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The backend import package is now docsgpt, the name it will carry on PyPI; application was far too generic to install into anyone's site-packages. git mv plus a mechanical rewrite of every import, dotted string and path reference: 734 Python files, the compose files, Dockerfile, workflows, docs, setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage config, .gitignore. Behaviour is unchanged. Kept for one release: - A top-level application package whose meta-path finder resolves application.x.y to the already-imported docsgpt.x.y object, so old imports and entry points (celery -A application.app.celery, uvicorn application.asgi:asgi_app) keep working with a FutureWarning. - Celery registers every application.* task name as an alias of its docsgpt.* task on start-up, so messages queued by the previous release still run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries the previous release wrote are left unread instead of firing twice. The backend image builds from the repository root (docker build -f docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore allow-lists docsgpt/ and application/ and keeps caches, local data, .env files, the sample index files and the Dockerfile out. Compose and the image workflows point at the new context.
181 lines
6.1 KiB
Python
181 lines
6.1 KiB
Python
import logging
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import psycopg
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from docsgpt.agents.tools.base import Tool
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logger = logging.getLogger(__name__)
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class PostgresTool(Tool):
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"""
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PostgreSQL Database Tool
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A tool for connecting to a PostgreSQL database using a connection string,
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executing SQL queries, and retrieving schema information.
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"""
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def __init__(self, config):
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self.config = config
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self.connection_string = config.get("token", "")
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def execute_action(self, action_name, **kwargs):
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actions = {
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"postgres_execute_sql": self._execute_sql,
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"postgres_get_schema": self._get_schema,
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}
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if action_name not in actions:
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raise ValueError(f"Unknown action: {action_name}")
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return actions[action_name](**kwargs)
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def _execute_sql(self, sql_query):
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"""
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Executes an SQL query against the PostgreSQL database using a connection string.
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"""
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conn = None
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try:
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conn = psycopg.connect(self.connection_string)
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cur = conn.cursor()
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cur.execute(sql_query)
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conn.commit()
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if sql_query.strip().lower().startswith("select"):
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column_names = (
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[desc[0] for desc in cur.description] if cur.description else []
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)
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results = []
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rows = cur.fetchall()
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for row in rows:
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results.append(dict(zip(column_names, row)))
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response_data = {"data": results, "column_names": column_names}
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else:
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row_count = cur.rowcount
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response_data = {
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"message": f"Query executed successfully, {row_count} rows affected."
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}
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cur.close()
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return {
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"status_code": 200,
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"message": "SQL query executed successfully.",
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"response_data": response_data,
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}
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except psycopg.Error as e:
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error_message = f"Database error: {e}"
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logger.error("PostgreSQL execute_sql error: %s", e)
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return {
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"status_code": 500,
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"message": "Failed to execute SQL query.",
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"error": error_message,
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}
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finally:
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if conn:
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conn.close()
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def _get_schema(self, db_name):
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"""
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Retrieves the schema of the PostgreSQL database using a connection string.
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"""
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conn = None
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try:
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conn = psycopg.connect(self.connection_string)
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cur = conn.cursor()
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cur.execute(
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"""
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SELECT
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table_name,
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column_name,
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data_type,
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column_default,
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is_nullable
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FROM
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information_schema.columns
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WHERE
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table_schema = 'public'
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ORDER BY
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table_name,
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ordinal_position;
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"""
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)
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schema_data = {}
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for row in cur.fetchall():
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table_name, column_name, data_type, column_default, is_nullable = row
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if table_name not in schema_data:
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schema_data[table_name] = []
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schema_data[table_name].append(
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{
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"column_name": column_name,
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"data_type": data_type,
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"column_default": column_default,
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"is_nullable": is_nullable,
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}
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)
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cur.close()
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return {
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"status_code": 200,
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"message": "Database schema retrieved successfully.",
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"schema": schema_data,
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}
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except psycopg.Error as e:
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error_message = f"Database error: {e}"
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logger.error("PostgreSQL get_schema error: %s", e)
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return {
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"status_code": 500,
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"message": "Failed to retrieve database schema.",
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"error": error_message,
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}
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finally:
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if conn:
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conn.close()
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def get_actions_metadata(self):
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return [
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{
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"name": "postgres_execute_sql",
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"description": "Execute an SQL query against the PostgreSQL database and return the results. Use this tool to interact with the database, e.g., retrieve specific data or perform updates. Only SELECT queries will return data, other queries will return execution status.",
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"parameters": {
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"type": "object",
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"properties": {
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"sql_query": {
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"type": "string",
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"description": "The SQL query to execute.",
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},
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},
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"required": ["sql_query"],
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"additionalProperties": False,
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},
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},
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{
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"name": "postgres_get_schema",
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"description": "Retrieve the schema of the PostgreSQL database, including tables and their columns. Use this to understand the database structure before executing queries. db_name is 'default' if not provided.",
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"parameters": {
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"type": "object",
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"properties": {
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"db_name": {
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"type": "string",
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"description": "The name of the database to retrieve the schema for.",
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},
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},
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"required": ["db_name"],
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"additionalProperties": False,
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},
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},
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]
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def get_config_requirements(self):
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return {
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"token": {
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"type": "string",
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"label": "Connection String",
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"description": "PostgreSQL database connection string",
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"required": True,
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"secret": True,
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"order": 1,
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},
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}
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