Correcting the retrieval of relevant documents

common/langchain/EveAIRetriever.py

@@ -4,19 +4,21 @@ from sqlalchemy.exc import SQLAlchemyError
 from pydantic import BaseModel, Field
 from typing import Any, Dict
 from flask import current_app
-from datetime import date
 
 from common.extensions import db
-from common.models.document import Document, DocumentVersion, Embedding
+from common.models.document import Document, DocumentVersion
+from common.utils.datetime_utils import get_date_in_timezone
 
 
 class EveAIRetriever(BaseRetriever):
     model_variables: Dict[str, Any] = Field(...)
+    tenant_info: Dict[str, Any] = Field(...)
 
-    def __init__(self, model_variables: Dict[str, Any]):
+    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):
@@ -27,7 +29,7 @@ class EveAIRetriever(BaseRetriever):
         k = self.model_variables['k']
 
         try:
-            current_date = date.today()
+            current_date = get_date_in_timezone(self.tenant_info['timezone'])
             # Subquery to find the latest version of each document
             subquery = (
                 db.session.query(
@@ -53,18 +55,17 @@ class EveAIRetriever(BaseRetriever):
                 .limit(k)
             )
 
-            # Print the generated SQL statement for debugging
-            current_app.logger.debug("SQL Statement:\n")
-            current_app.logger.debug(query_obj.statement.compile(compile_kwargs={"literal_binds": True}))
-
             res = query_obj.all()
 
-            # current_app.rag_tuning_logger.debug(f'Retrieved {len(res)} relevant documents')
+            if self.tenant_info['rag_tuning']:
-            # current_app.rag_tuning_logger.debug(f'---------------------------------------')
+                current_app.rag_tuning_logger.debug(f'Retrieved {len(res)} relevant documents')
+                current_app.rag_tuning_logger.debug(f'---------------------------------------')
+
             result = []
             for doc in res:
-                # current_app.rag_tuning_logger.debug(f'Document ID: {doc[0].id} - Distance: {doc[1]}\n')
+                if self.tenant_info['rag_tuning']:
-                # current_app.rag_tuning_logger.debug(f'Chunk: \n {doc[0].chunk}\n\n')
+                    current_app.rag_tuning_logger.debug(f'Document ID: {doc[0].id} - Distance: {doc[1]}\n')
+                    current_app.rag_tuning_logger.debug(f'Chunk: \n {doc[0].chunk}\n\n')
                 result.append(f'SOURCE: {doc[0].id}\n\n{doc[0].chunk}\n\n')
 
         except SQLAlchemyError as e:

common/models/user.py

@@ -67,6 +67,7 @@ class Tenant(db.Model):
             'website': self.website,
             'default_language': self.default_language,
             'allowed_languages': self.allowed_languages,
+            'timezone': self.timezone,
             'embedding_model': self.embedding_model,
             'llm_model': self.llm_model,
             'license_start_date': self.license_start_date,

common/utils/datetime_utils.py

@@ -0,0 +1,19 @@
+from datetime import datetime
+import pytz
+
+
+def get_date_in_timezone(timezone_str):
+    try:
+        # Get the timezone object from the string
+        timezone = pytz.timezone(timezone_str)
+
+        # Get the current time in the specified timezone
+        current_time = datetime.now(timezone)
+
+        # Extract the date part
+        current_date = current_time.date()
+
+        return current_date
+    except Exception as e:
+        print(f"Error getting date in timezone {timezone_str}: {e}")
+        return None

config/config.py

@@ -77,6 +77,7 @@ class Config(object):
     ```{text}```"""
     GPT3_5_SUMMARY_TEMPLATE = """Write a concise summary of the text in {language}. The text is delimited between triple backquotes.
         ```{text}```"""
+
     GPT4_RAG_TEMPLATE = """Answer the question based on the following context, delimited between triple backquotes. 
     Use the following {language} in your communication, and cite the sources used.
     If the question cannot be answered using the given context, say "I have insufficient information to answer this question."
@@ -92,6 +93,17 @@ class Config(object):
     Question:
     {question}"""
 
+    GPT4_HISTORY_TEMPLATE = """You are a helpful assistant that details a question based on a previous context,
+    in such a way that the question is understandable without the previous context. 
+    The context is a conversation history, with the HUMAN asking questions, the AI answering questions.
+    The history is delimited between triple backquotes.
+    Your answer by stating the question in {language}.
+    History:
+    ```{history}```
+    Question to be detailed:
+    {question}"""
+
+
     # SocketIO settings
     # SOCKETIO_ASYNC_MODE = 'threading'
     SOCKETIO_ASYNC_MODE = 'gevent'

eveai_app/views/user_forms.py

@@ -3,6 +3,8 @@ from flask_wtf import FlaskForm
 from wtforms import (StringField, PasswordField, BooleanField, SubmitField, EmailField, IntegerField, DateField,
                      SelectField, SelectMultipleField, FieldList, FormField, FloatField)
 from wtforms.validators import DataRequired, Length, Email, NumberRange, Optional
+import pytz
+
 from common.models.user import Role
 
 
@@ -12,6 +14,8 @@ class TenantForm(FlaskForm):
     # language fields
     default_language = SelectField('Default Language', choices=[], validators=[DataRequired()])
     allowed_languages = SelectMultipleField('Allowed Languages', choices=[], validators=[DataRequired()])
+    # Timezone
+    timezone = SelectField('Timezone', choices=[], validators=[DataRequired()])
     # LLM fields
     embedding_model = SelectField('Embedding Model', choices=[], validators=[DataRequired()])
     llm_model = SelectField('Large Language Model', choices=[], validators=[DataRequired()])
@@ -44,6 +48,8 @@ class TenantForm(FlaskForm):
         # initialise language fields
         self.default_language.choices = [(lang, lang.lower()) for lang in current_app.config['SUPPORTED_LANGUAGES']]
         self.allowed_languages.choices = [(lang, lang.lower()) for lang in current_app.config['SUPPORTED_LANGUAGES']]
+        # initialise timezone
+        self.timezone.choices = [(tz, tz) for tz in pytz.all_timezones]
         # initialise LLM fields
         self.embedding_model.choices = [(model, model) for model in current_app.config['SUPPORTED_EMBEDDINGS']]
         self.llm_model.choices = [(model, model) for model in current_app.config['SUPPORTED_LLMS']]

eveai_app/views/user_views.py

@@ -49,6 +49,7 @@ def tenant():
                             website=form.website.data,
                             default_language=form.default_language.data,
                             allowed_languages=form.allowed_languages.data,
+                            timezone=form.timezone.data,
                             embedding_model=form.embedding_model.data,
                             llm_model=form.llm_model.data,
                             license_start_date=form.license_start_date.data,

eveai_chat_workers/tasks.py

@@ -1,5 +1,5 @@
 from datetime import datetime as dt, timezone as tz
-from flask import current_app
+from flask import current_app, session
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnableParallel, RunnablePassthrough
 from langchain.globals import set_debug
@@ -46,9 +46,9 @@ def ask_question(tenant_id, question, language, session_id):
         # Ensure we are working in the correct database schema
         Database(tenant_id).switch_schema()
 
+        # Ensure we have a session to story history
         chat_session = ChatSession.query.filter_by(session_id=session_id).first()
         if not chat_session:
-            # Initialize a chat_session on the database
             try:
                 chat_session = ChatSession()
                 chat_session.session_id = session_id
@@ -66,22 +66,14 @@ def ask_question(tenant_id, question, language, session_id):
         new_interaction.question_at = dt.now(tz.utc)
         new_interaction.algorithm_used = current_app.config['INTERACTION_ALGORITHMS']['RAG_TENANT']['name']
 
-        # try:
-        #     db.session.add(new_interaction)
-        #     db.session.commit()
-        # except SQLAlchemyError as e:
-        #     current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
-        #     raise
-
-        current_app.logger.debug(f'ask_question: new_interaction: {new_interaction}')
-
         # Select variables to work with depending on tenant model
         model_variables = select_model_variables(tenant)
+        tenant_info = tenant.to_dict()
 
-        current_app.logger.debug(f'ask_question: model_variables: {model_variables}')
+        # Langchain debugging if required
+        # set_debug(True)
 
-        set_debug(True)
+        retriever = EveAIRetriever(model_variables, tenant_info)
-        retriever = EveAIRetriever(model_variables)
         llm = model_variables['llm']
         template = model_variables['rag_template']
         language_template = create_language_template(template, language)
@@ -141,7 +133,8 @@ def ask_question(tenant_id, question, language, session_id):
             current_app.logger.error(f'ask_question: Error saving interaction to database: {e}')
             raise
 
-        set_debug(False)
+        # Disable langchain debugging if set above.
+        # set_debug(False)
 
         result['algorithm'] = current_app.config['INTERACTION_ALGORITHMS']['RAG_TENANT']['name']
         result['interaction_id'] = new_interaction.id

requirements.txt

@@ -15,4 +15,5 @@ redis~=5.0.4
 itsdangerous~=2.2.0
 pydantic~=2.7.1
 chardet~=5.2.0
-langcodes~=3.4.0
+langcodes~=3.4.0
+pytz~=2024.1