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Prompt parser for QAFD cluster output.
Converts KG query clusters into formatted schema documentation
(CREATE TABLE statements with constraints and sample rows).
Matches the CoFD-M prompt.txt format for text2sql pipelines.
"""
import json
from typing import List, Dict, Any, Optional
def parse_qafd_clusters(
clusters_data: List[Dict[str, Any]],
add_sample_rows: bool = True,
schema_data: Optional[Dict] = None,
format_type: str = "create_table",
) -> str:
"""
Parse QAFD clusters into formatted schema documentation.
Args:
clusters_data: List of cluster dicts from KG query (return_raw_clusters=True)
add_sample_rows: Whether to include sample rows from schema
schema_data: Original DB summary JSON for sample rows and column types
format_type: "create_table" or "simple"
Returns:
Formatted schema string
"""
if not clusters_data:
return "No schema information available."
table_info = extract_tables_from_clusters(clusters_data)
if not table_info:
return "No tables found in cluster data."
if format_type == "create_table":
return format_as_create_table(table_info, schema_data, add_sample_rows)
else:
return format_as_simple(table_info, schema_data, add_sample_rows)
def extract_tables_from_clusters(clusters_data: List[Dict[str, Any]]) -> Dict[str, Dict]:
"""Extract table/column info from cluster entities."""
table_info = {}
for cluster in clusters_data:
entities = cluster.get("entities", cluster.get("nodes", []))
for entity in entities:
entity_name = entity.get("entity", "").strip('"')
entity_type = entity.get("entity_type", entity.get("type", ""))
description = entity.get("description", "")
if not entity_name:
continue
if entity_type == "complete_table":
if entity_name not in table_info:
table_info[entity_name] = {
"columns": {},
"description": description,
"rank": entity.get("rank", 0),
}
elif description and not table_info[entity_name].get("description"):
table_info[entity_name]["description"] = description
elif entity_type == "column" and "." in entity_name:
parts = entity_name.split(".", 1)
if len(parts) == 2:
table_name, column_name = parts
if table_name not in table_info:
table_info[table_name] = {
"columns": {},
"description": "",
"rank": 0,
}
table_info[table_name]["columns"][column_name] = {
"description": description,
"rank": entity.get("rank", 0),
}
return table_info
def format_as_simple(
table_info: Dict[str, Dict],
schema_data: Optional[Dict] = None,
add_sample_rows: bool = True,
) -> str:
"""Format as simple bullet-point list."""
output = []
for table_name, info in sorted(table_info.items()):
output.append(f"\n{'=' * 60}")
output.append(f"Table: {table_name}")
if info.get("description"):
output.append(f"Description: {info['description']}")
if info.get("columns"):
output.append("\nColumns:")
for col_name, col_info in sorted(info["columns"].items()):
desc = col_info.get("description", "")
output.append(f" - {col_name}: {desc}")
if add_sample_rows and schema_data:
samples = _get_sample_rows_json(table_name, schema_data)
if samples:
output.append(samples)
return "\n".join(output)
def format_as_create_table(
table_info: Dict[str, Dict],
schema_data: Optional[Dict] = None,
add_sample_rows: bool = True,
) -> str:
"""Format as CREATE TABLE statements (CoFD-M prompt.txt format)."""
output = []
sorted_tables = sorted(
table_info.items(),
key=lambda x: x[1].get("rank", 0),
reverse=True,
)
for table_name, info in sorted_tables:
create_stmt = _generate_create_table(table_name, info, schema_data)
output.append(create_stmt)
if add_sample_rows and schema_data:
samples = _get_sample_rows_json(table_name, schema_data)
if samples:
output.append(samples)
output.append("")
return "\n".join(output)
def _generate_create_table(
table_name: str,
table_info: Dict,
schema_data: Optional[Dict] = None,
) -> str:
"""
Generate CREATE TABLE matching CoFD-M prompt.txt format:
-- table_name
CREATE TABLE `table_name` (
`col` TYPE NOT NULL,
`col2` TYPE PRIMARY KEY,
,FOREIGN KEY (`col`) REFERENCES `other_table`(`other_col`),
,CHECK (`col` BETWEEN min AND max)
);
"""
rag_columns = table_info.get("columns", {})
table_desc = table_info.get("description", "")
col_defs = []
constraints = []
if schema_data and "tables" in schema_data:
schema_table = _find_table_in_schema(table_name, schema_data)
if schema_table and "columns" in schema_table:
schema_col_lookup = {
col.get("name", ""): col for col in schema_table["columns"]
}
for col_name in rag_columns:
schema_col = schema_col_lookup.get(col_name, {})
col_type = schema_col.get("type", "TEXT")
col_line = f" `{col_name}` {col_type}"
if schema_col.get("is_primary_key"):
col_line += " PRIMARY KEY"
elif schema_col.get("not_null"):
col_line += " NOT NULL"
col_defs.append(col_line)
# FK constraint
if schema_col.get("is_foreign_key") and schema_col.get("references_table"):
ref_table = schema_col["references_table"]
ref_col = schema_col.get("references_column", col_name)
constraints.append(
f" ,FOREIGN KEY (`{col_name}`) REFERENCES `{ref_table}`(`{ref_col}`)"
)
# CHECK constraint with min/max range
col_min = schema_col.get("min")
col_max = schema_col.get("max")
if col_min is not None and col_max is not None and col_min != col_max:
if isinstance(col_min, str):
constraints.append(
f" ,CHECK (`{col_name}` BETWEEN '{col_min}' AND '{col_max}')"
)
else:
constraints.append(
f" ,CHECK (`{col_name}` BETWEEN {col_min} AND {col_max})"
)
else:
# No schema_data — use RAG info only
for col_name in rag_columns:
col_defs.append(f" `{col_name}` TEXT")
lines = [f"-- {table_name}"]
lines.append(f"CREATE TABLE `{table_name}` (")
if col_defs or constraints:
all_lines = col_defs + constraints
lines.append(",\n".join(all_lines))
else:
lines.append(" -- No columns found")
lines.append(");")
return "\n".join(lines)
def _find_table_in_schema(table_name: str, schema_data: Dict) -> Optional[Dict]:
"""Find table in schema, handling qualified names."""
tables = schema_data.get("tables", {})
if table_name in tables:
return tables[table_name]
short_name = table_name.split(".")[-1]
for key, value in tables.items():
if key.split(".")[-1] == short_name:
return value
return None
def _get_sample_rows_json(table_name: str, schema_data: Dict) -> str:
"""
Get sample rows in JSON format (CoFD-M prompt.txt style).
/* Sample rows:
[
{"col1": val1, "col2": val2},
{"col1": val3, "col2": val4}
]
*/
"""
table_data = _find_table_in_schema(table_name, schema_data)
if not table_data:
return ""
sample_rows = None
for key in ["sample_rows", "samples", "example_rows", "rows"]:
if key in table_data:
sample_rows = table_data[key]
break
if not sample_rows:
return ""
rows_as_dicts = []
# Dict-of-lists format
if isinstance(sample_rows, dict):
col_names = list(sample_rows.keys())
max_rows = max(
(len(v) for v in sample_rows.values() if isinstance(v, list)),
default=0,
)
for i in range(min(3, max_rows)):
row = {}
for col in col_names:
col_data = sample_rows.get(col, [])
if isinstance(col_data, list) and i < len(col_data):
val = col_data[i]
if isinstance(val, str) and len(val) > 80:
val = val[:77] + "..."
row[col] = val
else:
row[col] = None
rows_as_dicts.append(row)
# List-of-dicts format
elif isinstance(sample_rows, list) and sample_rows:
for row in sample_rows[:3]:
if isinstance(row, dict):
cleaned = {}
for k, v in row.items():
if isinstance(v, str) and len(v) > 80:
v = v[:77] + "..."
cleaned[k] = v
rows_as_dicts.append(cleaned)
if not rows_as_dicts:
return ""
json_str = json.dumps(rows_as_dicts, indent=2, ensure_ascii=False, default=str)
return f"/* Sample rows:\n{json_str}\n*/"
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