1. SQL End-to-End Pipelines

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Code Sample: Open-Source NLP-to-SQL with Hugging Face

Step 1: Install Open-Source Tools

pip install transformers openai-whisper sqlalchemy

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Step 2: Create a Database

Set up a simple SQLite database for testing (same as before):

import sqlite3

# Create SQLite database and table
conn = sqlite3.connect('employees.db')
cursor = conn.cursor()

cursor.execute('''
    CREATE TABLE employees (
        id INTEGER PRIMARY KEY,
        name TEXT,
        age INTEGER,
        profession TEXT,
        achievements TEXT
    )
''')

# Insert sample employee data
employees = [
    (1, 'Fiona Davis', 32, 'Mobile Developer', 'Developed app with 100k downloads'),
    (2, 'Ian Clark', 45, 'Cybersecurity Expert', 'Led red-teaming for Fortune 500'),
    (3, 'Alice Cooper', 39, 'Data Scientist', 'Built a predictive analytics model')
]
cursor.executemany('INSERT INTO employees VALUES (?, ?, ?, ?, ?)', employees)
conn.commit()

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Step 3: Text-to-SQL with Hugging Face Grappa

We use Salesforce Grappa (open-source) for converting natural language into SQL.

from transformers import pipeline

# Load Hugging Face's Grappa model for text-to-SQL
text_to_sql = pipeline("text-to-sql", model="Salesforce/grappa-text-to-sql")

# Natural language query
query = "Who are the employees with the profession 'Cybersecurity Expert'?"

# Translate natural language to SQL
sql_query = text_to_sql(query, db_path="employees.db")['query']

# Execute the generated SQL query
cursor.execute(sql_query)
results = cursor.fetchall()

# Display results
for row in results:
    print(f"ID: {row[0]}, Name: {row[1]}, Age: {row[2]}, Profession: {row[3]}, Achievements: {row[4]}")

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Optional: Voice-to-SQL with Whisper + Grappa

If you want to add voice input, integrate OpenAI's Whisper for speech-to-text before feeding it to Grappa.

Code for Speech-to-SQL

import whisper

# Load Whisper model for speech recognition
whisper_model = whisper.load_model("base")

# Convert speech input to text
audio_file = "query_audio.wav"  # Replace with your audio file
result = whisper_model.transcribe(audio_file)
speech_query = result['text']

# Use Grappa to convert the text to SQL
sql_query = text_to_sql(speech_query, db_path="employees.db")['query']

# Execute SQL query
cursor.execute(sql_query)
results = cursor.fetchall()

# Display results
for row in results:
    print(f"ID: {row[0]}, Name: {row[1]}, Age: {row[2]}, Profession: {row[3]}, Achievements: {row[4]}")

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Sample Output

Natural Language Input:

Who are the employees with the profession 'Cybersecurity Expert'?

Generated SQL Query:

SELECT * FROM employees WHERE profession = 'Cybersecurity Expert';

Final Output:

ID: 2, Name: Ian Clark, Age: 45, Profession: Cybersecurity Expert, Achievements: Led red-teaming for Fortune 500

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This solution uses entirely open-source tools (Hugging Face’s Grappa + Whisper) to achieve text/voice-to-SQL functionality. Let me know if this works or needs more tweaking! 🚀

2. Explainable AI in SQL

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Table Update

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Code Example for Explainable SQL

Step 1: Install Open-Source Libraries

pip install transformers sqlalchemy

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Step 2: Create a Database

Let’s reuse a simple SQLite database setup:

import sqlite3

# Create SQLite database and table
conn = sqlite3.connect('employees.db')
cursor = conn.cursor()

cursor.execute('''
    CREATE TABLE employees (
        id INTEGER PRIMARY KEY,
        name TEXT,
        age INTEGER,
        profession TEXT,
        achievements TEXT
    )
''')

# Insert employee data
employees = [
    (1, 'Fiona Davis', 32, 'Mobile Developer', 'Developed app with 100k downloads'),
    (2, 'Ian Clark', 45, 'Cybersecurity Expert', 'Led red-teaming for Fortune 500'),
    (3, 'Alice Cooper', 39, 'Data Scientist', 'Built a predictive analytics model')
]
cursor.executemany('INSERT INTO employees VALUES (?, ?, ?, ?, ?)', employees)
conn.commit()

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Step 3: Natural Language to SQL with Explanations

We use Hugging Face's Grappa model to generate SQL queries and provide manual explanations for each part of the query.

from transformers import pipeline

# Load Grappa model for text-to-SQL
text_to_sql = pipeline("text-to-sql", model="Salesforce/grappa-text-to-sql")

# User's natural language query
query = "Who are the employees with the profession 'Cybersecurity Expert'?"

# Generate SQL query
result = text_to_sql(query, db_path="employees.db")
sql_query = result['query']

# Explanation of SQL query
explanation = {
    "SELECT *": "Retrieve all columns from the table.",
    "FROM employees": "Query the 'employees' table.",
    "WHERE profession = 'Cybersecurity Expert'": "Filter results where the profession column equals 'Cybersecurity Expert'."
}

# Execute SQL query
cursor.execute(sql_query)
results = cursor.fetchall()

# Display results
print("SQL Query:", sql_query)
print("\nQuery Explanation:")
for clause, meaning in explanation.items():
    print(f"{clause}: {meaning}")

print("\nResults:")
for row in results:
    print(f"ID: {row[0]}, Name: {row[1]}, Age: {row[2]}, Profession: {row[3]}, Achievements: {row[4]}")

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Sample Output

Input Query:

Who are the employees with the profession 'Cybersecurity Expert'?

Generated SQL Query:

SELECT * FROM employees WHERE profession = 'Cybersecurity Expert';

Explanation:

SELECT *: Retrieve all columns from the table.
FROM employees: Query the 'employees' table.
WHERE profession = 'Cybersecurity Expert': Filter results where the profession column equals 'Cybersecurity Expert'.

Results:

ID: 2, Name: Ian Clark, Age: 45, Profession: Cybersecurity Expert, Achievements: Led red-teaming for Fortune 500

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How This Works

1. Query Translation: Hugging Face's Grappa translates natural language to SQL.

2. Explainability: Manual annotations break down the SQL query into understandable parts, helping users trust the process.

3. Open-Source: Fully open-source tools are used to ensure flexibility and accessibility.

3. Fast search w/ Weaviate

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Code Example with Weaviate

Step 1: Install Required Libraries

pip install weaviate-client transformers

Step 2: Start Weaviate Locally

Use Docker to start an open-source Weaviate instance:

docker run -d -p 8080:8080 semitechnologies/weaviate

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Step 3: Set Up the Database and Add Data

import weaviate
from transformers import AutoTokenizer, AutoModel

# Connect to Weaviate
client = weaviate.Client("http://localhost:8080")

# Define the schema for employee data
schema = {
    "class": "Employee",
    "properties": [
        {"name": "name", "dataType": ["text"]},
        {"name": "profession", "dataType": ["text"]},
        {"name": "achievements", "dataType": ["text"]}
    ]
}
client.schema.create_class(schema)

# Load Hugging Face embedding model
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")

# Helper function to generate embeddings
def get_embedding(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    outputs = model(**inputs)
    return outputs.last_hidden_state.mean(dim=1).detach().numpy()[0]

# Add employee data
employees = [
    {"name": "Fiona Davis", "profession": "Mobile Developer", "achievements": "Developed app with 100k downloads"},
    {"name": "Ian Clark", "profession": "Cybersecurity Expert", "achievements": "Led red-teaming for Fortune 500"},
    {"name": "Alice Cooper", "profession": "Data Scientist", "achievements": "Built predictive analytics model"}
]

for employee in employees:
    vector = get_embedding(f"{employee['name']} {employee['profession']} {employee['achievements']}")
    client.data_object.create(
        data_object=employee,
        class_name="Employee",
        vector=vector
    )

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Step 4: Perform Scalable Hybrid Search

Use semantic search for context-based matches combined with keyword filtering.

# Define a query
query_text = "Find a Cybersecurity Expert to secure our systems."
query_vector = get_embedding(query_text)

# Perform hybrid search (semantic + keyword)
results = client.query.get("Employee", ["name", "profession", "achievements"]) \
    .with_near_vector({"vector": query_vector.tolist()}) \
    .with_where({
        "path": ["profession"],
        "operator": "Equal",
        "valueText": "Cybersecurity Expert"
    }) \
    .with_limit(2) \
    .do()

# Display results
print("Results:")
for result in results["data"]["Get"]["Employee"]:
    print(f"Name: {result['name']}, Profession: {result['profession']}, Achievements: {result['achievements']}")

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Sample Output

Input Query:

Find a Cybersecurity Expert to secure our systems.

Results:

Name: Ian Clark, Profession: Cybersecurity Expert, Achievements: Led red-teaming for Fortune 500

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Why Weaviate?

• Scalable: Handles millions of embeddings efficiently.

• Hybrid Search: Combines semantic vectors with exact keyword matches.

• Open-Source: Free and flexible for any project.

Here’s how to implement real-time querying of streaming data using open-source tools like Apache Kafka (for streaming) and Materialize (for real-time SQL). These tools enable live queries on dynamic data pipelines.

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Code Example for Real-Time Querying

Step 1: Install Required Tools

1. Kafka: Set up a Kafka cluster locally (Docker recommended).

2. Materialize: Install Materialize for real-time SQL on streaming data.

    docker run -d -p 6875:6875 materialize/materialized:latest
   

Install Kafka and Materialize Python clients:

pip install confluent-kafka psycopg2

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Step 2: Start Kafka for Streaming

Run Kafka with Docker:

docker-compose up -d

Create a Kafka topic for streaming employee data:

docker exec broker kafka-topics --create --topic employee-data --bootstrap-server localhost:9092

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Step 3: Stream Data to Kafka

Simulate real-time data streaming for employee updates.

from confluent_kafka import Producer
import json
import time

# Kafka producer configuration
producer = Producer({"bootstrap.servers": "localhost:9092"})

# Stream employee data
employee_updates = [
    {"name": "Fiona Davis", "profession": "Mobile Developer", "achievements": "Developed app with 200k downloads"},
    {"name": "Ian Clark", "profession": "Cybersecurity Expert", "achievements": "Secured Fortune 500 systems"},
    {"name": "Alice Cooper", "profession": "Data Scientist", "achievements": "Improved model efficiency by 20%"}
]

for update in employee_updates:
    producer.produce("employee-data", json.dumps(update).encode("utf-8"))
    print(f"Sent: {update}")
    time.sleep(1)  # Simulate real-time streaming

producer.flush()

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Step 4: Query Streaming Data with Materialize

Materialize continuously syncs and allows querying streaming Kafka topics.

-- In Materialize SQL Shell
-- Create a Kafka source
CREATE SOURCE employee_source
FROM KAFKA BROKER 'localhost:9092' TOPIC 'employee-data'
FORMAT AVRO USING CONFLUENT SCHEMA REGISTRY 'http://localhost:8081';

-- Create a real-time view for querying
CREATE MATERIALIZED VIEW employee_view AS
SELECT
    data->>'name' AS name,
    data->>'profession' AS profession,
    data->>'achievements' AS achievements
FROM employee_source;

-- Query the real-time view
SELECT * FROM employee_view WHERE profession = 'Cybersecurity Expert';

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Step 5: Query Using Python

Query the Materialize database in real time via Python.

import psycopg2

# Connect to Materialize
conn = psycopg2.connect(
    dbname="materialize", user="materialize", password="", host="localhost", port=6875
)
cursor = conn.cursor()

# Query the materialized view
cursor.execute("SELECT * FROM employee_view WHERE profession = 'Cybersecurity Expert';")
results = cursor.fetchall()

# Display results
for row in results:
    print(f"Name: {row[0]}, Profession: {row[1]}, Achievements: {row[2]}")

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Sample Output

Streaming Data Sent to Kafka:

Sent: {'name': 'Ian Clark', 'profession': 'Cybersecurity Expert', 'achievements': 'Secured Fortune 500 systems'}

Real-Time Query Output:

Name: Ian Clark, Profession: Cybersecurity Expert, Achievements: Secured Fortune 500 systems

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Why Kafka + Materialize?

1. Real-Time Processing: Materialize enables live SQL queries on Kafka streams.

2. Open-Source: Both Kafka and Materialize are free and scalable.

3. Scalability: Handle large-scale streaming pipelines seamlessly.

Here’s how lightweight, open-source local models like Mistral 7B or Llama 2 can replace cloud-hosted solutions to save costs and enhance privacy.

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4. Free cloud host SQL

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Code Example: Using Mistral 7B Locally

Step 1: Install Required Libraries

pip install transformers accelerate

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Step 2: Load and Use Mistral 7B

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the Mistral 7B model and tokenizer
model_name = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, device_map="auto", torch_dtype="auto"
)

# Run a local inference
def ask_model(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  # Use GPU if available
    outputs = model.generate(inputs["input_ids"], max_length=100, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Test the model with a natural language query
query = "What are the benefits of using open-source AI models?"
response = ask_model(query)
print("Response:", response)

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Sample Output

Input Prompt:

What are the benefits of using open-source AI models?

Model Output:

Open-source AI models are cost-effective, promote transparency, and provide greater flexibility for customization.

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Using Llama 2 Locally

Step 3: Load and Use Llama 2

Llama 2 models are also highly efficient for local use, especially with Hugging Face.

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load Llama 2 model and tokenizer
model_name = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, device_map="auto", torch_dtype="auto"
)

# Run a local inference
def ask_llama(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")  # Use GPU if available
    outputs = model.generate(inputs["input_ids"], max_length=100, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Test with a natural language query
query = "How does Llama 2 compare to Mistral 7B for local AI use?"
response = ask_llama(query)
print("Response:", response)

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Sample Output

Input Prompt:

How does Llama 2 compare to Mistral 7B for local AI use?

Model Output:

Both Llama 2 and Mistral 7B are highly efficient open-source models, but Mistral 7B is optimized for lower resource usage.

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Why Use Local Models?

1. Cost Savings: No cloud costs—run models on local GPUs.

2. Privacy: No sensitive data sent to external servers.

3. Flexibility: Fully customizable for your needs.

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