---
title: PhraseMatcher
teaser: Match sequences of tokens, based on documents
tag: class
source: spacy/matcher/phrasematcher.pyx
new: 2
---
The `PhraseMatcher` lets you efficiently match large terminology lists. While
the [`Matcher`](/api/matcher) lets you match sequences based on lists of token
descriptions, the `PhraseMatcher` accepts match patterns in the form of `Doc`
objects. See the [usage guide](/usage/rule-based-matching#phrasematcher) for
examples.
## PhraseMatcher.\_\_init\_\_ {#init tag="method"}
Create the rule-based `PhraseMatcher`. Setting a different `attr` to match on
will change the token attributes that will be compared to determine a match. By
default, the incoming `Doc` is checked for sequences of tokens with the same
`ORTH` value, i.e. the verbatim token text. Matching on the attribute `LOWER`
will result in case-insensitive matching, since only the lowercase token texts
are compared. In theory, it's also possible to match on sequences of the same
part-of-speech tags or dependency labels.
If `validate=True` is set, additional validation is performed when pattern are
added. At the moment, it will check whether a `Doc` has attributes assigned that
aren't necessary to produce the matches (for example, part-of-speech tags if the
`PhraseMatcher` matches on the token text). Since this can often lead to
significantly worse performance when creating the pattern, a `UserWarning` will
be shown.
> #### Example
>
> ```python
> from spacy.matcher import PhraseMatcher
> matcher = PhraseMatcher(nlp.vocab)
> ```
| Name | Description |
| --------------------------------------- | ------------------------------------------------------------------------------------------------------ |
| `vocab` | The vocabulary object, which must be shared with the documents the matcher will operate on. ~~Vocab~~ |
| `attr` 2.1 | The token attribute to match on. Defaults to `ORTH`, i.e. the verbatim token text. ~~Union[int, str]~~ |
| `validate` 2.1 | Validate patterns added to the matcher. ~~bool~~ |
## PhraseMatcher.\_\_call\_\_ {#call tag="method"}
Find all token sequences matching the supplied patterns on the `Doc`.
> #### Example
>
> ```python
> from spacy.matcher import PhraseMatcher
>
> matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", [nlp("Barack Obama")])
> doc = nlp("Barack Obama lifts America one last time in emotional farewell")
> matches = matcher(doc)
> ```
| Name | Description |
| ----------- | ----------------------------------- |
| `doc` | The document to match over. ~~Doc~~ |
| **RETURNS** | list | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end]`. The `match_id` is the ID of the added match pattern. ~~List[Tuple[int, int, int]]~~ |
Because spaCy stores all strings as integers, the `match_id` you get back will
be an integer, too – but you can always get the string representation by looking
it up in the vocabulary's `StringStore`, i.e. `nlp.vocab.strings`:
```python
match_id_string = nlp.vocab.strings[match_id]
```
## PhraseMatcher.pipe {#pipe tag="method"}
Match a stream of documents, yielding them in turn.
> #### Example
>
> ```python
> from spacy.matcher import PhraseMatcher
> matcher = PhraseMatcher(nlp.vocab)
> for doc in matcher.pipe(docs, batch_size=50):
> pass
> ```
| Name | Description |
| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `docs` | A stream of documents. ~~Iterable[Doc]~~ |
| `batch_size` | The number of documents to accumulate into a working set. ~~int~~ |
| `return_matches` 2.1 | Yield the match lists along with the docs, making results `(doc, matches)` tuples. ~~bool~~ |
| `as_tuples` | Interpret the input stream as `(doc, context)` tuples, and yield `(result, context)` tuples out. If both `return_matches` and `as_tuples` are `True`, the output will be a sequence of `((doc, matches), context)` tuples. ~~bool~~ |
| **YIELDS** | Documents and optional matches or context in order. ~~Union[Doc, Tuple[Doc, Any], Tuple[Tuple[Doc, Any], Any]]~~ |
## PhraseMatcher.\_\_len\_\_ {#len tag="method"}
Get the number of rules added to the matcher. Note that this only returns the
number of rules (identical with the number of IDs), not the number of individual
patterns.
> #### Example
>
> ```python
> matcher = PhraseMatcher(nlp.vocab)
> assert len(matcher) == 0
> matcher.add("OBAMA", [nlp("Barack Obama")])
> assert len(matcher) == 1
> ```
| Name | Description |
| ----------- | ---------------------------- |
| **RETURNS** | The number of rules. ~~int~~ |
## PhraseMatcher.\_\_contains\_\_ {#contains tag="method"}
Check whether the matcher contains rules for a match ID.
> #### Example
>
> ```python
> matcher = PhraseMatcher(nlp.vocab)
> assert "OBAMA" not in matcher
> matcher.add("OBAMA", [nlp("Barack Obama")])
> assert "OBAMA" in matcher
> ```
| Name | Description |
| ----------- | -------------------------------------------------------------- |
| `key` | The match ID. ~~str~~ |
| **RETURNS** | Whether the matcher contains rules for this match ID. ~~bool~~ |
## PhraseMatcher.add {#add tag="method"}
Add a rule to the matcher, consisting of an ID key, one or more patterns, and a
callback function to act on the matches. The callback function will receive the
arguments `matcher`, `doc`, `i` and `matches`. If a pattern already exists for
the given ID, the patterns will be extended. An `on_match` callback will be
overwritten.
> #### Example
>
> ```python
> def on_match(matcher, doc, id, matches):
> print('Matched!', matches)
>
> matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", [nlp("Barack Obama")], on_match=on_match)
> matcher.add("HEALTH", [nlp("health care reform"), nlp("healthcare reform")], on_match=on_match)
> doc = nlp("Barack Obama urges Congress to find courage to defend his healthcare reforms")
> matches = matcher(doc)
> ```
As of spaCy v3.0, `PhraseMatcher.add` takes a list of patterns as the second
argument (instead of a variable number of arguments). The `on_match` callback
becomes an optional keyword argument.
```diff
patterns = [nlp("health care reform"), nlp("healthcare reform")]
- matcher.add("HEALTH", on_match, *patterns)
+ matcher.add("HEALTH", patterns, on_match=on_match)
```
| Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `match_id` | str | An ID for the thing you're matching. ~~str~~ |
| `docs` | `Doc` objects of the phrases to match. ~~List[Doc]~~ |
| _keyword-only_ | | |
| `on_match` | Callback function to act on matches. Takes the arguments `matcher`, `doc`, `i` and `matches`. ~~Optional[Callable[[Matcher, Doc, int, List[tuple], Any]]~~ |
## PhraseMatcher.remove {#remove tag="method" new="2.2"}
Remove a rule from the matcher by match ID. A `KeyError` is raised if the key
does not exist.
> #### Example
>
> ```python
> matcher = PhraseMatcher(nlp.vocab)
> matcher.add("OBAMA", [nlp("Barack Obama")])
> assert "OBAMA" in matcher
> matcher.remove("OBAMA")
> assert "OBAMA" not in matcher
> ```
| Name | Description |
| ----- | --------------------------------- |
| `key` | The ID of the match rule. ~~str~~ |