9 Best Embedding Models for Semantic Search

Imagine you're shopping online for a new pair of running shoes. You type "best shoes for marathon training" into the search bar, expecting options designed for long distances. But instead, you get casual sneakers, heels, and even hiking boots. Not exactly helpful.

We've all been there - searching for one thing and getting results that miss the mark. The problem is traditional keyword-based search engines lack the context and intent recognition we expect today.

Enter semantic search powered by text embedding models. These models are the unsung heroes that can actually understand the meaning behind your queries.

In this post, I'll explore text embedding models - from how they work to choosing the right one for semantic search. You'll uncover why embedding models are fundamental to semantic search, learn the top text embedding models, get tips on implementation, and how to gauge their impact.

Whether you're a developer looking to elevate your search capabilities or just embedding-curious, you'll gain a deeper appreciation for these transformative technologies.

Why Text Embedding Models Are Critical for Semantic Search

Now that I've established some context, you may be wondering - what exactly are text embedding models and why are they so important for semantic search?

Let's first look at the limitations of traditional keyword-based search to understand why we need better solutions:

• No understanding of context or intent behind search queries
• Results rely solely on matching input keywords, often leading to irrelevant hits
• Struggles with synonyms - "sneakers" won't match "running shoes"

Text embedding models address these issues by transforming words into numeric vector representations. Each word gets "embedded" as coordinates in a multidimensional space.

The key is that related words cluster together in this vector space. So "sneakers" and "running shoes" would be near each other based on context, even though the keywords differ.

This numerical understanding of language is what enables semantic search. The engine can analyze embedded search queries and results in context to deliver relevant hits - even if the exact keywords don't match.

Text embeddings provide a deeper layer of intelligence for identifying meaningful patterns and relationships in language. That's why they are fundamental building blocks for semantic search.

Choosing the Right Embedding Model for Semantic Search

So, you're convinced that embedding models are essential for improving your search capabilities. But with so many options available, how do you choose the right one? Each model has its own set of strengths and weaknesses, making the decision crucial yet challenging.

Pre-trained vs. Custom Models

Your first decision will be whether to use a pre-trained model or to develop a custom one. Pre-trained models are like ready-made solutions; they're quick to implement and often effective right out of the box. Custom models, on the other hand, are built specifically for your needs but require more time and resources to develop.

Key Factors to Consider

When selecting an embedding model, consider the following key factors:

• Accuracy: How well does the model understand the nuances of your specific industry or product range?
• Speed: Is the model capable of delivering results in near real-time?
• Scalability: Can the model handle increased data loads as your business grows?

By focusing on these factors, you can make a more informed decision and select an embedding model that aligns well with your specific needs and objectives.

Compare multiple embedding models with your data in Graft.

9 Best Embedding Models for Semantic Search (updated April 2024)

1. Word2Vec
2. GTE-Base
3. GloVe
4. BERT
5. GPT-3 and Ada-002
6. FAISS
7. Universal Sentence Encoder
8. Sentence-BERT (SBERT)
9. Cohere Embed 3.0

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1. Word2Vec

Word2Vec is one of the pioneering models in the field of word embeddings. Developed by researchers at Google, it's a two-layer neural network that turns text into a numerical form that machines can understand.

Pros:

• Simple to implement
• Efficient on large datasets
• Good at capturing the context of words

Cons:

• Struggles with out-of-vocabulary words
• Doesn't capture multiple meanings well
• Older model

Use-case Example

Imagine you're building a recommendation engine for a book e-commerce site. Word2Vec can help you understand the context in which certain books are often discussed, enabling more contextual recommendations.

2. GTE-Base

GTE-Base is a recently open-sourced text embedding model developed by experts at TheNLPer, optimized for semantic search.

Pros:

• State-of-the-art performance on semantic search benchmarks
• Compact and efficient for scalable deployment
• Pre-trained models available in TensorFlow/PyTorch
• Balanced accuracy and speed

Cons:

• Less customizable than training your own model
• Limited to sequence lengths under 512 tokens

Use Case Example

Let's say you want to add conversational semantic search capabilities to a customer support chatbot. GTE-Base provides an off-the-shelf embedding model tailored for this use case - enabling precise results without slowed response times from overly complex models.

3. GloVe

GloVe, short for "Global Vectors for Word Representation," is an unsupervised learning algorithm that can obtain vector representations for words. Developed by Stanford, it's designed to scale to very large corpora.

Pros:

• Good at capturing global statistics
• Handles multiple word meanings better than Word2Vec

Cons:

• Requires more computational resources
• Not as straightforward to implement

Use-case Example

Let's say you're working on a news aggregation platform. GloVe can help categorize articles based on the global context, making the recommendations more nuanced.

4. BERT

BERT (Bidirectional Encoder Representations from Transformers) is a more recent model developed by Google. It's designed to pre-train deep bidirectional representations by considering the context from both the left and the right side of a word in all layers.

Pros:

• Excellent at understanding the context
• Pre-trained models available for various tasks

Cons:

• Resource-intensive
• Can be overkill for simple tasks

Use-case Example

If you're in charge of a customer support chatbot, BERT can help the bot understand the nuances of customer queries, making the interactions more natural and effective.

5. GPT-3 and Ada-002

GPT-3 (Generative Pre-trained Transformer) are models developed by OpenAI. They are designed to perform various tasks without task-specific training data, making them very versatile.

Pros:

• Extremely versatile
• Capable of understanding and generating human-like text

Cons:

• Requires a lot of computational power
• Not open-source

Use-case Example

Imagine you're developing a content curation platform. GPT-3 can not only help in categorizing the content but also in generating summaries or even creating new content.

6. FAISS

FAISS (Facebook AI Similarity Search) is an embedding model developed by Facebook specifically for efficient similarity search in high dimensional spaces like text.

Pros:

• Optimized for blazingly fast nearest neighbor search in vector spaces
• Scales to index billions of embedded data points
• Enables tunable tradeoff between speed and accuracy
• Open source with Python integration

Cons:

• Less versatile than general purpose models like BERT
• Approximate search can reduce accuracy

Use Case Example

Let's say you operate an e-commerce site with 50 million product listings. Using FAISS, you could index the text embeddings for all product titles and quickly find the most relevant matches for a user's search query in milliseconds. The speed at scale makes it ideal for large real-world semantic search applications.

7. Universal Sentence Encoder

Universal Sentence Encoder is a text embedding model by Google designed to convert entire sentences into fixed-length vectors, aiming for a higher understanding of semantic meaning.

Pros:

• Capable of embedding full sentences, providing richer context
• Pre-trained on a variety of data, making it versatile
• Easy-to-use with TensorFlow Hub integration
• Can work well with both short and long text strings

Cons:

• Can be slower than word-level models like Word2Vec
• Not as optimized for nearest neighbor search as models like FAISS

Use Case Example: 

Imagine you're building a customer support chatbot that needs to understand the intent behind entire sentences or even paragraphs. Using the Universal Sentence Encoder, you could embed customer queries to match them with the most relevant pre-defined responses, improving user experience through accurate and contextually appropriate answers.

8. Sentence-BERT (SBERT)

Sentence-BERT (SBERT) is a specialized adaptation of the BERT model, tweaked for generating sentence embeddings. It's particularly good for semantic search tasks and is well-suited for projects demanding both speed and accuracy.

Pros:

• Fast, thanks to sentence-level embeddings
• Higher accuracy in capturing semantic meaning compared to word-level models
• Pre-trained variants for specific tasks available
• Easy to use with Hugging Face's Transformers library

Cons:

• Requires more resources for training compared to simpler models
• Might be overkill for projects that don't require high accuracy

Use Case Example: Consider a document retrieval system for a law firm where each file can be several pages long. Using Sentence-BERT, you can quickly generate embeddings for chunks of text within each document. When lawyers search for case precedents, SBERT can find the most semantically relevant documents in a flash, making research efficient and effective.

This high-level overview summarizes how some top models compare. For your application, test different models against factors like domain relevance, speed, and scalability to determine the optimal fit.

9. Cohere Embed 3.0

Cohere's Embed v3 represents the latest advancement in embedding models, offering unparalleled performance in semantic search applications. This model stands out for its ability to discern not only the topic relevance but also the quality of content in documents, making it highly effective in environments with noisy data.

Pros:

• Superior performance in noisy, real-world data scenarios.
• Special compression-aware training reduces infrastructure costs significantly.
• Offers both English and multilingual versions, supporting over 100 languages.
• Compatible with various similarity metrics like dot product and cosine similarity.
• State-of-the-art performance on trusted benchmarks like MTEB and BEIR.

Cons:

• May require more computational resources due to its advanced capabilities.
• The complexity of the model might be unnecessary for simpler search tasks.

Use Case Example:Imagine a global news aggregation platform that needs to sift through thousands of articles daily in multiple languages. Embed v3 can efficiently process and rank these articles not just by topic relevance but also by content quality. This ensures that users are presented with the most informative and relevant articles, enhancing user engagement and satisfaction.

In summary, Cohere's Embed v3 is a robust choice for applications where accuracy, content quality, and support for multiple languages are paramount. Its advanced features make it ideal for complex semantic search tasks in diverse and challenging data environments.

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Be sure to check out the 14 Best Open Source Text Embedding Models

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How to Implement Text Embedding Models for Semantic Search

So you've picked your embedding model, and you're ready to implement it. But where do you start? The first step is to integrate the model into your existing search infrastructure. This might sound daunting, but with today's tools and platforms, it's easier than you'd think.

Once you've selected a text embedding model, integrating it into your semantic search system involves three key phases:

1. API Integration

Properly leveraging the model's API is crucial for a smooth integration. Thoroughly review the API documentation and guides to understand the endpoints, payloads, and responses. Look for tutorials and code samples for your specific language and environment. For example, TensorFlow Serving provides Python, JavaScript and REST APIs for hosting models like BERT.

2. Index Pipeline Augmentation

Embed search corpus documents and queries during indexing. This stores vectors alongside original text for efficiency. Most APIs provide out-of-the-box batch embedding capabilities.

3. Search Algorithm Update

Update the search ranking algorithm to compare document and query vectors when scoring relevance. Computing similarity between embedded representations gives semantic matching.

Additionally, consider:

• Batch Processing - Embed offline for efficiency then index.
• Re-embedding Cadence - Regenerate vectors periodically to pick up new data.
• Monitoring - Track embedding coverage, freshness, and server load.
• Testing - Evaluate precision and recall on sample test queries before full launch.

For a smooth implementation:

• Review documentation and existing integration guides
• Start with a small prototype before scale out
• Monitor for spikes in latency or errors

With diligent preparation, you can avoid pitfalls and successfully leverage text embeddings to enable intelligent semantic search experiences.

Measuring the Impact of Text Embeddings on Semantic Search

When assessing the effectiveness of text embeddings, consider metrics aligned to semantic search success:

1. Query Latency - Does embedding increase latency vs keyword search? Aim for <200 ms.
2. Precision - % of retrieved results that are relevant to the query. Compare keyword vs embedded search.
3. Recall - % of total relevant results that are retrieved. Recall of ~80% is reasonable.
4. Latency: The time it takes for the model to return results. Lower latency usually leads to better user experience.
5. Click-Through-Rate - Are users clicking on embedded search results? Increased CTR indicates higher engagement, and high engagement often indicates that the search results are relevant.
6. Session Duration - Do users spend more time on search results pages? More time suggests they find the results valuable.
7. Fallback Rate - % of searches where users have to reformulate the query. Lower fallback indicates queries are understood.
8. Resource Utilization: Keep an eye on CPU and memory usage. A model that's too resource-intensive may not be practical for your application.

Monitor these metrics before and after embedding integration to quantify impact. Analyze sample queries, user cohorts, and categories to identify areas of improvement.

Text embeddings empower more meaningful search experiences. But optimizing their application takes diligent measurement and iteration based on how they perform for your users. Start by tracking metrics like relevance, engagement and query understanding to maximize value.

Experiment with these models. Tinker with their settings. Measure their effectiveness using the KPIs we discussed. The future of search is semantic, and the tools to build that future are at your fingertips.

Combine the Power of Semantic Search with the Simplicity of Graft

We've explored the world of text embeddings and seen how they can enable more intelligent semantic search experiences. But what about actually implementing these models in a production system at scale?

While it's possible to build your own infrastructure for embedding models, search indexing, and more, this approach has downsides:

• Time consuming to stitch together different components like APIs, databases, frontends.
• Ongoing maintenance overhead as things need updating, monitoring, troubleshooting.
• Scaling challenges as data volumes and queries spike.
• Replicating state-of-the-art techniques requires significant ML expertise.
• Difficult to iterate quickly and test different approaches.

That's where a no-code AI Platform like Graft comes in. Get an enterprise-grade semantic search engine out-of-the-box, letting you skip the headaches of piecing it together.

With Graft, you get:

• A unified AI platform with embeddings, indexing, search, and monitoring.
• Instant access to state-of-the-art semantic models like GTE-Base.
• Easy front end integration.
• Built-in scalability and zero maintenance.
• Rapid experimentation to find the optimal model.
• Continuous improvements as new data and models come out.

By leveraging Graft's end-to-end semantic search solution, you can focus your time on high-value problems vs. building infrastructure. The platform empowers anyone to harness the magic of semantic search for their applications with ease.

Ready to elevate your search? Experience the power of Graft!