> 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/only_alphabets_fuzzy-match.md). # ONLY\_ALPHABETS\_FUZZY Match Strips all numeric characters first, then applies fuzzy matching to the remaining alphabetic characters. Built for address fields where street names vary & numbers are handled separately. ### What ONLY\_ALPHABETS\_FUZZY does `ONLY_ALPHABETS_FUZZY` removes all numeric characters from both field values and then applies fuzzy string similarity to the remaining alphabetic characters. Two values match based on how similar their alphabetic portions are with tolerance for typos, abbreviations, and spelling variants. ### What **`ONLY_ALPHABETS_FUZZY`** matches and what it does not
Value AValue BMatch?
42 Main Street44 Main StreetYes - numbers stripped, "Main Street" is identical
42 Main St44 Main StreetYes - "Main St" vs "Main Street" scores a high fuzzy similarity
42 Main St42 Oak StNo - "Main St" vs "Oak St" too different alphabetically
[null]Main StreetYes - a null/blank value on either side is an automatic match; add NULL_OR_BLANK if you want nulls excluded
### When to use **`ONLY_ALPHABETS_FUZZY`**
Street address lines where number and name are in the same field The most common use case: an address line like "42 Main Street" where you want to match "Main Street" fuzzily but not use the number 42 in that comparison. "42 Main St" and "42 Main Street" - `ONLY_ALPHABETS_FUZZY` matches "Main St" to "Main Street".
Product model/catalog fields with variant numbers "iPhone 15 Pro Max 256GB" vs "iPhone 15 Pro Max 512GB" - stripping the numbers isolates "iPhone Pro Max GB", grouping storage/color variants under the same parent product.
Corporate, brand, and store-location fields "Walmart Store #1024" vs "Walmart Store #4412" - stripping the store numbers isolates "Walmart Store", letting branch-level records roll up to the parent brand.
Course or curriculum codes "CS 101: Intro to Python" vs "CS 102: Intro to Python" - stripping the course numbers isolates "CS: Intro to Python", letting transcripts map across curriculum revisions.
Drug/chemical name fields, when dosage doesn't matter for the match "Amoxicillin 250mg" vs "Amoxicillin 500mg" - stripping the dosage isolates "Amoxicillin mg", useful for flagging an allergy to the drug itself regardless of strength. Only applies when the field represents the drug name, not the prescribed dose - see below for when the number itself is what matters.
### When not to use **`ONLY_ALPHABETS_FUZZY`**
When the alphabetic portions must match exactly Use `ONLY_ALPHABETS_EXACT` when you need the alphabetic component to be identical - for example, building name codes where any alphabetic variation means a different location.
When the number is a unique identifier, not a variant Patent numbers ("US9123456B2" vs "US9123457B2"), invoice numbers ("Invoice #5001" vs "#5002"), and flight numbers ("Flight AA 402" vs "AA 403") all collapse to the same alphabetic string once stripped ("Patent USB", "Invoice #", "Flight AA"), merging records that must stay distinct.
When the number is a safety- or spec-critical value, not noise Drug dosage ("Insulin 10 Units" vs "100 Units"), material grade ("Grade 40" vs "Grade 60" steel rebar), and size ("Nike Air Max Size 6" vs "Size 11") all lose the one value that makes the records different. Stripping the number here risks a dosing error, a structural failure, or shipping the wrong size - not a false match.
When you want a single match type covering the whole string If your address data is structured enough that `FUZZY` on the full string gives good results, use `FUZZY`. `ONLY_ALPHABETS_FUZZY` adds complexity and is most valuable when the number component is causing false positives in a `FUZZY` match.
{% hint style="success" icon="right-long" %} **Related types**: * `NUMERIC` - use this for numeric fields * `ONLY_ALPHABETS_EXACT` - use when the name must match exactly (no abbreviation tolerance) * `ONLY_ALPHABETS_FUZZY_OPTIMISED` - same match type, faster at scale * `FUZZY` - simpler alternative for the full 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/only_alphabets_fuzzy-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.