[QnA Maker] Multi-turn support for python

libraries/botbuilder-ai/botbuilder/ai/qna/models/__init__.py

@@ -9,7 +9,10 @@
 from .feedback_records import FeedbackRecords
 from .generate_answer_request_body import GenerateAnswerRequestBody
 from .metadata import Metadata
+from .prompt import Prompt
 from .qnamaker_trace_info import QnAMakerTraceInfo
+from .qna_request_context import QnARequestContext
+from .qna_response_context import QnAResponseContext
 from .query_result import QueryResult
 from .query_results import QueryResults
 from .train_request_body import TrainRequestBody
@@ -19,7 +22,10 @@
     "FeedbackRecords",
     "GenerateAnswerRequestBody",
     "Metadata",
+    "Prompt",
     "QnAMakerTraceInfo",
+    "QnARequestContext",
+    "QnAResponseContext",
     "QueryResult",
     "QueryResults",
     "TrainRequestBody",

libraries/botbuilder-ai/botbuilder/ai/qna/models/generate_answer_request_body.py

@@ -6,6 +6,7 @@
 from msrest.serialization import Model
 
 from .metadata import Metadata
+from .qna_request_context import QnARequestContext
 
 
 class GenerateAnswerRequestBody(Model):
@@ -16,6 +17,7 @@ class GenerateAnswerRequestBody(Model):
         "top": {"key": "top", "type": "int"},
         "score_threshold": {"key": "scoreThreshold", "type": "float"},
         "strict_filters": {"key": "strictFilters", "type": "[Metadata]"},
+        "context": {"key": "context", "type": "QnARequestContext"},
     }
 
     def __init__(
@@ -24,6 +26,7 @@ def __init__(
         top: int,
         score_threshold: float,
         strict_filters: List[Metadata],
+        context: QnARequestContext = None,
         **kwargs
     ):
         """
@@ -36,7 +39,10 @@ def __init__(
 
         score_threshold: Threshold for answers returned based on score.
 
-        strict_filters: Find only answers that contain these metadata.
+        strict_filters: Find answers that contains these metadata.
+
+        context: The context from which the QnA was extracted.
+
         """
 
         super().__init__(**kwargs)
@@ -45,3 +51,4 @@ def __init__(
         self.top = top
         self.score_threshold = score_threshold
         self.strict_filters = strict_filters
+        self.context = context

libraries/botbuilder-ai/botbuilder/ai/qna/models/prompt.py

@@ -0,0 +1,45 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+from msrest.serialization import Model
+
+
+class Prompt(Model):
+    """ Prompt Object. """
+
+    _attribute_map = {
+        "display_order": {"key": "displayOrder", "type": "int"},
+        "qna_id": {"key": "qnaId", "type": "int"},
+        "qna": {"key": "qna", "type": "object"},
+        "display_text": {"key": "displayText", "type": "str"},
+    }
+
+    def __init__(
+        self,
+        *,
+        display_order: int,
+        qna_id: int,
+        display_text: str,
+        qna: object = None,
+        **kwargs
+    ):
+        """
+        Parameters:
+        -----------
+
+        display_order: Index of the prompt - used in ordering of the prompts.
+
+        qna_id: QnA ID.
+
+        display_text: Text displayed to represent a follow up question prompt.
+
+        qna: The QnA object returned from the API (Optional).
+
+        """
+
+        super(Prompt, self).__init__(**kwargs)
+
+        self.display_order = display_order
+        self.qna_id = qna_id
+        self.display_text = display_text
+        self.qna = qna

libraries/botbuilder-ai/botbuilder/ai/qna/models/qna_request_context.py

@@ -0,0 +1,31 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+from msrest.serialization import Model
+
+
+class QnARequestContext(Model):
+    """
+    The context associated with QnA.
+    Used to mark if the current prompt is relevant with a previous question or not.
+    """
+
+    _attribute_map = {
+        "previous_qna_id": {"key": "previousQnAId", "type": "int"},
+        "prvious_user_query": {"key": "previousUserQuery", "type": "string"},
+    }
+
+    def __init__(self, previous_qna_id: int, prvious_user_query: str, **kwargs):
+        """
+        Parameters:
+        -----------
+
+        previous_qna_id: The previous QnA Id that was returned.
+
+        prvious_user_query: The previous user query/question.
+        """
+
+        super().__init__(**kwargs)
+
+        self.previous_qna_id = previous_qna_id
+        self.prvious_user_query = prvious_user_query

libraries/botbuilder-ai/botbuilder/ai/qna/models/qna_response_context.py

@@ -0,0 +1,35 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+from typing import List
+from msrest.serialization import Model
+from .prompt import Prompt
+
+
+class QnAResponseContext(Model):
+    """
+    The context associated with QnA.
+    Used to mark if the qna response has related prompts.
+    """
+
+    _attribute_map = {
+        "is_context_only": {"key": "isContextOnly", "type": "bool"},
+        "prompts": {"key": "prompts", "type": "[Prompt]"},
+    }
+
+    def __init__(
+        self, *, is_context_only: bool = False, prompts: List[Prompt] = None, **kwargs
+    ):
+        """
+        Parameters:
+        -----------
+
+        is_context_only: Whether this prompt is context only.
+
+        prompts: The prompts collection of related prompts.
+
+        """
+
+        super(QnAResponseContext, self).__init__(**kwargs)
+        self.is_context_only = is_context_only
+        self.prompts = prompts

libraries/botbuilder-ai/botbuilder/ai/qna/models/qnamaker_trace_info.py

@@ -6,10 +6,11 @@
 from botbuilder.schema import Activity
 from .metadata import Metadata
 from .query_result import QueryResult
+from .qna_request_context import QnARequestContext
 
 
 class QnAMakerTraceInfo:
-    """ Represents all the trice info that we collect from the QnAMaker Middleware. """
+    """ Represents all the trace info that we collect from the QnAMaker Middleware. """
 
     def __init__(
         self,
@@ -19,6 +20,7 @@ def __init__(
         score_threshold: float,
         top: int,
         strict_filters: List[Metadata],
+        context: QnARequestContext = None,
     ):
         """
         Parameters:
@@ -35,10 +37,13 @@ def __init__(
         top: Number of ranked results that are asked to be returned.
 
         strict_filters: Filters used on query.
+
+        context: (Optional) The context from which the QnA was extracted.
         """
         self.message = message
         self.query_results = query_results
         self.knowledge_base_id = knowledge_base_id
         self.score_threshold = score_threshold
         self.top = top
         self.strict_filters = strict_filters
+        self.context = context

libraries/botbuilder-ai/botbuilder/ai/qna/models/query_result.py

@@ -2,21 +2,35 @@
 # Licensed under the MIT License.
 
 from typing import List
-
+from msrest.serialization import Model
 from .metadata import Metadata
+from .qna_response_context import QnAResponseContext
 
 
-class QueryResult:
+class QueryResult(Model):
     """ Represents an individual result from a knowledge base query. """
 
+    _attribute_map = {
+        "questions": {"key": "questions", "type": "[str]"},
+        "answer": {"key": "answer", "type": "str"},
+        "score": {"key": "score", "type": "float"},
+        "metadata": {"key": "metadata", "type": "object"},
+        "source": {"key": "source", "type": "str"},
+        "id": {"key": "id", "type": "int"},
+        "context": {"key": "context", "type": "object"},
+    }
+
     def __init__(
         self,
+        *,
         questions: List[str],
         answer: str,
         score: float,
         metadata: object = None,
         source: str = None,
         id: int = None,  # pylint: disable=invalid-name
+        context: QnAResponseContext = None,
+        **kwargs
     ):
         """
         Parameters:
@@ -33,10 +47,14 @@ def __init__(
         source: The source from which the QnA was extracted (if any).
 
         id: The index of the answer in the knowledge base. V3 uses 'qnaId', V4 uses 'id' (if any).
+
+        context: The context from which the QnA was extracted.
         """
+        super(QueryResult, self).__init__(**kwargs)
         self.questions = questions
         self.answer = answer
         self.score = score
         self.metadata = list(map(lambda meta: Metadata(**meta), metadata))
         self.source = source
+        self.context = QnAResponseContext(**context) if context is not None else None
         self.id = id  # pylint: disable=invalid-name

libraries/botbuilder-ai/botbuilder/ai/qna/qnamaker.py

@@ -47,12 +47,6 @@ def __init__(
                 "QnAMaker.__init__(): endpoint is not an instance of QnAMakerEndpoint"
             )
 
-        if endpoint.host.endswith("v2.0"):
-            raise ValueError(
-                "v2.0 of QnA Maker service is no longer supported in the Bot Framework. Please upgrade your QnA Maker"
-                " service at www.qnamaker.ai."
-            )
-
         self._endpoint: str = endpoint
 
         opt = options or QnAMakerOptions()

libraries/botbuilder-ai/botbuilder/ai/qna/qnamaker_options.py

@@ -1,8 +1,7 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-from .models import Metadata
-
+from .models import Metadata, QnARequestContext
 
 # figure out if 300 milliseconds is ok for python requests library...or 100000
 class QnAMakerOptions:
@@ -12,8 +11,10 @@ def __init__(
         timeout: int = 0,
         top: int = 0,
         strict_filters: [Metadata] = None,
+        context: [QnARequestContext] = None,
     ):
         self.score_threshold = score_threshold
         self.timeout = timeout
         self.top = top
         self.strict_filters = strict_filters or []
+        self.context = context

libraries/botbuilder-ai/botbuilder/ai/qna/utils/generate_answer_utils.py

@@ -127,18 +127,22 @@ def _hydrate_options(self, query_options: QnAMakerOptions) -> QnAMakerOptions:
             ):
                 hydrated_options.timeout = query_options.timeout
 
+            if query_options.context:
+                hydrated_options.context = query_options.context
+
         return hydrated_options
 
     async def _query_qna_service(
-        self, context: TurnContext, options: QnAMakerOptions
+        self, turn_context: TurnContext, options: QnAMakerOptions
     ) -> List[QueryResult]:
         url = f"{ self._endpoint.host }/knowledgebases/{ self._endpoint.knowledge_base_id }/generateAnswer"
 
         question = GenerateAnswerRequestBody(
-            question=context.activity.text,
+            question=turn_context.activity.text,
             top=options.top,
             score_threshold=options.score_threshold,
             strict_filters=options.strict_filters,
+            context=options.context,
         )
 
         http_request_helper = HttpRequestUtils(self._http_client)
@@ -161,6 +165,7 @@ async def _emit_trace_info(
             score_threshold=options.score_threshold,
             top=options.top,
             strict_filters=options.strict_filters,
+            context=options.context,
         )
 
         trace_activity = Activity(
@@ -189,15 +194,6 @@ async def _format_qna_result(
             answers_within_threshold, key=lambda ans: ans["score"], reverse=True
         )
 
-        # The old version of the protocol returns the id in a field called qnaId
-        # The following translates this old structure to the new
-        is_legacy_protocol: bool = self._endpoint.host.endswith(
-            "v2.0"
-        ) or self._endpoint.host.endswith("v3.0")
-        if is_legacy_protocol:
-            for answer in answers_within_threshold:
-                answer["id"] = answer.pop("qnaId", None)
-
         answers_as_query_results = list(
             map(lambda answer: QueryResult(**answer), sorted_answers)
         )

libraries/botbuilder-ai/tests/qna/test_data/AnswerWithHighScoreProvidedContext.json

@@ -0,0 +1,18 @@
+{
+    "answers": [
+      {
+        "questions": [
+          "Where can I buy cleaning products?"
+        ],
+        "answer": "Any DIY store",
+        "score": 100,
+        "id": 55,
+        "source": "Editorial",
+        "metadata": [],
+        "context": {
+          "isContextOnly": true,
+          "prompts": []
+        }
+      }
+    ]
+  }