[GenAIOrchestrator] Add QA chain

gen-ai/orchestrator-server/src/main/python/server/src/gen_ai_orchestrator/main.py

@@ -29,10 +29,11 @@
 from gen_ai_orchestrator.routers.app_monitors_router import (
     application_check_router,
 )
-from gen_ai_orchestrator.routers.em_providers_router import em_providers_router
 from gen_ai_orchestrator.routers.completion_router import completion_router
+from gen_ai_orchestrator.routers.em_providers_router import em_providers_router
 from gen_ai_orchestrator.routers.llm_providers_router import llm_providers_router
 from gen_ai_orchestrator.routers.observability_providers_router import observability_providers_router
+from gen_ai_orchestrator.routers.qa_router import qa_router
 from gen_ai_orchestrator.routers.rag_router import rag_router
 
 # configure logging
@@ -54,6 +55,7 @@
 app.include_router(em_providers_router)
 app.include_router(observability_providers_router)
 app.include_router(rag_router)
+app.include_router(qa_router)
 app.include_router(completion_router)
 
 logger.info('Generative AI Orchestrator - Startup')

gen-ai/orchestrator-server/src/main/python/server/src/gen_ai_orchestrator/models/rag/rag_models.py

@@ -15,40 +15,49 @@
 """Module for RAG Models"""
 
 from enum import Enum, unique
-from typing import Optional, List
+from typing import List, Optional
 
 from pydantic import AnyUrl, BaseModel, Field, HttpUrl
 
-from gen_ai_orchestrator.models.vector_stores.vector_stores_types import DocumentSearchParams
+from gen_ai_orchestrator.models.vector_stores.vector_stores_types import (
+    DocumentSearchParams,
+)
 
 
-class Footnote(BaseModel):
-    """A footnote model, used to associate document sources with the RAG answer"""
+class Source(BaseModel):
+    """A source model, used to associate document sources with the QA response"""
 
-    identifier: str = Field(description='Footnote identifier', examples=['1'])
-    title: str = Field(description='Footnote title', examples=['Tock Documentation'])
+    title: str = Field(description='Source title', examples=['Tock Documentation'])
     url: Optional[AnyUrl] = Field(
-        description='Footnote url', examples=['https://doc.tock.ai/tock/'], default=None
+        description='Source url', examples=['https://doc.tock.ai/tock/'], default=None
+    )
+    content: str = Field(
+        description='Source content', examples=['Tock: The Open Conversation Kit']
     )
-    content: str = Field(description='Footnote content', examples=['Tock: The Open Conversation Kit'])
 
     def __eq__(self, other):
         """
-        Footnotes are identified by their title and URL.
-        When the identifier or the content are not the same for identical footnotes,
+        Source are identified by their title and URL.
+        When the identifier or the content are not the same for identical sources,
         this means that the sources are parts of the same document.
         """
         return (
-                isinstance(other, Footnote)
-                and self.title == other.title
-                and self.url == other.url
-                and self.content == other.content
+            isinstance(other, Source)
+            and self.title == other.title
+            and self.url == other.url
+            and self.content == other.content
         )
 
     def __hash__(self):
         return hash((self.title, self.url, self.content))
 
 
+class Footnote(Source):
+    """A footnote model, used to associate document sources with the RAG answer"""
+
+    identifier: str = Field(description='Footnote identifier', examples=['1'])
+
+
 class TextWithFootnotes(BaseModel):
     """Text with its footnotes. Used for RAG response"""
 
@@ -82,73 +91,75 @@ class RagDocumentMetadata(BaseModel):
     index_session_id: str = Field(
         description='The indexing session id.', examples=['123f-ed01-gt21-gg08']
     )
-    id: str = Field(
-        description='The document id.', examples=['e014-g24-0f11-1g3e']
-    )
-    title: str = Field(
-        description='The document title.',
-        examples=['Tracking shot'])
+    id: str = Field(description='The document id.', examples=['e014-g24-0f11-1g3e'])
+    title: str = Field(description='The document title.', examples=['Tracking shot'])
     url: Optional[HttpUrl] = Field(
         description='The document url.',
         examples=['https://en.wikipedia.org/wiki/Tracking_shot'],
-        default=None
-    )
-    chunk: str = Field(
-        description='The document chunk.',
-        examples=['1/3']
+        default=None,
     )
+    chunk: str = Field(description='The document chunk.', examples=['1/3'])
 
 
 class RagDocument(BaseModel):
     """The definition of RAG document"""
 
     content: str = Field(
         description='The document content.',
-        examples=['In cinematography, a tracking shot is any shot where the camera follows backward, '
-                  'forward or moves alongside the subject being recorded.']
+        examples=[
+            'In cinematography, a tracking shot is any shot where the camera follows backward, '
+            'forward or moves alongside the subject being recorded.'
+        ],
     )
     metadata: RagDocumentMetadata = Field(
         description='The document metadata.',
     )
 
 
-class RagDebugData(BaseModel):
-    """A RAG debug data"""
+class QADebugData(BaseModel):
+    """A QA debug data. This class is not currently used in the QA chain as Langfuse is now supported."""
 
     user_question: Optional[str] = Field(
-        description='The user\'s initial question.',
-        examples=["I'm interested in going to Morocco"]
+        description="The user's initial question.",
+        examples=["I'm interested in going to Morocco"],
+    )
+    documents: List[RagDocument] = Field(
+        description='Documents retrieved from the vector store.'
+    )
+    document_index_name: str = Field(
+        description='Index name corresponding to a document collection in the vector database.',
+    )
+    document_search_params: DocumentSearchParams = Field(
+        description='The document search parameters. Ex: number of documents, metadata filter',
     )
+    duration: float = Field(
+        description='The duration of RAG in seconds.', examples=['7.2']
+    )
+
+
+class RagDebugData(QADebugData):
+    """A RAG debug data"""
+
     condense_question_prompt: Optional[str] = Field(
         description='The prompt of the question rephrased with the history of the conversation.',
-        examples=["""Given the following conversation and a follow up question,
+        examples=[
+            """Given the following conversation and a follow up question,
         rephrase the follow up question to be a standalone question, in its original language.
         Chat History:
         Human: What travel offers are you proposing?
         Assistant: We offer trips to all of Europe and North Africa.
         Follow Up Input: I'm interested in going to Morocco
-        Standalone question:"""]
+        Standalone question:"""
+        ],
     )
     condense_question: Optional[str] = Field(
         description='The question rephrased with the history of the conversation.',
-        examples=['Hello, how to plan a trip to Morocco ?']
+        examples=['Hello, how to plan a trip to Morocco ?'],
     )
     question_answering_prompt: Optional[str] = Field(
         description='The question answering prompt.',
-        examples=['Question: Hello, how to plan a trip to Morocco ?. Answer in French.']
-    )
-    documents: List[RagDocument] = Field(
-        description='Documents retrieved from the vector store.'
-    )
-    document_index_name: str = Field(
-        description='Index name corresponding to a document collection in the vector database.',
-    )
-    document_search_params: DocumentSearchParams = Field(
-        description='The document search parameters. Ex: number of documents, metadata filter',
-    )
-    answer: str = Field(
-        description='The RAG answer.'
-    )
-    duration: float = Field(
-        description='The duration of RAG in seconds.', examples=['7.2']
+        examples=[
+            'Question: Hello, how to plan a trip to Morocco ?. Answer in French.'
+        ],
     )
+    answer: str = Field(description='The RAG answer.')

gen-ai/orchestrator-server/src/main/python/server/src/gen_ai_orchestrator/routers/qa_router.py

@@ -0,0 +1,32 @@
+#   Copyright (C) 2023-2024 Credit Mutuel Arkea
+#
+#   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.
+#
+"""QA Router Module"""
+
+from fastapi import APIRouter
+
+from gen_ai_orchestrator.routers.requests.requests import QAQuery
+from gen_ai_orchestrator.routers.responses.responses import QAResponse
+from gen_ai_orchestrator.services.qa.qa_service import qa
+
+qa_router = APIRouter(prefix='/qa', tags=['Question Answering'])
+
+
+@qa_router.post('')
+async def ask_qa(query: QAQuery) -> QAResponse:
+    """
+    ## Ask a QA System
+    Ask question to a QA System, and return sources founded in a knowledge base (documents)
+    """
+    return await qa(query)