haystack/docs/_src/api/api/crawler.md

<a id="crawler"></a>

# Module crawler

<a id="crawler.Crawler"></a>

## Crawler

```python
class Crawler(BaseComponent)
```

Crawl texts from a website so that we can use them later in Haystack as a corpus for search / question answering etc.

**Example:**
```python
|    from haystack.nodes.connector import Crawler
|
|    crawler = Crawler(output_dir="crawled_files")
|    # crawl Haystack docs, i.e. all pages that include haystack.deepset.ai/overview/
|    docs = crawler.crawl(urls=["https://haystack.deepset.ai/overview/get-started"],
|                         filter_urls= ["haystack.deepset.ai/overview/"])
```

<a id="crawler.Crawler.__init__"></a>

#### Crawler.\_\_init\_\_

```python
def __init__(output_dir: str, urls: Optional[List[str]] = None, crawler_depth: int = 1, filter_urls: Optional[List] = None, overwrite_existing_files=True, id_hash_keys: Optional[List[str]] = None, extract_hidden_text=True, loading_wait_time: Optional[int] = None, crawler_naming_function: Optional[Callable[[str, str], str]] = None)
```

Init object with basic params for crawling (can be overwritten later).

**Arguments**:

- `output_dir`: Path for the directory to store files
- `urls`: List of http(s) address(es) (can also be supplied later when calling crawl())
- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
0: Only initial list of urls
1: Follow links found on the initial URLs (but no further)
- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
All URLs not matching at least one of the regular expressions will be dropped.
- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
- `id_hash_keys`: Generate the document id from a custom list of strings that refer to the document's
attributes. If you want to ensure you don't have duplicate documents in your DocumentStore but texts are
not unique, you can modify the metadata and pass e.g. `"meta"` to this field (e.g. [`"content"`, `"meta"`]).
In this case the id will be generated by using the content and the defined metadata.
- `extract_hidden_text`: Whether to extract the hidden text contained in page.
E.g. the text can be inside a span with style="display: none"
- `loading_wait_time`: Seconds to wait for page loading before scraping. Recommended when page relies on
dynamic DOM manipulations. Use carefully and only when needed. Crawler will have scraping speed impacted.
E.g. 2: Crawler will wait 2 seconds before scraping page
- `crawler_naming_function`: A function mapping the crawled page to a file name.
By default, the file name is generated from the processed page url (string compatible with Mac, Unix and Windows paths) and the last 6 digits of the MD5 sum of this unprocessed page url.
E.g. 1) crawler_naming_function=lambda url, page_content: re.sub("[<>:'/\\|?*\0 ]", "_", link)
        This example will generate a file name from the url by replacing all characters that are not allowed in file names with underscores.
     2) crawler_naming_function=lambda url, page_content: hashlib.md5(f"{url}{page_content}".encode("utf-8")).hexdigest()
        This example will generate a file name from the url and the page content by using the MD5 hash of the concatenation of the url and the page content.

<a id="crawler.Crawler.crawl"></a>

#### Crawler.crawl

```python
def crawl(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None, id_hash_keys: Optional[List[str]] = None, extract_hidden_text: Optional[bool] = None, loading_wait_time: Optional[int] = None, crawler_naming_function: Optional[Callable[[str, str], str]] = None) -> List[Path]
```

Craw URL(s), extract the text from the HTML, create a Haystack Document object out of it and save it (one JSON

file per URL, including text and basic meta data).
You can optionally specify via `filter_urls` to only crawl URLs that match a certain pattern.
All parameters are optional here and only meant to overwrite instance attributes at runtime.
If no parameters are provided to this method, the instance attributes that were passed during __init__ will be used.

**Arguments**:

- `output_dir`: Path for the directory to store files
- `urls`: List of http addresses or single http address
- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
0: Only initial list of urls
1: Follow links found on the initial URLs (but no further)
- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
All URLs not matching at least one of the regular expressions will be dropped.
- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
- `id_hash_keys`: Generate the document id from a custom list of strings that refer to the document's
attributes. If you want to ensure you don't have duplicate documents in your DocumentStore but texts are
not unique, you can modify the metadata and pass e.g. `"meta"` to this field (e.g. [`"content"`, `"meta"`]).
In this case the id will be generated by using the content and the defined metadata.
- `loading_wait_time`: Seconds to wait for page loading before scraping. Recommended when page relies on
dynamic DOM manipulations. Use carefully and only when needed. Crawler will have scraping speed impacted.
E.g. 2: Crawler will wait 2 seconds before scraping page
- `crawler_naming_function`: A function mapping the crawled page to a file name.
By default, the file name is generated from the processed page url (string compatible with Mac, Unix and Windows paths) and the last 6 digits of the MD5 sum of this unprocessed page url.
E.g. 1) crawler_naming_function=lambda url, page_content: re.sub("[<>:'/\\|?*\0 ]", "_", link)
        This example will generate a file name from the url by replacing all characters that are not allowed in file names with underscores.
     2) crawler_naming_function=lambda url, page_content: hashlib.md5(f"{url}{page_content}".encode("utf-8")).hexdigest()
        This example will generate a file name from the url and the page content by using the MD5 hash of the concatenation of the url and the page content.

**Returns**:

List of paths where the crawled webpages got stored

<a id="crawler.Crawler.run"></a>

#### Crawler.run

```python
def run(output_dir: Union[str, Path, None] = None, urls: Optional[List[str]] = None, crawler_depth: Optional[int] = None, filter_urls: Optional[List] = None, overwrite_existing_files: Optional[bool] = None, return_documents: Optional[bool] = False, id_hash_keys: Optional[List[str]] = None, extract_hidden_text: Optional[bool] = True, loading_wait_time: Optional[int] = None, crawler_naming_function: Optional[Callable[[str, str], str]] = None) -> Tuple[Dict[str, Union[List[Document], List[Path]]], str]
```

Method to be executed when the Crawler is used as a Node within a Haystack pipeline.

**Arguments**:

- `output_dir`: Path for the directory to store files
- `urls`: List of http addresses or single http address
- `crawler_depth`: How many sublinks to follow from the initial list of URLs. Current options:
0: Only initial list of urls
1: Follow links found on the initial URLs (but no further)
- `filter_urls`: Optional list of regular expressions that the crawled URLs must comply with.
All URLs not matching at least one of the regular expressions will be dropped.
- `overwrite_existing_files`: Whether to overwrite existing files in output_dir with new content
- `return_documents`: Return json files content
- `id_hash_keys`: Generate the document id from a custom list of strings that refer to the document's
attributes. If you want to ensure you don't have duplicate documents in your DocumentStore but texts are
not unique, you can modify the metadata and pass e.g. `"meta"` to this field (e.g. [`"content"`, `"meta"`]).
In this case the id will be generated by using the content and the defined metadata.
- `extract_hidden_text`: Whether to extract the hidden text contained in page.
E.g. the text can be inside a span with style="display: none"
- `loading_wait_time`: Seconds to wait for page loading before scraping. Recommended when page relies on
dynamic DOM manipulations. Use carefully and only when needed. Crawler will have scraping speed impacted.
E.g. 2: Crawler will wait 2 seconds before scraping page
- `crawler_naming_function`: A function mapping the crawled page to a file name.
By default, the file name is generated from the processed page url (string compatible with Mac, Unix and Windows paths) and the last 6 digits of the MD5 sum of this unprocessed page url.
E.g. 1) crawler_naming_function=lambda url, page_content: re.sub("[<>:'/\\|?*\0 ]", "_", link)
        This example will generate a file name from the url by replacing all characters that are not allowed in file names with underscores.
     2) crawler_naming_function=lambda url, page_content: hashlib.md5(f"{url}{page_content}".encode("utf-8")).hexdigest()
        This example will generate a file name from the url and the page content by using the MD5 hash of the concatenation of the url and the page content.

**Returns**:

Tuple({"paths": List of filepaths, ...}, Name of output edge)