renamed variables for clarity

eval/gemini_runner.py

@@ -42,9 +42,10 @@ def generate_prompt(
     question_instructions = question + " " + instructions
 
     if table_metadata_string == "":
-        pruned_metadata_str = prune_metadata_str(
+        pruned_metadata_ddl, join_str = prune_metadata_str(
             question_instructions, db_name, public_data, num_columns_to_keep, shuffle
         )
+        pruned_metadata_str = pruned_metadata_ddl + join_str
     else:
         pruned_metadata_str = table_metadata_string
 

eval/mistral_runner.py

@@ -45,9 +45,10 @@ def generate_prompt(
     question_instructions = question + " " + instructions
 
     if table_metadata_string == "":
-        pruned_metadata_str = prune_metadata_str(
+        pruned_metadata_ddl, join_str = prune_metadata_str(
             question_instructions, db_name, public_data, columns_to_keep, shuffle
         )
+        pruned_metadata_str = pruned_metadata_ddl + join_str
     else:
         pruned_metadata_str = table_metadata_string
 

query_generators/anthropic.py

@@ -99,13 +99,14 @@ def generate_query(
             model_prompt = file.read()
         question_instructions = question + " " + instructions
         if table_metadata_string == "":
-            pruned_metadata_str = prune_metadata_str(
+            pruned_metadata_ddl, join_str = prune_metadata_str(
                 question_instructions,
                 self.db_name,
                 self.use_public_data,
                 columns_to_keep,
                 shuffle,
             )
+            pruned_metadata_str = pruned_metadata_ddl + join_str
         else:
             pruned_metadata_str = table_metadata_string
         prompt = model_prompt.format(

query_generators/openai.py

@@ -140,13 +140,14 @@ def generate_query(
         question_instructions = question + " " + instructions
         if table_metadata_string == "":
             if columns_to_keep > 0:
-                table_metadata_string = prune_metadata_str(
+                table_metadata_ddl, join_str = prune_metadata_str(
                     question_instructions,
                     self.db_name,
                     self.use_public_data,
                     columns_to_keep,
                     shuffle,
                 )
+                table_metadata_string = table_metadata_ddl + join_str
             elif columns_to_keep == 0:
                 md = dbs[self.db_name]["table_metadata"]
                 table_metadata_string = to_prompt_schema(md, shuffle)

utils/gen_prompt.py

@@ -134,15 +134,15 @@ def generate_prompt(
         if columns_to_keep > 0:
             from utils.pruning import prune_metadata_str
 
-            table_metadata_string, join_str = prune_metadata_str(
+            table_metadata_ddl, join_str = prune_metadata_str(
                 question_instructions,
                 db_name,
                 public_data,
                 columns_to_keep,
                 shuffle_metadata,
             )
             # remove triple backticks
-            table_metadata_string = table_metadata_string.replace("```", "").strip()
+            table_metadata_ddl = table_metadata_ddl.replace("```", "").strip()
         elif columns_to_keep == 0:
             if public_data:
                 import defog_data.supplementary as sup
@@ -155,7 +155,7 @@ def generate_prompt(
 
             md = dbs[db_name]["table_metadata"]
             table_names = list(md.keys())
-            table_metadata_string = to_prompt_schema(md, shuffle_metadata)
+            table_metadata_ddl = to_prompt_schema(md, shuffle_metadata)
 
             # get join_str from column_join
             join_list = []
@@ -173,34 +173,34 @@ def generate_prompt(
             raise ValueError("columns_to_keep must be >= 0")
 
         # add schema creation statements if relevant
-        schema_names = get_schema_names(table_metadata_string)
+        schema_names = get_schema_names(table_metadata_ddl)
         if schema_names:
             for schema_name in schema_names:
-                table_metadata_string = (
+                table_metadata_ddl = (
                     f"CREATE SCHEMA IF NOT EXISTS {schema_name};\n"
-                    + table_metadata_string
+                    + table_metadata_ddl
                 )
         # transform metadata string to target dialect if necessary
         if db_type in ["postgres", "snowflake"]:
-            table_metadata_string = table_metadata_string + join_str
+            table_metadata_string = table_metadata_ddl + join_str
         elif db_type == "bigquery":
             table_metadata_string = (
-                ddl_to_bigquery(table_metadata_string, "postgres", db_name, "")[0]
+                ddl_to_bigquery(table_metadata_ddl, "postgres", db_name, "")[0]
                 + join_str
             )
         elif db_type == "mysql":
             table_metadata_string = (
-                ddl_to_mysql(table_metadata_string, "postgres", db_name, "")[0]
+                ddl_to_mysql(table_metadata_ddl, "postgres", db_name, "")[0]
                 + join_str
             )
         elif db_type == "sqlite":
             table_metadata_string = (
-                ddl_to_sqlite(table_metadata_string, "postgres", db_name, "")[0]
+                ddl_to_sqlite(table_metadata_ddl, "postgres", db_name, "")[0]
                 + join_str
             )
         elif db_type == "tsql":
             table_metadata_string = (
-                ddl_to_tsql(table_metadata_string, "postgres", db_name, "")[0]
+                ddl_to_tsql(table_metadata_ddl, "postgres", db_name, "")[0]
                 + join_str
             )
         else:

utils/pruning.py

@@ -212,7 +212,7 @@ def prune_metadata_str(
     emb = emb_tuple[0]
     csv_descriptions = emb_tuple[1]
     try:
-        table_metadata_csv, join_str = get_md_emb(
+        table_metadata_ddl, join_str = get_md_emb(
             question,
             emb[db_name],
             csv_descriptions[db_name],
@@ -226,4 +226,4 @@ def prune_metadata_str(
             raise ValueError(f"DB name `{db_name}` not found in public data")
         else:
             raise ValueError(f"DB name `{db_name}` not found in private data")
-    return table_metadata_csv, join_str
+    return table_metadata_ddl, join_str