Update naming

Author: BenConstable9

text_2_sql/autogen/README.md

@@ -134,7 +134,7 @@ Each agent can be configured with specific parameters and prompts to optimize it
 
 ## Query Cache Implementation Details
 
-The vector based with query cache uses the `fetch_queries_from_cache()` method to fetch the most relevant previous query and injects it into the prompt before the initial LLM call. The use of Auto-Function Calling here is avoided to reduce the response time as the cache index will always be used first.
+The vector based with query cache uses the `fetch_sql_queries_with_schemas_from_cache()` method to fetch the most relevant previous query and injects it into the prompt before the initial LLM call. The use of Auto-Function Calling here is avoided to reduce the response time as the cache index will always be used first.
 
 If the score of the top result is higher than the defined threshold, the query will be executed against the target data source and the results included in the prompt. This allows us to prompt the LLM to evaluated whether it can use these results to answer the question, **without further SQL Query generation** to speed up the process.
 

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

@@ -152,8 +152,14 @@ async def consume_inner_messages_from_agentic_flow(
                         # Search for specific message types and add them to the final output object
                         if isinstance(parsed_message, dict):
                             # Check if the message contains pre-run results
-                            if ("contains_pre_run_results" in parsed_message) and (
-                                parsed_message["contains_pre_run_results"] is True
+                            if (
+                                "contains_cached_sql_queries_with_schemas_from_cache_database_results"
+                                in parsed_message
+                            ) and (
+                                parsed_message[
+                                    "contains_cached_sql_queries_with_schemas_from_cache_database_results"
+                                ]
+                                is True
                             ):
                                 logging.info("Contains pre-run results")
                                 for pre_run_sql_query, pre_run_result in parsed_message[

text_2_sql/previous_iterations/semantic_kernel/README.md

@@ -134,9 +134,9 @@ This method is called by the Semantic Kernel framework automatically, when instr
 
 The search text passed is vectorised against the entity level **Description** columns. A hybrid Semantic Reranking search is applied against the **EntityName**, **Entity**, **Columns/Name** fields.
 
-#### fetch_queries_from_cache()
+#### fetch_sql_queries_with_schemas_from_cache()
 
-The vector based with query cache uses the `fetch_queries_from_cache()` method to fetch the most relevant previous query and injects it into the prompt before the initial LLM call. The use of Auto-Function Calling here is avoided to reduce the response time as the cache index will always be used first.
+The vector based with query cache uses the `fetch_sql_queries_with_schemas_from_cache()` method to fetch the most relevant previous query and injects it into the prompt before the initial LLM call. The use of Auto-Function Calling here is avoided to reduce the response time as the cache index will always be used first.
 
 If the score of the top result is higher than the defined threshold, the query will be executed against the target data source and the results included in the prompt. This allows us to prompt the LLM to evaluated whether it can use these results to answer the question, **without further SQL Query generation** to speed up the process.
 

text_2_sql/previous_iterations/semantic_kernel/plugins/vector_based_sql_plugin/vector_based_sql_plugin.py

@@ -137,7 +137,7 @@ async def fetch_schemas_from_store(self, search: str) -> list[dict]:
 
         return schemas
 
-    async def fetch_queries_from_cache(self, question: str) -> str:
+    async def fetch_sql_queries_with_schemas_from_cache(self, question: str) -> str:
         """Fetch the queries from the cache based on the question.
 
         Args:
@@ -151,7 +151,7 @@ async def fetch_queries_from_cache(self, question: str) -> str:
         if not self.use_query_cache:
             return None
 
-        cached_schemas = await self.ai_search.run_ai_search_query(
+        sql_queries_with_schemas = await self.ai_search.run_ai_search_query(
             question,
             ["QuestionEmbedding"],
             ["Question", "SqlQueryDecomposition"],
@@ -164,26 +164,28 @@ async def fetch_queries_from_cache(self, question: str) -> str:
             minimum_score=1.5,
         )
 
-        if len(cached_schemas) == 0:
+        if len(sql_queries_with_schemas) == 0:
             return None
         else:
             database = os.environ["Text2Sql__DatabaseName"]
-            for entry in cached_schemas["SqlQueryDecomposition"]:
+            for entry in sql_queries_with_schemas["SqlQueryDecomposition"]:
                 for schema in entry["Schemas"]:
                     entity = schema["Entity"]
                     schema["SelectFromEntity"] = f"{database}.{entity}"
 
                     self.schemas[entity] = schema
 
         pre_fetched_results_string = ""
-        if self.pre_run_query_cache and len(cached_schemas) > 0:
-            logging.info("Cached schemas: %s", cached_schemas)
+        if self.pre_run_query_cache and len(sql_queries_with_schemas) > 0:
+            logging.info(
+                "Cached SQL Queries with Schemas: %s", sql_queries_with_schemas
+            )
 
             # check the score
-            if cached_schemas[0]["@search.reranker_score"] > 2.75:
+            if sql_queries_with_schemas[0]["@search.reranker_score"] > 2.75:
                 logging.info("Score is greater than 3")
 
-                sql_queries = cached_schemas[0]["SqlQueryDecomposition"]
+                sql_queries = sql_queries_with_schemas[0]["SqlQueryDecomposition"]
                 query_result_store = {}
 
                 query_tasks = []
@@ -208,7 +210,7 @@ async def fetch_queries_from_cache(self, question: str) -> str:
                 return pre_fetched_results_string
 
         formatted_sql_cache_string = f"""[BEGIN CACHED QUERIES AND SCHEMAS]:\n{
-            json.dumps(cached_schemas, default=str)}[END CACHED QUERIES AND SCHEMAS]"""
+            json.dumps(sql_queries_with_schemas, default=str)}[END CACHED QUERIES AND SCHEMAS]"""
 
         return formatted_sql_cache_string
 
@@ -230,7 +232,9 @@ async def sql_prompt_injection(
         self.set_mode()
 
         if self.use_query_cache:
-            query_cache_string = await self.fetch_queries_from_cache(question)
+            query_cache_string = await self.fetch_sql_queries_with_schemas_from_cache(
+                question
+            )
         else:
             query_cache_string = None
 

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

@@ -260,7 +260,7 @@ def handle_node(node):
             logging.info("SQL Query is valid.")
             return True
 
-    async def fetch_queries_from_cache(
+    async def fetch_sql_queries_with_schemas_from_cache(
         self, question: str, injected_parameters: dict = None
     ) -> str:
         """Fetch the queries from the cache based on the question.
@@ -276,14 +276,14 @@ async def fetch_queries_from_cache(
         # Return empty results if AI Search is disabled
         if not self.use_ai_search:
             return {
-                "contains_pre_run_results": False,
-                "cached_questions_and_schemas": None,
+                "contains_cached_sql_queries_with_schemas_from_cache_database_results": False,
+                "cached_sql_queries_with_schemas_from_cache": None,
             }
 
         if injected_parameters is None:
             injected_parameters = {}
 
-        cached_schemas = await self.ai_search_connector.run_ai_search_query(
+        sql_queries_with_schemas = await self.ai_search_connector.run_ai_search_query(
             question,
             ["QuestionEmbedding"],
             ["Question", "SqlQueryDecomposition"],
@@ -294,51 +294,51 @@ async def fetch_queries_from_cache(
             minimum_score=1.5,
         )
 
-        if len(cached_schemas) == 0:
+        if len(sql_queries_with_schemas) == 0:
             return {
-                "contains_pre_run_results": False,
-                "cached_questions_and_schemas": None,
+                "contains_cached_sql_queries_with_schemas_from_cache_database_results": False,
+                "cached_sql_queries_with_schemas_from_cache": None,
             }
 
         # loop through all sql queries and populate the template in place
-        for schema in cached_schemas:
-            sql_queries = schema["SqlQueryDecomposition"]
-            for sql_query in sql_queries:
+        for queries_with_schemas in sql_queries_with_schemas:
+            for sql_query in queries_with_schemas["SqlQueryDecomposition"]:
                 sql_query["SqlQuery"] = Template(sql_query["SqlQuery"]).render(
                     **injected_parameters
                 )
 
-        logging.info("Cached schemas: %s", cached_schemas)
-        if self.pre_run_query_cache and len(cached_schemas) > 0:
+        logging.info("Cached SQL Queries with Schemas: %s", sql_queries_with_schemas)
+        if self.pre_run_query_cache and len(sql_queries_with_schemas) > 0:
             # check the score
-            if cached_schemas[0]["@search.reranker_score"] > 2.75:
+            if sql_queries_with_schemas[0]["@search.reranker_score"] > 2.75:
                 logging.info("Score is greater than 3")
 
-                sql_queries = cached_schemas[0]["SqlQueryDecomposition"]
                 query_result_store = {}
 
                 query_tasks = []
 
-                for sql_query in sql_queries:
+                for sql_query in sql_queries_with_schemas[0]["SqlQueryDecomposition"]:
                     logging.info("SQL Query: %s", sql_query)
 
                     # Run the SQL query
                     query_tasks.append(self.query_execution(sql_query["SqlQuery"]))
 
                 sql_results = await asyncio.gather(*query_tasks)
 
-                for sql_query, sql_result in zip(sql_queries, sql_results):
+                for sql_query, sql_result in zip(
+                    sql_queries_with_schemas[0]["SqlQueryDecomposition"], sql_results
+                ):
                     query_result_store[sql_query["SqlQuery"]] = {
                         "sql_rows": sql_result,
                         "schemas": sql_query["Schemas"],
                     }
 
                 return {
-                    "contains_pre_run_results": True,
-                    "cached_questions_and_schemas": query_result_store,
+                    "contains_cached_sql_queries_with_schemas_from_cache_database_results": True,
+                    "cached_sql_queries_with_schemas_from_cache": query_result_store,
                 }
 
         return {
-            "contains_pre_run_results": False,
-            "cached_questions_and_schemas": cached_schemas,
+            "contains_cached_sql_queries_with_schemas_from_cache_database_results": False,
+            "cached_sql_queries_with_schemas_from_cache": sql_queries_with_schemas,
         }

text_2_sql/text_2_sql_core/src/text_2_sql_core/custom_agents/sql_query_cache_agent.py

@@ -13,26 +13,33 @@ async def process_message(
     ) -> dict:
         # Initialize results dictionary
         cached_results = {
-            "cached_questions_and_schemas": [],
-            "contains_pre_run_results": False,
+            "cached_sql_queries_with_schemas_from_cache": [],
+            "contains_cached_sql_queries_with_schemas_from_cache_database_results": False,
         }
 
         # Process each question sequentially
         for message in messages:
             # Fetch the queries from the cache based on the question
             logging.info(f"Fetching queries from cache for question: {message}")
-            cached_query = await self.sql_connector.fetch_queries_from_cache(
-                message, injected_parameters=injected_parameters
+            cached_query = (
+                await self.sql_connector.fetch_sql_queries_with_schemas_from_cache(
+                    message, injected_parameters=injected_parameters
+                )
             )
 
             # 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
+            if cached_query.get(
+                "contains_cached_sql_queries_with_schemas_from_cache_database_results",
+                False,
+            ):
+                cached_results[
+                    "contains_cached_sql_queries_with_schemas_from_cache_database_results"
+                ] = True
 
             # Add the cached results for this question
-            if cached_query.get("cached_questions_and_schemas"):
-                cached_results["cached_questions_and_schemas"].extend(
-                    cached_query["cached_questions_and_schemas"]
+            if cached_query.get("cached_sql_queries_with_schemas_from_cache"):
+                cached_results["cached_sql_queries_with_schemas_from_cache"].extend(
+                    cached_query["cached_sql_queries_with_schemas_from_cache"]
                 )
 
         logging.info(f"Final cached results: {cached_results}")