Started to work on interaction views. However, need a quick check in because of a python upgrade systemwide that breaks code.
This commit is contained in:
Josako
@@ -18,7 +18,7 @@ from langchain_core.exceptions import LangChainException
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from common.utils.database import Database
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from common.models.document import DocumentVersion, EmbeddingMistral, EmbeddingSmallOpenAI, Embedding
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from common.models.user import Tenant
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from common.models.interaction import ChatSession, Interaction, InteractionEmbedding
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from common.models.interaction import ChatSession, Interaction, InteractionEmbedding
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from common.extensions import db
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from common.utils.celery_utils import current_celery
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from common.utils.model_utils import select_model_variables, create_language_template, replace_variable_in_template
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@@ -33,12 +33,11 @@ def detail_question(question, language, model_variables, session_id):
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language_template = create_language_template(template, language)
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full_template = replace_variable_in_template(language_template, "{tenant_context}", model_variables['rag_context'])
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history_prompt = ChatPromptTemplate.from_template(full_template)
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setup_and_retrieval = RunnableParallel({"history": retriever,"question": RunnablePassthrough()})
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setup_and_retrieval = RunnableParallel({"history": retriever, "question": RunnablePassthrough()})
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output_parser = StrOutputParser()
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chain = setup_and_retrieval | history_prompt | llm | output_parser
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try:
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answer = chain.invoke(question)
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return answer
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@@ -48,7 +47,7 @@ def detail_question(question, language, model_variables, session_id):
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@current_celery.task(name='ask_question', queue='llm_interactions')
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def ask_question(tenant_id, question, language, session_id):
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def ask_question(tenant_id, question, language, session_id, user_timezone):
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"""returns result structured as follows:
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result = {
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'answer': 'Your answer here',
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@@ -75,103 +74,178 @@ def ask_question(tenant_id, question, language, session_id):
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chat_session = ChatSession()
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chat_session.session_id = session_id
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chat_session.session_start = dt.now(tz.utc)
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chat_session.timezone = user_timezone
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db.session.add(chat_session)
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db.session.commit()
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except SQLAlchemyError as e:
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current_app.logger.error(f'ask_question: Error initializing chat session in database: {e}')
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raise
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new_interaction = Interaction()
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new_interaction.question = question
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new_interaction.language = language
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new_interaction.appreciation = None
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new_interaction.chat_session_id = chat_session.id
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new_interaction.question_at = dt.now(tz.utc)
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new_interaction.algorithm_used = current_app.config['INTERACTION_ALGORITHMS']['RAG_TENANT']['name']
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# Select variables to work with depending on tenant model
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model_variables = select_model_variables(tenant)
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tenant_info = tenant.to_dict()
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# Langchain debugging if required
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# set_debug(True)
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detailed_question = detail_question(question, language, model_variables, session_id)
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current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
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new_interaction.detailed_question = detailed_question
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new_interaction.detailed_question_at = dt.now(tz.utc)
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retriever = EveAIRetriever(model_variables, tenant_info)
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llm = model_variables['llm']
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template = model_variables['rag_template']
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language_template = create_language_template(template, language)
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full_template = replace_variable_in_template(language_template, "{tenant_context}", model_variables['rag_context'])
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rag_prompt = ChatPromptTemplate.from_template(full_template)
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setup_and_retrieval = RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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new_interaction_embeddings = []
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if not model_variables['cited_answer_cls']: # The model doesn't support structured feedback
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output_parser = StrOutputParser()
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chain = setup_and_retrieval | rag_prompt | llm | output_parser
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# Invoke the chain with the actual question
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answer = chain.invoke(detailed_question)
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new_interaction.answer = answer
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result = {
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'answer': answer,
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'citations': []
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}
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else: # The model supports structured feedback
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structured_llm = llm.with_structured_output(model_variables['cited_answer_cls'])
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chain = setup_and_retrieval | rag_prompt | structured_llm
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result = chain.invoke(detailed_question).dict()
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current_app.logger.debug(f'ask_question: result answer: {result['answer']}')
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current_app.logger.debug(f'ask_question: result citations: {result["citations"]}')
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new_interaction.answer = result['answer']
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# Filter out the existing Embedding IDs
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given_embedding_ids = [int(emb_id) for emb_id in result['citations']]
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embeddings = (
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db.session.query(Embedding)
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.filter(Embedding.id.in_(given_embedding_ids))
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.all()
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)
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existing_embedding_ids = [emb.id for emb in embeddings]
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urls = [emb.document_version.url for emb in embeddings]
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for emb_id in existing_embedding_ids:
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new_interaction_embedding = InteractionEmbedding(embedding_id=emb_id)
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new_interaction_embedding.interaction = new_interaction
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new_interaction_embeddings.append(new_interaction_embedding)
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result['citations'] = urls
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new_interaction.answer_at = dt.now(tz.utc)
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chat_session.session_end = dt.now(tz.utc)
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try:
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db.session.add(chat_session)
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db.session.add(new_interaction)
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db.session.add_all(new_interaction_embeddings)
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db.session.commit()
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except SQLAlchemyError as e:
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current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
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raise
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# Disable langchain debugging if set above.
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# set_debug(False)
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result, interaction = answer_using_tenant_rag(question, language, tenant, chat_session)
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result['algorithm'] = current_app.config['INTERACTION_ALGORITHMS']['RAG_TENANT']['name']
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result['interaction_id'] = new_interaction.id
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result['interaction_id'] = interaction.id
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if result['insufficient_info']:
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if 'LLM' in tenant.fallback_algorithms:
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result, interaction = answer_using_llm(question, language, tenant, chat_session)
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result['algorithm'] = current_app.config['INTERACTION_ALGORITHMS']['LLM']['name']
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result['interaction_id'] = interaction.id
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return result
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except Exception as e:
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current_app.logger.error(f'ask_question: Error processing question: {e}')
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raise
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def answer_using_tenant_rag(question, language, tenant, chat_session):
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new_interaction = Interaction()
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new_interaction.question = question
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new_interaction.language = language
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new_interaction.timezone = chat_session.timezone
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new_interaction.appreciation = None
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new_interaction.chat_session_id = chat_session.id
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new_interaction.question_at = dt.now(tz.utc)
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new_interaction.algorithm_used = current_app.config['INTERACTION_ALGORITHMS']['RAG_TENANT']['name']
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# Select variables to work with depending on tenant model
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model_variables = select_model_variables(tenant)
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tenant_info = tenant.to_dict()
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# Langchain debugging if required
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# set_debug(True)
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detailed_question = detail_question(question, language, model_variables, chat_session.session_id)
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current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
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new_interaction.detailed_question = detailed_question
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new_interaction.detailed_question_at = dt.now(tz.utc)
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retriever = EveAIRetriever(model_variables, tenant_info)
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llm = model_variables['llm']
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template = model_variables['rag_template']
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language_template = create_language_template(template, language)
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full_template = replace_variable_in_template(language_template, "{tenant_context}", model_variables['rag_context'])
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rag_prompt = ChatPromptTemplate.from_template(full_template)
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setup_and_retrieval = RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
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new_interaction_embeddings = []
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if not model_variables['cited_answer_cls']: # The model doesn't support structured feedback
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output_parser = StrOutputParser()
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chain = setup_and_retrieval | rag_prompt | llm | output_parser
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# Invoke the chain with the actual question
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answer = chain.invoke(detailed_question)
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new_interaction.answer = answer
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result = {
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'answer': answer,
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'citations': [],
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'insufficient_info': False
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}
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else: # The model supports structured feedback
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structured_llm = llm.with_structured_output(model_variables['cited_answer_cls'])
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chain = setup_and_retrieval | rag_prompt | structured_llm
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result = chain.invoke(detailed_question).dict()
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current_app.logger.debug(f'ask_question: result answer: {result['answer']}')
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current_app.logger.debug(f'ask_question: result citations: {result["citations"]}')
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current_app.logger.debug(f'ask_question: insufficient information: {result["insufficient_info"]}')
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new_interaction.answer = result['answer']
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# Filter out the existing Embedding IDs
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given_embedding_ids = [int(emb_id) for emb_id in result['citations']]
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embeddings = (
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db.session.query(Embedding)
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.filter(Embedding.id.in_(given_embedding_ids))
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.all()
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)
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existing_embedding_ids = [emb.id for emb in embeddings]
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urls = [emb.document_version.url for emb in embeddings]
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for emb_id in existing_embedding_ids:
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new_interaction_embedding = InteractionEmbedding(embedding_id=emb_id)
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new_interaction_embedding.interaction = new_interaction
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new_interaction_embeddings.append(new_interaction_embedding)
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result['citations'] = urls
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# Disable langchain debugging if set above.
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# set_debug(False)
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new_interaction.answer_at = dt.now(tz.utc)
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chat_session.session_end = dt.now(tz.utc)
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try:
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db.session.add(chat_session)
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db.session.add(new_interaction)
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db.session.add_all(new_interaction_embeddings)
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db.session.commit()
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return result, new_interaction
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except SQLAlchemyError as e:
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current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
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raise
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def answer_using_llm(question, language, tenant, chat_session):
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new_interaction = Interaction()
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new_interaction.question = question
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new_interaction.language = language
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new_interaction.timezone = chat_session.timezone
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new_interaction.appreciation = None
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new_interaction.chat_session_id = chat_session.id
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new_interaction.question_at = dt.now(tz.utc)
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new_interaction.algorithm_used = current_app.config['INTERACTION_ALGORITHMS']['LLM']['name']
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# Select variables to work with depending on tenant model
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model_variables = select_model_variables(tenant)
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tenant_info = tenant.to_dict()
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# Langchain debugging if required
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# set_debug(True)
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detailed_question = detail_question(question, language, model_variables, chat_session.session_id)
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current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
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new_interaction.detailed_question = detailed_question
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new_interaction.detailed_question_at = dt.now(tz.utc)
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retriever = EveAIRetriever(model_variables, tenant_info)
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llm = model_variables['llm_no_rag']
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template = model_variables['encyclopedia_template']
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language_template = create_language_template(template, language)
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rag_prompt = ChatPromptTemplate.from_template(language_template)
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setup = RunnablePassthrough()
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output_parser = StrOutputParser()
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new_interaction_embeddings = []
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chain = setup | rag_prompt | llm | output_parser
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input_question = {"question": detailed_question}
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# Invoke the chain with the actual question
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answer = chain.invoke(input_question)
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new_interaction.answer = answer
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result = {
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'answer': answer,
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'citations': [],
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'insufficient_info': False
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}
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# Disable langchain debugging if set above.
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# set_debug(False)
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new_interaction.answer_at = dt.now(tz.utc)
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chat_session.session_end = dt.now(tz.utc)
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try:
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db.session.add(chat_session)
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db.session.add(new_interaction)
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db.session.commit()
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return result, new_interaction
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except SQLAlchemyError as e:
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current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
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raise
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def tasks_ping():
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return 'pong'
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