Include history to improve query if required.

common/langchain/EveAIHistoryRetriever.py

@@ -0,0 +1,44 @@
+from langchain_core.retrievers import BaseRetriever
+from sqlalchemy import asc
+from sqlalchemy.exc import SQLAlchemyError
+from pydantic import BaseModel, Field
+from typing import Any, Dict
+from flask import current_app
+
+from common.extensions import db
+from common.models.interaction import ChatSession, Interaction
+from common.utils.datetime_utils import get_date_in_timezone
+
+
+class EveAIHistoryRetriever(BaseRetriever):
+    model_variables: Dict[str, Any] = Field(...)
+    session_id: str = Field(...)
+
+    def __init__(self, model_variables: Dict[str, Any], session_id: str):
+        super().__init__()
+        self.model_variables = model_variables
+        self.session_id = session_id
+
+    def _get_relevant_documents(self, query: str):
+        current_app.logger.debug(f'Retrieving history of interactions for query: {query}')
+
+        try:
+            query_obj = (
+                db.session.query(Interaction)
+                .join(ChatSession, Interaction.chat_session_id == ChatSession.id)
+                .filter(ChatSession.session_id == self.session_id)
+                .order_by(asc(Interaction.id))
+            )
+
+            interactions = query_obj.all()
+
+            result = []
+            for interaction in interactions:
+                result.append(f'HUMAN:\n{interaction.detailed_question}\n\nAI: \n{interaction.answer}\n\n')
+
+        except SQLAlchemyError as e:
+            current_app.logger.error(f'Error retrieving history of interactions: {e}')
+            db.session.rollback()
+            return []
+
+        return result

common/langchain/EveAIRetriever.py

@@ -16,10 +16,8 @@ class EveAIRetriever(BaseRetriever):
 
     def __init__(self, model_variables: Dict[str, Any], tenant_info: Dict[str, Any]):
         super().__init__()
-        current_app.logger.debug('Initializing EveAIRetriever')
         self.model_variables = model_variables
         self.tenant_info = tenant_info
-        current_app.logger.debug('EveAIRetriever initialized')
 
     def _get_relevant_documents(self, query: str):
         current_app.logger.debug(f'Retrieving relevant documents for query: {query}')

common/models/interaction.py

@@ -21,6 +21,7 @@ class Interaction(db.Model):
     id = db.Column(db.Integer, primary_key=True)
     chat_session_id = db.Column(db.Integer, db.ForeignKey(ChatSession.id), nullable=False)
     question = db.Column(db.Text, nullable=False)
+    detailed_question = db.Column(db.Text, nullable=True)
     answer = db.Column(db.Text, nullable=True)
     algorithm_used = db.Column(db.String(20), nullable=True)
     language = db.Column(db.String(2), nullable=False)
@@ -28,6 +29,7 @@ class Interaction(db.Model):
 
     # Timing information
     question_at = db.Column(db.DateTime, nullable=False)
+    detailed_question_at = db.Column(db.DateTime, nullable=True)
     answer_at = db.Column(db.DateTime, nullable=True)
 
     # Relations

common/utils/model_utils.py

@@ -103,15 +103,18 @@ def select_model_variables(tenant):
                 case 'gpt-4-turbo' | 'gpt-4o':
                     summary_template = current_app.config.get('GPT4_SUMMARY_TEMPLATE')
                     rag_template = current_app.config.get('GPT4_RAG_TEMPLATE')
+                    history_template = current_app.config.get('GPT4_HISTORY_TEMPLATE')
                     tool_calling_supported = True
                 case 'gpt-3-5-turbo':
                     summary_template = current_app.config.get('GPT3_5_SUMMARY_TEMPLATE')
                     rag_template = current_app.config.get('GPT3_5_RAG_TEMPLATE')
+                    history_template = current_app.config.get('GPT3_5_HISTORY_TEMPLATE')
                 case _:
                     raise Exception(f'Error setting model variables for tenant {tenant.id} '
                                     f'error: Invalid chat model')
             model_variables['summary_template'] = summary_template
             model_variables['rag_template'] = rag_template
+            model_variables['history_template'] = history_template
             if tool_calling_supported:
                 model_variables['cited_answer_cls'] = CitedAnswer
         case _:

eveai_chat_workers/tasks.py

@@ -23,6 +23,27 @@ from common.extensions import db
 from common.utils.celery_utils import current_celery
 from common.utils.model_utils import select_model_variables, create_language_template
 from common.langchain.EveAIRetriever import EveAIRetriever
+from common.langchain.EveAIHistoryRetriever import EveAIHistoryRetriever
+
+
+def detail_question(question, language, model_variables, session_id):
+    retriever = EveAIHistoryRetriever(model_variables, session_id)
+    llm = model_variables['llm']
+    template = model_variables['history_template']
+    language_template = create_language_template(template, language)
+    history_prompt = ChatPromptTemplate.from_template(language_template)
+    setup_and_retrieval = RunnableParallel({"history": retriever,"question": RunnablePassthrough()})
+    output_parser = StrOutputParser()
+
+    chain = setup_and_retrieval | history_prompt | llm | output_parser
+
+
+    try:
+        answer = chain.invoke(question)
+        return answer
+    except LangChainException as e:
+        current_app.logger.error(f'Error detailing question: {e}')
+        raise
 
 
 @current_celery.task(name='ask_question', queue='llm_interactions')
@@ -73,6 +94,11 @@ def ask_question(tenant_id, question, language, session_id):
         # Langchain debugging if required
         # set_debug(True)
 
+        detailed_question = detail_question(question, language, model_variables, session_id)
+        current_app.logger.debug(f'Original question:\n {question}\n\nDetailed question: {detailed_question}')
+        new_interaction.detailed_question = detailed_question
+        new_interaction.detailed_question_at = dt.now(tz.utc)
+
         retriever = EveAIRetriever(model_variables, tenant_info)
         llm = model_variables['llm']
         template = model_variables['rag_template']
@@ -87,7 +113,7 @@ def ask_question(tenant_id, question, language, session_id):
             chain = setup_and_retrieval | rag_prompt | llm | output_parser
 
             # Invoke the chain with the actual question
-            answer = chain.invoke(question)
+            answer = chain.invoke(detailed_question)
             new_interaction.answer = answer
             result = {
                 'answer': answer,
@@ -99,7 +125,7 @@ def ask_question(tenant_id, question, language, session_id):
 
             chain = setup_and_retrieval | rag_prompt | structured_llm
 
-            result = chain.invoke(question).dict()
+            result = chain.invoke(detailed_question).dict()
             current_app.logger.debug(f'ask_question: result answer: {result['answer']}')
             current_app.logger.debug(f'ask_question: result citations: {result["citations"]}')
             new_interaction.answer = result['answer']