> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md). # NUMERIC Match Extracts numbers from strings and compares how many are shared across both values. Built for street numbers, apartment numbers, and building numbers embedded in address strings. ### What `NUMERIC` does `NUMERIC` extracts all numbers from a string field value and scores the two values using Similarity functions - which gives a continuous score in \[0, 1]. Numbers are extracted into a set, so a number repeated within one value is only counted once - "10 Main St, Apt 10" contributes the number 10 a single time, not twice. This makes it useful for address fields where a number is embedded in a string - "42 Main St" and "42 Main Street" share the number 42, which `NUMERIC` extracts and matches. The alphabetic parts are not compared by `NUMERIC` - use `ONLY_ALPHABETS_FUZZY` or `FUZZY` for those. **Exception:** every other match type in this reference treats a null/blank value as an automatic match. `NUMERIC` does not - a null or blank value extracts no numbers, so it has nothing to share with the other side and scores a deterministic non-match (0.0). ### What `NUMERIC` matches and what it does not

Value A	Value B	Score	Notes
42 Main St	42 Main Street	1.0 (Yes)	42 extracted from both
42B Main St	42 Main St	1.0 (Yes)	42 extracted from both - "B" isn't a digit, so it's dropped
42 Main St	43 Main St	0.0 (No)	{42} vs {43} share nothing
Suite 12, Floor 3	Suite 12, Floor 4	0.33	{12,3} vs {12,4} share one of three distinct numbers
10 Main St, Apt 10	10 Main St	1.0 (Yes)	"10" appears twice on the left but dedupes to the set {10}, same as the right side
[no numbers]	42 Main St	0.0 (No)	One side extracts no numbers, so there's nothing to intersect
[null]	42 Main St	0.0 (No)	The null-matches-anything default every other match type uses does not apply here

### When to use `NUMERIC`

Street numbers and apartment numbers in address fields

When your address data has the street number embedded in the same field as the street name - "42 Main Street"; use `NUMERIC` to match the number component.

Building identifiers, floor numbers, unit numbers

Any field where a number is the primary discriminating element and it is embedded in a string with non-numeric context.

### When not to use `NUMERIC`

Fields where the full string matters, not just the numbers

`NUMERIC` ignores alphabetic characters. "42 Main Street" and "42 Oak Avenue" both contain "42" - `NUMERIC` would score them as matching. The street name difference is invisible to NUMERIC. Use `FUZZY` so both the number and the text are compared.

Product codes with units

For "16gb", "500ml", and similar specifications where the unit matters as much as the number, use `NUMERIC_WITH_UNITS` instead.

{% hint style="success" icon="right-long" %} **Related matches:** * `NUMERIC_WITH_UNITS` - for product codes that include units (16gb, 500ml) * `FUZZY` - if you want a single match type covering the whole string **Read more**: [Match Types](/zingg/zingg-concepts/zingg-configuration/field-definition/match-types.md) {% endhint %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zingg.ai/zingg/zingg-concepts/zingg-configuration/field-definition/match-types/numeric-match.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.