> 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/running-zingg/set-deterministic-matching-conditions.md).

# Set Deterministic Matching Conditions

{% hint style="info" icon="right-long" %}
Deterministic matching is Enterprise only. Available in Enterprise Lite and above.
{% endhint %}

To configure deterministic matching, define each rule as a `DeterministicMatching` condition in Python or as a `matchCondition` block in JSON, then pass all conditions to `setDeterministicMatchingCondition()`. Full Python and JSON examples → [Configure Zingg](/zingg/running-zingg/configure-zingg.md)

### How the conditions work

Each `matchCondition` is evaluated independently. Any pair that satisfies at least one condition is resolved as a match with a score of 1. You can define as many conditions as your data requires.

Using the example above:

<table><thead><tr><th width="252.984375" valign="top">Condition</th><th valign="top">What it means</th></tr></thead><tbody><tr><td valign="top"><code>fname</code> + <code>stNo</code> + <code>add1</code> match <code>exactly</code></td><td valign="top">Same first name at the same street number and address line → treated as same entity, score 1.</td></tr><tr><td valign="top"><code>fname</code> + <code>dob</code> + <code>ssn</code> match <code>exactly</code></td><td valign="top">Same first name, date of birth, and SSN → treated as same entity, score 1.</td></tr><tr><td valign="top"><code>fname</code> + <code>email</code> match <code>exactly</code></td><td valign="top">Same first name and email address → treated as same entity, score 1.</td></tr></tbody></table>

<details>

<summary><strong>When should I use deterministic matching?</strong></summary>

Use deterministic matching when your data contains reliable unique identifier fields where an exact match between two records should always mean they are the same entity.

Good candidates for deterministic rules:

* First name + email address
* First name + date of birth + national ID
* First name + street number + address line 1

Do not use deterministic matching on fields that are frequently incomplete or inconsistent. Null values or formatting variations will cause valid matches to be missed. Apply deterministic rules only to fields you trust.

If you are unsure, start with probabilistic matching only. Add deterministic rules once you have reviewed match output and identified high-confidence identifier combinations in your data.

{% hint style="success" icon="right-long" %}
**Read more:** [Configure Zingg](/zingg/running-zingg/configure-zingg.md) | [Concepts glossary](/zingg/frequently-asked-questions/concept-glossary.md)
{% endhint %}

</details>


---

# 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/running-zingg/set-deterministic-matching-conditions.md?ask=<question>&goal=<user_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.
