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Updated SQLite to include demo functionality and external API LLM call

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Garvan
2024-11-21 12:52:29 -05:00
parent 145f123555
commit 034f95c788
5 changed files with 548 additions and 211 deletions
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90
src/sqlite/README.md
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@@ -1,62 +1,48 @@
# Simple SQLite Notes Server
# SQLite MCP Server
A basic MCP server implementation that demonstrates note-taking functionality using the three core MCP primitives: Resources, Prompts, and Tools.
## Overview
A Model Context Protocol (MCP) server implementation that provides database interaction and business intelligence capabilities through SQLite. This server enables running SQL queries, analyzing business data, and automatically generating business insight memos that can be enhanced with Claude's analysis when an Anthropic API key is provided.
## Core Concepts
## Components
### Resources
Resources are how clients access data from the server. In this case, they're notes stored in SQLite.
```python
# Access notes through a custom URI scheme
note:///example_note # Gets the content of 'example_note'
```
The server exposes a single dynamic resource:
- `memo://insights`: A continuously updated business insights memo that aggregates discovered insights during analysis
- Auto-updates as new insights are discovered via the append-insight tool
- Optional enhancement through Claude for professional formatting (requires Anthropic API key)
### Prompts
Prompts allow generating text based on server state. Our server has a note summarization prompt that can be styled.
```python
# Example prompt with style argument
{
"name": "summarize-notes",
"arguments": {
"style": "academic" # Can be any style descriptor
}
}
```
The server provides a demonstration prompt:
- `mcp-demo`: Interactive prompt that guides users through database operations
- Required argument: `topic` - The business domain to analyze
- Generates appropriate database schemas and sample data
- Guides users through analysis and insight generation
- Integrates with the business insights memo
### Tools
Tools modify server state. We have a simple tool to add new notes.
The server offers six core tools:
```python
# Adding a new note
{
"name": "add-note",
"arguments": {
"name": "my_note",
"content": "This is my note content"
}
}
#### Query Tools
- `read-query`: Execute SELECT queries on the database
- `write-query`: Execute INSERT, UPDATE, or DELETE queries
- `create-table`: Create new database tables
#### Schema Tools
- `list-tables`: Get a list of all tables in the database
- `describe-table`: View the schema of a specific table
#### Analysis Tools
- `append-insight`: Add new business insights to the memo resource
## Installation
```bash
# Required: Python 3.10+
python -m pip install mcp-sqlite-server
# Run with default settings
mcp-sqlite-server
# Run with custom database and Anthropic integration
mcp-sqlite-server --db-path ~/analysis.db --anthropic-key sk-xxx
```
## Key Implementation Details
### Handler Registration
All decorated handlers must be inside `__init__`:
```python
def __init__(self):
super().__init__("sqlite")
@self.list_resources()
async def handle_list_resources():
# Handler code here
@self.read_resource()
async def handle_read_resource():
# Handler code here
```
### Storage
- Uses SQLite for persistent storage
- Helper methods handle database operations
- Clients are notified of state changes

3
src/sqlite/pyproject.toml
View File

@@ -5,7 +5,8 @@ description = "A simple SQLite MCP server"
readme = "README.md"
requires-python = ">=3.11"
dependencies = [
"mcp>=0.9.0",
"mcp>=0.9.1",
"anthropic>=0.39.0",
]
[build-system]

13
src/sqlite/src/sqlite/__init__.py
View File

@@ -1,10 +1,21 @@
from . import server
import asyncio
import argparse
import os
def main():
"""Main entry point for the package."""
asyncio.run(server.main())
parser = argparse.ArgumentParser(description='SQLite MCP Server')
parser.add_argument('--db-path',
default="./sqlite_mcp_server.db",
help='Path to SQLite database file')
parser.add_argument('--anthropic-api-key',
default=os.environ.get('ANTHROPIC_API_KEY'),
help='Anthropic API key (can also be set via ANTHROPIC_API_KEY environment variable)')
args = parser.parse_args()
asyncio.run(server.main(args.db_path, args.anthropic_api_key))
# Optionally expose other important items at package level

460
src/sqlite/src/sqlite/server.py
View File

@@ -1,21 +1,31 @@
import sqlite3
import logging
from logging.handlers import RotatingFileHandler
from contextlib import closing
import textwrap
from pathlib import Path
from mcp.server.models import InitializationOptions
import mcp.types as types
from mcp.server import NotificationOptions, Server
from pydantic import AnyUrl
from mcp.server import NotificationOptions, Server, AnyUrl
import mcp.server.stdio
from anthropic import Anthropic
# Set up logging to file
log_file = Path('mcp_server.log')
handler = RotatingFileHandler(log_file, maxBytes=10*1024*1024, backupCount=5)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger = logging.getLogger('mcp_sqlite_server')
logger.setLevel(logging.DEBUG)
logger.addHandler(handler)
logger.info("Starting MCP SQLite Server")
class McpServer(Server):
"""
Example MCP server using SQLite for persistent storage instead of an in-memory dictionary.
"""
def _init_database(self):
"""Initialize the SQLite database with the notes table"""
"""Initialize connection to the SQLite database"""
logger.debug("Initializing database connection")
with closing(sqlite3.connect(self.db_path)) as conn:
<<<<<<< HEAD
with closing(conn.cursor()) as cursor:
cursor.execute("""
CREATE TABLE IF NOT EXISTS notes (
@@ -31,24 +41,43 @@ class McpServer(Server):
("example", "This is an example note."),
)
conn.commit()
=======
conn.row_factory = sqlite3.Row
conn.close()
def _synthesize_memo(self) -> str:
"""Synthesizes business insights into a formatted memo"""
logger.debug(f"Synthesizing memo with {len(self.insights)} insights")
if not self.insights:
return "No business insights have been discovered yet."
insights = "\n".join(f"- {insight}" for insight in self.insights)
if self.anthropic_api_key is None:
memo = "📊 Business Intelligence Memo 📊\n\n"
memo += "Key Insights Discovered:\n\n"
memo += insights
if len(self.insights) > 1:
memo += "\nSummary:\n"
memo += f"Analysis has revealed {len(self.insights)} key business insights that suggest opportunities for strategic optimization and growth."
logger.debug("Generated basic memo format")
return memo
else:
try:
logger.debug("Requesting memo generation from Anthropic")
prompt = """
You are tasked with summarizing a set of business insights into a formal business memo. The insights are typically 1-2 sentences each and cover various aspects of the business. Your goal is to create a concise, well-organized memo that effectively communicates these insights to the recipient.
>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md)
def _get_notes(self) -> dict[str, str]:
"""Helper method to get all notes from the database"""
with closing(sqlite3.connect(self.db_path)) as conn:
with closing(conn.cursor()) as cursor:
cursor.execute("SELECT name, content FROM notes")
return dict(cursor.fetchall())
Here are the business insights you need to summarize:
def _get_note(self, name: str) -> str:
"""Helper method to get a single note from the database"""
with closing(sqlite3.connect(self.db_path)) as conn:
with closing(conn.cursor()) as cursor:
cursor.execute("SELECT content FROM notes WHERE name = ?", (name,))
result = cursor.fetchone()
if result is None:
raise ValueError(f"Note not found: {name}")
return result[0]
<insights>
{insights}
</insights>
<<<<<<< HEAD
def _add_note(self, name: str, content: str):
"""Helper method to add or update a note in the database"""
with closing(sqlite3.connect(self.db_path)) as conn:
@@ -56,68 +85,147 @@ class McpServer(Server):
cursor.execute(
"INSERT OR REPLACE INTO notes (name, content) VALUES (?, ?)",
(name, content),
)
conn.commit()
=======
To create the memo, follow these steps:
1. Review all the insights carefully.
2. Group related insights together under appropriate subheadings.
3. Summarize each group of insights into 1-2 concise paragraphs.
4. Ensure the memo flows logically from one point to the next.
5. Use professional language and maintain a formal tone throughout the memo.
Format the memo using these guidelines:
- Single-space the content, with a blank line between paragraphs
- Use bullet points or numbered lists where appropriate
- Keep the entire memo to one page if possible, two pages maximum
Write your final memo within <memo> tags. Ensure that all components of the memo are included and properly formatted.
""".format(insights=insights)
message = self.anthropic_client.messages.create(
max_tokens=4096,
messages=[
{
"role": "user",
"content": prompt
},
{
"role": "assistant",
"content": "<memo>"
}
],
model="claude-3-sonnet-20240229",
stop_sequences=["</memo>"],
>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md)
)
logger.debug("Successfully received memo from Anthropic")
return message.content[0].text.strip()
except Exception as e:
logger.error(f"Error generating memo with Anthropic: {e}")
return insights
<<<<<<< HEAD
def __init__(self):
super().__init__("sqlite")
# Initialize SQLite database
self.db_path = "notes.db"
self._init_database()
=======
def _execute_query(self, query: str, params=None) -> list[dict]:
"""Execute a SQL query and return results as a list of dictionaries"""
logger.debug(f"Executing query: {query}")
try:
with closing(sqlite3.connect(self.db_path)) as conn:
conn.row_factory = sqlite3.Row
with closing(conn.cursor()) as cursor:
if params:
cursor.execute(query, params)
else:
cursor.execute(query)
if query.strip().upper().startswith(('INSERT', 'UPDATE', 'DELETE', 'CREATE', 'DROP', 'ALTER')):
conn.commit()
affected = cursor.rowcount
logger.debug(f"Write query affected {affected} rows")
return [{"affected_rows": affected}]
results = [dict(row) for row in cursor.fetchall()]
logger.debug(f"Read query returned {len(results)} rows")
return results
except Exception as e:
logger.error(f"Database error executing query: {e}")
raise
# RESOURCE HANDLERS
def __init__(self, db_path: str = "~/sqlite_mcp_server.db", anthropic_api_key: str | None = None):
logger.info("Initializing McpServer")
super().__init__("sqlite-manager")
>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md)
# Initialize SQLite database
self.db_path = str(Path(db_path).expanduser())
Path(self.db_path).parent.mkdir(parents=True, exist_ok=True)
self._init_database()
logger.debug(f"Initialized database at {self.db_path}")
# Initialize Anthropic API key
self.anthropic_api_key = anthropic_api_key
if anthropic_api_key:
self.anthropic_client = Anthropic(api_key=anthropic_api_key)
logger.debug("Initialized Anthropic client")
# Initialize insights list
self.insights = []
# REGISTER HANDLERS
logger.debug("Registering handlers")
@self.list_resources()
async def handle_list_resources() -> list[types.Resource]:
"""List available note resources from the SQLite database"""
notes = self._get_notes()
logger.debug("Handling list_resources request")
return [
types.Resource(
uri=AnyUrl(f"note:///{name}"),
name=f"Note: {name}",
description=f"A simple note named {name}",
uri=AnyUrl("memo://insights"), # Changed from memo:///insights
name="Business Insights Memo",
description="A living document of discovered business insights",
mimeType="text/plain",
)
for name in notes
]
@self.read_resource()
async def handle_read_resource(uri: AnyUrl) -> str:
"""Read a specific note's content by its URI from the SQLite database"""
if uri.scheme != "note":
logger.debug(f"Handling read_resource request for URI: {uri}")
if uri.scheme != "memo":
logger.error(f"Unsupported URI scheme: {uri.scheme}")
raise ValueError(f"Unsupported URI scheme: {uri.scheme}")
path = str(uri).replace("memo://", "") # Changed to match new URI format
if not path or path != "insights":
logger.error(f"Unknown resource path: {path}")
raise ValueError(f"Unknown resource path: {path}")
return self._synthesize_memo()
name = uri.path
if name is not None:
name = name.lstrip("/")
return self._get_note(name)
raise ValueError(f"Note not found: {name}")
# PROMPT HANDLERS
@self.list_prompts()
async def handle_list_prompts() -> list[types.Prompt]:
"""List available prompts"""
logger.debug("Handling list_prompts request")
return [
types.Prompt(
name="summarize-notes",
description="Creates a summary of all notes",
name="mcp-demo",
description="A prompt to seed the database with initial data and demonstrate what you can do with an SQLite MCP Server + Claude",
arguments=[
types.PromptArgument(
name="style",
description="Style of the summary (brief/detailed)",
required=False,
name="topic",
description="Topic to seed the database with initial data",
required=True,
)
],
)
]
@self.get_prompt()
async def handle_get_prompt(
name: str, arguments: dict[str, str] | None
) -> types.GetPromptResult:
"""Generate a prompt using notes from the database"""
if name != "summarize-notes":
async def handle_get_prompt(name: str, arguments: dict[str, str] | None) -> types.GetPromptResult:
logger.debug(f"Handling get_prompt request for {name} with args {arguments}")
if name != "mcp-demo":
logger.error(f"Unknown prompt: {name}")
raise ValueError(f"Unknown prompt: {name}")
<<<<<<< HEAD
notes = (
"<notes>\n"
+ "\n".join(
@@ -132,15 +240,102 @@ class McpServer(Server):
{notes}
Ensure that the summary is in {style} style.
""".format(notes=notes, style=style)
=======
if not arguments or "topic" not in arguments:
logger.error("Missing required argument: topic")
raise ValueError("Missing required argument: topic")
topic = arguments["topic"]
template = f"""
The assistants goal is to walkthrough an informative demo of MCP. To demonstrate the Model Context Protocol (MCP) we will leverage this example server to interact with an SQLite database.
It is important that you first explain to the user what is going on. The user has downloaded and installed the SQLite MCP Server and is now ready to use it.
The have selected the MCP menu item which is contained within a parent menu denoted by the paperclip icon. Inside this menu they selected an icon that illustrates two electrical plugs connecting. This is the MCP menu.
Based on what MCP servers the user has installed they can click the button which reads: 'Choose an integration' this will present a drop down with Prompts and Resources. The user hase selected the prompt titled: 'mcp-demo'.
This text file is that prompt. The goal of the following instructions is to walk the user through the process of using the 3 core aspects of an MCP server. These are: Prompts, Tools, and Resources.
They have already used a prompt and provided a topic. The topic is: {topic}. The user is now ready to begin the demo.
Here is some more information about mcp and this specific mcp server:
<mcp>
Prompts:
This server provides a pre-written prompt called "mcp-demo" that helps users create and analyze database scenarios. The prompt accepts a "topic" argument and guides users through creating tables, analyzing data, and generating insights. For example, if a user provides "retail sales" as the topic, the prompt will help create relevant database tables and guide the analysis process. Prompts basically serve as interactive templates that help structure the conversation with the LLM in a useful way.
Resources:
This server exposes one key resource: "memo://insights", which is a business insights memo that gets automatically updated throughout the analysis process. As users analyze the database and discover insights, the memo resource gets updated in real-time to reflect new findings. The memo can even be enhanced with Claude's help if an Anthropic API key is provided, turning raw insights into a well-structured business document. Resources act as living documents that provide context to the conversation.
Tools:
This server provides several SQL-related tools:
"read-query": Executes SELECT queries to read data from the database
"write-query": Executes INSERT, UPDATE, or DELETE queries to modify data
"create-table": Creates new tables in the database
"list-tables": Shows all existing tables
"describe-table": Shows the schema for a specific table
"append-insight": Adds a new business insight to the memo resource
</mcp>
<demo-instructions>
You are an AI assistant tasked with generating a comprehensive business scenario based on a given topic.
Your goal is to create a narrative that involves a data-driven business problem, develop a database structure to support it, generate relevant queries, create a dashboard, and provide a final solution.
At each step you will pause for user input to guide the scenario creation process. Overall ensure the scenario is engaging, informative, and demonstrates the capabilities of the SQLite MCP Server.
You should guide the scenario to completion. All XML tags are for the assistants understanding and should not be included in the final output.
1. The user has chosen the topic: {topic}.
2. Create a business problem narrative:
a. Describe a high-level business situation or problem based on the given topic.
b. Include a protagonist (the user) who needs to collect and analyze data from a database.
c. Add an external, potentially comedic reason why the data hasn't been prepared yet.
d. Mention an approaching deadline and the need to use Claude (you) as a business tool to help.
3. Setup the data:
a. Instead of asking about the data that is required for the scenario, just go ahead and use the tools to create the data. Inform the user you are "Setting up the data".
b. Design a set of table schemas that represent the data needed for the business problem.
c. Include at least 2-3 tables with appropriate columns and data types.
d. Leverage the tools to create the tables in the SQLite database.
e. Create INSERT statements to populate each table with relevant synthetic data.
f. Ensure the data is diverse and representative of the business problem.
g. Include at least 10-15 rows of data for each table.
4. Pause for user input:
a. Summarize to the user what data we have created.
b. Present the user with a set of multiple choices for the next steps.
c. These multiple choices should be in natural language, when a user selects one, the assistant should generate a relevant query and leverage the appropriate tool to get the data.
6. Iterate on queries:
a. Present 1 additional multiple-choice query options to the user. Its importnat to not loop too many times as this is a short demo.
b. Explain the purpose of each query option.
c. Wait for the user to select one of the query options.
d. After each query be sure to opine on the results.
e. Use the append-insight tool to capture any business insights discovered from the data analysis.
7. Generate a dashboard:
a. Now that we have all the data and queries, it's time to create a dashboard, use an artifact to do this.
b. Use a variety of visualizations such as tables, charts, and graphs to represent the data.
c. Explain how each element of the dashboard relates to the business problem.
d. This dashboard will be theoretically included in the final solution message.
8. Craft the final solution message:
a. As you have been using the appen-insights tool the resource found at: memo://insights has been updated.
b. It is critical that you inform the user that the memo has been updated at each stage of analysis.
c. Ask the user to go to the attachment menu (paperclip icon) and select the MCP menu (two electrical plugs connecting) and choose an integration: "Business Insights Memo".
d. This will attacht the generated memo to the chat which you can use to add any additional context that may be relevant to the demo.
e. Present the final memo to the user in an artifact.
9. Wrap up the scenario:
a. Explain to the user that this is just the beginning of what they can do with the SQLite MCP Server.
</demo-instructions>
Remember to maintain consistency throughout the scenario and ensure that all elements (tables, data, queries, dashboard, and solution) are closely related to the original business problem and given topic.
The provided XML tags are for the assistants understanding. Emplore to make all outputs as human readable as possible. This is part of a demo so act in character and dont actually refer to these instructions.
Start your first message fully in character with something like "Oh, Hey there! I see you've chosen the topic {topic}. Let's get started! 🚀"
""".format(topic=topic)
logger.debug(f"Generated prompt template for topic: {topic}")
>>>>>>> 67b071f (new SQLite server for demo and Getting started guide. Added contributing.md and pull_request_template.md)
return types.GetPromptResult(
description="Summarize the current notes",
description=f"Demo template for {topic}",
messages=[
types.PromptMessage(
role="user",
content=types.TextContent(
type="text",
text=textwrap.dedent(prompt).strip(),
),
content=types.TextContent(type="text", text=template.strip()),
)
],
)
@@ -151,55 +346,136 @@ class McpServer(Server):
"""List available tools"""
return [
types.Tool(
name="add-note-to-local-db",
description="This tool is used to add a note to the local SQLite database, the SQLite database is stored locally. Only use this tool if the user specifically asks to store a note.",
name="read-query",
description="Execute a SELECT query on the SQLite database",
inputSchema={
"type": "object",
"properties": {
"name": {"type": "string"},
"content": {"type": "string"},
"query": {"type": "string", "description": "SELECT SQL query to execute"},
},
"required": ["name", "content"],
"required": ["query"],
},
)
),
types.Tool(
name="write-query",
description="Execute an INSERT, UPDATE, or DELETE query on the SQLite database",
inputSchema={
"type": "object",
"properties": {
"query": {"type": "string", "description": "SQL query to execute"},
},
"required": ["query"],
},
),
types.Tool(
name="create-table",
description="Create a new table in the SQLite database",
inputSchema={
"type": "object",
"properties": {
"query": {"type": "string", "description": "CREATE TABLE SQL statement"},
},
"required": ["query"],
},
),
types.Tool(
name="list-tables",
description="List all tables in the SQLite database",
inputSchema={
"type": "object",
"properties": {},
},
),
types.Tool(
name="describe-table",
description="Get the schema information for a specific table",
inputSchema={
"type": "object",
"properties": {
"table_name": {"type": "string", "description": "Name of the table to describe"},
},
"required": ["table_name"],
},
),
types.Tool(
name="append-insight",
description="Add a business insight to the memo",
inputSchema={
"type": "object",
"properties": {
"insight": {"type": "string", "description": "Business insight discovered from data analysis"},
},
"required": ["insight"],
},
),
]
@self.call_tool()
async def handle_call_tool(
name: str, arguments: dict | None
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
"""Handle tool execution requests using the SQLite database"""
if name != "add-note":
raise ValueError(f"Unknown tool: {name}")
"""Handle tool execution requests"""
try:
if name == "list-tables":
results = self._execute_query(
"SELECT name FROM sqlite_master WHERE type='table'"
)
return [types.TextContent(type="text", text=str(results))]
elif name == "describe-table":
if not arguments or "table_name" not in arguments:
raise ValueError("Missing table_name argument")
results = self._execute_query(
f"PRAGMA table_info({arguments['table_name']})"
)
return [types.TextContent(type="text", text=str(results))]
if not arguments:
raise ValueError("Missing arguments")
elif name == "append-insight":
if not arguments or "insight" not in arguments:
raise ValueError("Missing insight argument")
self.insights.append(arguments["insight"])
memo = self._synthesize_memo()
# Notify clients that the memo resource has changed
await self.request_context.session.send_resource_updated("memo://insights") # Changed from memo:///insights
return [types.TextContent(type="text", text="Insight added to memo")]
if not arguments:
raise ValueError("Missing arguments")
note_name = arguments.get("name")
content = arguments.get("content")
if name == "read-query":
if not arguments["query"].strip().upper().startswith("SELECT"):
raise ValueError("Only SELECT queries are allowed for read-query")
results = self._execute_query(arguments["query"])
return [types.TextContent(type="text", text=str(results))]
if not note_name or not content:
raise ValueError("Missing name or content")
elif name == "write-query":
if arguments["query"].strip().upper().startswith("SELECT"):
raise ValueError("SELECT queries are not allowed for write-query")
results = self._execute_query(arguments["query"])
return [types.TextContent(type="text", text=str(results))]
# Update database
self._add_note(note_name, content)
elif name == "create-table":
if not arguments["query"].strip().upper().startswith("CREATE TABLE"):
raise ValueError("Only CREATE TABLE statements are allowed")
self._execute_query(arguments["query"])
return [types.TextContent(type="text", text="Table created successfully")]
# Notify clients that resources have changed
await self.request_context.session.send_resource_list_changed()
else:
raise ValueError(f"Unknown tool: {name}")
return [
types.TextContent(
type="text",
text=f"Added note '{note_name}' with content: {content}",
)
]
except sqlite3.Error as e:
return [types.TextContent(type="text", text=f"Database error: {str(e)}")]
except Exception as e:
return [types.TextContent(type="text", text=f"Error: {str(e)}")]
async def main():
server = McpServer()
# Run the server using stdin/stdout streams
async def main(db_path: str, anthropic_api_key: str | None = None):
logger.info(f"Starting SQLite MCP Server with DB path: {db_path}")
server = McpServer(db_path, anthropic_api_key)
async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
logger.info("Server running with stdio transport")
await server.run(
read_stream,
write_stream,
@@ -208,7 +484,9 @@ async def main():
server_version="0.1.0",
capabilities=server.get_capabilities(
notification_options=NotificationOptions(),
experimental_capabilities={},
experimental_capabilities={
"anthropic_api_key": {"key": anthropic_api_key}
} if anthropic_api_key else {},
),
),
)

193
src/sqlite/uv.lock generated
View File

@@ -1,21 +1,5 @@
version = 1
requires-python = ">=3.11"
resolution-markers = [
"python_full_version < '3.13'",
"python_full_version >= '3.13'",
]
[[package]]
name = "aiosqlite"
version = "0.20.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "typing-extensions" },
]
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