Proximity search: Improve relevance & deliver smarter results

If you’ve ever wondered why specific results rank higher even when they don’t match a query exactly, you’ve encountered proximity search in action.

Modern search engines don’t just check whether terms exist in a document – they evaluate how close those terms appear, in what order, and under which constraints.

Proximity search is the mechanism that bridges the gap between simple keyword matching and semantic retrieval.

Here is what we’ll cover in our guide:

• The definition of proximity search and its role in improving search ranking.
• How it works based on distance, word order, and other specified distance rules.
• Why you can’t overlook the importance of proximity search for user satisfaction and result quality.
• The most common proximity operators and how they differ from phrase search.
• Practical application of proximity search for Google, PubMed, and Boolean searches.
• A step-by-step guide on how you can implement a proximity search algorithm.
• The most common mistakes developers must avoid, as well as the limitations of proximity search.
• How engines like Meilisearch complement proximity search to deliver better full-text results.

Let’s get into it.

What is proximity search?

Proximity search is a technique that finds documents where the search terms appear close to each other, rather than anywhere in the text.

For example, a user is looking for documents containing ‘error handling.’

Document A contains the following sentence: ‘This guide explains error handling in distributed systems.’ While document B includes this one: ‘This guide explains errors in distributed systems and later discusses handling user input.’

Proximity search will prefer document A and rank it higher in the results because the words ‘error’ and ‘handling’ are closer together – right next to each other, in fact – in the text.

This helps search engines and databases return results that make more sense than just keyword matching.

How does proximity search work?

Proximity search works based on a simple principle: how close search terms appear to each other within a document or page.

Instead of matching keywords anywhere in the full text, the search engine measures the distance between the search terms or the number of words between them. If the terms fall within a specific range, the result will rank higher.

Different systems support proximity search in various ways. They can rely on proximity operators, adjacency operators, or custom syntax such as the NEAR operator, quotation marks, or a tilde to indicate distance.

Thanks to these rules, relevance tuning becomes more predictable and consistent across various search engines or databases.

For related concepts, developers often combine proximity rules with fuzzy search techniques for handling misspellings and variations.

Now, let’s take a look at why proximity search plays such an important role in relevance.

Why is proximity search important?

Proximity search is so important because it improves search relevance. It ranks documents in which search terms appear close together rather than scattered throughout the page.

As a result, there’s a significant decrease in useless or irrelevant matches, especially where word order affects meaning.

Academic IR evaluations and health sciences search systems like PubMed and the National Library of Medicine highlighted that keyword matching is easily outperformed by proximity matching. PubMed’s own search methodology and proximity operators explain how enforcing term closeness in titles and abstracts improves precision and reduces irrelevant matches in extensive biomedical collections.

Search engineering experts agree that proximity is one of the strongest signals for semantic closeness without requiring full semantic models. This conclusion is supported by recent research such as Text Retrieval in Restricted Domains by Pairwise Term Co-occurrence, which demonstrates that incorporating proximity-based co-occurrence information significantly improves retrieval effectiveness compared to keyword-only matching across multiple text types.

Now, let’s look at the operators that enable proximity control.

What are proximity operators?

Proximity operators are special tools used in proximity searching. They control how far apart search terms may appear within a document by defining the maximum number of words between terms.

You’ll find these operators most commonly used in advanced search platforms, such as databases, academic engines, PubMed, and so on.

The most common proximity operators include:

• NEAR / N: Finds terms within a specified distance; for example, term1 NEAR/5 term2 is a match when both appear within five words of each other.
• AROUND(): Mostly used by Google to set the number of words that are allowed between terms.
• ~ (tilde): For example, climate change~3. This means that the terms must appear within three words of each other.
• ADJ: An adjacency operator that requires terms to appear next to each other.
• W / WITHIN: This proximity operator matches terms within a defined word span in specific systems.

These different proximity operators ensure that your search is both flexible and precise.

How does proximity search differ from phrase search?

The main difference between proximity and phrase search is that proximity search allows terms to appear within a specified distance.

Phrase search is more strict. It requires the exact phrase in the exact word order. It uses double quotes or quotation marks to enforce the strict matching.

For example, let’s say that a user is searching with the phrase ‘data consistency.’

Document A contains the sentence: ‘This paper discusses data consistency models in distributed databases.’

Document B includes the sentence: ‘This paper discusses the consistency of data across distributed databases.’

Even though both documents talk about the same idea, phrase search won’t treat document B as a relevant result, while proximity search will.

This added tolerance for variation in proximity search can lead to more context-rich matches. However, in domains where accuracy is non-negotiable, you should stick to phrase search.

Let’s take a look at use cases where proximity search genuinely makes a difference.

What are the main use cases for proximity search?

The real value of proximity search lies in use cases where context and relationships are crucial to the quality of results.

The most common use cases of proximity search are:

• Academic research: Using databases like PubMed, NLM, EBSCO, and ProQuest.
• Legal search: Proximity search helps lawyers or law students find cases in which concepts appear near each other, enabling them to improve their interpretation and citations.
• AI and semantic search: AI models combine keyword logic with AI ranking, so the relevance tuning requires the inclusion of proximity search.
• Recruitment search: It helps recruiters surface resumes where skills appear closely together rather than scattered.
• Enterprise and government search: Proximity search plays a huge part in ensuring accuracy in large document repositories across business and government systems.

Now, let’s move on to the most prominent search engines and databases that use proximity search. First up is Google.

How do you use proximity search in Google?

Google supports proximity search through the AROUND() operator. It instructs the engine to find search terms within a specified distance of each other.

The syntax is: term1 AROUND(number of words) term2

This will help you retrieve tightly related concepts rather than broad keyword matches.

Here’s how it works step-by-step:

1. First, you will enter your two terms.
2. Then, you insert AROUND() between them.
3. After the operator, place the number of words allowed in parentheses.
4. You run the query and review the results where the terms appear close together.

Examples:

• Batman AROUND(3) Joker

Or, if you want a slightly wider distance:

• Batman AROUND(5) Gotham

These queries rank results higher when terms appear within the specified range.

Feeling like a master searcher yet? Let’s talk about proximity search in PubMed.

How do you use proximity search in PubMed?

PubMed is fond of the ADJ operator, which finds terms within a specified distance and within the same word order.

For reference, the syntax is: term1[Field] ADJ[number of words] term2[Field]

This is part of PubMed’s NLM search engine and is crucial if you want to narrow results in health sciences research.

Step-by-step:

1. First, you enter your two search terms.
2. Next, add the ADJ plus the maximum number of words allowed between them.
3. To move on, you will specify the field, such as [TIAB] for title/abstract.
4. Run the search to filter studies where terms appear close together.

Example:

coronavirus[TIAB] ADJ3 vaccine[TIAB]

This finds articles in which the word ‘coronavirus’ appears within three words of ‘vaccine’ in the title or abstract.

Now, let’s look at proximity search for Boolean searches.

How do you use proximity search in Boolean searches?

Proximity search fits naturally into Boolean logic by adding distance control to operators like AND, OR, and NOT. These proximity operators will define how close the terms must appear.

Many systems also use NEAR, NEAR/n, or WITHIN to combine proximity with Boolean expressions.

You’ll notice that this allows more precise search techniques than using AND alone, which matches terms anywhere in the full text.

For example:

batman NEAR/5 joker AND gotham

This finds documents in which ‘Batman’ appears within five words of ‘Joker’ and ‘Gotham’ appears anywhere in the document.

Another example:

superhero NEAR/3 origin NOT "secret identity"

Here, proximity is combined with exclusion through NOT to refine results. We would get results with ‘superhero’ and ‘origin’ within three words of each other, provided that the document does not contain the phrase ‘secret identity’ anywhere in its text.

Now, let’s walk through the steps of implementing a proximity search algorithm.

How do you implement a proximity search algorithm?

The outline of our implementation here includes:

1. Cleaning the documents we will be working with (parsing and tokenizing them).
2. Building a document index.
3. Matching queries to the documents based on defined distances.

We will also explain how (and why) to use Meilisearch to simplify this process.

1. Parse documents and record term positions

First things first, you will normalize and tokenize each document, then store the term positions.

_from collections import defaultdict_

_from typing import Dict, List_

_def index_document(doc_id: str, text: str) -> List[tuple[str, int]]:_

_"""_

_Tokenize text and return (term, position) pairs._

_"""_

_tokens = text.lower().split()_

_return [(term, pos) for pos, term in enumerate(tokens)]_

2. Build a positional inverted index

Next up, you will build a positional inverted index: term → doc_id → [positions].

_PosIndex = Dict[str, Dict[str, List[int]]]_

_def build_positional_index(docs: Dict[str, str]) -> PosIndex:_

_"""_

_Build a positional inverted index from {doc_id: text}._

_"""_

_index: PosIndex = defaultdict(lambda: defaultdict(list))_

_for doc_id, text in docs.items():_

_for term, pos in index_document(doc_id, text):_

_index[term][doc_id].append(pos)_

_return index_

3. Match queries and compute distances

It is crucial that, at query time, you compare positions and check whether they fall within the max distance range.

_def within_distance(positions_a: List[int],_

_positions_b: List[int],_

_max_dist: int) -> bool:_

_"""_

_Return True if any pair of positions is within max_dist._

_Assumes positions_a and positions_b are sorted._

_"""_

_i, j = 0, 0_

_while i < len(positions_a) and j < len(positions_b):_

_diff = positions_a[i] - positions_b[j]_

_if abs(diff) <= max_dist:_

_return True_

_if diff < 0:_

_i += 1_

_else:_

_j += 1_

_return False_

You would wrap this in a function that, for a given pair of query terms and a max_dist, scans the positional index and returns matching document IDs.

In a custom engine, you’d have to do all of this on your own: parsing, indexing, and distance logic.

With Meilisearch, you eliminate most of this complexity. In Meilisearch, proximity is a built-in ranking rule: you define searchableAttributes, tune rankingRules (including proximity), and let Meilisearch handle proximity scoring under the hood, instead of manually managing each positional index.

The following are the common mistakes teams make with proximity search.

What are common mistakes with proximity search?

These are the most common mistakes we’ve noticed when working with proximity search:

• Using the wrong proximity operators or adjacency operators. As a result, the system interprets the search terms literally rather than by a specified distance, which undermines the purpose of proximity search.
• Setting the number of words too narrowly or too broadly. This can either bring back irrelevant search results or none at all.
• Ignoring word order in systems that require a specific sequence can return no results.
• Incorrectly mixing proximity searching with Boolean operators, overriding constraints in advanced search queries.

With these issues covered, we can now look at the broader limitations of proximity search itself.

What are the limitations of proximity search?

Different systems use different syntax, proximity operators, and adjacency operators. This creates problems when you want to use queries across platforms.

Some databases restrict the maximum number of words allowed per query. In fact, some even require controlled vocabulary or strict word order to return accurate citations.

Extensive full-text indexes can slow down performance because the engine must track positional data for every search term. In advanced workflows, these constraints can even clash with Boolean operators.

Why proximity search matters for modern information retrieval

Proximity search is your best option for strengthening relevance by rewarding documents in which the search terms appear close together in the full text.

It reduces noise and improves search accuracy. Plus, the advanced search techniques it supports play a considerable role in domains such as health sciences, legal research, and large government or academic databases.