Setting Up Ollama with Snowflake for Data Warehousing

What is Ollama?

What is Snowflake?

The Benefits of Combining Ollama & Snowflake

• Enhanced Data Accessibility: Snowflake’s architecture allows for easy and quick access to data, which Ollama can leverage to perform real-time analysis using LLMs.
• Powerful Natural Language Queries: By running Ollama models locally, you can articulate queries in natural language and receive precise SQL queries to interact with your Snowflake warehouse.
• Cost Efficiency: Local model execution reduces the costs associated with using third-party AI services, enabling businesses to save money on API usage while still benefiting from advanced AI capabilities.
• Data Security: Running models locally ensures that sensitive data remains within your organizational infrastructure without sending it to external cloud services.

Setting Up Your Environment

1. AWS or Cloud Service for Snowflake
   • Ensure you have a Snowflake account, and note your credentials for use in later steps. If you don’t have an account, you can sign up for a 30-day trial account to explore its capabilities for FREE!
2. Ollama Installation
   • You will need to install Ollama on your local machine to run the large language models. For MacOS or Linux, you can run:
     1 2 bash curl https://ollama.ai/install.sh | sh
   • For Windows users, remember to enable WSL (Windows Subsystem for Linux) to proceed with the installation.
3. Python Packages
   • Ensure you have the necessary Python packages installed. Use pip to install the Snowflake connector:
     1 2 bash pip install snowflake-connector-python
   • For making API calls to Ollama, ensure you have the
     1 requests
     library installed:
     1 2 bash pip install requests

Step-by-Step Integration Process

Step 1: Run Ollama Locally

Step 2: Connect Ollama to Snowflake

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import snowflake.connector

def get_connection(USER, PASSWORD, ACCOUNT, WAREHOUSE, DATABASE, SCHEMA):
    conn = snowflake.connector.connect(
        user=USER,
        password=PASSWORD,
        account=ACCOUNT,
        warehouse=WAREHOUSE,
        database=DATABASE,
        schema=SCHEMA
    )
    return conn

Step 3: Crafting Queries with Ollama

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import requests

model = 'mistral'  # Use the desired model name

def generate_sql(prompt, context):
    r = requests.post('http://localhost:11434/api/generate', json={
        'model': model,
        'prompt': prompt,
        'context': context,
    })
    return r.json()  # Return the model's response

Step 4: Executing SQL Queries

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def execute_query(conn, query):
    cursor = conn.cursor()
    try:
        cursor.execute(query)
        return cursor.fetchall()
    finally:
        cursor.close()

Generate the SQL query using Ollama

Step 5: Tailoring Responses

Best Practices for Ollama & Snowflake Integration

• Reference Your Schema: Ensure that your prompts inform Ollama about the schema of your data in Snowflake.
• Test Regularly: Regularly test your model with diverse queries to ensure reliability.
• Keep your environment updated: Update both Ollama and Snowflake components routinely to benefit from the latest features and security patches.
• Use Efficient Context Management: To ensure accurate responses, maintain a clean context state that reflects your ongoing dialogue with Ollama. Use it to inform your model how to handle subsequent queries.

Leverage Arsturn for Enhanced Engagement

Conclusion