Update parameters

Author: BenConstable9

text_2_sql/autogen/src/autogen_text_2_sql/autogen_text_2_sql.py

@@ -22,6 +22,7 @@
     AnswerWithSources,
     UserInformationRequest,
     ProcessingUpdate,
+    ChatHistoryItem,
 )
 from autogen_agentchat.base import Response, TaskResult
 from typing import AsyncGenerator
@@ -164,16 +165,16 @@ def extract_sources(self, messages: list) -> AnswerWithSources:
     async def process_question(
         self,
         question: str,
-        chat_history: list[str] = None,
-        parameters: dict = None,
+        chat_history: list[ChatHistoryItem] = None,
+        injected_parameters: dict = None,
     ) -> AsyncGenerator[AnswerWithSources | UserInformationRequest, None]:
         """Process the complete question through the unified system.
 
         Args:
         ----
             task (str): The user question to process.
             chat_history (list[str], optional): The chat history. Defaults to None.
-            parameters (dict, optional): Parameters to pass to agents. Defaults to None.
+            injected_parameters (dict, optional): Parameters to pass to agents. Defaults to None.
 
         Returns:
         -------
@@ -185,13 +186,14 @@ async def process_question(
         agent_input = {
             "question": question,
             "chat_history": {},
-            "parameters": parameters,
+            "injected_parameters": injected_parameters,
         }
 
         if chat_history is not None:
             # Update input
             for idx, chat in enumerate(chat_history):
-                agent_input[f"chat_{idx}"] = chat
+                # For now only consider the user query
+                agent_input[f"chat_{idx}"] = chat.user_query
 
         async for message in self.agentic_flow.run_stream(task=json.dumps(agent_input)):
             logging.debug("Message: %s", message)

text_2_sql/autogen/src/autogen_text_2_sql/custom_agents/sql_query_cache_agent.py

@@ -43,9 +43,9 @@ async def on_messages_stream(
         last_response = messages[-1].content
         try:
             user_questions = json.loads(last_response)
-            user_parameters = json.loads(parameter_input)["parameters"]
+            injected_parameters = json.loads(parameter_input)["injected_parameters"]
             logging.info(f"Processing questions: {user_questions}")
-            logging.info(f"Input Parameters: {user_parameters}")
+            logging.info(f"Input Parameters: {injected_parameters}")
 
             # Initialize results dictionary
             cached_results = {
@@ -58,7 +58,7 @@ async def on_messages_stream(
                 # Fetch the queries from the cache based on the question
                 logging.info(f"Fetching queries from cache for question: {question}")
                 cached_query = await self.sql_connector.fetch_queries_from_cache(
-                    question, parameters=user_parameters
+                    question, injected_parameters=injected_parameters
                 )
 
                 # If any question has pre-run results, set the flag

text_2_sql/autogen/src/autogen_text_2_sql/inner_autogen_text_2_sql.py

@@ -189,14 +189,14 @@ def agentic_flow(self):
     def process_question(
         self,
         question: str,
-        parameters: dict = None,
+        injected_parameters: dict = None,
     ):
         """Process the complete question through the unified system.
 
         Args:
         ----
             task (str): The user question to process.
-            parameters (dict, optional): Parameters to pass to agents. Defaults to None.
+            injected_parameters (dict, optional): Parameters to pass to agents. Defaults to None.
 
         Returns:
         -------
@@ -207,7 +207,7 @@ def process_question(
         agent_input = {
             "question": question,
             "chat_history": {},
-            "parameters": parameters,
+            "injected_parameters": injected_parameters,
         }
 
         return self.agentic_flow.run_stream(task=json.dumps(agent_input))

text_2_sql/text_2_sql_core/src/text_2_sql_core/connectors/sql.py

@@ -153,7 +153,7 @@ async def query_validation(
             return True
 
     async def fetch_queries_from_cache(
-        self, question: str, parameters: dict = None
+        self, question: str, injected_parameters: dict = None
     ) -> str:
         """Fetch the queries from the cache based on the question.
 
@@ -166,21 +166,21 @@ async def fetch_queries_from_cache(
             str: The formatted string of the queries fetched from the cache. This is injected into the prompt.
         """
 
-        if parameters is None:
-            parameters = {}
+        if injected_parameters is None:
+            injected_parameters = {}
 
-        # Populate the parameters
-        if "date" not in parameters:
-            parameters["date"] = self.get_current_date()
+        # Populate the injected_parameters
+        if "date" not in injected_parameters:
+            injected_parameters["date"] = self.get_current_date()
 
-        if "time" not in parameters:
-            parameters["time"] = self.get_current_time()
+        if "time" not in injected_parameters:
+            injected_parameters["time"] = self.get_current_time()
 
-        if "datetime" not in parameters:
-            parameters["datetime"] = self.get_current_datetime()
+        if "datetime" not in injected_parameters:
+            injected_parameters["datetime"] = self.get_current_datetime()
 
-        if "unix_timestamp" not in parameters:
-            parameters["unix_timestamp"] = self.get_current_unix_timestamp()
+        if "unix_timestamp" not in injected_parameters:
+            injected_parameters["unix_timestamp"] = self.get_current_unix_timestamp()
 
         cached_schemas = await self.ai_search_connector.run_ai_search_query(
             question,
@@ -204,7 +204,7 @@ async def fetch_queries_from_cache(
             sql_queries = schema["SqlQueryDecomposition"]
             for sql_query in sql_queries:
                 sql_query["SqlQuery"] = Template(sql_query["SqlQuery"]).render(
-                    **parameters
+                    **injected_parameters
                 )
 
         logging.info("Cached schemas: %s", cached_schemas)

text_2_sql/text_2_sql_core/src/text_2_sql_core/payloads/__init__.py

@@ -1,5 +1,12 @@
 from text_2_sql_core.payloads.answer_with_sources import AnswerWithSources, Source
 from text_2_sql_core.payloads.user_information_request import UserInformationRequest
 from text_2_sql_core.payloads.processing_update import ProcessingUpdate
+from text_2_sql_core.payloads.chat_history import ChatHistoryItem
 
-__all__ = ["AnswerWithSources", "Source", "UserInformationRequest", "ProcessingUpdate"]
+__all__ = [
+    "AnswerWithSources",
+    "Source",
+    "UserInformationRequest",
+    "ProcessingUpdate",
+    "ChatHistoryItem",
+]

text_2_sql/text_2_sql_core/src/text_2_sql_core/payloads/chat_history.py

@@ -0,0 +1,9 @@
+from pydantic import BaseModel
+from text_2_sql_core.payloads.answer_with_sources import AnswerWithSources
+
+
+class ChatHistoryItem(BaseModel):
+    """Chat history item with user message and agent response."""
+
+    user_query: str
+    agent_response: AnswerWithSources

text_2_sql/text_2_sql_core/src/text_2_sql_core/prompts/answer_agent.yaml

@@ -2,7 +2,7 @@ model: "4o-mini"
 description: "An agent that generates a response to a user's question."
 system_message: |
   <role_and_objective>
-      You are a helpful AI Assistant specializing in answering a user's question.
+      You are a helpful AI Assistant specializing in answering a user's question about {{ use_case }}.
   </role_and_objective>
 
   Use the information obtained to generate a response to the user's question. The question has been broken down into a series of SQL queries and you need to generate a response based on the results of these queries.