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OpenAI Swarm implementation of an AI agent swarm to manage a SQL database

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Cole Medin
2024-10-16 16:16:41 -05:00
parent 0a6d1f430d
commit 0fa98dbdee
7 changed files with 396 additions and 1 deletions
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1
.gitignore vendored
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@@ -11,3 +11,4 @@ credentials.json
token.json token.json
node_modules node_modules
venv venv
*.db

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sql-ai-agent/ai-news-complete-mock-data.sql Normal file
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-- Insert mock data into rss_feeds table
INSERT OR IGNORE INTO rss_feeds (name, url, description, site_link, language) VALUES
('AI Daily', 'https://ai-daily.com/feed', 'Daily AI news and updates', 'https://ai-daily.com', 'en'),
('ML Weekly', 'https://mlweekly.com/rss', 'Weekly roundup of machine learning news', 'https://mlweekly.com', 'en'),
('IA Nouvelles', 'https://ia-nouvelles.fr/flux', 'Actualités sur l''intelligence artificielle en français', 'https://ia-nouvelles.fr', 'fr'),
('Data Science Digest', 'https://datasciencedigest.com/feed', 'Comprehensive coverage of data science topics', 'https://datasciencedigest.com', 'en'),
('AI Ethics Blog', 'https://aiethicsblog.org/rss', 'Exploring ethical implications of AI', 'https://aiethicsblog.org', 'en');
-- Insert mock data into categories table
INSERT OR IGNORE INTO categories (name, description) VALUES
('Machine Learning', 'News related to machine learning algorithms and techniques'),
('Natural Language Processing', 'Updates on NLP research and applications'),
('Computer Vision', 'Advancements in image and video processing using AI'),
('Ethics in AI', 'Discussions on ethical considerations in AI development and deployment'),
('Robotics', 'News about AI in robotics and automation'),
('AI in Healthcare', 'Applications of AI in medicine and healthcare'),
('Deep Learning', 'Focused on deep neural networks and related technologies');
-- Insert mock data into rss_items table
INSERT OR IGNORE INTO rss_items (rss_feed_id, title, link, description, content, published_date, author) VALUES
(1, 'New breakthrough in reinforcement learning', 'https://ai-daily.com/articles/reinforcement-learning-breakthrough', 'Researchers achieve significant progress in RL algorithms', 'Full content of the article...', '2023-04-15 09:30:00', 'Jane Doe'),
(2, 'GPT-4 shows impressive results in medical diagnosis', 'https://mlweekly.com/news/gpt4-medical-diagnosis', 'OpenAI''s latest language model demonstrates potential in healthcare', 'Detailed article content...', '2023-04-14 14:45:00', 'John Smith'),
(3, 'L''IA générative révolutionne la création artistique', 'https://ia-nouvelles.fr/articles/ia-generative-art', 'Comment l''IA transforme le processus créatif des artistes', 'Contenu complet de l''article...', '2023-04-13 11:15:00', 'Marie Dupont'),
(4, 'Advancements in Computer Vision for Autonomous Vehicles', 'https://datasciencedigest.com/articles/cv-autonomous-vehicles', 'Recent developments in CV improving self-driving car capabilities', 'Full article content...', '2023-04-16 10:00:00', 'Alex Johnson'),
(5, 'The Ethics of AI in Hiring Processes', 'https://aiethicsblog.org/posts/ai-in-hiring', 'Examining the implications of using AI for job candidate selection', 'Detailed blog post content...', '2023-04-17 13:20:00', 'Samantha Lee');
-- Insert mock data into rss_item_categories junction table
INSERT OR IGNORE INTO rss_item_categories (rss_item_id, category_id) VALUES
(1, 1), (1, 7), -- Reinforcement learning article tagged with Machine Learning and Deep Learning
(2, 2), (2, 6), -- GPT-4 article tagged with NLP and AI in Healthcare
(3, 1), (3, 4), -- Generative AI article tagged with Machine Learning and Ethics in AI
(4, 3), (4, 5), -- Computer Vision article tagged with Computer Vision and Robotics
(5, 4), (5, 6); -- AI Ethics article tagged with Ethics in AI and AI in Healthcare
-- Insert mock data into users table
INSERT OR IGNORE INTO users (username, email, password_hash, created_at, last_login) VALUES
('alice_ai', 'alice@example.com', 'hashed_password_1', '2023-01-01 10:00:00', '2023-04-15 14:30:00'),
('bob_ml', 'bob@example.com', 'hashed_password_2', '2023-02-15 11:30:00', '2023-04-14 09:15:00'),
('charlie_nlp', 'charlie@example.com', 'hashed_password_3', '2023-03-20 09:45:00', '2023-04-13 16:45:00'),
('dana_cv', 'dana@example.com', 'hashed_password_4', '2023-03-25 14:00:00', '2023-04-16 11:30:00'),
('evan_ethics', 'evan@example.com', 'hashed_password_5', '2023-04-01 08:30:00', '2023-04-17 10:45:00');
-- Insert mock data into user_category_preferences table
INSERT OR IGNORE INTO user_category_preferences (user_id, category_id) VALUES
(1, 1), (1, 2), (1, 7), -- Alice is interested in Machine Learning, NLP, and Deep Learning
(2, 1), (2, 3), (2, 5), -- Bob is interested in Machine Learning, Computer Vision, and Robotics
(3, 2), (3, 4), (3, 6), -- Charlie is interested in NLP, Ethics in AI, and AI in Healthcare
(4, 3), (4, 5), (4, 7), -- Dana is interested in Computer Vision, Robotics, and Deep Learning
(5, 4), (5, 6), (5, 1); -- Evan is interested in Ethics in AI, AI in Healthcare, and Machine Learning
-- Insert mock data into user_feed_preferences table
INSERT OR IGNORE INTO user_feed_preferences (user_id, rss_feed_id) VALUES
(1, 1), (1, 2), (1, 4), -- Alice follows AI Daily, ML Weekly, and Data Science Digest
(2, 2), (2, 3), (2, 4), -- Bob follows ML Weekly, IA Nouvelles, and Data Science Digest
(3, 1), (3, 3), (3, 5), -- Charlie follows AI Daily, IA Nouvelles, and AI Ethics Blog
(4, 1), (4, 2), (4, 4), -- Dana follows AI Daily, ML Weekly, and Data Science Digest
(5, 3), (5, 4), (5, 5); -- Evan follows IA Nouvelles, Data Science Digest, and AI Ethics Blog
-- Insert mock data into article_interactions table
INSERT OR IGNORE INTO article_interactions (user_id, rss_item_id, interaction_type, interaction_time) VALUES
(1, 1, 'view', '2023-04-15 10:15:00'),
(1, 1, 'like', '2023-04-15 10:20:00'),
(1, 2, 'view', '2023-04-15 10:30:00'),
(2, 2, 'view', '2023-04-14 15:00:00'),
(2, 2, 'like', '2023-04-14 15:05:00'),
(2, 2, 'share', '2023-04-14 15:10:00'),
(3, 3, 'view', '2023-04-13 17:00:00'),
(3, 3, 'like', '2023-04-13 17:10:00'),
(4, 4, 'view', '2023-04-16 11:45:00'),
(4, 4, 'share', '2023-04-16 11:50:00'),
(5, 5, 'view', '2023-04-17 14:00:00'),
(5, 5, 'like', '2023-04-17 14:15:00');
-- Insert mock data into feed_views table
INSERT OR IGNORE INTO feed_views (user_id, rss_feed_id, viewed_at) VALUES
(1, 1, '2023-04-15 10:00:00'),
(1, 2, '2023-04-15 10:25:00'),
(2, 2, '2023-04-14 14:55:00'),
(3, 3, '2023-04-13 16:50:00'),
(4, 4, '2023-04-16 11:40:00'),
(5, 5, '2023-04-17 13:55:00');
-- Insert mock data into user_sessions table
INSERT OR IGNORE INTO user_sessions (user_id, session_token, created_at, expires_at) VALUES
(1, 'token_alice_1', '2023-04-15 14:30:00', '2023-04-16 14:30:00'),
(2, 'token_bob_1', '2023-04-14 09:15:00', '2023-04-15 09:15:00'),
(3, 'token_charlie_1', '2023-04-13 16:45:00', '2023-04-14 16:45:00'),
(4, 'token_dana_1', '2023-04-16 11:30:00', '2023-04-17 11:30:00'),
(5, 'token_evan_1', '2023-04-17 10:45:00', '2023-04-18 10:45:00');

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sql-ai-agent/ai-news-complete-tables.sql Normal file
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-- Create the RSS feeds table
CREATE TABLE IF NOT EXISTS rss_feeds (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name VARCHAR(255) NOT NULL,
url VARCHAR(255) NOT NULL UNIQUE,
description TEXT,
site_link VARCHAR(255),
language VARCHAR(50)
);
-- Create the categories table
CREATE TABLE IF NOT EXISTS categories (
id INTEGER PRIMARY KEY AUTOINCREMENT,
name VARCHAR(100) NOT NULL UNIQUE,
description TEXT
);
-- Create the RSS news feed items table
CREATE TABLE IF NOT EXISTS rss_items (
id INTEGER PRIMARY KEY AUTOINCREMENT,
rss_feed_id INTEGER NOT NULL,
title VARCHAR(255) NOT NULL,
link VARCHAR(255) NOT NULL,
description TEXT,
content TEXT,
published_date DATETIME,
author VARCHAR(255),
FOREIGN KEY (rss_feed_id) REFERENCES rss_feeds(id)
);
-- Create a junction table for the many-to-many relationship between rss_items and categories
CREATE TABLE IF NOT EXISTS rss_item_categories (
rss_item_id INTEGER NOT NULL,
category_id INTEGER NOT NULL,
PRIMARY KEY (rss_item_id, category_id),
FOREIGN KEY (rss_item_id) REFERENCES rss_items(id),
FOREIGN KEY (category_id) REFERENCES categories(id)
);
-- Create users table
CREATE TABLE IF NOT EXISTS users (
id INTEGER PRIMARY KEY AUTOINCREMENT,
username VARCHAR(50) NOT NULL UNIQUE,
email VARCHAR(100) NOT NULL UNIQUE,
password_hash VARCHAR(255) NOT NULL,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
last_login DATETIME
);
-- Create user preferences table for categories
CREATE TABLE IF NOT EXISTS user_category_preferences (
user_id INTEGER NOT NULL,
category_id INTEGER NOT NULL,
PRIMARY KEY (user_id, category_id),
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (category_id) REFERENCES categories(id)
);
-- Create user preferences table for RSS feeds
CREATE TABLE IF NOT EXISTS user_feed_preferences (
user_id INTEGER NOT NULL,
rss_feed_id INTEGER NOT NULL,
PRIMARY KEY (user_id, rss_feed_id),
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (rss_feed_id) REFERENCES rss_feeds(id)
);
-- Create table for tracking article interactions
CREATE TABLE IF NOT EXISTS article_interactions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
user_id INTEGER NOT NULL,
rss_item_id INTEGER NOT NULL,
interaction_type VARCHAR(20) NOT NULL, -- 'view', 'like', 'share', etc.
interaction_time DATETIME DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (rss_item_id) REFERENCES rss_items(id)
);
-- Create table for tracking feed views
CREATE TABLE IF NOT EXISTS feed_views (
id INTEGER PRIMARY KEY AUTOINCREMENT,
user_id INTEGER NOT NULL,
rss_feed_id INTEGER NOT NULL,
viewed_at DATETIME DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (user_id) REFERENCES users(id),
FOREIGN KEY (rss_feed_id) REFERENCES rss_feeds(id)
);
-- Create table for user sessions
CREATE TABLE IF NOT EXISTS user_sessions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
user_id INTEGER NOT NULL,
session_token VARCHAR(255) NOT NULL UNIQUE,
created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
expires_at DATETIME NOT NULL,
FOREIGN KEY (user_id) REFERENCES users(id)
);

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sql-ai-agent/load_sql_data.py Normal file
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import sqlite3
"""
This script is used to create a SQLlite database, add tables
to it, and insert mock data, all for the OpenAI Swarm demonstration
with the other Python script in this directory.
Simply run this script with the command:
python load_sql_data.py
And then you will have a database loaded and ready to use
with the agent swarm!
"""
def execute_sql_script(cursor, script_file):
# Opens the .sql file given as script_file
with open(script_file, 'r') as sql_file:
sql_script = sql_file.read()
# Gets all the sql commands and executes them one at a time
statements = sql_script.split(';')
for statement in statements:
if statement.strip():
cursor.execute(statement)
def main():
# Connect to the database
conn = sqlite3.connect('rss-feed-database.db')
cursor = conn.cursor()
# Execute SQL script to create tables for the AI RSS Feed system
execute_sql_script(cursor, 'ai-news-complete-tables.sql')
conn.commit()
# Execute SQL script to insert mock data for the AI RSS Feed system
execute_sql_script(cursor, 'ai-news-complete-mock-data.sql')
conn.commit()
# Query table to make sure things are looking good
cursor.execute("SELECT * FROM rss_feeds")
feeds = cursor.fetchall()
for feed in feeds:
print(feed)
# Close the connection
conn.close()
if __name__ == "__main__":
main()

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sql-ai-agent/requirements.txt Normal file
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aiohappyeyeballs==2.4.3
aiohttp==3.10.10
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.2.post1
attrs==24.2.0
certifi==2024.8.30
cfgv==3.4.0
charset-normalizer==3.4.0
click==8.1.7
colorama==0.4.6
distlib==0.3.9
distro==1.9.0
docstring_parser==0.16
filelock==3.16.1
frozenlist==1.4.1
h11==0.14.0
httpcore==1.0.6
httpx==0.27.2
identify==2.6.1
idna==3.10
iniconfig==2.0.0
instructor==1.5.2
jiter==0.5.0
markdown-it-py==3.0.0
mdurl==0.1.2
multidict==6.1.0
nodeenv==1.9.1
numpy==2.1.2
openai==1.51.2
packaging==24.1
platformdirs==4.3.6
pluggy==1.5.0
pre_commit==4.0.1
propcache==0.2.0
pydantic==2.9.2
pydantic_core==2.23.4
Pygments==2.18.0
pytest==8.3.3
PyYAML==6.0.2
requests==2.32.3
rich==13.9.2
shellingham==1.5.4
sniffio==1.3.1
swarm @ git+https://github.com/openai/swarm.git@9db581cecaacea0d46a933d6453c312b034dbf47
tenacity==8.5.0
tqdm==4.66.5
typer==0.12.5
typing_extensions==4.12.2
urllib3==2.2.3
virtualenv==20.26.6
yarl==1.15.2

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sql-ai-agent/run.py Normal file
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from swarm.repl import run_demo_loop
from sql_agents import sql_router_agent
if __name__ == "__main__":
run_demo_loop(sql_router_agent)

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sql-ai-agent/sql_agents.py Normal file
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from swarm import Agent
import sqlite3
conn = sqlite3.connect('rss-feed-database.db')
cursor = conn.cursor()
with open("ai-news-complete-tables.sql", "r") as table_schema_file:
table_schemas = table_schema_file.read()
def run_sql_select_statement(sql_statement):
"""Executes a SQL SELECT statement and returns the results of running the SELECT. Make sure you have a full SQL SELECT query created before calling this function."""
print(f"Executing SQL statement: {sql_statement}")
cursor.execute(sql_statement)
records = cursor.fetchall()
if not records:
return "No results found."
# Get column names
column_names = [description[0] for description in cursor.description]
# Calculate column widths
col_widths = [len(name) for name in column_names]
for row in records:
for i, value in enumerate(row):
col_widths[i] = max(col_widths[i], len(str(value)))
# Format the results
result_str = ""
# Add header
header = " | ".join(name.ljust(width) for name, width in zip(column_names, col_widths))
result_str += header + "\n"
result_str += "-" * len(header) + "\n"
# Add rows
for row in records:
row_str = " | ".join(str(value).ljust(width) for value, width in zip(row, col_widths))
result_str += row_str + "\n"
return result_str
def get_sql_router_agent_instructions():
return """You are an orchestrator of different SQL data experts and it is your job to
determine which of the agent is best suited to handle the user's request,
and transfer the conversation to that agent."""
def get_sql_agent_instructions():
return f"""You are a SQL expert who takes in a request from a user for information
they want to retrieve from the DB, creates a SELECT statement to retrieve the
necessary information, and then invoke the function to run the query and
get the results back to then report to the user the information they wanted to know.
Here are the table schemas for the DB you can query:
{table_schemas}
Write all of your SQL SELECT statements to work 100% with these schemas and nothing else.
You are always willing to create and execute the SQL statements to answer the user's question.
"""
sql_router_agent = Agent(
name="Router Agent",
instructions=get_sql_router_agent_instructions()
)
rss_feed_agent = Agent(
name="RSS Feed Agent",
instructions=get_sql_agent_instructions() + "\n\nHelp the user with data related to RSS feeds. Be super enthusiastic about how many great RSS feeds there are in every one of your responses.",
functions=[run_sql_select_statement]
)
user_agent = Agent(
name="User Agent",
instructions=get_sql_agent_instructions() + "\n\nHelp the user with data related to users.",
functions=[run_sql_select_statement],
)
analytics_agent = Agent(
name="Analytics Agent",
instructions=get_sql_agent_instructions() + "\n\nHelp the user gain insights from the data with analytics. Be super accurate in reporting numbers and citing sources.",
functions=[run_sql_select_statement],
)
def transfer_back_to_router_agent():
"""Call this function if a user is asking about data that is not handled by the current agent."""
return sql_router_agent
def transfer_to_rss_feeds_agent():
return rss_feed_agent
def transfer_to_user_agent():
return user_agent
def transfer_to_analytics_agent():
return analytics_agent
sql_router_agent.functions = [transfer_to_rss_feeds_agent, transfer_to_user_agent, transfer_to_analytics_agent]
rss_feed_agent.functions.append(transfer_back_to_router_agent)
user_agent.functions.append(transfer_back_to_router_agent)
analytics_agent.functions.append(transfer_back_to_router_agent)