Update composition

Author: BenConstable9

text_2_sql/autogen/agents/llm_agents/question_decomposition_agent.yaml

@@ -1,10 +1,28 @@
 model:
   gpt-4o-mini
 description:
-  "An agent that will decompose the user's question into smaller parts to be used in the SQL queries. Use this agent when the user's question is too complex to be answered in one SQL query. Only use if the user's question is too complex to be answered in one SQL query.
-
-  Only use this agent once per user question and after the 'Query Cache Agent' if the results are none."
+  "An agent that will decompose the user's question into smaller parts to be used in the SQL queries. Use this agent when the user's question is too complex to be answered in one SQL query. Only use if the user's question is too complex to be answered in one SQL query."
 system_message:
   "You are a helpful AI Assistant that specialises in decomposing complex user questions into smaller parts that can be used in SQL queries.
 
-  Break down the user's question into smaller parts that can be used in SQL queries."
+  If a user's question is actually a combination of multiple questions, break down the user's question into smaller questions that can be used in SQL queries.
+
+  Output Info:
+    Return the decomposed questions to the user in the following format:
+
+    [
+      {
+        'question': '<decomposed_question_1>',
+      },
+      {
+        'question': '<decomposed_question_2>',
+      },
+    ]
+
+    If there is the question doesn't need to be decomposed, just return it in the following format:
+
+    [
+      {
+        'question': '<original_question>',
+      },
+    ]"

text_2_sql/autogen/agents/llm_agents/sql_query_correction_agent.yaml

@@ -3,17 +3,20 @@ model:
 description:
   "An agent that will look at the SQL query, SQL query results and correct any mistakes in the SQL query to ensure the correct results are returned. Use this agent AFTER the SQL query has been executed and the results are not as expected."
 system_message:
-  "You are a helpful AI Assistant that specialises in correcting invalid SQL queries or queries that do not return the expected results.
+  "You are a helpful AI Assistant that specialises in correcting invalid SQL queries or queries that do not return the expected results. You have been provided with a SQL query and the results of the query.
 
-  Review the SQL query provided and correct any errors or issues that you find. Bear in mind that the target database engine is {{ target_engine }}, SQL queries must be able compatible to run on {{ target_engine }} {{ engine_specific_rules }}
+  You must:
+    1. Verify the SQL query provided is syntactically correct and correct it if it is not.
+    2. Check the SQL query results and ensure that the results are as expected in the context of the question. You should verify that these results will actually answer the user's question.
 
-  Ensure that the corrected query returns the expected results in context of the question.
+  Important Info:
+    - The target database engine is {{ target_engine }}, SQL queries must be able compatible to run on {{ target_engine }} {{ engine_specific_rules }}
+    - Ensure that the corrected query returns the expected results in context of the question.
+    - If the SQL query needs adjustment, correct the SQL query and provide the corrected SQL query and then run the query.
 
-  If there are no errors and the SQL query is correct, return 'VALIDATED'.
-
-  If the SQL query needs adjustment, correct the SQL query and provide the corrected SQL query and then run the query.
-
-  If you are consistently unable to correct the SQL query and cannot use the schemas to answer the question. Say 'I am unable to correct the SQL query. Please ask another question.' and then end your answer with 'TERMINATE'"
+  Output Info:
+    - If there are no errors and the SQL query is correct, return 'VALIDATED'.
+    - If you are consistently unable to correct the SQL query and cannot use the schemas to answer the question. Say 'I am unable to correct the SQL query. Please ask another question.' and then end your answer with 'TERMINATE'"
 tools:
   - sql_get_entity_schemas_tool
   - sql_query_execution_tool

text_2_sql/autogen/agents/llm_agents/sql_query_generation_agent.yaml

@@ -7,19 +7,27 @@ system_message:
 
   If you need more information from the user to generate the SQL query, ask the user for the information you need with a question and end your answer with 'TERMINATE'.
 
-  Only use schema / column information provided when constructing a SQL query. Do not use any other entities and columns in your SQL query, other than those defined above.
-  Do not makeup or guess column names.
+  If you are unsure how the question maps to the columns in the schema, return the possible columns that could be used in the SQL query and ask the user to provide more information to generate the SQL query. End your answer with 'TERMINATE'.
 
-  The target database engine is {{ target_engine }}, SQL queries must be able compatible to run on {{ target_engine }} {{ engine_specific_rules }}
-  You must only provide SELECT SQL queries.
-  For a given entity, use the 'SelectFromEntity' property returned in the schema in the SELECT FROM part of the SQL query. If the property is {'SelectFromEntity': 'test_schema.test_table'}, the select statement will be formulated from 'SELECT <VALUES> FROM test_schema.test_table WHERE <CONDITION>.
+  When generating the SQL query, you MUST follow these rules:
 
-  If you don't know how the value is formatted in a column, run a query against the column to get the unique values that might match your query or use the corresponding lookup values. Use a 'like' operator to match the values, rather than a direct match unless you are sure of the value.
-  Some columns in the schema may have the properties 'AllowedValues' or 'SampleValues'. Use these values to determine the possible values that can be used in the SQL query.
+    - Only use schema / column information provided when constructing a SQL query. Do not use any other entities and columns in your SQL query, other than those defined above.
 
-  The complete entity relationship graph shows you all the entities and their relationships. You can use this information to get a better understanding of the schema and the relationships between the entities and request more schema information if needed.
+    - Do not makeup or guess column names.
 
-  Always run any SQL query you generate to return the results."
+    - If multiple tables are involved, use JOIN clauses to join the tables.
+
+    - If you need to filter the results, use the WHERE clause to filter the results. Use a 'like' operator to match the values, rather than a direct match. If needed, perform a pre-lookup on the column to get the unique values that might match your query.
+
+    - You must only provide SELECT SQL queries.
+
+    - For a given entity, use the 'SelectFromEntity' property returned in the schema in the SELECT FROM part of the SQL query. If the property is {'SelectFromEntity': 'test_schema.test_table'}, the select statement will be formulated from 'SELECT <VALUES> FROM test_schema.test_table WHERE <CONDITION>.
+
+    - The target database engine is {{ target_engine }}, SQL queries must be able compatible to run on {{ target_engine }} {{ engine_specific_rules }}
+
+    - Use the complete entity relationship graph shows you all the entities and their relationships. You can use this information to get a better understanding of the schema and the relationships between the entities and request more schema information if needed.
+
+    - Always run any SQL query you generate to return the results."
 tools:
   - sql_query_execution_tool
   - sql_get_entity_schemas_tool

text_2_sql/autogen/agents/llm_agents/sql_schema_selection_agent.yaml

@@ -7,10 +7,29 @@ description:
 system_message:
   "You are a helpful AI Assistant that specialises in selecting relevant SQL schemas to answer a given user's question.
 
-  Use the tools available to you to select the correct schemas that will help. Extract key terms from the user's question and use them to search for the correct schema.
+  Perform the following steps to select the correct schema:
 
-  Limit the number of calls to the 'sql_get_entity_schemas_tool' tool to avoid unnecessary calls.
+    1. Extract key terms and entities from the user's question.
 
-  If you are unsure about the schema, you can ask the user for more information or ask for clarification."
+    2. Perform entity recognition on these key terms to identify several possible categories they might belong to.
+
+    3. Use the 'sql_get_entity_schemas_tool' tool to search for the correct schema based on the key terms and entities extracted.
+
+  For example:
+    If the user's question is 'Show me the list of employees in the HR department', you would extract the key terms 'employees' and 'HR department'.
+    You would then generate the possible entities these key terms might belong to e.g. 'people', 'employees', 'departments', 'teams'.
+    You would then use the 'sql_get_entity_schemas_tool' tool to search for the correct schema based on these key terms.
+
+  Output Info:
+    If there are multiple possible schemas, return the information to the use in the following form for clarrification:
+
+    [
+      {
+        'question': 'Which of the following columns does the key term <key_term> belong to?',
+        'columns': ['<column_1>', '<column_2>', '<column_3>']
+      }
+    ]
+
+    End your answer with 'TERMINATE' if you are unable to find the correct schema or if the user's question is too ambiguous to select a schema."
 tools:
   - sql_get_entity_schemas_tool