Initial implementation of NLU YAML parser

data/rasa_yaml_examples/nlu.yml

@@ -0,0 +1,10 @@
+nlu:
+- intent: estimate_emissions
+  # Arbitrary metadata
+  metadata:
+    author: Some example metadata!
+    key: value
+  # Multiline examples, each line is a separate training example.
+  examples: |
+     how much CO2 will that use?
+     how much carbon will a one way flight from [new york]{"entity": "city", "role": "from"} to california produce?

rasa/nlu/featurizers/sparse_featurizer/regex_featurizer.py

@@ -4,12 +4,12 @@
 from typing import Any, Dict, List, Optional, Text, Union, Type, Tuple
 
 import numpy as np
+import scipy.sparse
 
-from rasa.constants import DOCS_URL_TRAINING_DATA_NLU
 import rasa.utils.io
 import rasa.utils.io
-import scipy.sparse
 from rasa.nlu import utils
+from rasa.nlu.components import Component
 from rasa.nlu.config import RasaNLUModelConfig
 from rasa.nlu.constants import (
     RESPONSE,
@@ -19,12 +19,10 @@
     FEATURE_TYPE_SEQUENCE,
     FEATURIZER_CLASS_ALIAS,
 )
-from rasa.nlu.tokenizers.tokenizer import Tokenizer
-from rasa.nlu.components import Component
 from rasa.nlu.featurizers.featurizer import SparseFeaturizer, Features
-from rasa.nlu.training_data import Message, TrainingData
-import rasa.utils.common as common_utils
 from rasa.nlu.model import Metadata
+from rasa.nlu.tokenizers.tokenizer import Tokenizer
+from rasa.nlu.training_data import Message, TrainingData
 
 logger = logging.getLogger(__name__)
 
@@ -158,13 +156,6 @@ def _generate_lookup_regex(
         # if it's a list, it should be the elements directly
         if isinstance(lookup_elements, list):
             elements_to_regex = lookup_elements
-            common_utils.raise_warning(
-                "Directly including lookup tables as a list is deprecated since Rasa "
-                "1.6.",
-                FutureWarning,
-                docs=DOCS_URL_TRAINING_DATA_NLU + "#lookup-tables",
-            )
-
         # otherwise it's a file path.
         else:
 

rasa/nlu/training_data/__init__.py

@@ -1,3 +1,6 @@
+import rasa.nlu.training_data.entities_parser
+import rasa.nlu.training_data.synonyms_parser
+import rasa.nlu.training_data.lookup_tables_parser
 from rasa.nlu.training_data.loading import load_data
 from rasa.nlu.training_data.message import Message
 from rasa.nlu.training_data.training_data import TrainingData

rasa/nlu/training_data/entities_parser.py

@@ -0,0 +1,167 @@
+import re
+from json import JSONDecodeError
+from typing import Text, List, Dict, Match, Optional, NamedTuple, Any
+
+from rasa.constants import DOCS_URL_TRAINING_DATA_NLU
+from rasa.nlu.constants import (
+    ENTITY_ATTRIBUTE_GROUP,
+    ENTITY_ATTRIBUTE_TYPE,
+    ENTITY_ATTRIBUTE_ROLE,
+    ENTITY_ATTRIBUTE_VALUE,
+)
+from rasa.utils.common import raise_warning
+
+GROUP_ENTITY_VALUE = "value"
+GROUP_ENTITY_TYPE = "entity"
+GROUP_ENTITY_DICT = "entity_dict"
+GROUP_ENTITY_TEXT = "entity_text"
+GROUP_COMPLETE_MATCH = 0
+
+# regex for: `[entity_text]((entity_type(:entity_synonym)?)|{entity_dict})`
+ENTITY_REGEX = re.compile(
+    r"\[(?P<entity_text>[^\]]+?)\](\((?P<entity>[^:)]+?)(?:\:(?P<value>[^)]+))?\)|\{(?P<entity_dict>[^}]+?)\})"
+)
+
+
+class EntityAttributes(NamedTuple):
+    """Attributes of an entity defined in markdown data."""
+
+    type: Text
+    value: Text
+    text: Text
+    group: Optional[Text]
+    role: Optional[Text]
+
+
+def find_entities_in_training_example(example: Text) -> List[Dict[Text, Any]]:
+    """Extracts entities from an intent example.
+
+    Args:
+        example: Intent example.
+
+    Returns:
+        Extracted entities.
+    """
+    import rasa.nlu.utils as rasa_nlu_utils
+
+    entities = []
+    offset = 0
+
+    for match in re.finditer(ENTITY_REGEX, example):
+        entity_attributes = extract_entity_attributes(match)
+
+        start_index = match.start() - offset
+        end_index = start_index + len(entity_attributes.text)
+        offset += len(match.group(0)) - len(entity_attributes.text)
+
+        entity = rasa_nlu_utils.build_entity(
+            start_index,
+            end_index,
+            entity_attributes.value,
+            entity_attributes.type,
+            entity_attributes.role,
+            entity_attributes.group,
+        )
+        entities.append(entity)
+
+    return entities
+
+
+def extract_entity_attributes(match: Match) -> EntityAttributes:
+    """Extract the entity attributes, i.e. type, value, etc., from the
+    regex match.
+
+    Args:
+        match: Regex match to extract the entity attributes from.
+
+    Returns:
+        EntityAttributes object.
+    """
+    entity_text = match.groupdict()[GROUP_ENTITY_TEXT]
+
+    if match.groupdict()[GROUP_ENTITY_DICT]:
+        return extract_entity_attributes_from_dict(entity_text, match)
+
+    entity_type = match.groupdict()[GROUP_ENTITY_TYPE]
+
+    if match.groupdict()[GROUP_ENTITY_VALUE]:
+        entity_value = match.groupdict()[GROUP_ENTITY_VALUE]
+    else:
+        entity_value = entity_text
+
+    return EntityAttributes(entity_type, entity_value, entity_text, None, None)
+
+
+def extract_entity_attributes_from_dict(
+    entity_text: Text, match: Match
+) -> EntityAttributes:
+    """Extract entity attributes from dict format.
+
+    Args:
+        entity_text: Original entity text.
+        match: Regex match.
+
+    Returns:
+        Extracted entity attributes.
+    """
+    entity_dict_str = match.groupdict()[GROUP_ENTITY_DICT]
+    entity_dict = get_validated_dict(entity_dict_str)
+    return EntityAttributes(
+        entity_dict.get(ENTITY_ATTRIBUTE_TYPE),
+        entity_dict.get(ENTITY_ATTRIBUTE_VALUE, entity_text),
+        entity_text,
+        entity_dict.get(ENTITY_ATTRIBUTE_GROUP),
+        entity_dict.get(ENTITY_ATTRIBUTE_ROLE),
+    )
+
+
+def get_validated_dict(json_str: Text) -> Dict[Text, Text]:
+    """Converts the provided `json_str` to a valid dict containing the entity
+    attributes.
+
+    Users can specify entity roles, synonyms, groups for an entity in a dict, e.g.
+    [LA]{"entity": "city", "role": "to", "value": "Los Angeles"}.
+
+    Args:
+        json_str: The entity dict as string without "{}".
+
+    Raises:
+        ValidationError if validation of entity dict fails.
+        JSONDecodeError if provided entity dict is not valid json.
+
+    Returns:
+        Deserialized and validated `json_str`.
+    """
+    import json
+    import rasa.utils.validation as validation_utils
+    import rasa.nlu.schemas.data_schema as schema
+
+    # add {} as they are not part of the regex
+    try:
+        data = json.loads(f"{{{json_str}}}")
+    except JSONDecodeError as e:
+        raise_warning(
+            f"Incorrect training data format ('{{{json_str}}}'). Make sure your "
+            f"data is valid.",
+            docs=DOCS_URL_TRAINING_DATA_NLU,
+        )
+        raise e
+
+    validation_utils.validate_training_data(data, schema.entity_dict_schema())
+
+    return data
+
+
+def replace_entities(training_example: Text) -> Text:
+    """Replace special symbols related to the entities in the provided
+       training example.
+
+    Args:
+        training_example: Original training example with special symbols.
+
+    Returns:
+        String with removed special symbols.
+    """
+    return re.sub(
+        ENTITY_REGEX, lambda m: m.groupdict()[GROUP_ENTITY_TEXT], training_example
+    )

rasa/nlu/training_data/formats/__init__.py

@@ -1,3 +1,4 @@
+from rasa.nlu.training_data.formats.rasa_yaml import RasaYAMLReader
 from rasa.nlu.training_data.formats.dialogflow import DialogflowReader
 from rasa.nlu.training_data.formats.luis import LuisReader
 from rasa.nlu.training_data.formats.markdown import MarkdownReader, MarkdownWriter
@@ -7,3 +8,4 @@
     NLGMarkdownReader,
     NLGMarkdownWriter,
 )
+from rasa.nlu.training_data.formats.rasa_yaml import RasaYAMLReader