Embeddings & Semantic Search

What Is Embeddings & Semantic Search?

Embeddings are a way to turn something “human” (text, images, audio) into something computers can compare: a vector (a list of numbers). If two pieces of content are similar in meaning, their vectors tend to end up close together in this numeric space.

Analogy: imagine every sentence is a pin on a giant map. Sentences about pets cluster in one area, sentences about finance in another, and “how to bake sourdough” lives somewhere between “cooking” and “chemistry experiments gone right.” An embedding model is the cartographer that decides where each pin goes.

Semantic search uses embeddings to search by meaning, not exact wording. Instead of matching “refund policy” only when those exact words appear, semantic search can also find “return rules,” “money back,” or “can I get a refund?”—because the underlying meaning lands nearby in vector space.

Technically:

• An embedding model maps input data to a high-dimensional vector.
• Semantic search embeds the query, then retrieves the most similar embedded documents/chunks using a similarity metric (often cosine similarity or dot product).

Why Does It Matter?

Because keyword search is literal, and humans are not.

Semantic search helps when:

• People use different words for the same thing (“bug” vs “issue” vs “unexpected behavior”).
• The data is messy (support tickets, chat logs, notes, PDFs).
• You want “closest meaning,” not “exact phrase.”

Real-world impact:

• Better search UX: fewer “no results” moments.
• Better RAG: retrieval is the make-or-break step in Retrieval-Augmented Generation. If you retrieve the wrong chunks, the LLM is forced to guess.
• Better organization: clustering and deduplication become easier because “similarity” becomes a measurable thing.

It’s one of those quietly powerful ideas: once you can compare meaning numerically, a lot of workflows become simpler.

How It Works

Here’s the core mechanism in a clean sequence you can visualize and implement.

1) Choose what you’re embedding

You can embed:

• single words
• sentences
• paragraphs
• document chunks (common for RAG)
• product descriptions, tickets, code snippets, etc.

Rule of thumb: embed units you might want to retrieve. For a knowledge hub, that’s usually chunks (e.g., 200–800 tokens) rather than whole documents.

2) Convert text to vectors (create embeddings)

You run each chunk through an embedding model:

• Input: "Refunds are available within 30 days for unopened items."
• Output: a vector like [0.02, -0.11, 0.44, ...]

These vectors can be hundreds or thousands of numbers long. You don’t interpret the individual numbers. What matters is geometry: which vectors are close.

3) Store embeddings in an index

To search fast, you store vectors in a structure built for “nearest neighbor” lookup (often called a vector index or vector database). You keep:

• the vector
• the original chunk text
• metadata (document title, URL, date, section, tags, permissions)

Metadata is crucial for filtering (“only docs from Team X” or “only updated after 2024”).

4) Embed the query the same way

When a user searches:

• Query: “Can I return an opened item?”
• Embed query → query vector

Key principle: query and documents must be embedded with the same model (or a compatible pair), otherwise “distance” becomes meaningless.

5) Compute similarity and retrieve top-k

You score how close the query vector is to each document vector. A common metric is cosine similarity:

$\cos(\theta)=\frac{\mathbf{q}\cdot \mathbf{d}}{|\mathbf{q}||\mathbf{d}|}$

Intuition:

• The dot product $\mathbf{q}\cdot \mathbf{d}$ measures alignment.
• Dividing by magnitudes makes it about direction, not length.
• High cosine similarity means the vectors “point the same way,” which often corresponds to similar meaning.

Then you return the top results (top-k), such as the best 5–20 chunks.

6) (Often) rerank or hybridize for higher quality

In many production systems, the first retrieval step is “fast and pretty good,” then a second step improves precision:

• Reranking: a stronger model re-scores the top candidates.
• Hybrid search: combine semantic search (embeddings) with lexical search (keywords/BM25). This helps with exact names, IDs, and rare terms.

A tiny concrete example

Documents contain:

1. “Unopened items: refunds within 30 days.”
2. “Opened items: eligible for store credit only.”

User query: “Can I get my money back if I opened it?”

A good semantic search returns (2) even if the doc never says “money back,” because “refund” and “money back” are semantically close. That retrieval step is what makes the final system feel smart.

Key Terminology

• Embedding: A vector representation of content designed so similar meaning lands near each other in vector space.
• Vector space: The geometric space where embeddings live; “closeness” corresponds to similarity.
• Similarity metric: A way to score closeness (cosine similarity, dot product, Euclidean distance).
• Nearest neighbors (top-k): The most similar vectors to a query vector—your search results.
• Reranking / hybrid retrieval: Techniques to improve relevance after initial retrieval (semantic + lexical often wins).

Real-World Applications

• RAG retrieval for knowledge assistants: Find the best chunks from docs, then have an LLM answer using them.
• Customer support search: Suggest similar past tickets and solutions even when wording differs.
• Recommendations: “Users who liked this also liked…” based on embedding similarity of items or user interactions.
• Clustering and topic discovery: Group documents by meaning to create taxonomies or “related content” sections.
• Deduplication and near-duplicate detection: Identify repeated or paraphrased content across a corpus.

Common Misconceptions

1. “Embeddings are a perfect meaning detector.” They’re a learned approximation. They can be excellent, but they’re not truth. They capture patterns from training data and can miss nuance, sarcasm, or domain-specific meanings.

2. “Semantic search replaces keyword search.” Not always. IDs, exact product names, error codes, and legal phrasing often benefit from lexical matching. Hybrid approaches are common because each method covers the other’s blind spots.

3. “If retrieval is wrong, the model will still figure it out.” Usually not. RAG systems are only as good as what they retrieve. Bad retrieval leads to confident nonsense, because the generator tries to be helpful even when context is missing.

Further Reading

• Efficient Estimation of Word Representations in Vector Space (Mikolov et al.) — classic foundation for word embeddings.
• Sentence-BERT (Reimers & Gurevych) — sentence embeddings designed for similarity search.
• OpenAI documentation: Embeddings guide — practical overview and common use cases.