[DERCBOT-1037] Use of PromptTemplate

bot/engine/src/main/kotlin/engine/config/RAGAnswerHandler.kt

@@ -181,10 +181,14 @@ object RAGAnswerHandler : AbstractProactiveAnswerHandler {
                     query = RAGQuery(
                         history = getDialogHistory(dialog),
                         questionAnsweringLlmSetting = ragConfiguration.llmSetting,
-                        questionAnsweringPromptInputs = mapOf(
-                            "question" to action.toString(),
-                            "locale" to userPreferences.locale.displayLanguage,
-                            "no_answer" to ragConfiguration.noAnswerSentence
+                        questionAnsweringPrompt = PromptTemplate(
+                            formatter = Formatter.F_STRING.id,
+                            template = ragConfiguration.llmSetting.prompt,
+                            inputs = mapOf(
+                                "question" to action.toString(),
+                                "locale" to userPreferences.locale.displayLanguage,
+                                "no_answer" to ragConfiguration.noAnswerSentence
+                            )
                         ),
                         embeddingQuestionEmSetting = ragConfiguration.emSetting,
                         documentIndexName = indexName,

gen-ai/orchestrator-client/src/main/kotlin/ai/tock/genai/orchestratorclient/requests/RAGQuery.kt

@@ -23,10 +23,10 @@ import ai.tock.genai.orchestratorcore.models.vectorstore.VectorStoreSetting
 
 data class RAGQuery(
     // val condenseQuestionLlmSetting: LLMSetting,
-    // val condenseQuestionPromptInputs: Map<String, String>,
+    // val condenseQuestionPrompt: PromptTemplate,
     val history: List<ChatMessage> = emptyList(),
     val questionAnsweringLlmSetting: LLMSetting,
-    val questionAnsweringPromptInputs: Map<String, String>,
+    val questionAnsweringPrompt: PromptTemplate,
     val embeddingQuestionEmSetting: EMSetting,
     val documentIndexName: String,
     val documentSearchParams: DocumentSearchParamsBase,

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

@@ -37,8 +37,3 @@ class BaseLLMSetting(BaseModel):
         ge=0,
         le=2,
     )
-    prompt: str = Field(
-        description='The prompt to generate completions for.',
-        examples=['How to learn to ride a bike without wheels!'],
-        min_length=1,
-    )

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

@@ -131,20 +131,17 @@ class RagQuery(BaseQuery):
     history: list[ChatMessage] = Field(
         description="Conversation history, used to reformulate the user's question."
     )
-    question_answering_prompt_inputs: Any = Field(
-        description='Key-value inputs for the llm prompt when used as a template. Please note that the '
-        'chat_history field must not be specified here, it will be override by the history field',
-    )
     # condense_question_llm_setting: LLMSetting =
     #   Field(description="LLM setting, used to condense the user's question.")
-    # condense_question_prompt_inputs: Any = (
-    #         Field(
-    #             description='Key-value inputs for the condense question llm prompt, when used as a template.',
-    #         ),
+    # condense_question_prompt: PromptTemplate = Field(
+    #         description='Prompt template, used to create a prompt with inputs for jinja and fstring format'
     #     )
     question_answering_llm_setting: LLMSetting = Field(
         description='LLM setting, used to perform a QA Prompt.'
     )
+    question_answering_prompt : PromptTemplate = Field(
+        description='Prompt template, used to create a prompt with inputs for jinja and fstring format'
+    )
 
     model_config = {
         'json_schema_extra': {
@@ -164,7 +161,11 @@ class RagQuery(BaseQuery):
                             'value': 'ab7***************************A1IV4B',
                         },
                         'temperature': 1.2,
-                        'prompt': """Use the following context to answer the question at the end.
+                        'model': 'gpt-3.5-turbo',
+                    },
+                    'question_answering_prompt': {
+                        'formatter': 'f-string',
+                        'template': """Use the following context to answer the question at the end.
 If you don't know the answer, just say {no_answer}.
 
 Context:
@@ -174,12 +175,11 @@ class RagQuery(BaseQuery):
 {question}
 
 Answer in {locale}:""",
-                        'model': 'gpt-3.5-turbo',
-                    },
-                    'question_answering_prompt_inputs': {
-                        'question': 'How to get started playing guitar ?',
-                        'no_answer': "Sorry, I don't know.",
-                        'locale': 'French',
+                        'inputs': {
+                            'question': 'How to get started playing guitar ?',
+                            'no_answer': 'Sorry, I don t know.',
+                            'locale': 'French',
+                        }
                     },
                     'embedding_question_em_setting': {
                         'provider': 'OpenAI',

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

@@ -16,23 +16,14 @@
 
 import logging
 import time
-from typing import Optional
 
-from jinja2 import Template, TemplateError
 from langchain_core.output_parsers import NumberedListOutputParser
 from langchain_core.prompts import PromptTemplate as LangChainPromptTemplate
-from langchain_core.runnables import RunnableConfig
 
-from gen_ai_orchestrator.errors.exceptions.exceptions import (
-    GenAIPromptTemplateException,
-)
 from gen_ai_orchestrator.errors.handlers.openai.openai_exception_handler import (
     openai_exception_handler,
 )
-from gen_ai_orchestrator.models.errors.errors_models import ErrorInfo
 from gen_ai_orchestrator.models.observability.observability_trace import ObservabilityTrace
-from gen_ai_orchestrator.models.prompt.prompt_formatter import PromptFormatter
-from gen_ai_orchestrator.models.prompt.prompt_template import PromptTemplate
 from gen_ai_orchestrator.routers.requests.requests import (
     SentenceGenerationQuery,
 )
@@ -42,6 +33,7 @@
 from gen_ai_orchestrator.services.langchain.factories.langchain_factory import (
     get_llm_factory, create_observability_callback_handler,
 )
+from gen_ai_orchestrator.services.utils.prompt_utility import validate_prompt_template
 
 logger = logging.getLogger(__name__)
 
@@ -90,29 +82,3 @@ async def generate_and_split_sentences(
         )
 
     return SentenceGenerationResponse(sentences=sentences)
-
-
-def validate_prompt_template(prompt: PromptTemplate):
-    """
-    Prompt template validation
-
-    Args:
-        prompt: The prompt template
-
-    Returns:
-        Nothing.
-    Raises:
-        GenAIPromptTemplateException: if template is incorrect
-    """
-    if PromptFormatter.JINJA2 == prompt.formatter:
-        try:
-            Template(prompt.template).render(prompt.inputs)
-        except TemplateError as exc:
-            logger.error('Prompt completion - template validation failed!')
-            logger.error(exc)
-            raise GenAIPromptTemplateException(
-                ErrorInfo(
-                    error=exc.__class__.__name__,
-                    cause=str(exc),
-                )
-            )

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

@@ -18,15 +18,14 @@
 """
 
 import logging
-import re
 import time
 from logging import ERROR, WARNING
 from typing import List, Optional
 
 from langchain.chains import ConversationalRetrievalChain
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.documents import Document
-from langchain_core.prompts import PromptTemplate
+from langchain_core.prompts import PromptTemplate as LangChainPromptTemplate
 
 from gen_ai_orchestrator.errors.exceptions.exceptions import (
     GenAIGuardCheckException,
@@ -47,9 +46,6 @@
     RagDocumentMetadata,
     TextWithFootnotes,
 )
-from gen_ai_orchestrator.models.vector_stores.vectore_store_provider import (
-    VectorStoreProvider,
-)
 from gen_ai_orchestrator.routers.requests.requests import RagQuery
 from gen_ai_orchestrator.routers.responses.responses import RagResponse
 from gen_ai_orchestrator.services.langchain.callbacks.retriever_json_callback_handler import (
@@ -60,6 +56,7 @@
     get_llm_factory,
     get_vector_store_factory, create_observability_callback_handler,
 )
+from gen_ai_orchestrator.services.utils.prompt_utility import validate_prompt_template
 
 logger = logging.getLogger(__name__)
 
@@ -93,7 +90,7 @@ async def execute_qa_chain(query: RagQuery, debug: bool) -> RagResponse:
             message_history.add_ai_message(msg.text)
 
     inputs = {
-        **query.question_answering_prompt_inputs,
+        **query.question_answering_prompt.inputs,
         'chat_history': message_history.messages,
     }
 
@@ -180,6 +177,11 @@ def create_rag_chain(query: RagQuery) -> ConversationalRetrievalChain:
                                                     index_name=query.document_index_name,
                                                     embedding_function=em_factory.get_embedding_model())
 
+    logger.info('RAG chain - LLM template validation')
+    validate_prompt_template(query.question_answering_prompt)
+
+
+
     logger.debug('RAG chain - Document index name: %s', query.document_index_name)
     logger.debug('RAG chain - Create a ConversationalRetrievalChain from LLM')
     return ConversationalRetrievalChain.from_llm(
@@ -188,27 +190,13 @@ def create_rag_chain(query: RagQuery) -> ConversationalRetrievalChain:
         return_source_documents=True,
         return_generated_question=True,
         combine_docs_chain_kwargs={
-            'prompt': PromptTemplate(
-                template=llm_factory.setting.prompt,
-                input_variables=__find_input_variables(llm_factory.setting.prompt),
+            'prompt': LangChainPromptTemplate.from_template(
+                template=query.question_answering_prompt.template,
+                template_format=query.question_answering_prompt.formatter.value,
             )
         },
     )
 
-
-def __find_input_variables(template):
-    """
-    Search for input variables on a given template
-
-    Args:
-        template: the template to search on
-    """
-
-    motif = r'\{([^}]+)\}'
-    variables = re.findall(motif, template)
-    return variables
-
-
 def __rag_guard(inputs, response):
     """
     If a 'no_answer' input was given as a rag setting,
@@ -315,7 +303,7 @@ def get_rag_debug_data(
     """RAG debug data assembly"""
 
     return RagDebugData(
-        user_question=query.question_answering_prompt_inputs['question'],
+        user_question=query.question_answering_prompt.inputs['question'],
         condense_question_prompt=get_llm_prompts(records_callback_handler)[0],
         condense_question=get_condense_question(records_callback_handler),
         question_answering_prompt=get_llm_prompts(records_callback_handler)[1],

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

@@ -51,28 +51,3 @@ async def check_llm_setting(query: LLMProviderSettingStatusQuery) -> bool:
             trace_name=ObservabilityTrace.CHECK_LLM_SETTINGS.value)
 
     return await get_llm_factory(query.setting).check_llm_setting(langfuse_callback_handler)
-
-
-def llm_inference_with_parser(
-    llm_factory: LangChainLLMFactory, parser: BaseOutputParser
-) -> AIMessage:
-    """
-    Perform LLM inference and format the output content based on the given parser.
-
-    :param llm_factory: LangChain LLM Factory.
-    :param parser: Parser to format the output.
-
-    :return: Result of the language model inference with the content formatted.
-    """
-
-    # Change the prompt with added format instructions
-    format_instructions = parser.get_format_instructions()
-    formatted_prompt = llm_factory.setting.prompt + '\n' + format_instructions
-
-    # Inference of the LLM with the formatted prompt
-    llm_output = llm_factory.invoke(formatted_prompt)
-
-    # Apply the parsing on the LLM output
-    llm_output.content = parser.parse(llm_output.content)
-
-    return llm_output

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

@@ -0,0 +1,14 @@
+#   Copyright (C) 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.
+#

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

@@ -0,0 +1,37 @@
+import logging
+
+from jinja2 import Template, TemplateError
+
+from gen_ai_orchestrator.errors.exceptions.exceptions import (
+    GenAIPromptTemplateException,
+)
+from gen_ai_orchestrator.models.errors.errors_models import ErrorInfo
+from gen_ai_orchestrator.models.prompt.prompt_formatter import PromptFormatter
+from gen_ai_orchestrator.models.prompt.prompt_template import PromptTemplate
+
+logger = logging.getLogger(__name__)
+
+def validate_prompt_template(prompt: PromptTemplate):
+    """
+    Prompt template validation
+
+    Args:
+        prompt: The prompt template
+
+    Returns:
+        Nothing.
+    Raises:
+        GenAIPromptTemplateException: if template is incorrect
+    """
+    if PromptFormatter.JINJA2 == prompt.formatter:
+        try:
+            Template(prompt.template).render(prompt.inputs)
+        except TemplateError as exc:
+            logger.error('Prompt completion - template validation failed!')
+            logger.error(exc)
+            raise GenAIPromptTemplateException(
+                ErrorInfo(
+                    error=exc.__class__.__name__,
+                    cause=str(exc),
+                )
+            )

gen-ai/orchestrator-server/src/main/python/server/tests/services/test_completion_service.py

@@ -13,9 +13,7 @@
 #   limitations under the License.
 #
 from gen_ai_orchestrator.models.prompt.prompt_template import PromptTemplate
-from gen_ai_orchestrator.services.completion.completion_service import (
-    validate_prompt_template,
-)
+from gen_ai_orchestrator.services.utils.prompt_utility import validate_prompt_template
 
 
 def test_validate_prompt_template():