File size: 11,873 Bytes
c399543 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 |
"""
Prompt Engine for SQL Generation
Constructs intelligent prompts for SQL generation using retrieved examples and best practices.
"""
import json
from typing import List, Dict, Any, Optional
from pathlib import Path
from loguru import logger
class PromptEngine:
"""Intelligent prompt construction for SQL generation."""
def __init__(self, prompts_dir: str = "./prompts"):
"""
Initialize the prompt engine.
Args:
prompts_dir: Directory containing prompt templates
"""
self.prompts_dir = Path(prompts_dir)
self.prompts_dir.mkdir(parents=True, exist_ok=True)
# Load prompt templates
self.templates = self._load_prompt_templates()
# Default system prompt
self.default_system_prompt = """You are an expert SQL developer. Your task is to convert natural language questions into accurate SQL queries.
Key Guidelines:
1. Always use the exact table column names provided
2. Generate standard SQL syntax (compatible with most databases)
3. Use appropriate JOINs when multiple tables are involved
4. Apply proper WHERE clauses for filtering
5. Use GROUP BY for aggregations when needed
6. Ensure queries are efficient and readable
7. Handle edge cases appropriately
Table Schema: {table_schema}
Retrieved Examples:
{examples}
Question: {question}
Generate the SQL query:"""
def _load_prompt_templates(self) -> Dict[str, str]:
"""Load prompt templates from files."""
templates = {}
# Create default templates if they don't exist
default_templates = {
"sql_generation.txt": self._get_default_sql_prompt(),
"few_shot_examples.txt": self._get_default_few_shot_prompt(),
"error_correction.txt": self._get_default_error_correction_prompt()
}
for filename, content in default_templates.items():
template_path = self.prompts_dir / filename
if not template_path.exists():
with open(template_path, 'w', encoding='utf-8') as f:
f.write(content)
logger.info(f"Created default template: {filename}")
# Load the template
with open(template_path, 'r', encoding='utf-8') as f:
templates[filename.replace('.txt', '')] = f.read()
return templates
def _get_default_sql_prompt(self) -> str:
"""Get default SQL generation prompt template."""
return """You are an expert SQL developer. Convert the natural language question to SQL.
Table Schema: {table_schema}
Examples:
{examples}
Question: {question}
Generate SQL:"""
def _get_default_few_shot_prompt(self) -> str:
"""Get default few-shot learning prompt template."""
return """Given these examples, generate SQL for the new question:
Examples:
{examples}
New Question: {question}
Table Schema: {table_schema}
SQL Query:"""
def _get_default_error_correction_prompt(self) -> str:
"""Get default error correction prompt template."""
return """The following SQL query has an error. Please correct it:
Original Question: {question}
Table Schema: {table_schema}
Incorrect SQL: {incorrect_sql}
Error: {error_message}
Corrected SQL:"""
def construct_sql_prompt(self,
question: str,
table_headers: List[str],
retrieved_examples: List[Dict[str, Any]],
prompt_type: str = "sql_generation") -> str:
"""
Construct a prompt for SQL generation.
Args:
question: Natural language question
table_headers: List of table column names
retrieved_examples: List of retrieved relevant examples
prompt_type: Type of prompt to use
Returns:
Constructed prompt string
"""
# Format table schema
table_schema = self._format_table_schema(table_headers)
# Format examples
examples_text = self._format_examples(retrieved_examples)
# Get template
template = self.templates.get(prompt_type, self.templates["sql_generation"])
# Fill template
prompt = template.format(
question=question,
table_schema=table_schema,
examples=examples_text
)
return prompt
def construct_enhanced_prompt(self,
question: str,
table_headers: List[str],
retrieved_examples: List[Dict[str, Any]],
additional_context: Optional[Dict[str, Any]] = None) -> str:
"""
Construct an enhanced prompt with additional context and examples.
Args:
question: Natural language question
table_headers: List of table column names
retrieved_examples: List of retrieved relevant examples
additional_context: Additional context information
Returns:
Enhanced prompt string
"""
# Start with system prompt
prompt_parts = [self.default_system_prompt]
# Add table schema
table_schema = self._format_table_schema(table_headers)
prompt_parts.append(f"Table Schema: {table_schema}\n")
# Add retrieved examples with relevance scores
if retrieved_examples:
prompt_parts.append("Relevant Examples (ordered by relevance):")
for i, example in enumerate(retrieved_examples[:3], 1): # Top 3 examples
relevance = example.get("final_score", example.get("similarity_score", 0))
prompt_parts.append(f"\nExample {i} (Relevance: {relevance:.2f}):")
prompt_parts.append(f"Question: {example['question']}")
prompt_parts.append(f"SQL: {example['sql']}")
prompt_parts.append(f"Table: {example['table_headers']}")
# Add additional context if provided
if additional_context:
prompt_parts.append("\nAdditional Context:")
for key, value in additional_context.items():
prompt_parts.append(f"{key}: {value}")
# Add the current question
prompt_parts.append(f"\nCurrent Question: {question}")
prompt_parts.append("\nGenerate the SQL query:")
return "\n".join(prompt_parts)
def construct_few_shot_prompt(self,
question: str,
table_headers: List[str],
examples: List[Dict[str, Any]]) -> str:
"""
Construct a few-shot learning prompt.
Args:
question: Natural language question
table_headers: List of table column names
examples: List of examples for few-shot learning
Returns:
Few-shot prompt string
"""
template = self.templates["few_shot_examples"]
# Format examples in a structured way
examples_text = ""
for i, example in enumerate(examples[:5], 1): # Use top 5 examples
examples_text += f"\n--- Example {i} ---\n"
examples_text += f"Question: {example['question']}\n"
examples_text += f"Table: {example['table_headers']}\n"
examples_text += f"SQL: {example['sql']}\n"
table_schema = self._format_table_schema(table_headers)
return template.format(
examples=examples_text,
question=question,
table_schema=table_schema
)
def construct_error_correction_prompt(self,
question: str,
table_headers: List[str],
incorrect_sql: str,
error_message: str) -> str:
"""
Construct a prompt for error correction.
Args:
question: Natural language question
table_headers: List of table column names
incorrect_sql: The incorrect SQL query
error_message: Error message or description
Returns:
Error correction prompt string
"""
template = self.templates["error_correction"]
table_schema = self._format_table_schema(table_headers)
return template.format(
question=question,
table_schema=table_schema,
incorrect_sql=incorrect_sql,
error_message=error_message
)
def _format_table_schema(self, table_headers: List[str]) -> str:
"""Format table headers into a readable schema."""
if not table_headers:
return "No table schema provided"
# Group headers by type for better readability
schema_parts = []
# Primary keys and IDs
pk_headers = [h for h in table_headers if 'id' in h.lower() or 'key' in h.lower()]
if pk_headers:
schema_parts.append(f"Primary Keys: {', '.join(pk_headers)}")
# Text fields
text_headers = [h for h in table_headers if any(word in h.lower() for word in ['name', 'title', 'description', 'text'])]
if text_headers:
schema_parts.append(f"Text Fields: {', '.join(text_headers)}")
# Numeric fields
numeric_headers = [h for h in table_headers if any(word in h.lower() for word in ['age', 'count', 'price', 'salary', 'amount', 'number'])]
if numeric_headers:
schema_parts.append(f"Numeric Fields: {', '.join(numeric_headers)}")
# Date fields
date_headers = [h for h in table_headers if any(word in h.lower() for word in ['date', 'time', 'created', 'updated', 'birth'])]
if date_headers:
schema_parts.append(f"Date Fields: {', '.join(date_headers)}")
# Boolean fields
bool_headers = [h for h in table_headers if any(word in h.lower() for word in ['is_', 'has_', 'active', 'enabled', 'status'])]
if bool_headers:
schema_parts.append(f"Boolean Fields: {', '.join(bool_headers)}")
# Other fields
other_headers = [h for h in table_headers if h not in pk_headers + text_headers + numeric_headers + date_headers + bool_headers]
if other_headers:
schema_parts.append(f"Other Fields: {', '.join(other_headers)}")
return "\n".join(schema_parts)
def _format_examples(self, examples: List[Dict[str, Any]]) -> str:
"""Format retrieved examples for prompt inclusion."""
if not examples:
return "No relevant examples found."
formatted_examples = []
for i, example in enumerate(examples[:3], 1): # Use top 3 examples
relevance = example.get("final_score", example.get("similarity_score", 0))
formatted_examples.append(f"Example {i} (Relevance: {relevance:.2f}):")
formatted_examples.append(f" Question: {example['question']}")
formatted_examples.append(f" SQL: {example['sql']}")
formatted_examples.append(f" Table: {example['table_headers']}")
return "\n".join(formatted_examples)
def get_prompt_statistics(self) -> Dict[str, Any]:
"""Get statistics about the prompt engine."""
return {
"available_templates": list(self.templates.keys()),
"prompts_directory": str(self.prompts_dir),
"template_count": len(self.templates)
}
|