Economical Text Embedding: Ollama Local Models for Azure SQL Data Optimization

Cost-Effective Text Embedding: Leveraging Ollama Local Models with Azure SQL Databases

Embedding text using a local model can provide significant cost advantages and flexibility over cloud-based services. In this blog post, we explore how to set up and use a local model for text embedding and how this approach can be integrated with Azure SQL databases for advanced querying capabilities.

Cost Considerations

When choosing between a paid service and setting up a local model for text embedding, it’s crucial to consider the cost implications based on the scale of your data and the frequency of usage.

Pay Model Cost Estimate:

Using a paid model like OpenAI’s Ada V2 for embedding 1 terabyte of OCR texts can cost around $25,000. This estimation is based on converting every 4 characters into one token.

Local Model Cost Estimate:

The initial investment for setting up a local model can range from $4,050 to $12,750, depending on the selection of components. This one-time cost can be amortized over many uses and datasets, offering a more cost-effective solution, especially for large data volumes.

Overall Financial Implications:

While the upfront cost for a local model might seem high, it becomes significantly more economical with increased data volumes and repeated use. In contrast, the cost of using a pay model scales linearly with data volume, leading to potentially high ongoing expenses.

Considering these factors, the local model offers a cost advantage and greater control over data processing and security, making it an attractive option for organizations handling large quantities of sensitive data.

Exploring Local Models

In recent tests with local embedding models like BGE-M3, NOMIC-EMBED-TEXT, and others, we found unique capabilities and limitations worth considering for various machine learning tasks.

When exploring open-source embedding models, specific strengths and applications should be taken into account:

NOMIC-EMBED-TEXT: Suitable for tasks involving long-context text processing.
BGE-M3: Adapted for sentence similarity tasks and multilingual support.
MXBAI-EMBED-LARGE: Noted for feature extraction capabilities and generalization across text types.

Each model brings unique capabilities that cater to specific machine learning requirements. Testing these models with relevant data is crucial to determine the best fit for your project.

Comparing Results

Comparing the performance of different models against OpenAI’s Text-embedding-ada-002 reveals valuable insights into their suitability for various tasks.

By analyzing similarity percentages and conducting cluster search tests, we can determine the effectiveness of each model in different scenarios.

Furthermore, embedding text locally using models like Ollama offers a scalable and cost-effective solution for handling large volumes of data. By integrating these embeddings into Azure SQL databases, organizations can enhance their querying capabilities and extract meaningful insights efficiently.

Additional Resources

For further information and detailed guides on text similarity analysis and leveraging generative AI for querying in datasets, refer to the following resources:

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