Semantic Search
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What is Semantic Search?
Semantic search is a search technique that uses natural language processing (NLP) and machine learning (ML) to understand the context and meaning behind a user's search query. Here are some key terms related to semantic search:
Natural Language Processing (NLP)
A branch of artificial intelligence (AI) that focuses on enabling computers to understand and process human language. NLP is used in semantic search to help computers understand the meaning behind a user's search query.
Machine Learning (ML)
A type of AI that involves training computer algorithms to learn from data and improve their performance over time. ML is used in semantic search to help computers understand the context and intent of a user's search query.
Semantic Understanding
Semantic understanding is the ability of a computer to understand the meaning and context behind a user's search query. Semantic understanding is a crucial component of semantic search.
What is a Semantic Search Engine?
A Semantic Search Engine (sometimes called a Vector Database) is specifically designed to conduct a semantic similarity search. Semantic Search Engines will use a specific index algorithm to build an index of a set of vector embeddings. Milvus has 11 different Index options, but most Semantic Search Engines only have one (typically HNSW). With the Index and similarity metrics, users can query for similar items with the Semantic Search Engine.
How to Implement a Semantic Search Engine?
There are several options to implement Semantic Search. Here are a few options
- Python Semantic Search Engine. You can build a custom Semantic Search on your own corpus of data using Python, a machine model, and a Vector Index Algorithm like FAISS, HNSW, or even ANNOY. Here is a tutorial to walk you through how to implement Semantic Search with Facebook AI Similarity Search (FAISS).
- Traditional keyword based Search Engines like ElasticSearch also have added Vector Search capabilities. The benefit is that you can easily add vector search to a solution already using Elasticsearch.
- Popular database solutions like PostgreSQL have added extensions like Pgvector to support vector search. Here is a tutorial to walk you through how to get started using Pgvector.
- Vector Databases Another great option is to use a Vector Database to implement Semantic Search. With a vector database, you store and index the vector embeddings that you generate with your chosen machine learning algorithm. With most vector databases, you use HSNW to generate the index, with Milvus, you can choose from 11 different index types to best fit your use case. When you start your search, you will convert to a vector embedding then do a query against your dataset to find the most similar items.
Benefits of a Semantic Search Engine
There are several advantages to performing a semantic search. One benefit is that semantic search enables you to search for concepts or ideas instead of specific words or phrases, eliminating the need for guesswork in your search queries. In addition, Semantic search can better understand query intent, and as a result, it can generate search results that are more relevant to the user. In this case study from Lucidworks, you can learn how to build a semantic search solution to see for yourself how this can make your solution even better.
Keyword Search vs Semantic Search
Typically, keyword search utilizes tools like Elasticsearch to search and rank queried items. When a user conducts a search, Elasticsearch is queried to rank the outcomes based on the query. Each word in Elasticsearch is stored as a sequence of numbers representing ASCII (or UTF) codes for each letter. Elasticsearch builds an inverted index to identify which documents contain words from the user query quickly. It then uses various scoring algorithms to find the best match among these documents, considering word frequency and proximity factors. However, these scoring algorithms do not consider the meaning of the words but instead focus on their occurrence and proximity. While ASCII representation can convey semantics, there is currently no efficient algorithm for computers to compare the meaning of ASCII-encoded words to search results that are more relevant to the user.
On the other hand, Semantic Search converts unstructured data (emails, images, videos, audio files, etc) into vector embeddings using Machine Learning models to represent data points as vectors in a high-dimensional space. The next step is to index these embeddings using one of many available algorithms (HNSW, FAISS, ANNOY, etc.). Then, you can conduct a nearest-neighbor search to find items or data points similar or closely related to a given query vector. Unlike a keyword search, semantic search aims to efficiently search and retrieve the most relevant vectors that are similar or nearest to a query vector.
Lexical Search vs Semantic Search
These terms refer to different facets of language. "Semantic" relates to meaning, while "lexical" refers to vocabulary.
- Semantic: It's common for films to be based on popular books, and often, people judge these movies by how well they line up with the images that pop into their heads while reading.
- Semantic refers to understanding the relationships between words and how language interprets meaning.
- It's all about digging into the deep meanings and connections that words or parts of a sentence have with each other.
- In semantics, one might explore how the word "bank" can refer to a financial institution or the side of a river, depending on the context.
- Lexical: Lexical relates to the vocabulary or words of a language. It's all about digging into single words - their shapes, what they mean, and how we throw them around when we talk.
- Lexical analysis examines the structure, usage, spelling, pronunciation, and meaning of individual words within a language.
- Example: In lexical analysis, one might examine the variations of a word (e.g., "run," "ran," "running") and how these variations contribute to the overall meaning.
Let's break this down. Think of "semantic" as the big picture guru - it tackles language in a way similar to understanding the story behind an art piece. Now, "lexical"? That's your detail detective; it zeroes in on every word like each one is a unique brushstroke that adds depth to the masterpiece. This dance between semantics and lexical makes us savvy conversationalists and powers cool tech advancements such as natural language processing.
Semantic Search vs Cognitive Search
Semantic search and cognitive search are both advanced search technologies that aim to enhance the accuracy and relevance of search results, but they differ in their approaches and capabilities:
- Semantic Search:
- Focus: Semantic search focuses on understanding the meaning of words and the intent behind a search query.
- Technology: It utilizes natural language processing (NLP) and machine learning algorithms to comprehend context, semantics, and user intent.
- Capabilities: Semantic search goes beyond keyword matching and considers the relationships between words, synonyms, and the overall context of the query. Its goal is to provide results that hit the mark by fully grasping what you're saying and considering all its nuances.
- Cognitive Search:
- Focus: Cognitive search extends beyond semantic understanding to incorporate additional cognitive capabilities, often involving artificial intelligence (AI) technologies.
- Technology: AI-like machine learning and knowledge graphs help cognitive search understand language and connections better over time.
- Capabilities: Cognitive search understands language semantics and can learn and adapt over time. It's like a brainiac on steroids - it pulls in info from all over, breaks down the messy stuff, and spits out some pretty intelligent takeaways.
Cognitive search is the big picture, and semantic search is just one piece of that puzzle. Think of cognitive search as a high-tech Sherlock Holmes, using AI and other brainy skills to crack the code of intricate questions, juggle various data types, and serve richer knowledge nuggets. While semantic search is all about understanding language, cognitive search takes it up a notch by grasping not just the info but also how users interact with it.
Does Zilliz Offer Semantic Search Tools?
Zilliz Cloud is a Vector Database with Open-source Milvus at its heart. A Vector Database’s core functionality is to provide semantic search capabilities. In addition, a purpose-built vector database like Zilliz Cloud provides the following advanced capabilities:
- CRUD support, data consistency, and filter search
- System availability with strong data persistency and better disaster recovery
- System scalability with load balancing support, a distributed architecture that separates computing and storage, and better usability
- RBAC with support for multi-tenant SDKs of various programming languages (Python, Javascript, C, Ruby, Go) and a monitoring system.