Fix bad changes (#101)

text_2_sql/autogen/src/autogen_text_2_sql/autogen_text_2_sql.py

@@ -18,11 +18,12 @@
 from autogen_agentchat.base import Response
 import json
 import os
-import asyncio
 from datetime import datetime
 
+
 class EmptyResponseUserProxyAgent(UserProxyAgent):
     """UserProxyAgent that automatically responds with empty messages."""
+
     def __init__(self, name):
         super().__init__(name=name)
         self._has_responded = False
@@ -35,6 +36,7 @@ async def on_messages_stream(self, messages, sender=None, config=None):
             yield message
         yield Response(chat_message=message)
 
+
 class AutoGenText2Sql:
     def __init__(self, engine_specific_rules: str, **kwargs: dict):
         self.use_query_cache = False
@@ -65,32 +67,31 @@ def get_all_agents(self):
         """Get all agents for the complete flow."""
         # Get current datetime for the Query Rewrite Agent
         current_datetime = datetime.now()
-        
+
         QUERY_REWRITE_AGENT = LLMAgentCreator.create(
-            "query_rewrite_agent",
-            current_datetime=current_datetime
+            "query_rewrite_agent", current_datetime=current_datetime
         )
-        
+
         SQL_QUERY_GENERATION_AGENT = LLMAgentCreator.create(
             "sql_query_generation_agent",
             target_engine=self.target_engine,
             engine_specific_rules=self.engine_specific_rules,
             **self.kwargs,
         )
-        
+
         SQL_SCHEMA_SELECTION_AGENT = SqlSchemaSelectionAgent(
             target_engine=self.target_engine,
             engine_specific_rules=self.engine_specific_rules,
             **self.kwargs,
         )
-        
+
         SQL_QUERY_CORRECTION_AGENT = LLMAgentCreator.create(
             "sql_query_correction_agent",
             target_engine=self.target_engine,
             engine_specific_rules=self.engine_specific_rules,
             **self.kwargs,
         )
-        
+
         SQL_DISAMBIGUATION_AGENT = LLMAgentCreator.create(
             "sql_disambiguation_agent",
             target_engine=self.target_engine,
@@ -101,11 +102,9 @@ def get_all_agents(self):
         QUESTION_DECOMPOSITION_AGENT = LLMAgentCreator.create(
             "question_decomposition_agent"
         )
-        
+
         # Auto-responding UserProxyAgent
-        USER_PROXY = EmptyResponseUserProxyAgent(
-            name="user_proxy"
-        )
+        USER_PROXY = EmptyResponseUserProxyAgent(name="user_proxy")
 
         agents = [
             USER_PROXY,
@@ -114,7 +113,7 @@ def get_all_agents(self):
             SQL_SCHEMA_SELECTION_AGENT,
             SQL_QUERY_CORRECTION_AGENT,
             QUESTION_DECOMPOSITION_AGENT,
-            SQL_DISAMBIGUATION_AGENT
+            SQL_DISAMBIGUATION_AGENT,
         ]
 
         if self.use_query_cache:
@@ -192,7 +191,6 @@ def agentic_flow(self):
             allow_repeated_speaker=False,
             model_client=LLMModelCreator.get_model("4o-mini"),
             termination_condition=self.termination_condition,
-            selector_func=self.selector,
             selector_func=self.unified_selector,
         )
         return flow

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

@@ -47,19 +47,21 @@ async def on_messages_stream(
             # Initialize results dictionary
             cached_results = {
                 "cached_questions_and_schemas": [],
-                "contains_pre_run_results": False
+                "contains_pre_run_results": False,
             }
 
             # Process each question sequentially
             for question in user_questions:
                 # 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)
-
+                cached_query = await self.sql_connector.fetch_queries_from_cache(
+                    question
+                )
+
                 # If any question has pre-run results, set the flag
                 if cached_query.get("contains_pre_run_results", False):
                     cached_results["contains_pre_run_results"] = True
-                
+
                 # Add the cached results for this question
                 if cached_query.get("cached_questions_and_schemas"):
                     cached_results["cached_questions_and_schemas"].extend(
@@ -75,7 +77,9 @@ async def on_messages_stream(
         except json.JSONDecodeError:
             # If not JSON array, process as single question
             logging.info(f"Processing single question: {last_response}")
-            cached_queries = await self.sql_connector.fetch_queries_from_cache(last_response)
+            cached_queries = await self.sql_connector.fetch_queries_from_cache(
+                last_response
+            )
             yield Response(
                 chat_message=TextMessage(
                     content=json.dumps(cached_queries), source=self.name

text_2_sql/text_2_sql_core/src/text_2_sql_core/connectors/ai_search.py

@@ -198,21 +198,19 @@ async def get_entity_schemas(
         logging.info("Search Text: %s", text)
 
         retrieval_fields = [
-            # "FQN",
+            "FQN",
             "Entity",
             "EntityName",
-            # "Schema",
-            # "Definition",
-            "Description",
+            "Schema",
+            "Definition",
             "Columns",
             "EntityRelationships",
             "CompleteEntityRelationshipsGraph",
         ] + engine_specific_fields
 
         schemas = await self.run_ai_search_query(
             text,
-            # ["DefinitionEmbedding"],
-            ["DescriptionEmbedding"],
+            ["DefinitionEmbedding"],
             retrieval_fields,
             os.environ["AIService__AzureSearchOptions__Text2SqlSchemaStore__Index"],
             os.environ[
@@ -227,7 +225,7 @@ async def get_entity_schemas(
         for schema in schemas:
             filtered_schemas = []
 
-            # del schema["FQN"]
+            del schema["FQN"]
 
             if (
                 schema["CompleteEntityRelationshipsGraph"] is not None

text_2_sql/text_2_sql_core/src/text_2_sql_core/connectors/databricks_sql.py

@@ -98,13 +98,12 @@ async def get_entity_schemas(
         )
 
         for schema in schemas:
-            # schema["SelectFromEntity"] = ".".join(
-            #     [schema["Catalog"], schema["Schema"], schema["Entity"]]
-            # )
-            schema["SelectFromEntity"] = schema["Entity"]
+            schema["SelectFromEntity"] = ".".join(
+                [schema["Catalog"], schema["Schema"], schema["Entity"]]
+            )
 
             del schema["Entity"]
-            # del schema["Schema"]
+            del schema["Schema"]
             del schema["Catalog"]
 
         if as_json:

text_2_sql/text_2_sql_core/src/text_2_sql_core/prompts/query_rewrite_agent.yaml

@@ -14,7 +14,7 @@ system_message:
   - Use the current date/time above as reference point
   - Replace relative dates like 'last month', 'this year', 'previous quarter' with absolute dates
   - Maintain consistency in date formats (YYYY-MM-DD)
-  
+
   Examples of date resolution (assuming current date is {{ current_datetime }}):
   - 'last month' -> specific month name and year
   - 'this year' -> {{ current_datetime.year }}

text_2_sql/text_2_sql_core/src/text_2_sql_core/prompts/sql_disambiguation_agent.yaml

@@ -48,6 +48,7 @@ system_message:
       <unsuccessful_mapping_entry>
         - If you cannot map it to a column, add en entry to the disambiguation list with the clarification question you need from the user:
         - If there are multiple possible options, or you are unsure how it maps, make sure to ask a clarification question.
+        - If there are no possible options, ask a clarification question for more detail.
 
         {
           \"disambiguation\": [