Semantic Search and Vector Database

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Why do we need Vector DB?

Let’s start with a common scenario: You’re searching for “affordable laptop with a good battery life” on an e-commerce site. A keyword-based search engine might return results that include the words “affordable,” “laptop,” and “battery life.” But it might also miss products that are described as “budget-friendly” or “long-lasting battery,” simply because they don’t match the exact keywords. The result? A frustrating search experience where you might not find what you’re really looking for, even though the perfect product is just a few clicks away.

Keyword-based search systems struggle with:

  • Synonyms: Different words can mean the same thing (e.g., “cheap” and “affordable”).
  • Polysemy: The same word can have multiple meanings depending on context (e.g., “bank” as in a financial institution vs. “bank” as in the side of a river).
  • Contextual Understanding: Words and phrases can have different meanings based on the context in which they’re used.

These limitations highlight the need for a more advanced approach to search—one that can understand and process the meaning behind words, rather than just matching them with predefined keywords.

Enter Semantic Search: The Need for Understanding

Semantic search aims to address these issues by focusing on the intent and contextual meaning of the query. Instead of just looking for exact keyword matches, semantic search systems try to understand what the user is really asking for. This involves analyzing the relationships between words, understanding context, and even considering the user’s past behavior to provide more relevant results.

For example, if you search for “best budget laptop,” a semantic search engine would return results that include laptops described as “cheap,” “affordable,” or “cost-effective,” even if those exact words weren’t in your query.

To achieve this level of understanding, semantic search engines rely on machine learning models that can transform words, phrases, and even entire documents into vector representations—essentially, mathematical objects that capture the meaning of the data in a multi-dimensional space. And this is where vector databases come into play.

How does Vector Databases work? 

Let’s dive deeper into how vector databases can revolutionize semantic search with a practical example. Imagine you're on a job search platform, and you type in the query, "remote software engineer jobs with Python." Instead of relying on exact keyword matches, a vector database can understand the intent and meaning behind your words, ensuring you find the most relevant job listings, even if the exact words you typed aren't present.

Step 1: Creating Embeddings for Job Descriptions

To understand how this works, let’s start by looking at five different job descriptions:

  1. Job 1: "Work-from-home developer needed for a project requiring expertise in Python."
  2. Job 2: "Hiring a software engineer with experience in remote coding."
  3. Job 3: "Looking for a programmer proficient in Python, with options to telecommute."
  4. Job 4: "Python developer wanted for a remote position at a tech startup."
  5. Job 5: "Join our team as a software engineer working remotely, with Python experience required."

Each of these job descriptions has a different wording but ultimately refers to a similar role—someone who works remotely, is skilled in Python, and is a developer or software engineer. To make these descriptions searchable in a semantic search system, we first need to convert them into vector embeddings.

What Are Vector Embeddings?

Vector embeddings are numerical representations of data points in a high-dimensional space. Each vector captures the essence of the job description, including the key concepts and relationships between words. For example:

  • Job 1 Embedding: [0.45, 0.78, 0.22, 0.95, 0.11, ...]
  • Job 2 Embedding: [0.40, 0.82, 0.19, 0.91, 0.15, ...]
  • Job 3 Embedding: [0.46, 0.77, 0.23, 0.94, 0.13, ...]
  • Job 4 Embedding: [0.48, 0.79, 0.21, 0.92, 0.12, ...]
  • Job 5 Embedding: [0.44, 0.80, 0.20, 0.93, 0.14, ...]

Each of these vectors is a simplified representation of the job description. The vector values (which would typically have hundreds of dimensions) capture different aspects of the job, such as the role (e.g., "developer" or "engineer"), the location (e.g., "remote" or "work-from-home"), and the required skills (e.g., "Python").

Step 2: Creating an Embedding for the User Query

Now, let’s look at your search query: "remote software engineer jobs with Python."

Just like the job descriptions, this query is also transformed into a vector embedding:

  • Query Embedding: [0.47, 0.81, 0.22, 0.94, 0.13, ...]

This vector represents the meaning behind your search, capturing the essence of what you’re looking for—remote work, software engineering, and Python expertise.

Step 3: Measuring Similarity Between the Query and Job Descriptions

The power of a vector database lies in its ability to compare these embeddings and find the ones that are most similar. Similarity between vectors can be measured using metrics like cosine similarity, which calculates the angle between two vectors in the high-dimensional space. The closer the vectors are, the more similar the meanings of the job description and the query.

Here’s how the similarity might look:

  • Similarity with Job 1: 0.98 (Highly Similar)
  • Similarity with Job 2: 0.85 (Moderately Similar)
  • Similarity with Job 3: 0.95 (Highly Similar)
  • Similarity with Job 4: 0.97 (Highly Similar)
  • Similarity with Job 5: 0.99 (Highly Similar)

In this case, the vector database identifies that Jobs 1, 3, 4, and 5 are highly similar to your query, even though they don’t use the exact words "remote software engineer jobs with Python." Instead, the system understands that "work-from-home developer" or "Python developer" with "remote" options are close matches to what you’re looking for.

Step 4: Returning the Most Relevant Results

Finally, the vector database ranks these job descriptions based on their similarity to your query and returns the most relevant ones at the top of your search results. This way, you get to see job listings that truly match your intent, rather than just those with matching keywords.



Example :



How It Works:

  1. Model Initialization: We use a pre-trained model from sentence-transformers to create embeddings.
  2. Job Descriptions and Query: We have a list of job descriptions and a user query.
  3. Embedding Creation: Both the job descriptions and the query are converted into embeddings.
  4. Cosine Similarity Calculation: We calculate cosine similarity between the query embedding and each job description embedding.
  5. Output: The job description with the highest similarity to the query is printed.

Deep Dive: How Advanced Techniques Improve Vector Embeddings

Vector embeddings can be further enhanced using techniques like attention mechanisms and positional encoding.

  • Attention Mechanisms: By focusing on the most relevant parts of the input data, attention mechanisms ensure that the vector captures the most important features of the text. For example, in a job description, the model might pay more attention to the phrases "remote work" and "Python developer" while creating the embedding, ensuring that these critical aspects are reflected in the final vector.

  • Positional Encoding: Positional encoding adds information about the order of words in the text, which can be crucial for understanding context. For example, in the query "Python developer with remote work experience," the order of words matters, and positional encoding helps the model capture this context, leading to more accurate embeddings.

Conclusion: Making Search More Intelligent with Vector Databases

By transforming both queries and data into vectors and measuring their similarity, vector databases make it possible to understand the meaning behind your search and deliver results that are relevant to your needs. Whether it’s job search, product recommendations, or any other application where understanding context and intent is crucial, vector databases are the backbone of modern, intelligent search systems.

So, the next time you find exactly what you’re looking for with a single search, remember that a powerful vector database is working behind the scenes, connecting the dots in a way that traditional search engines simply can’t.


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