "TC3A";

// TensorFlow Lite model for suggesting actions.
namespace libtextclassifier3;
table TensorflowLiteModelSpec {
  // TensorFlow Lite model for suggesting actions.
  tflite_model:[ubyte] (force_align: 16);

  // Input specification.
  // (num messages,) int32 tensor, the user id per message.
  input_user_id:int = 0;

  // (num messages,) string tensor, each message of the conversation.
  input_context:int = 1;

  // int, the number of messages in the conversation.
  input_context_length:int = 2;

  // (num messages,) float tensor, the time difference in seconds of the
  // messages in the conversation.
  input_time_diffs:int = 3;

  // int, the number of smart replies to produce.
  input_num_suggestions:int = 4;

  // float, the output diversification distance parameter.
  input_diversification_distance:int = -1;

  // float, the empirical probability factor parameter.
  input_empirical_probability_factor:int = -1;

  // float, the confidence threshold.
  input_confidence_threshold:int = -1;

  // Input port for hashed and embedded tokens, a (num messages, max tokens,
  // embedding size) float tensor specifying the embeddings of each token of
  // each message in the conversation.
  input_token_embeddings:int = -1;

  // Input port for the number of tokens per message.
  // (num messages) int32 tensor specifying the number of tokens in each message
  // in the conversation.
  input_num_tokens:int = -1;

  // Output specification.
  output_replies:int = 0;

  output_replies_scores:int = 1;
  output_sensitive_topic_score:int = 3;
  output_triggering_score:int = 4;
  output_actions_scores:int = 5;

  // Model setup.
  // When true, the inputs are resized to the concrete input sizes before
  // inference otherwise, it's assumed that the model has the correct input
  // shapes set.
  resize_inputs:bool = false;

  // Input port for the hashed, embedded and flattened/concatenated tokens.
  // A (max tokens, embedding_size) float tensor specifying the embeddings of
  // each token.
  input_flattened_token_embeddings:int = -1;
}

// Configuration for the tokenizer.
namespace libtextclassifier3;
table ActionsTokenizerOptions {
  type:TokenizationType = INTERNAL_TOKENIZER;

  // If true, white space tokens will be kept when using the icu tokenizer.
  icu_preserve_whitespace_tokens:bool = false;

  // Codepoint ranges that determine what role the different codepoints play
  // during tokenized. The ranges must not overlap.
  tokenization_codepoint_config:[TokenizationCodepointRange];

  // A set of codepoint ranges to use in the mixed tokenization mode to identify
  // stretches of tokens to re-tokenize using the internal tokenizer.
  internal_tokenizer_codepoint_ranges:[CodepointRange];

  // If true, tokens will be also split when the codepoint's script_id changes
  // as defined in TokenizationCodepointRange.
  tokenize_on_script_change:bool = false;
}

// Configuration for the feature processor.
namespace libtextclassifier3;
table ActionsTokenFeatureProcessorOptions {
  // Tokenizer options.
  tokenizer_options:ActionsTokenizerOptions;

  // Serialized TensorFlow Lite model with weights for the token embeddings.
  embedding_model:[ubyte] (force_align: 16);

  // Size of the embedding.
  embedding_size:int = -1;

  // Number of bits for quantization for embeddings.
  embedding_quantization_bits:int = 8;

  // Number of buckets used for hashing charactergrams.
  num_buckets:int = -1;

  // Orders of charactergrams to extract, e.g. 2 means character bigrams, 3
  // character trigrams etc.
  chargram_orders:[int];

  // Whether to extract the token case feature.
  extract_case_feature:bool;

  // If true, will use the unicode-aware functionality for extracting features.
  unicode_aware_features:bool;

  // Regexp features to extract.
  regexp_features:[string];

  // Whether to remap digits to a single number.
  remap_digits:bool;

  // Whether to lowercase all tokens.
  lowercase_tokens:bool;

  // Maximum length of a word.
  max_token_length:int = 20;

  // The `max_num_tokens_per_message` and `min_num_tokens_per_message` are
  // applied when tokens are embedded per message.
  // If set and the number of tokens of a message is bigger than this limit,
  // tokens at the beginning of the message are dropped to fit the limit.
  max_num_tokens_per_message:int = -1;

  // If set, the tokens of each message will be padded to this fixed number of
  // tokens.
  min_num_tokens_per_message:int = -1;

  // If set and the total number of concatenated tokens is bigger than this
  // limit, tokens at the start of the conversation are dropped.
  max_num_total_tokens:int = -1;

  // If set and the total number of concatenaed tokens is smaller than this
  // limit, the conversation is padded with padding tokens.
  min_num_total_tokens:int = -1;

  // Id that is used as encoding of the padding token.
  padding_token_id:int = 0;

  // Id that is used as encoding of the start of message token.
  start_token_id:int = 1;

  // Id that is used as encoding of the end of message token.
  end_token_id:int = 2;
}

// N-Gram based linear regression model.
namespace libtextclassifier3;
table NGramLinearRegressionModel {
  // A flat list of all the hashed n-grams concatenated back to back. Elements
  // should only ever be accessed via the offset table below.
  hashed_ngram_tokens:[uint];

  // Offsets to the start of the n-grams in hashed_ngram_tokens. The last
  // element in this array is the length of hashed_ngrams to make it easier to
  // compute n-gram lengths.
  ngram_start_offsets:[ushort];

  // Weights of the n-grams.
  ngram_weights:[float];

  // The default weight assigned to n-grams that weren't matched.
  default_token_weight:float;

  // Maximum n-gram length to consider when calculating the denominatior.
  // This should usually be the same as max_ngram_length but can diverge
  // if additional (longer) n-grams are added to a model as part of a minor
  // update.
  max_denom_ngram_length:int;

  // If non-zero, the order of the skip-gram to match.
  max_skips:int;

  // The threshold above which the model output is considered positive.
  threshold:float;

  // Model specific tokenizer options.
  // If not specified, will reuse the feature processor tokenizer.
  tokenizer_options:ActionsTokenizerOptions;
}

namespace libtextclassifier3;
table TriggeringPreconditions {
  // Lower bound thresholds for the smart reply model prediction output.
  min_smart_reply_triggering_score:float;

  // Maximum sensitive score for which actions and smart replies are shown.
  max_sensitive_topic_score:float = 1;

  // Whether to suppress all model output when a conversation is classified as
  // sensitive.
  suppress_on_sensitive_topic:bool = true;

  // Thresholds on the model prediction input.
  // The minimal length of input to consider for prediction.
  min_input_length:int = 0;

  // The maximal length of input to consider for prediciton, -1 if unbounded.
  max_input_length:int = -1;

  // Minimal fraction of messages in the input conversation that need to match
  // a locale that the model can handle.
  min_locale_match_fraction:float = 0.75;

  handle_missing_locale_as_supported:bool = false;
  handle_unknown_locale_as_supported:bool = false;

  // Filter input with low-confidence triggers.
  suppress_on_low_confidence_input:bool = true;

  // Same as low_confidence_rules in ActionsModel.
  // NOTE: Only fill this when the TriggeringPreconditions are pushed separately
  // as a flag value (i.e. as overlay).
  low_confidence_rules:RulesModel;

  // Smart reply thresholds.
  diversification_distance_threshold:float = 0;

  confidence_threshold:float = 0;
  empirical_probability_factor:float = 0;
  min_reply_score_threshold:float = 0;
}

namespace libtextclassifier3;
table ActionSuggestionSpec {
  // Type of the action suggestion.
  type:string;

  // Text of a smart reply action.
  response_text:string;

  // Score.
  score:float;

  // Serialized entity information.
  serialized_entity_data:string;

  // Priority score used for internal conflict resolution.
  priority_score:float = 0;
}

// Options to specify triggering behaviour per action class.
namespace libtextclassifier3;
table ActionTypeOptions {
  // The name of the predicted action.
  name:string;

  // Triggering behaviour.
  // Whether the action class is considered in the model output or not.
  enabled:bool = true;

  // Minimal output score threshold.
  min_triggering_score:float = 0;

  // The action to trigger.
  action:ActionSuggestionSpec;
}

namespace libtextclassifier3.AnnotationActionsSpec_;
table AnnotationMapping {
  // The annotation collection.
  annotation_collection:string;

  // The action name to use.
  action:ActionSuggestionSpec;

  // Whether to use the score of the annotation as the action score.
  use_annotation_score:bool = true;

  // Minimum threshold for the annotation score for filtering.
  min_annotation_score:float;

  // If set, the text of the annotation will be used to set a field in the
  // action entity data.
  entity_field:FlatbufferFieldPath;
}

// Configuration for actions based on annotatations.
namespace libtextclassifier3;
table AnnotationActionsSpec {
  annotation_mapping:[AnnotationActionsSpec_.AnnotationMapping];

  // Whether to deduplicate annotations by type and text prior to generating
  // actions.
  deduplicate_annotations:bool = true;

  // Annotation usecase to specify for text annotation.
  annotation_usecase:AnnotationUsecase = ANNOTATION_USECASE_SMART;

  // Maximum number of recent messages to consider from any person.
  // We consider at most `max_history_from_any_person` many recent messages if
  // they were received from different users or at most the maximum of this and
  // `max_history_from_last_person` if they are all from the same user.
  max_history_from_any_person:int = 1;

  // Maximum number of recent messages to consider from the last person.
  max_history_from_last_person:int = 1;

  // Whether to include messages from the local user.
  include_local_user_messages:bool = false;

  // Whether to only consider messages up to the last one sent by the local
  // user.
  only_until_last_sent:bool = true;

  // If true, annotator would populare serialized_entity_data in the results.
  is_serialized_entity_data_enabled:bool = true;
}

// Ranking options.
namespace libtextclassifier3;
table RankingOptions {
  // When true, actions suggestions are deduplicated by `type`, `response_text`
  // and associated annotations, keeping the higher scoring actions.
  deduplicate_suggestions:bool = true;

  // When true, actions are deduplicated by the span they are referring to.
  deduplicate_suggestions_by_span:bool = true;

  // Optional script to run for ranking and filtering the action suggestions.
  // The following global variables are available to the script:
  // * input: (optionally deduplicated) action suggestions, via the `actions`
  // global
  // * output: indices of the actions to keep in the provided order.
  lua_ranking_script:string;

  compressed_lua_ranking_script:CompressedBuffer;

  // If true, suppresses smart replies if other smart actions are suggested.
  suppress_smart_replies_with_actions:bool = false;

  // If true, keep actions from the same entities together for ranking.
  group_by_annotations:bool = true;
}

// Entity data to set from capturing groups.
namespace libtextclassifier3.RulesModel_.Rule_.RuleActionSpec_;
table RuleCapturingGroup {
  // The id of group.
  group_id:int;

  // If set, the text of the capturing group will be used to set a field
  // in the action entity data.
  entity_field:FlatbufferFieldPath;

  // If set, the capturing group will be used to create a text annotation
  // with the given name and type.
  annotation_type:string;

  annotation_name:string;

  // If set, the capturing group text will be used to create a text
  // reply.
  text_reply:ActionSuggestionSpec;
}

// The actions to produce upon triggering.
namespace libtextclassifier3.RulesModel_.Rule_;
table RuleActionSpec {
  // The action.
  action:ActionSuggestionSpec;

  capturing_group:[RuleActionSpec_.RuleCapturingGroup];
}

// List of regular expression matchers.
namespace libtextclassifier3.RulesModel_;
table Rule {
  // The regular expression pattern.
  pattern:string;

  compressed_pattern:CompressedBuffer;
  actions:[Rule_.RuleActionSpec];

  // Patterns for post-checking the outputs.
  output_pattern:string;

  compressed_output_pattern:CompressedBuffer;
}

// Rule based actions.
namespace libtextclassifier3;
table RulesModel {
  rule:[RulesModel_.Rule];

  // If true, will compile the regexes only on first use.
  lazy_regex_compilation:bool = true;
}

namespace libtextclassifier3;
table ActionsModel {
  // Comma-separated list of locales supported by the model as BCP 47 tags.
  locales:string;

  // Version of the actions model.
  version:int;

  // A name for the model that can be used e.g. for logging.
  name:string;

  tflite_model_spec:TensorflowLiteModelSpec;

  // Output classes.
  smart_reply_action_type:string;

  action_type:[ActionTypeOptions];

  // Triggering conditions of the model.
  preconditions:TriggeringPreconditions;

  // Default number of smart reply predictions.
  num_smart_replies:int = 3;

  // Length of message history to consider, -1 if unbounded.
  max_conversation_history_length:int = 1;

  // Configuration for mapping annotations to action suggestions.
  annotation_actions_spec:AnnotationActionsSpec;

  // Configuration for rules.
  rules:RulesModel;

  // Configuration for intent generation on Android.
  android_intent_options:IntentFactoryModel;

  // Model resources.
  resources:ResourcePool;

  // Schema data for handling entity data.
  actions_entity_data_schema:[ubyte];

  // Action ranking options.
  ranking_options:RankingOptions;

  // Lua based actions.
  lua_actions_script:string;

  compressed_lua_actions_script:CompressedBuffer;

  // Low confidence classifiers.
  low_confidence_rules:RulesModel;

  low_confidence_ngram_model:NGramLinearRegressionModel;

  // Feature processor options.
  feature_processor_options:ActionsTokenFeatureProcessorOptions;
}

root_type libtextclassifier3.ActionsModel;