How to get more from your data with embeddings for MongoDB Vector Search

You could be forgiven for thinking the term “embeddings” is just another tech buzzword. But these mathematical representations of data are transforming the way we approach information. 

Embeddings are algorithms that convert complex data, like text and images, into a structured numerical format that machines can easily understand. They also act as a bridge between human language and the exacting requirements of machines. 

Just as a translator interprets nuances in conversation, embeddings translate the essence of language into a form that machine learning models can use to perform tasks like search, recommendations, and classifications. 

The amount of information businesses need to deal with is growing year-on-year, so knowing how to deliver data quality and how to use embeddings can have significant benefits. A McKinsey study found data-driven organizations are 23 times more likely to attract new customers and 19 times more likely to be profitable.

What is MongoDB Vector Search?

One of the most popular ways of making use of embeddings is with MongoDB Vector Search, which can also be used to efficiently manage and retrieve enterprise data. MongoDB Vector Search uses advanced algorithms to simplify even the largest datasets.

For example, users of a movie database can search for films based on interests, rather than just matching keywords. MongoDB Vector Search analyzes the underlying meanings of queries and presents the most relevant films through semantic similarity. This transforms search results from a simple list of titles into a more intelligent, context-rich conversation.

Commercial vs open source embeddings

To choose the right embeddings, it’s important to know your options. Embeddings are broadly classified into commercial and open source categories.

MongoDB Vector Search supports both, which gives flexibility and adaptability depending on project needs. It also allows you to tap into the strengths of different models, creating an optimal environment for your unique applications.

Commercial embeddings

Commercial options typically promise ease of use, dedicated technical support and regular updates, but can sometimes come with higher operational costs. Some well-known examples include:

OpenAI’s GPT Models

Mainly used for text generation, embeddings are highly contextualized and trained on large corpora. For example, the text-embedding-ada-002 model provides high-quality embeddings with 1,536 dimensions.

Cohere

The Cohere Embed model is designed for semantic search and can handle large volumes of queries with low latency.

Google’s BERT

A transformer-based model trained to understand context in sentences. It produces embeddings that are particularly useful for tasks requiring nuanced understanding and semantic relationships in text.

AWS Comprehend

A natural language processing service that provides text analytics including sentiment analysis and entity recognition while also enabling users to create embeddings for custom applications.

Open-source embeddings

Open-source embeddings offer a more customizable approach. Here are some examples:

Word2Vec

Developed by Google, it effectively turns words into numerical vectors by considering their context in the training corpus. It generally offers two models: Continuous Bag of Words and Skip-Gram, producing embeddings that are easy to train and use.

GloVe (Global Vectors for Word Representation)

This model uses global word co-occurrence statistics to learn word representations. It efficiently captures the semantic relationships through a limited number of dimensions (often around 100 to 300), making it well-suited for various NLP tasks.

FastText

Developed by Facebook, FastText improves upon Word2Vec’s limitations by considering subword information. It generates embeddings for words in various languages, providing a richer understanding of unrecognized or compound words.

Hugging Face transformers

An extensive collection of pre-trained embedding models (including DistilBERT and RoBERTa) that are widely adopted in research and production scenarios. Their flexibility allows quick adaptation for custom tasks.

Sentence Transformers

This model family allows users to derive sentence and paragraph embeddings in addition to simple word embeddings. It’s useful for semantic search as it captures meaning beyond tokens.

The pros and cons of embeddings

As with any technology, there are pros and cons to consider. The case for using embeddings includes:

• Support for complex queries 
• Improved user experience, thanks to more intuitive search results
• Multimodal capabilities
• Scalability
• AI/machine learning integration

However, potential drawbacks include:

• Complexity of properly configuring and maintaining embedding models
• Resource intensive 
• Need for data quality
• Need for additional storage

Factors to consider when choosing embeddings

Selecting the right embedding models is a crucial task that requires careful consideration. Best practices suggest that organizations should first define their goals, such as improving search capabilities or enhancing personalization, and align their embedding choices with these objectives.

Budget constraints, workflow requirements, and anticipated outcomes should also guide the decision process. With the right insights, decision makers can navigate the landscape of embeddings confidently.

Hardware requirements

Choosing the right hardware can feel like a daunting task, but understanding your infrastructure needs is crucial for implementing MongoDB Vector Search successfully. You’ll need the right server capabilities to handle high-dimensional vector data effectively.

Memory is important, as having enough RAM ensures that your system can handle large embeddings without lagging. Scalability is key too and your server architecture should allow you to expand as your data demands increase.

Implementation costs

Analyze the cost implications of different embedding models, along with their integration into MongoDB Vector Search. While embedded solutions offer extensive functionalities, they may also require investment in hardware and ongoing support.

Considering the potential Return on Investment from implementing MongoDB Vector Search can provide critical insights. When used correctly, embeddings can lead to gains not just in efficiency but also in enhanced customer satisfaction, ultimately contributing positively to the bottom line.

Performance insights

Performance is often the primary metric by which technology choices are judged. With embeddings and MongoDB Vector Search, rapid retrieval times are possible even when handling expansive datasets.

MongoDB’s architecture is designed for speed, employing approximate nearest neighbor search methods that optimize query handling. With scalable infrastructure backing, businesses can expect efficiency across the board, so they can meet customer demands and operational needs effectively.

Strategic benefits

The integration of MongoDB Vector Search with embeddings is more than just a technical upgrade, it has strategic implications for your organization. Companies can use embeddings to gain deeper insights into customer behaviors, leading to more informed decision-making.

Embeddings can also improve customer engagement through personalized recommendations and tailored content. 

How to choose between different embedding models

Here’s a step-by-step guide to help you choose between different embedding models:

1. Define your use case: Start by identifying the specific demands of your application. Are you focused on semantic search, text classification, or a recommendation system? Different models excel in different tasks. For example, semantic search models like BERT and sentence-transformers excel in their contextual understanding. Models like Word2Vec are suitable for simpler word-level tasks but may struggle with context.
2. Evaluate performance: Conduct tests to evaluate how each model performs on relevant tasks. Consider using a subset of your data to run benchmark queries and measure accuracy and retrieval time. Analyze how each model handles your specific data types (for example, long-form text, short comments).
3. Analyze latency needs: If immediate feedback is critical for user experience, latency becomes crucial. Test models under conditions that simulate real-world usage to identify any bottlenecks.
4. Assess scalability: As your user base grows, so might your data. Choose models that are proven to scale without degradation in performance. For example, models like FastText offer speed and scalability, while some transformers may require more hardware to maintain the same performance level with increased load.
5. Consider dimensionality: Higher-dimensional embeddings offer a more detailed representation of data, but can be computationally heavier. Aim for a balance: trade-off between performance and resource requirements. If using MongoDB, ensure your infrastructure can handle the chosen dimensionality to maximize efficiency.
6. Examine training time and resources: If your organization lacks extensive resources, opt for models that are pretrained or readily available through APIs. These can save you development time while providing solid baseline performance.
7. Conduct a cost-benefit analysis: Finally, assess the total cost of ownership for each embedding option, including infrastructure costs and potential ROI from applications powered by each model. Commercial options often come with support and higher accuracy, but weigh that against ongoing costs. Open-source models may save on licensing but require more maintenance.

How embeddings can set you up for future success

Embeddings have an important role to play in data processing, especially when integrated with MongoDB Vector Search. The choices you make about embeddings today can significantly influence your future ability to use data for innovation. 

In an era where information is power, embeddings and Studio 3T’s ability to assist with quick and accurate information retrieval can set you on the path towards confident decision making, improved customer interactions, and streamlined operations.