# What is Semantic Search (opens new window)?
In the realm of search technology, semantic search stands out as a game-changer. It goes beyond mere keyword matching to understand the intent and context behind a query. Unlike traditional methods that rely solely on specific words, semantic search delves into the deeper meaning of the search terms.
# Why Semantic Search Matters
Imagine searching for "best pizza near me." With semantic search, results won't just list restaurants with those exact words; it will consider factors like your location, preferences, and even reviews to provide tailored suggestions. This level of personalization and relevance is why semantic search is crucial in today's data-driven world.
In everyday scenarios, like looking up medical information or finding local services, semantic search ensures you get precisely what you need without sifting through irrelevant results.
Announcement: MyScaleDB, the Revolutionary SQL vector database, Goes Open-Source (opens new window).
# The Role of Embedding Models (opens new window) in Semantic Search
In the realm of semantic search, embedding models play a pivotal role in transforming how search engines understand and process information. Let's delve into the core aspects that make these models indispensable.
# What are Embedding Models?
Embedding models function as mathematical representations that capture the essence of words or phrases in a continuous vector space. This transformation enables machines to interpret language nuances and relationships more effectively. By mapping words to vectors, these models can grasp semantic similarities and differences, enhancing the depth of understanding within search queries.
# Semantic Embeddings vs. Search Embeddings
Semantic Embeddings and Search Embeddings (opens new window) both transform text into meaningful vector representations, but they serve different purposes and focus on distinct aspects of text processing.
Semantic Embeddings: These embeddings capture the semantic similarity between texts. They understand how closely different words or phrases are related in terms of meaning. Typically, semantic embeddings are used in natural language understanding tasks such as sentiment analysis, text classification, and language translation. These embeddings are often generated using language models like BERT or GPT, which are good at grasping deep contextual relationships within text.
Search Embeddings: On the other hand, search embeddings are specifically designed to efficiently retrieve the most relevant pieces of text from a wide range of data based on user queries. These embeddings are optimized to find the best possible match between user queries and available documents. Their primary application is in information retrieval systems, such as search engines and recommendation systems, where they are trained with techniques that focus on query-document relevance rather than solely on semantic closeness.
Both are extremely useful in their respective areas but mostly they are used in applications where accurate and efficient text processing is required.
# The Connection Between Embedding Models and Semantic Search
The integration of embedding models into search algorithms significantly improves the accuracy and relevance of search results. As we have discussed, traditional search methods rely on matching keywords directly against keywords found in documents. However, when embedding models are integrated with search, both user queries and documents are converted into embeddings. This conversion makes it easier for machines to understand the text better.
In this way, the system can effectively compare the embeddings of the user's query with those of the documents. This comparison is based on semantic similarity rather than just a keyword matching. As a result, the search engine can identify documents that are semantically related to the query, even if the exact keywords are not present. This method ensures that the search results are not only more aligned with the user's intent but also more contextually relevant.
So, selecting an optimal embedding model is crucial because it significantly impacts the precision and quality of retrieved information. This careful selection ensures that search results are not only accurate but also highly relevant to user queries.
Related Blog: MyScale now supports Powerful Full-Text and Hybrid Search (opens new window)
# Comparing the Best Embedding Models for Semantic Search
When evaluating the best embedding models for semantic search, it's essential to consider specific criteria that can impact performance. The key factors for comparison typically revolve around accuracy, speed, and versatility.
Accuracy: This crucial metric assesses how precisely an embedding model captures the semantic relationships between words or phrases. Higher accuracy implies a better understanding of language nuances, leading to more relevant search results.
Speed: The speed of an embedding model determines how quickly it can process text into vector representations. Faster models can enhance the user experience by enabling search systems to operate more swiftly, delivering quick and accurate search outcomes.
Versatility: A versatile embedding model can adapt to various domains, languages, and data types. Versatility ensures that the model remains effective across different contexts and applications, catering to diverse user needs.
# A Look at the Contenders
There are so many embedding models available in the market righ now, but we have picked some of the leading models.
# Cohere Embed v3 (opens new window)
Cohere Embed v3 is a cutting-edge embedding model designed for enhancing semantic search and generative AI. This model has shown very good results in various benchmarks like the Massive Text Embedding Benchmark (MTEB) (opens new window) and BEIR (opens new window), proving it as an high performance embedding model across different tasks and domains. Some of it's key features are:
Compression-Aware Training: This approach optimizes efficiency without sacrificing quality and allows the model to handle billions of embeddings without significant infrastructure costs.
Multilingual Support: It supports over 100 languages, making it highly versatile for cross-language searches.
High Performance: Particularly effective in noisy real-world data scenarios, ranking high-quality documents by evaluating content quality and relevance
# Usage
To use Cohere embedding model in in your application, you first need to install Cohere using pip install -U cohere. After that, you can get the embeddings of your docs like this:
import cohere
import numpy as np
cohere_key = "{YOUR_COHERE_API_KEY}" #Get your API key from www.cohere.com
co = cohere.Client(cohere_key)
docs = ["MyScaleDB is a SQL vector database",
"It has outperformed specialized vector databases in terms of performance.",
"It has been especially designed for large scale AI applications."]
#Encode your documents with input type 'search_document'
doc_emb = co.embed(texts=docs, input_type="search_document", model="embed-english-v3.0").embeddings
doc_emb = np.asarray(doc_emb)
# OpenAI’s Embedding Models (opens new window)
OpenAI has recently introduced their new advanced embedding models (opens new window), including text-embedding-3-small and text-embedding-3-large. These models offer better performance and are more cost-efficient.
Performance: The
text-embedding-3-largemodel supports embeddings with up to 3072 dimensions. This allows for detailed and nuanced text representation. It has also outperformed previous models on benchmarks like MIRACL and MTEB.Cost-Effectiveness: The previous models of OpenAI like
text-embedding-ada-002had some pricing issues because it was a bit on the expensive side. But the newer modeltext-embedding-3-smallis almost five times more cost-effective compared to its predecessor,text-embedding-ada-002.
# Usage
To use OpenAI embedding models in your application, you first need to install OpenAI using pip install -U openai.After that, you can get the embeddings of your docs like this:
from openai import OpenAI
client = OpenAI(openai_api_key="your-api-key-here")
def get_embedding(text, model="text-embedding-3-small"):
text = text.replace("\n", " ")
return client.embeddings.create(input = [text], model=model).data[0].embedding
embeddings=get_embedding("MyScaleDB is a SQL vector database.")
# Mistral (opens new window)
The Mistral family includes some of the high-performing, open-source Large Language Models including an embedding model, E5-mistral-7b-instruct. This model is initialized from Mistral-7B-v0.1 and fine-tuned on a mixture of multilingual datasets. As a result, it has some multilingual capability.
Instruction Following: Specifically designed to perform better in tasks that require understanding and following complex instructions, making it ideal for applications in education and interactive AI systems.
Large-Scale Training: Pre-trained on extensive web-scale data, fine-tuned for a variety of NLP tasks to ensure robust and reliable performance.
High Efficiency: Optimized for efficient processing, capable of handling large datasets and delivering high-quality embeddings across diverse use cases.
Selecting the best embedding model for semantic search optimization involves evaluating each model's strengths against specific task requirements and objectives. Each model offers unique capabilities that suit different use cases within semantic search applications.
# Usage
To use OpenAI embedding models in your application, you first need to install OpenAI using pip install torch transformers.After that, you can get the embeddings of your docs like this:
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct')
model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct')
inputs = tokenizer("Your text here", return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
embeddings = outputs.last_hidden_state
# My Top Picks and Why
After exploring all three embedding models for semantic search, they all have some distinct strengths and applications. Let's see how.
# The Winner for Accuracy
Among the contenders, Cohere Embed v3 is the best choice for accuracy in many applications. Its design captures detailed meanings accurately, ensuring search results are both relevant and high-quality. Cohere Embed v3 also handles multilingual queries and noisy data well, making it reliable for tasks that need high accuracy.
# The Best for Speed
In terms of speed optimization, OpenAI’s Embedding Models lead with their efficient embedding capabilities. Models like text-embedding-3-small provides quick processing speeds without compromising result quality. These models' high-dimensional embeddings and cost-effectiveness make them ideal for scenarios that require fast and affordable search outcomes.
# The Most Versatile Option
When versatility is essential, Mistral's E5-mistral-7b-instruct is the most adaptable choice across different domains and languages. Its instruction-following design and large-scale training ensure robust performance across various NLP tasks. Whether handling multilingual queries or complex instructions, E5-mistral-7b-instruct adjusts seamlessly to different needs, making it a versatile solution for a wide range of semantic search applications.
# MyScaleDB: The SQL Vector Database
As we wrap up our discussion on top embedding models for semantic search, let's introduce a search engine in this field: the MyScale (opens new window) SQL vector database. This advanced database works perfectly with embedding models, making it easier to store and retrieve vector data efficiently. MyScale stands out with its Multi-Scale Tree Graph (MSTG) technology, which outperforms other specialized vector databases (opens new window). It’s built for quick vector operations that are crucial for fast, real-time semantic search applications. Plus, MyScale is keen on making this technology accessible by giving every new user 5 million free vector storage, making it a key player in enhancing data-driven, AI-powered search platforms.
