documentation for the REST API is now complete

mc_backend/csm/app/api/textcomplexityAPI.py

+import rapidjson as json
 from mcserver.app.models import AnnisResponse, TextComplexity
 from mcserver.app.services import NetworkService, CorpusService, TextComplexityService
 from openapi.openapi_server.models import TextComplexityForm
@@ -5,7 +6,7 @@ from openapi.openapi_server.models import TextComplexityForm
 
 def post(complexity_data: dict):
     tcf: TextComplexityForm = TextComplexityForm.from_dict(complexity_data)
-    ar: AnnisResponse = AnnisResponse.from_dict(
-        tcf.annis_response.to_dict()) if tcf.annis_response else CorpusService.get_corpus(tcf.urn, is_csm=True)
+    ar: AnnisResponse = AnnisResponse.from_dict(json.loads(tcf.annis_response)) if tcf.annis_response \
+        else CorpusService.get_corpus(tcf.urn, is_csm=True)
     tc: TextComplexity = TextComplexityService.text_complexity(tcf.measure, tcf.urn, True, ar.graph_data)
     return NetworkService.make_json_response(tc.to_dict())

mc_backend/csm/csm_api.yaml

@@ -39,4 +39,4 @@ paths:
         content:
           application/x-www-form-urlencoded:
             schema:
-              $ref: '../openapi_models.yaml#/components/schemas/TextComplexityFormBase'
+              $ref: '../openapi_models.yaml#/components/schemas/TextComplexityForm'

mc_backend/mcserver/app/api/exerciseListAPI.py

@@ -7,6 +7,7 @@ from mcserver.app import db
 from mcserver.app.models import Language, VocabularyCorpus, ResourceType
 from mcserver.app.services import NetworkService, FileService
 from mcserver.models_auto import Exercise, UpdateInfo
+from openapi.openapi_server.models import MatchingExercise
 
 
 def get(lang: str, frequency_upper_bound: int, last_update_time: int, vocabulary: str = ""):
@@ -29,13 +30,11 @@ def get(lang: str, frequency_upper_bound: int, last_update_time: int, vocabulary
         lang = Language.English
     exercises: List[Exercise] = db.session.query(Exercise).filter_by(language=lang.value)
     db.session.commit()
-    ret_val: List[dict] = [NetworkService.serialize_exercise(x, compress=True) for x in exercises]
-    matching_degrees: List[float] = []
+    matching_exercises: List[MatchingExercise] = [MatchingExercise.from_dict(x.to_dict()) for x in exercises]
     if len(vocabulary_set):
-        for exercise in exercises:
+        for exercise in matching_exercises:
             conll: List[TokenList] = conllu.parse(exercise.conll)
             lemmata: List[str] = [tok["lemma"] for sent in conll for tok in sent.tokens]
-            matching_degrees.append(sum((1 if x in vocabulary_set else 0) for x in lemmata) / len(lemmata) * 100)
-        for i in range(len(ret_val)):
-            ret_val[i]["matching_degree"] = matching_degrees[i]
+            exercise.matching_degree = sum((1 if x in vocabulary_set else 0) for x in lemmata) / len(lemmata) * 100
+    ret_val: List[dict] = [NetworkService.serialize_exercise(x, compress=True) for x in matching_exercises]
     return NetworkService.make_json_response(ret_val)

mc_backend/mcserver/app/api/fileAPI.py

@@ -4,15 +4,10 @@ import os
 import uuid
 from datetime import datetime
 from typing import List, Union
-
 import connexion
-import flask
 from connexion.lifecycle import ConnexionResponse
 from flask import send_from_directory, Response
-from flask_restful import Resource, abort
-from flask_restful.reqparse import RequestParser
 from werkzeug.wrappers import ETagResponseMixin
-
 from mcserver.app import db
 from mcserver.app.models import FileType, ResourceType, DownloadableFile, MimeType, XapiStatement, LearningResultMC
 from mcserver.app.services import FileService, NetworkService

mc_backend/mcserver/app/api/validReffAPI.py

@@ -2,15 +2,17 @@ from typing import List, Union
 import connexion
 from connexion.lifecycle import ConnexionResponse
 from flask import Response
-
 from mcserver import Config
 from mcserver.app.services import CorpusService, NetworkService, CustomCorpusService
 
 
 def get(urn: str) -> Union[Response, ConnexionResponse]:
     """The GET method for the valid references REST API. It provides references for the desired text."""
-    reff: List[str] = CustomCorpusService.get_custom_corpus_reff(urn) if CustomCorpusService.is_custom_corpus_urn(
-        urn) else CorpusService.get_standard_corpus_reff(urn)
+    try:
+        reff: List[str] = CustomCorpusService.get_custom_corpus_reff(urn) if CustomCorpusService.is_custom_corpus_urn(
+            urn) else CorpusService.get_standard_corpus_reff(urn)
+    except ValueError:
+        return connexion.problem(400, Config.ERROR_TITLE_BAD_REQUEST, Config.ERROR_MESSAGE_BAD_REQUEST)
     if not reff:
         return connexion.problem(404, Config.ERROR_TITLE_NOT_FOUND, Config.ERROR_MESSAGE_CORPUS_NOT_FOUND)
     return NetworkService.make_json_response(reff)

mc_backend/mcserver/app/services/customCorpusService.py

@@ -2,12 +2,9 @@ import ntpath
 import os
 from collections import OrderedDict
 from typing import List, Tuple, Set, Dict
-
 import conllu
 import rapidjson as json
 from conllu import TokenList
-from flask_restful import abort
-
 from mcserver import Config
 from mcserver.app.models import CustomCorpus, CitationLevel, TextPart, Citation, CorpusMC
 from mcserver.app.services import AnnotationService, FileService
@@ -132,7 +129,10 @@ class CustomCorpusService:
         if cts_urn == target_corpus.corpus.source_urn:
             disk_reff = [(":".join([cts_urn, str(x.citation.value)])) for x in target_corpus.text_parts]
         else:
-            disk_reff = CustomCorpusService.get_custom_corpus_sub_reff(cts_urn, target_corpus)
+            try:
+                disk_reff = CustomCorpusService.get_custom_corpus_sub_reff(cts_urn, target_corpus)
+            except ValueError:
+                raise
         with open(os.path.join(Config.REFF_CACHE_DIRECTORY, disk_urn), "w+") as f:
             f.write(json.dumps(disk_reff))
         return disk_reff
@@ -145,7 +145,7 @@ class CustomCorpusService:
         try:
             citation_parts = [int(x) for x in urn_parts[-1].split(".")]
         except ValueError:
-            abort(400)
+            raise
         target_text_parts: List[TextPart] = target_corpus.text_parts
         if len(citation_parts) > 1:
             target_text_parts = next(

mc_backend/mcserver/app/services/networkService.py

@@ -9,7 +9,8 @@ from flask_restful.reqparse import RequestParser
 
 from mcserver import Config
 from mcserver.app.models import StaticExercise
-from mcserver.models_auto import Exercise, TExercise
+from mcserver.models_auto import Exercise
+from openapi.openapi_server.models import MatchingExercise
 
 
 class NetworkService:
@@ -33,12 +34,12 @@ class NetworkService:
         return response
 
     @staticmethod
-    def serialize_exercise(exercise: TExercise, compress: bool) -> dict:
+    def serialize_exercise(exercise: MatchingExercise, compress: bool) -> dict:
         """ Serializes an exercise to JSON format. """
-        ret_val: dict = exercise.to_dict()
-        ret_val["conll"] = "" if compress else exercise.conll
+        exercise.conll = "" if compress else exercise.conll
         # convert the POSIX timestamp to JSON / Javascript, i.e. from seconds to milliseconds
-        ret_val["last_access_time"] = exercise.last_access_time * 1000
+        exercise.last_access_time = exercise.last_access_time * 1000
+        ret_val: dict = exercise.to_dict()
         ret_val["search_values"] = json.loads(exercise.search_values)
-        ret_val["solutions"] = "[]" if compress else json.loads(exercise.solutions)
+        ret_val["solutions"] = [] if compress else json.loads(exercise.solutions)
         return ret_val

mc_backend/mcserver/app/services/textComplexityService.py

@@ -5,6 +5,7 @@ import requests
 from mcserver import Config
 from mcserver.app.models import GraphData, TextComplexity, TextComplexityMeasure, AnnisResponse
 from mcserver.app.services import TextService, AnnotationService, CorpusService
+from openapi.openapi_server.models import TextComplexityForm
 
 
 class TextComplexityService:
@@ -183,6 +184,7 @@ class TextComplexityService:
             url: str = f"{Config.INTERNET_PROTOCOL}{Config.HOST_IP_CSM}:" + \
                        f"{Config.CORPUS_STORAGE_MANAGER_PORT}{Config.SERVER_URI_TEXT_COMPLEXITY}"
             ar: AnnisResponse = AnnisResponse(graph_data=gd)
-            response: requests.Response = requests.post(url, data=json.dumps(
-                dict(urn=urn, measure=TextComplexityMeasure.all.name, annis_response=ar.to_dict())))
+            tcf: TextComplexityForm = TextComplexityForm(urn=urn, measure=TextComplexityMeasure.all.name,
+                                                         annis_response=json.dumps(ar.to_dict()))
+            response: requests.Response = requests.post(url, data=tcf.to_dict())
             return TextComplexity.from_dict(json.loads(response.text))

mc_backend/mcserver/config.py

@@ -73,6 +73,9 @@ class Config(object):
     DEBUG = False
     DOCKER_SERVICE_NAME_CSM = "csm"
     DOCKER_SERVICE_NAME_MCSERVER = "mcserver"
+    ERROR_MESSAGE_BAD_REQUEST = \
+        "The server cannot or will not process the request due to something that is perceived to be a client " \
+        "error (e.g., malformed request syntax, invalid request message framing, or deceptive request routing)."
     ERROR_MESSAGE_CORPUS_NOT_FOUND = "A corpus with the specified ID was not found!"
     ERROR_MESSAGE_EXERCISE_NOT_FOUND = "An exercise with the specified ID was not found!"
     ERROR_MESSAGE_INTERNAL_SERVER_ERROR = \
@@ -83,6 +86,7 @@ class Config(object):
     ERROR_MESSAGE_UNPROCESSABLE_ENTITY = \
         "The server understands the content type of the request entity, and the syntax of the request entity is " \
         "correct, but it was unable to process the contained instructions."
+    ERROR_TITLE_BAD_REQUEST = "Bad Request"
     ERROR_TITLE_INTERNAL_SERVER_ERROR = "Internal Server Error"
     ERROR_TITLE_NOT_FOUND = "Not found"
     ERROR_TITLE_SERVICE_UNAVAILABLE = "Service Unavailable"

mc_backend/mcserver/mcserver_api.yaml

@@ -12,7 +12,7 @@ paths:
       summary: Returns a list of corpora.
       operationId: mcserver.app.api.corpusListAPI.get
       responses:
-        200:
+        "200":
           description: Corpus list
           content:
             application/json:
@@ -39,7 +39,7 @@ paths:
       summary: Deletes a single corpus by ID.
       operationId: mcserver.app.api.corpusAPI.delete
       responses:
-        200:
+        "200":
           description: Indication of success
           content:
             application/json:
@@ -50,7 +50,7 @@ paths:
       summary: Returns a single corpus by ID.
       operationId: mcserver.app.api.corpusAPI.get
       responses:
-        200:
+        "200":
           description: Corpus object
           content:
             application/json:
@@ -60,7 +60,7 @@ paths:
       summary: Updates a single corpus by ID.
       operationId: mcserver.app.api.corpusAPI.patch
       responses:
-        200:
+        "200":
           description: Corpus object
           content:
             application/json:
@@ -93,7 +93,7 @@ paths:
       summary: Returns exercise data by ID.
       operationId: mcserver.app.api.exerciseAPI.get
       responses:
-        200:
+        "200":
           description: Exercise data object, including a graph model for linguistic annotations.
           content:
             application/json:
@@ -105,7 +105,7 @@ paths:
       summary: Creates a new exercise.
       operationId: mcserver.app.api.exerciseAPI.post
       responses:
-        200:
+        "200":
           description: Exercise data object
           content:
             application/json:
@@ -122,12 +122,14 @@ paths:
       summary: Provides metadata for all available exercises.
       operationId: mcserver.app.api.exerciseListAPI.get
       responses:
-        200:
+        "200":
           description: Data for interactive exercises, excluding the linguistic details.
           content:
             application/json:
               schema:
-                $ref: '../openapi_models.yaml#/components/schemas/Exercise'
+                type: array
+                items:
+                  $ref: '../openapi_models.yaml#/components/schemas/MatchingExercise'
       parameters:
         - $ref: '../openapi_models.yaml#/components/parameters/LangParam'
         - name: frequency_upper_bound
@@ -157,12 +159,18 @@ paths:
       summary: Provides the URL to download a specific file.
       operationId: mcserver.app.api.fileAPI.get
       responses:
-        200:
+        "200":
           description: Data for interactive exercises, excluding the linguistic details.
           content:
-            application/json:
+            application/pdf:
+              schema:
+                type: object
+            application/vnd.openxmlformats-officedocument.wordprocessingml.document:
+              schema:
+                type: object
+            application/xml:
               schema:
-                $ref: '../openapi_models.yaml#/components/schemas/Exercise'
+                type: object
       parameters:
         - name: id
           in: query
@@ -182,7 +190,7 @@ paths:
       summary: Serializes and persists learning results or HTML content for later access.
       operationId: mcserver.app.api.fileAPI.post
       responses:
-        200:
+        "200":
           description: Indication of success, possibly a reference to the resulting file.
           content:
             application/json:
@@ -218,7 +226,7 @@ paths:
       summary: Returns results for a frequency query from ANNIS for a given CTS URN.
       operationId: mcserver.app.api.frequencyAPI.get
       responses:
-        200:
+        "200":
           description: Frequency analysis, i.e. a list of frequency items.
           content:
             application/json:
@@ -234,7 +242,7 @@ paths:
       summary: Provides JSON templates for client-side H5P exercise layouts.
       operationId: mcserver.app.api.h5pAPI.get
       responses:
-        200:
+        "200":
           description: JSON template for an interactive H5P exercise.
           content:
             application/json:
@@ -251,7 +259,7 @@ paths:
       summary: Provides example contexts for a given phenomenon in a given corpus.
       operationId: mcserver.app.api.kwicAPI.post
       responses:
-        200:
+        "200":
           description: HTML strings with SVG elements for rendering KWIC images.
           content:
             application/json:
@@ -268,7 +276,7 @@ paths:
       summary: Provides the raw text for a requested text passage.
       operationId: mcserver.app.api.rawTextAPI.get
       responses:
-        200:
+        "200":
           description: Graph data for the text passage, including raw text and annotations.
           content:
             application/json:
@@ -281,7 +289,7 @@ paths:
       summary: Returns metadata for static exercises.
       operationId: mcserver.app.api.staticExercisesAPI.get
       responses:
-        200:
+        "200":
           description: Metadata for static exercises, mapped to their respective URIs in the frontend.
           content:
             application/json:
@@ -294,7 +302,7 @@ paths:
       summary: Gives users measures of text complexity for a given text.
       operationId: mcserver.app.api.textcomplexityAPI.get
       responses:
-        200:
+        "200":
           description: Text complexity measures for a given text.
           content:
             application/json:
@@ -314,7 +322,7 @@ paths:
       summary: Gives users all the citable text references for a corpus.
       operationId: mcserver.app.api.validReffAPI.get
       responses:
-        200:
+        "200":
           description: Valid references for the desired text.
           content:
             application/json:
@@ -330,7 +338,7 @@ paths:
       summary: Provides network data for the vectors in an AI model.
       operationId: mcserver.app.api.vectorNetworkAPI.get
       responses:
-        200:
+        "200":
           description: HTML string with SVG elements for rendering the network graph.
           content:
             application/json:
@@ -369,7 +377,7 @@ paths:
       summary: Provides network data for the vectors in an AI model.
       operationId: mcserver.app.api.vectorNetworkAPI.post
       responses:
-        200:
+        "200":
           description: Sentences whose content is similar to a given word.
           content:
             application/json:
@@ -395,7 +403,7 @@ paths:
       summary: Shows how well the vocabulary of a text matches a predefined reference vocabulary.
       operationId: mcserver.app.api.vocabularyAPI.get
       responses:
-        200:
+        "200":
           description: Retrieves sentence ID and matching degree for each sentence in the query text.
           content:
             application/json:
@@ -428,7 +436,7 @@ paths:
       summary: Shows how well the vocabulary of a text matches a predefined reference vocabulary.
       operationId: mcserver.app.api.vocabularyAPI.post
       responses:
-        200:
+        "200":
           description: Indicates for each token of a corpus whether it is covered by a reference vocabulary.
           content:
             application/json:
@@ -443,7 +451,7 @@ paths:
 # include this here so the data model gets generated correctly
 components:
   schemas:
-    TextComplexityForm:
+    TextComplexityFormExtension:
       type: object
       allOf:
-        - $ref: '../openapi_models.yaml#/components/schemas/TextComplexityFormBase'
+        - $ref: '../openapi_models.yaml#/components/schemas/TextComplexityForm'

mc_backend/mcserver/models_auto.py

@@ -167,10 +167,10 @@ class ExerciseDict(_ExerciseDictBase, total=False):
     work_title: str
     conll: str
     exercise_type: str
-    exercise_type_translation: str
     solutions: str
     text_complexity: float
     urn: str
+    exercise_type_translation: str
 
 
 class TExercise(typing.Protocol):
@@ -197,13 +197,13 @@ class TExercise(typing.Protocol):
             string.
         eid: Unique identifier (UUID) for the exercise.
         exercise_type: Type of exercise, concerning interaction and layout.
-        exercise_type_translation: Localized expression of the exercise type.
         last_access_time: When the exercise was last accessed (as POSIX
             timestamp).
         solutions: Correct solutions for the exercise.
         text_complexity: Overall text complexity as measured by the software's
             internal language analysis.
         urn: CTS URN for the text passage from which the exercise was created.
+        exercise_type_translation: Localized expression of the exercise type.
 
     """
 
@@ -225,11 +225,11 @@ class TExercise(typing.Protocol):
     conll: str
     eid: str
     exercise_type: str
-    exercise_type_translation: str
     last_access_time: float
     solutions: str
     text_complexity: float
     urn: str
+    exercise_type_translation: str
 
     def __init__(
         self,
@@ -246,10 +246,10 @@ class TExercise(typing.Protocol):
         work_title: str = "",
         conll: str = "",
         exercise_type: str = "",
-        exercise_type_translation: str = "",
         solutions: str = "[]",
         text_complexity: float = 0,
         urn: str = "",
+        exercise_type_translation: str = "",
     ) -> None:
         """
         Construct.
@@ -273,8 +273,6 @@ class TExercise(typing.Protocol):
                 single string.
             eid: Unique identifier (UUID) for the exercise.
             exercise_type: Type of exercise, concerning interaction and layout.
-            exercise_type_translation: Localized expression of the exercise
-                type.
             last_access_time: When the exercise was last accessed (as POSIX
                 timestamp).
             solutions: Correct solutions for the exercise.
@@ -282,6 +280,8 @@ class TExercise(typing.Protocol):
                 software's internal language analysis.
             urn: CTS URN for the text passage from which the exercise was
                 created.
+            exercise_type_translation: Localized expression of the exercise
+                type.
 
         """
         ...
@@ -302,10 +302,10 @@ class TExercise(typing.Protocol):
         work_title: str = "",
         conll: str = "",
         exercise_type: str = "",
-        exercise_type_translation: str = "",
         solutions: str = "[]",
         text_complexity: float = 0,
         urn: str = "",
+        exercise_type_translation: str = "",
     ) -> "TExercise":
         """
         Construct from a dictionary (eg. a POST payload).
@@ -329,8 +329,6 @@ class TExercise(typing.Protocol):
                 single string.
             eid: Unique identifier (UUID) for the exercise.
             exercise_type: Type of exercise, concerning interaction and layout.
-            exercise_type_translation: Localized expression of the exercise
-                type.
             last_access_time: When the exercise was last accessed (as POSIX
                 timestamp).
             solutions: Correct solutions for the exercise.
@@ -338,6 +336,8 @@ class TExercise(typing.Protocol):
                 software's internal language analysis.
             urn: CTS URN for the text passage from which the exercise was
                 created.
+            exercise_type_translation: Localized expression of the exercise
+                type.
 
         Returns:
             Model instance based on the dictionary.

mc_backend/mocks.py

@@ -761,7 +761,7 @@ class Mocks:
                                'Romanus', 'Solomon', 'amor']
     raw_text: str = "Caesar fortis est. Galli moriuntur."
     static_exercises_udpipe_string: str = "1\tscribere\tscribere\n1\tcommovere\tcommovere\n1\tC\tC\n1\tgaudere\tgaudere\n1\tsignum\tsignum\n1\tvas\tvas\n1\tclarus\tclarus\n1\tcondicio\tcondicio\n1\tcom\tcum\n1\tprae\tprae\n1\tmovere\tmovere\n1\tducere\tducere\n1\tde\tde\n1\tcum\tcum\n1\tistam\tiste\n1\tnationum\tnatio\n1\tclarissimae\tclarus\n1\tmoderationem\tmoderatio\n1\tanimi\tanimus\n1\tomnium\tomnis\n1\tgentium\tgens\n1\tac\tac\n1\tvirtutem\tvirtus\n1\tprovinciae\tprovincia\n1\tCaesar\tCaesar\n1\test\tesse\n1\tsatis\tsatis\n1\tgovernment\tgovernment\n1\tsocius\tsocius\n1\tprovincia\tprovincia\n1\tpublicus\tpublicus\n1\tcivis\tcivis\n1\tatque\tatque"
-    subgraph_json: str = '{"exercise_id":"","exercise_type":null,"frequency_analysis":null,"graph_data":{"directed":true,"graph":{},"links":[],"multigraph":true,"nodes":[{"annis_node_name":"urn:cts:latinLit:phi0448.phi001.perseus-lat2:1.1.1-1.1.1/doc1#sent1tok3","annis_node_type":"node","annis_tok":"Galli","annis_type":"node","id":"salt:/urn:cts:latinLit:phi0448.phi001.perseus-lat2:1.1.1-1.1.1/doc1#sent1tok3","is_oov":null,"udep_lemma":"Gallo","udep_upostag":"VERB","udep_xpostag":"L3|modQ|tem1|stAC","udep_feats":"Tense=Pres|VerbForm=Inf|Voice=Pass","solution":null}]},"solutions":[],"text_complexity":null,"uri":""}'
+    subgraph_json: str = '{"exercise_id":"","exercise_type":"","frequency_analysis":null,"graph_data":{"directed":true,"graph":{},"links":[],"multigraph":true,"nodes":[{"annis_node_name":"urn:cts:latinLit:phi0448.phi001.perseus-lat2:1.1.1-1.1.1/doc1#sent1tok3","annis_node_type":"node","annis_tok":"Galli","annis_type":"node","id":"salt:/urn:cts:latinLit:phi0448.phi001.perseus-lat2:1.1.1-1.1.1/doc1#sent1tok3","is_oov":null,"udep_lemma":"Gallo","udep_upostag":"VERB","udep_xpostag":"L3|modQ|tem1|stAC","udep_feats":"Tense=Pres|VerbForm=Inf|Voice=Pass","solution":null}]},"solutions":[],"text_complexity":null,"uri":""}'
     test_args: List[str] = ["tests.py", "-test"]
     text_complexity_json_string: str = '{"all":54.53,"avg_w_len":5.79,"avg_w_per_sent":17.33,"lex_den":0.73,"n_abl_abs":0,"n_clause":1,"n_gerund":1,"n_inf":1,"n_part":1,"n_punct":3,"n_sent":3,"n_subclause":0,"n_types":48,"n_w":52,"pos":11}'
     text_list: List[Tuple[str, str]] = [("urn:cts:latinLit:phi0448.phi001.perseus-lat2:1.1.1", raw_text.split(".")[0]),

mc_backend/openapi/openapi_server/controllers/default_controller.py

@@ -3,9 +3,9 @@ import six
 
 from openapi.openapi_server.models.annis_response import AnnisResponse  # noqa: E501
 from openapi.openapi_server.models.corpus import Corpus  # noqa: E501
-from openapi.openapi_server.models.exercise import Exercise  # noqa: E501
 from openapi.openapi_server.models.file_type import FileType  # noqa: E501
 from openapi.openapi_server.models.frequency_item import FrequencyItem  # noqa: E501
+from openapi.openapi_server.models.matching_exercise import MatchingExercise  # noqa: E501
 from openapi.openapi_server.models.sentence import Sentence  # noqa: E501
 from openapi.openapi_server.models.static_exercise import StaticExercise  # noqa: E501
 from openapi.openapi_server.models.text_complexity import TextComplexity  # noqa: E501
@@ -109,7 +109,7 @@ def mcserver_app_api_exercise_list_api_get(lang, frequency_upper_bound=None, las
     :param vocabulary: Identifier for a reference vocabulary.
     :type vocabulary: dict | bytes
 
-    :rtype: Exercise
+    :rtype: List[MatchingExercise]
     """
     if connexion.request.is_json:
         vocabulary =  VocabularyMC.from_dict(connexion.request.get_json())  # noqa: E501
@@ -128,7 +128,7 @@ def mcserver_app_api_file_api_get(id, type, solution_indices=None):  # noqa: E50
     :param solution_indices: Indices for the solutions that should be included in the download.
     :type solution_indices: List[int]
 
-    :rtype: Exercise
+    :rtype: object
     """
     if connexion.request.is_json:
         type =  FileType.from_dict(connexion.request.get_json())  # noqa: E501

mc_backend/openapi/openapi_server/models/__init__.py

@@ -5,9 +5,8 @@ from __future__ import absolute_import
 # import models into model package
 from openapi.openapi_server.models.annis_response import AnnisResponse
 from openapi.openapi_server.models.corpus import Corpus
-from openapi.openapi_server.models.exercise import Exercise
-from openapi.openapi_server.models.exercise_all_of import ExerciseAllOf
 from openapi.openapi_server.models.exercise_base import ExerciseBase
+from openapi.openapi_server.models.exercise_extension import ExerciseExtension
 from openapi.openapi_server.models.exercise_form import ExerciseForm
 from openapi.openapi_server.models.exercise_form_all_of import ExerciseFormAllOf
 from openapi.openapi_server.models.file_type import FileType
@@ -16,6 +15,8 @@ from openapi.openapi_server.models.graph_data import GraphData
 from openapi.openapi_server.models.inline_object import InlineObject
 from openapi.openapi_server.models.kwic_form import KwicForm
 from openapi.openapi_server.models.link import Link
+from openapi.openapi_server.models.matching_exercise import MatchingExercise
+from openapi.openapi_server.models.matching_exercise_all_of import MatchingExerciseAllOf
 from openapi.openapi_server.models.node_mc import NodeMC
 from openapi.openapi_server.models.phenomenon import Phenomenon
 from openapi.openapi_server.models.sentence import Sentence
@@ -24,7 +25,7 @@ from openapi.openapi_server.models.solution_element import SolutionElement
 from openapi.openapi_server.models.static_exercise import StaticExercise
 from openapi.openapi_server.models.text_complexity import TextComplexity
 from openapi.openapi_server.models.text_complexity_form import TextComplexityForm
-from openapi.openapi_server.models.text_complexity_form_base import TextComplexityFormBase
+from openapi.openapi_server.models.text_complexity_form_extension import TextComplexityFormExtension
 from openapi.openapi_server.models.vector_network_form import VectorNetworkForm
 from openapi.openapi_server.models.vocabulary_form import VocabularyForm
 from openapi.openapi_server.models.vocabulary_mc import VocabularyMC

mc_backend/openapi/openapi_server/models/annis_response.py

@@ -23,7 +23,7 @@ class AnnisResponse(Model):
     Do not edit the class manually.
     """
 
-    def __init__(self, exercise_id=None, exercise_type=None, frequency_analysis=None, graph_data=None, solutions=None, text_complexity=None, uri=None):  # noqa: E501
+    def __init__(self, exercise_id='', exercise_type='', frequency_analysis=None, graph_data=None, solutions=None, text_complexity=None, uri=''):  # noqa: E501
         """AnnisResponse - a model defined in OpenAPI
 
         :param exercise_id: The exercise_id of this AnnisResponse.  # noqa: E501

mc_backend/openapi/openapi_server/models/annis_response_frequency_analysis.py

-# coding: utf-8
-
-from __future__ import absolute_import
-from datetime import date, datetime  # noqa: F401
-
-from typing import List, Dict  # noqa: F401
-
-from openapi.openapi_server.models.base_model_ import Model
-from openapi.openapi_server import util
-
-
-class AnnisResponseFrequencyAnalysis(Model):