Similarity Search

What Is Similarity Search?

Similarity search retrieves items whose vector representations are most similar to a query vector. The query might come from a user's question, a document, an image, or another piece of content.

Why Embeddings Are Needed

A computer can compare strings directly, but semantic retrieval needs a representation that captures meaning. An embedding model converts content into numerical vectors that can be compared mathematically.

Query Vector and Stored Vectors

Similarity search compares one query vector with many stored vectors. Each stored vector normally has an ID and may have metadata such as a category, language, product ID, or document source.

For example, a search may compare one query against 100,000 document vectors and return only the best 5 matches. A vector database or vector index is responsible for making that retrieval efficient.

How Similarity Is Measured

Different systems use different metrics. The important point is that the ranking rule must match how your vectors are represented and how the index is configured.

Top-k Search

Top-k means returning the k highest-ranked candidates after comparing the query with the searchable vectors.

If k=3, the application receives the first three results. Choosing k is a retrieval-quality decision: too few results can miss useful context, while too many can add irrelevant information.

Python: Simple Cosine Similarity

Before using a vector database, it helps to understand the basic operation with plain Python. This example ranks a few small vectors against a query vector.

import math

def cosine_similarity(a, b):
    dot = sum(x * y for x, y in zip(a, b))
    norm_a = math.sqrt(sum(x * x for x in a))
    norm_b = math.sqrt(sum(y * y for y in b))

    if norm_a == 0 or norm_b == 0:
        return 0.0

    return dot / (norm_a * norm_b)

query = [1.0, 0.8, 0.1]

documents = {
    "doc-1": [0.9, 0.7, 0.2],
    "doc-2": [0.1, 0.2, 0.9],
    "doc-3": [0.8, 0.6, 0.1],
}

scores = [
    (doc_id, cosine_similarity(query, vector))
    for doc_id, vector in documents.items()
]

for doc_id, score in sorted(scores, key=lambda item: item[1], reverse=True):
    print(doc_id, round(score, 4))

Ranking Results

The search engine calculates a score or distance for each candidate and sorts the candidates according to the metric. Your application then takes the best results.

Similarity Search with Metadata Filters

Semantic similarity and metadata filtering solve different problems. Similarity finds semantically close records; filters enforce structured constraints.

For example, a support system could search semantically for "cancel my order" while filtering to language="en" and region="UK". This reduces irrelevant candidates before the application uses the results.

Similarity Search in a Vector Database

In production, the vector index is usually not a Python list. A vector database such as FAISS-based infrastructure, Qdrant, or Pinecone provides indexing and retrieval capabilities for larger collections.

Approximate vs Exact Search

Exact search compares the query with every relevant vector. This is simple and can be highly accurate, but scanning a very large collection can become expensive.

Approximate nearest-neighbor (ANN) methods trade some retrieval behavior for speed and scale. The right index depends on dataset size, latency targets, memory, and recall requirements.

Similarity Search for Semantic Search

A semantic search pipeline typically embeds both stored content and the incoming query. The query vector is then used to retrieve the nearest content vectors.

Similarity Search in RAG

Retrieval-augmented generation (RAG) uses similarity search to select external context before an LLM generates an answer.

If retrieval returns poor chunks, the LLM may receive weak evidence. Improving RAG therefore often requires improving chunking, embeddings, metadata, search parameters, and evaluation—not only changing the LLM prompt.

Similarity Thresholds

Top-k always returns candidates when candidates exist, but not every candidate is necessarily relevant. Some applications therefore combine ranking with a relevance threshold or a second-stage reranker.

Common Mistakes

How to Evaluate Similarity Search

Good retrieval should be measured using a representative test set. For each query, define which documents or chunks are relevant and check what the search system returns.