Improves disambiguation & answer agent

text_2_sql/autogen/pyproject.toml

@@ -5,12 +5,12 @@ description = "AutoGen Based Implementation"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "autogen-agentchat==0.4.0.dev9",
-    "autogen-core==0.4.0.dev9",
-    "autogen-ext[azure,openai]==0.4.0.dev9",
+    "autogen-agentchat==0.4.0.dev11",
+    "autogen-core==0.4.0.dev11",
+    "autogen-ext[azure,openai]==0.4.0.dev11",
     "grpcio>=1.68.1",
     "pyyaml>=6.0.2",
-    "text_2_sql_core",
+    "text_2_sql_core[snowflake,databricks]",
 ]
 
 [dependency-groups]

text_2_sql/autogen/src/autogen_text_2_sql/autogen_text_2_sql.py

@@ -50,15 +50,15 @@ def __init__(self, engine_specific_rules: str, **kwargs: dict):
     def set_mode(self):
         """Set the mode of the plugin based on the environment variables."""
         self.use_query_cache = (
-            os.environ.get("Text2Sql__UseQueryCache", "False").lower() == "true"
+            os.environ.get("Text2Sql__UseQueryCache", "True").lower() == "true"
         )
 
         self.pre_run_query_cache = (
-            os.environ.get("Text2Sql__PreRunQueryCache", "False").lower() == "true"
+            os.environ.get("Text2Sql__PreRunQueryCache", "True").lower() == "true"
         )
 
         self.use_column_value_store = (
-            os.environ.get("Text2Sql__UseColumnValueStore", "False").lower() == "true"
+            os.environ.get("Text2Sql__UseColumnValueStore", "True").lower() == "true"
         )
 
     def get_all_agents(self):
@@ -97,8 +97,10 @@ def get_all_agents(self):
             engine_specific_rules=self.engine_specific_rules,
             **self.kwargs,
         )
-
-        ANSWER_AGENT = LLMAgentCreator.create("answer_agent")
+
+        QUESTION_DECOMPOSITION_AGENT = LLMAgentCreator.create(
+            "question_decomposition_agent"
+        )
 
         # Auto-responding UserProxyAgent
         USER_PROXY = EmptyResponseUserProxyAgent(
@@ -111,8 +113,8 @@ def get_all_agents(self):
             SQL_QUERY_GENERATION_AGENT,
             SQL_SCHEMA_SELECTION_AGENT,
             SQL_QUERY_CORRECTION_AGENT,
-            SQL_DISAMBIGUATION_AGENT,
-            ANSWER_AGENT,
+            QUESTION_DECOMPOSITION_AGENT,
+            SQL_DISAMBIGUATION_AGENT
         ]
 
         if self.use_query_cache:
@@ -126,12 +128,15 @@ def termination_condition(self):
         """Define the termination condition for the chat."""
         termination = (
             TextMentionTermination("TERMINATE")
+            | (
+                TextMentionTermination("answer")
+                & TextMentionTermination("sources")
+                & SourceMatchTermination("sql_query_correction_agent")
+            )
             | MaxMessageTermination(20)
-            | SourceMatchTermination(["answer_agent"])
         )
         return termination
 
-    @staticmethod
     def unified_selector(messages):
         """Unified selector for the complete flow."""
         logging.info("Messages: %s", messages)
@@ -165,13 +170,14 @@ def unified_selector(messages):
             decision = "sql_disambiguation_agent"
         elif messages[-1].source == "sql_disambiguation_agent":
             decision = "sql_query_generation_agent"
+
+        elif messages[-1].source == "sql_query_correction_agent":
+            decision = "sql_query_generation_agent"
+
         elif messages[-1].source == "sql_query_generation_agent":
             decision = "sql_query_correction_agent"
         elif messages[-1].source == "sql_query_correction_agent":
-            if messages[-1].content == "VALIDATED":
-                decision = "answer_agent"
-            else:
-                decision = "sql_query_correction_agent"
+            decision = "sql_query_correction_agent"
         elif messages[-1].source == "answer_agent":
             return "user_proxy"  # Let user_proxy send TERMINATE
 
@@ -186,7 +192,8 @@ def agentic_flow(self):
             allow_repeated_speaker=False,
             model_client=LLMModelCreator.get_model("4o-mini"),
             termination_condition=self.termination_condition,
-            selector_func=AutoGenText2Sql.unified_selector,
+            selector_func=self.selector,
+            selector_func=self.unified_selector,
         )
         return flow
 

text_2_sql/autogen/src/autogen_text_2_sql/creators/llm_agent_creator.py

@@ -49,11 +49,6 @@ def get_tool(cls, sql_helper, ai_search_helper, tool_name: str):
                 ai_search_helper.get_column_values,
                 description="Gets the values of a column in the SQL Database by selecting the most relevant entity based on the search term. Several entities may be returned. Use this to get the correct value to apply against a filter for a user's question.",
             )
-        elif tool_name == "sql_query_validation_tool":
-            return FunctionTool(
-                sql_helper.query_validation,
-                description="Validates the SQL query to ensure that it is syntactically correct for the target database engine. Use this BEFORE executing any SQL statement.",
-            )
         elif tool_name == "current_datetime_tool":
             return FunctionTool(
                 sql_helper.get_current_datetime,

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

@@ -12,9 +12,9 @@
 
 
 class SqlQueryCacheAgent(BaseChatAgent):
-    def __init__(self):
+    def __init__(self, name: str = "sql_query_cache_agent"):
         super().__init__(
-            "sql_query_cache_agent",
+            name,
             "An agent that fetches the queries from the cache based on the user question.",
         )
 

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

@@ -101,15 +101,9 @@ async def on_messages_stream(
                 if schema not in final_schemas:
                     final_schemas.append(schema)
 
-        final_colmns = []
-        for column_value_result in column_value_results:
-            for column in column_value_result:
-                if column not in final_colmns:
-                    final_colmns.append(column)
-
         final_results = {
-            "schemas": final_schemas,
-            "column_values": final_colmns,
+            "COLUMN_OPTIONS_AND_VALUES_FOR_FILTERS": column_value_results,
+            "SCHEMA_OPTIONS": final_schemas,
         }
 
         logging.info(f"Final results: {final_results}")

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

@@ -3,7 +3,7 @@
 from azure.identity import DefaultAzureCredential
 from openai import AsyncAzureOpenAI
 from azure.core.credentials import AzureKeyCredential
-from azure.search.documents.models import VectorizedQuery
+from azure.search.documents.models import VectorizedQuery, QueryType
 from azure.search.documents.aio import SearchClient
 from text_2_sql_core.utils.environment import IdentityType, get_identity_type
 import os
@@ -69,11 +69,9 @@ async def run_ai_search_query(
             credential=credential,
         ) as search_client:
             if semantic_config is not None and vector_query is not None:
-                query_type = "semantic"
-            elif vector_query is not None:
-                query_type = "hybrid"
+                query_type = QueryType.SEMANTIC
             else:
-                query_type = "full"
+                query_type = QueryType.FULL
 
             results = await search_client.search(
                 top=top,
@@ -148,7 +146,7 @@ async def get_column_values(
                 "AIService__AzureSearchOptions__Text2SqlColumnValueStore__Index"
             ],
             semantic_config=None,
-            top=15,
+            top=50,
             include_scores=False,
             minimum_score=5,
         )
@@ -163,10 +161,14 @@ async def get_column_values(
 
             column_values[trimmed_fqn].append(value["Value"])
 
+        logging.info("Column Values: %s", column_values)
+
+        filter_to_column = {text: column_values}
+
         if as_json:
-            return json.dumps(column_values, default=str)
+            return json.dumps(filter_to_column, default=str)
         else:
-            return column_values
+            return filter_to_column
 
     async def get_entity_schemas(
         self,
@@ -193,20 +195,24 @@ async def get_entity_schemas(
             str: The schema of the views or tables in JSON format.
         """
 
+        logging.info("Search Text: %s", text)
+
         retrieval_fields = [
-            "FQN",
+            # "FQN",
             "Entity",
             "EntityName",
-            "Schema",
-            "Definition",
+            # "Schema",
+            # "Definition",
+            "Description",
             "Columns",
             "EntityRelationships",
             "CompleteEntityRelationshipsGraph",
         ] + engine_specific_fields
 
         schemas = await self.run_ai_search_query(
             text,
-            ["DefinitionEmbedding"],
+            # ["DefinitionEmbedding"],
+            ["DescriptionEmbedding"],
             retrieval_fields,
             os.environ["AIService__AzureSearchOptions__Text2SqlSchemaStore__Index"],
             os.environ[
@@ -221,7 +227,25 @@ async def get_entity_schemas(
         for schema in schemas:
             filtered_schemas = []
 
-            del schema["FQN"]
+            # del schema["FQN"]
+
+            if (
+                schema["CompleteEntityRelationshipsGraph"] is not None
+                and len(schema["CompleteEntityRelationshipsGraph"]) == 0
+            ):
+                del schema["CompleteEntityRelationshipsGraph"]
+
+            if (
+                schema["SammpleValues"] is not None
+                and len(schema["SammpleValues"]) == 0
+            ):
+                del schema["SammpleValues"]
+
+            if (
+                schema["EntityRelationships"] is not None
+                and len(schema["EntityRelationships"]) == 0
+            ):
+                del schema["EntityRelationships"]
 
             if schema["Entity"].lower() not in excluded_entities:
                 filtered_schemas.append(schema)

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

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

@@ -13,15 +13,15 @@
 class SqlConnector(ABC):
     def __init__(self):
         self.use_query_cache = (
-            os.environ.get("Text2Sql__UseQueryCache", "False").lower() == "true"
+            os.environ.get("Text2Sql__UseQueryCache", "True").lower() == "true"
         )
 
         self.pre_run_query_cache = (
-            os.environ.get("Text2Sql__PreRunQueryCache", "False").lower() == "true"
+            os.environ.get("Text2Sql__PreRunQueryCache", "True").lower() == "true"
         )
 
         self.use_column_value_store = (
-            os.environ.get("Text2Sql__UseColumnValueStore", "False").lower() == "true"
+            os.environ.get("Text2Sql__UseColumnValueStore", "True").lower() == "true"
         )
 
         self.ai_search_connector = ConnectorFactory.get_ai_search_connector()
@@ -91,7 +91,14 @@ async def query_execution_with_limit(
         -------
             list[dict]: The results of the SQL query.
         """
-        return await self.query_execution(sql_query, cast_to=None, limit=25)
+
+        # Validate the SQL query
+        validation_result = await self.query_validation(sql_query)
+
+        if isinstance(validation_result, bool) and validation_result:
+            return await self.query_execution(sql_query, cast_to=None, limit=25)
+        else:
+            return validation_result
 
     async def query_validation(
         self,
@@ -127,9 +134,7 @@ async def fetch_queries_from_cache(self, question: str) -> str:
             ["QuestionEmbedding"],
             ["Question", "SqlQueryDecomposition"],
             os.environ["AIService__AzureSearchOptions__Text2SqlQueryCache__Index"],
-            os.environ[
-                "AIService__AzureSearchOptions__Text2SqlQueryCache__SemanticConfig"
-            ],
+            None,
             top=1,
             include_scores=True,
             minimum_score=1.5,