Vectors & Semantic Search

In the last module, you learned about the importance of grounding to improve LLM accuracy and the concept of Retrieval Augmented Generation (RAG). RAG involves providing additional information to help the LLM form a response.

One of the challenges of RAG is understanding what the user is asking for and surfacing the correct information to pass to the LLM.

In this lesson, you will learn about semantic search and how vector indexes can help you implement it in Neo4j.

Vectors and Semantic Search

Semantic search aims to understand search phrases' intent and contextual meaning, rather than focusing on individual keywords.

Traditional keyword search often depends on exact-match keywords or proximity-based algorithms that find similar words.

For example, if you input "apple" in a traditional search, you might predominantly get results about the fruit.

However, in a semantic search, the engine tries to gauge the context: Are you searching about the fruit, the tech company, or something else?

The results are tailored based on the term and the perceived intent.

Vectors and embeddings

In natural language processing (NLP) and machine learning, numerical representations (known as vectors) represent words and phrases.

Each dimension in a vector can represent a particular semantic aspect of the word or phrase. When multiple dimensions are combined, they can convey the overall meaning of the word or phrase.

A vector will not directly encode tangible attributes like color, taste, or shape. Instead, the model will generate a list of numerical values that closely align the word with related words such as health, nutrition, and wellness.

When applied in a search context, the vector for "apple" can be compared to the vectors for other words or phrases to determine the most relevant results.

You can create vectors in various ways, but one of the most common methods is to use a large language model. These vectors are known as embeddings. With advanced models, these embeddings also contain contextual information.

For example, the embeddings for the word "apple" are 0.0077788467, -0.02306925, -0.007360777, -0.027743412, -0.0045747845, 0.01289164, -0.021863015, -0.008587573, 0.01892967, -0.029854324, -0.0027962727, 0.020108491, -0.004530236, 0.009129008, … and so on.

Reveal the completed embeddings for the word "apple"!

`0.0077788467, -0.02306925, -0.007360777, -0.027743412, -0.0045747845, 0.01289164, -0.021863015, -0.008587573, 0.01892967, -0.029854324, -0.0027962727, 0.020108491, -0.004530236, 0.009129008, -0.021451797, 0.002030382, 0.030813828, 9.744976e-05, 0.0019172973, -0.02568733, -0.020985752, -0.008066699, 0.02134214, -0.01222684, 0.0009980568, 0.005105939, 0.009999417, -0.000107408916, 0.015845545, -0.012980737, 0.020574536, -0.016160812, -0.018518453, 0.005263572, -0.019286057, -0.009293495, -0.012096621, -0.008854863, -0.005753605, -0.006157968, 0.010540851, 0.007724018, -0.0065554776, 0.00052944134, -0.023453051, 0.011089141, -0.021671113, -0.00061425474, -0.012754567, 0.015489157, -0.0054520466, -0.0020355221, -0.015050527, -0.0052944133, -0.0028082666, 0.0027431573, -0.019450543, 0.0063807103, -0.010725899, 0.0049243183, 0.005266999, 0.01513277, -0.027921606, 0.0055754115, -0.009183837, 0.00380718, -0.013624975, -0.0084710615, 0.012905347, 0.015667351, 0.033363372, 0.013268588, 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The vector for a word can change based on its surrounding context. For instance, the word bank will have a different vector in river bank than in savings bank.

Semantic search systems can use these contextual embeddings to understand user intent.

Creating Vector Embeddings

LLM providers typically expose API endpoints that convert a chunk of text into a vector embedding. Depending on the provider, the shape and size of the vector may differ.

For example, OpenAI’s text-embedding-ada-002 embedding model converts text into a vector of 1,536 dimensions.

You can use the distance or angle between vectors to gauge the semantic similarity between words or phrases.

A 3 dimensional chart illustrating the distance between vectors. The vectors are for the words "apple" and "fruit"

Words with similar meanings or contexts will have vectors that are close together, while unrelated words will be farther apart.

This principle is employed in semantic search to find contextually relevant results for a user’s query.

A semantic search involves the following steps:

The user submits a query.
The system creates a vector representation (embedding) of the query.
The system compares the query vector to the vectors of the indexed data.
The results are scored based on their similarity.
The system returns the most relevant results to the user.

Vectors can represent more than just words. They can also represent entire documents, images, audio, or other data types. They are instrumental in the operation of many other machine-learning tasks.

Vectors and Neo4j

Vectors are the backbone of semantic search. They enable systems to understand and represent the complex, multi-dimensional nature of language, context, and meaning.

Neo4j supports vector indexes and querying, allowing you to search for nodes based on their vector representations.

In the next lesson, you will learn how to use vectors to implement semantic search in Neo4j.

Check Your Understanding

1. Semantic vs. Traditional Search

In contrast to traditional search that may focus on exact keyword matches, what does semantic search emphasize?

❏ Matching images to text.
✓ Understanding intent and contextual meaning of search phrases.
❏ Predicting the user’s next search term.
❏ Relying solely on the popularity of search terms.

Hint

Traditional search often returns results based on exact keywords, while another type of search tries to gauge the context and intent behind the search.

Solution

The correct answer is Understanding intent and contextual meaning of search phrases.

2. Role of Vectors in Semantic Search

What role do vectors play in semantic search systems?

❏ They determine which advertisements to show alongside results.
❏ They help in adjusting the visual appearance of search results.
❏ They predict the likelihood of a user clicking on a result.
✓ They represent words or phrases numerically, aiding in understanding user intent.

Hint

In NLP, words are converted into numerical forms to capture their semantic essence.

Solution

Vectors represent words or phrases numerically, aiding in understanding user intent.

Lesson Summary

In this lesson, you learned how semantic search differs from traditional keyword search. Vectors, representing data numerically, facilitate this advanced search mechanism and are integral to many machine learning algorithms like LLMs.

In the next lesson, you will learn how to use vector indexes in Neo4j and implement semantic search.

Neo4j & LLM Fundamentals

Neo4j and Generative AI