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sql_chat_agent.py
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sql_chat_agent.py
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"""
Agent that allows interaction with an SQL database using SQLAlchemy library.
The agent can execute SQL queries in the database and return the result.
Functionality includes:
- adding table and column context
- asking a question about a SQL schema
"""
import logging
from typing import Any, Dict, List, Optional, Sequence, Union
from rich import print
from rich.console import Console
from langroid.exceptions import LangroidImportError
from langroid.utils.constants import DONE
try:
from sqlalchemy import MetaData, Row, create_engine, inspect, text
from sqlalchemy.engine import Engine
from sqlalchemy.exc import ResourceClosedError, SQLAlchemyError
from sqlalchemy.orm import Session, sessionmaker
except ImportError as e:
raise LangroidImportError(extra="sql", error=str(e))
from langroid.agent.chat_agent import ChatAgent, ChatAgentConfig
from langroid.agent.chat_document import ChatDocMetaData, ChatDocument
from langroid.agent.special.sql.utils.description_extractors import (
extract_schema_descriptions,
)
from langroid.agent.special.sql.utils.populate_metadata import (
populate_metadata,
populate_metadata_with_schema_tools,
)
from langroid.agent.special.sql.utils.system_message import (
DEFAULT_SYS_MSG,
SCHEMA_TOOLS_SYS_MSG,
)
from langroid.agent.special.sql.utils.tools import (
GetColumnDescriptionsTool,
GetTableNamesTool,
GetTableSchemaTool,
RunQueryTool,
)
from langroid.mytypes import Entity
from langroid.vector_store.base import VectorStoreConfig
logger = logging.getLogger(__name__)
console = Console()
DEFAULT_SQL_CHAT_SYSTEM_MESSAGE = f"""
{{mode}}
You do not need to attempt answering a question with just one query.
You could make a sequence of SQL queries to help you write the final query.
Also if you receive a null or other unexpected result,
(a) make sure you use the available TOOLs correctly, and
(b) see if you have made an assumption in your SQL query, and try another way,
or use `run_query` to explore the database table contents before submitting your
final query. For example when searching for "males" you may have used "gender= 'M'",
in your query, because you did not know that the possible genders in the table
are "Male" and "Female".
Start by asking what I would like to know about the data.
When you have FINISHED the given query or database update task,
say {DONE} and show your answer.
"""
ADDRESSING_INSTRUCTION = f"""
IMPORTANT - Whenever you are NOT writing a SQL query, make sure you address the user
using {{prefix}}User. You MUST use the EXACT syntax {{prefix}} !!!
In other words, you ALWAYS write EITHER:
- a SQL query using the `run_query` tool,
- OR address the user using {{prefix}}User, and include {DONE} to indicate your
task is FINISHED.
"""
SQL_ERROR_MSG = "There was an error in your SQL Query"
class SQLChatAgentConfig(ChatAgentConfig):
system_message: str = DEFAULT_SQL_CHAT_SYSTEM_MESSAGE
user_message: None | str = None
cache: bool = True # cache results
debug: bool = False
stream: bool = True # allow streaming where needed
database_uri: str = "" # Database URI
database_session: None | Session = None # Database session
vecdb: None | VectorStoreConfig = None
context_descriptions: Dict[str, Dict[str, Union[str, Dict[str, str]]]] = {}
use_schema_tools: bool = False
multi_schema: bool = False
addressing_prefix: str = ""
"""
Optional, but strongly recommended, context descriptions for tables, columns,
and relationships. It should be a dictionary where each key is a table name
and its value is another dictionary.
In this inner dictionary:
- The 'description' key corresponds to a string description of the table.
- The 'columns' key corresponds to another dictionary where each key is a
column name and its value is a string description of that column.
- The 'relationships' key corresponds to another dictionary where each key
is another table name and the value is a description of the relationship to
that table.
If multi_schema support is enabled, the tables names in the description
should be of the form 'schema_name.table_name'.
For example:
{
'table1': {
'description': 'description of table1',
'columns': {
'column1': 'description of column1 in table1',
'column2': 'description of column2 in table1'
}
},
'table2': {
'description': 'description of table2',
'columns': {
'column3': 'description of column3 in table2',
'column4': 'description of column4 in table2'
}
}
}
"""
class SQLChatAgent(ChatAgent):
"""
Agent for chatting with a SQL database
"""
used_run_query: bool = False
llm_responded: bool = False
def __init__(self, config: "SQLChatAgentConfig") -> None:
"""Initialize the SQLChatAgent.
Raises:
ValueError: If database information is not provided in the config.
"""
self._validate_config(config)
self.config: SQLChatAgentConfig = config
self._init_database()
self._init_metadata()
self._init_table_metadata()
self._init_message_tools()
def _validate_config(self, config: "SQLChatAgentConfig") -> None:
"""Validate the configuration to ensure all necessary fields are present."""
if config.database_session is None and config.database_uri is None:
raise ValueError("Database information must be provided")
def _init_database(self) -> None:
"""Initialize the database engine and session."""
if self.config.database_session:
self.Session = self.config.database_session
self.engine = self.Session.bind
else:
self.engine = create_engine(self.config.database_uri)
self.Session = sessionmaker(bind=self.engine)()
def _init_metadata(self) -> None:
"""Initialize the database metadata."""
if self.engine is None:
raise ValueError("Database engine is None")
self.metadata: MetaData | List[MetaData] = []
if self.config.multi_schema:
logger.info(
"Initializing SQLChatAgent with database: %s",
self.engine,
)
self.metadata = []
inspector = inspect(self.engine)
for schema in inspector.get_schema_names():
metadata = MetaData(schema=schema)
metadata.reflect(self.engine)
self.metadata.append(metadata)
logger.info(
"Initializing SQLChatAgent with database: %s, schema: %s, "
"and tables: %s",
self.engine,
schema,
metadata.tables,
)
else:
self.metadata = MetaData()
self.metadata.reflect(self.engine)
logger.info(
"SQLChatAgent initialized with database: %s and tables: %s",
self.engine,
self.metadata.tables,
)
def _init_table_metadata(self) -> None:
"""Initialize metadata for the tables present in the database."""
if not self.config.context_descriptions and isinstance(self.engine, Engine):
self.config.context_descriptions = extract_schema_descriptions(
self.engine, self.config.multi_schema
)
if self.config.use_schema_tools:
self.table_metadata = populate_metadata_with_schema_tools(
self.metadata, self.config.context_descriptions
)
else:
self.table_metadata = populate_metadata(
self.metadata, self.config.context_descriptions
)
def _init_message_tools(self) -> None:
"""Initialize message tools used for chatting."""
message = self._format_message()
self.config.system_message = self.config.system_message.format(mode=message)
if self.config.addressing_prefix != "":
self.config.system_message += ADDRESSING_INSTRUCTION.format(
prefix=self.config.addressing_prefix
)
super().__init__(self.config)
self.enable_message(RunQueryTool)
if self.config.use_schema_tools:
self._enable_schema_tools()
def _format_message(self) -> str:
if self.engine is None:
raise ValueError("Database engine is None")
"""Format the system message based on the engine and table metadata."""
return (
SCHEMA_TOOLS_SYS_MSG.format(dialect=self.engine.dialect.name)
if self.config.use_schema_tools
else DEFAULT_SYS_MSG.format(
dialect=self.engine.dialect.name, schema_dict=self.table_metadata
)
)
def _enable_schema_tools(self) -> None:
"""Enable tools for schema-related functionalities."""
self.enable_message(GetTableNamesTool)
self.enable_message(GetTableSchemaTool)
self.enable_message(GetColumnDescriptionsTool)
def llm_response(
self, message: Optional[str | ChatDocument] = None
) -> Optional[ChatDocument]:
self.llm_responded = True
return super().llm_response(message)
def user_response(
self,
msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
self.llm_responded = False
self.used_run_query = False
return super().user_response(msg)
def handle_message_fallback(
self, msg: str | ChatDocument
) -> str | ChatDocument | None:
if not self.llm_responded:
return None
if self.used_run_query:
prefix = (
self.config.addressing_prefix + "User"
if self.config.addressing_prefix
else ""
)
return (
DONE + prefix + (msg.content if isinstance(msg, ChatDocument) else msg)
)
else:
reminder = """
You may have forgotten to use the `run_query` tool to execute an SQL query
for the user's question/request
"""
if self.config.addressing_prefix != "":
reminder += f"""
OR you may have forgotten to address the user using the prefix
{self.config.addressing_prefix}
"""
return reminder
def _agent_response(
self,
msg: Optional[str | ChatDocument] = None,
) -> Optional[ChatDocument]:
# Your override code here
if msg is None:
return None
results = self.handle_message(msg)
if results is None:
return None
output = results
if SQL_ERROR_MSG in output:
output = "There was an error in the SQL Query. Press enter to retry."
console.print(f"[red]{self.indent}", end="")
print(f"[red]Agent: {output}")
sender_name = self.config.name
if isinstance(msg, ChatDocument) and msg.function_call is not None:
sender_name = msg.function_call.name
content = results.content if isinstance(results, ChatDocument) else results
return ChatDocument(
content=content,
metadata=ChatDocMetaData(
source=Entity.AGENT,
sender=Entity.AGENT,
sender_name=sender_name,
),
)
def retry_query(self, e: Exception, query: str) -> str:
"""
Generate an error message for a failed SQL query and return it.
Parameters:
e (Exception): The exception raised during the SQL query execution.
query (str): The SQL query that failed.
Returns:
str: The error message.
"""
logger.error(f"SQL Query failed: {query}\nException: {e}")
# Optional part to be included based on `use_schema_tools`
optional_schema_description = ""
if not self.config.use_schema_tools:
optional_schema_description = f"""\
This JSON schema maps SQL database structure. It outlines tables, each
with a description and columns. Each table is identified by a key, and holds
a description and a dictionary of columns, with column
names as keys and their descriptions as values.
```json
{self.config.context_descriptions}
```"""
# Construct the error message
error_message_template = f"""\
{SQL_ERROR_MSG}: '{query}'
{str(e)}
Run a new query, correcting the errors.
{optional_schema_description}"""
return error_message_template
def run_query(self, msg: RunQueryTool) -> str:
"""
Handle a RunQueryTool message by executing a SQL query and returning the result.
Args:
msg (RunQueryTool): The tool-message to handle.
Returns:
str: The result of executing the SQL query.
"""
query = msg.query
session = self.Session
self.used_run_query = True
try:
logger.info(f"Executing SQL query: {query}")
query_result = session.execute(text(query))
session.commit()
try:
# attempt to fetch results: should work for normal SELECT queries
rows = query_result.fetchall()
response_message = self._format_rows(rows)
except ResourceClosedError:
# If we get here, it's a non-SELECT query (UPDATE, INSERT, DELETE)
affected_rows = query_result.rowcount # type: ignore
response_message = f"""
Non-SELECT query executed successfully.
Rows affected: {affected_rows}
"""
except SQLAlchemyError as e:
session.rollback()
logger.error(f"Failed to execute query: {query}\n{e}")
response_message = self.retry_query(e, query)
finally:
session.close()
return response_message
def _format_rows(self, rows: Sequence[Row[Any]]) -> str:
"""
Format the rows fetched from the query result into a string.
Args:
rows (list): List of rows fetched from the query result.
Returns:
str: Formatted string representation of rows.
"""
# TODO: UPDATE FORMATTING
return (
",\n".join(str(row) for row in rows)
if rows
else "Query executed successfully."
)
def get_table_names(self, msg: GetTableNamesTool) -> str:
"""
Handle a GetTableNamesTool message by returning the names of all tables in the
database.
Returns:
str: The names of all tables in the database.
"""
if isinstance(self.metadata, list):
table_names = [", ".join(md.tables.keys()) for md in self.metadata]
return ", ".join(table_names)
return ", ".join(self.metadata.tables.keys())
def get_table_schema(self, msg: GetTableSchemaTool) -> str:
"""
Handle a GetTableSchemaTool message by returning the schema of all provided
tables in the database.
Returns:
str: The schema of all provided tables in the database.
"""
tables = msg.tables
result = ""
for table_name in tables:
table = self.table_metadata.get(table_name)
if table is not None:
result += f"{table_name}: {table}\n"
else:
result += f"{table_name} is not a valid table name.\n"
return result
def get_column_descriptions(self, msg: GetColumnDescriptionsTool) -> str:
"""
Handle a GetColumnDescriptionsTool message by returning the descriptions of all
provided columns from the database.
Returns:
str: The descriptions of all provided columns from the database.
"""
table = msg.table
columns = msg.columns.split(", ")
result = f"\nTABLE: {table}"
descriptions = self.config.context_descriptions.get(table)
for col in columns:
result += f"\n{col} => {descriptions['columns'][col]}" # type: ignore
return result