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refactor: rename the application package to docsgpt
The backend import package is now docsgpt, the name it will carry on PyPI; application was far too generic to install into anyone's site-packages. git mv plus a mechanical rewrite of every import, dotted string and path reference: 734 Python files, the compose files, Dockerfile, workflows, docs, setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage config, .gitignore. Behaviour is unchanged. Kept for one release: - A top-level application package whose meta-path finder resolves application.x.y to the already-imported docsgpt.x.y object, so old imports and entry points (celery -A application.app.celery, uvicorn application.asgi:asgi_app) keep working with a FutureWarning. - Celery registers every application.* task name as an alias of its docsgpt.* task on start-up, so messages queued by the previous release still run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries the previous release wrote are left unread instead of firing twice. The backend image builds from the repository root (docker build -f docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore allow-lists docsgpt/ and application/ and keeps caches, local data, .env files, the sample index files and the Dockerfile out. Compose and the image workflows point at the new context.
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from urllib.parse import urlparse, urljoin
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from bs4 import BeautifulSoup
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from docsgpt.parser.remote.base import BaseRemote
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from docsgpt.core.url_validation import validate_url, SSRFError
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from docsgpt.security.safe_url import UnsafeUserUrlError, pinned_request
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import re
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from markdownify import markdownify
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from docsgpt.parser.schema.base import Document
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import tldextract
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import os
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# The bundled public-suffix snapshot is enough for domain matching; the
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# default extractor would fetch the live list on first use and cache it on
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# disk, which is a network round trip the ingest worker should not depend on.
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_extract = tldextract.TLDExtract(suffix_list_urls=(), cache_dir=None)
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class CrawlerLoader(BaseRemote):
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def __init__(self, limit=10, allow_subdomains=False):
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"""
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Given a URL crawl web pages up to `self.limit`,
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convert HTML content to Markdown, and returning a list of Document objects.
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:param limit: The maximum number of pages to crawl.
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:param allow_subdomains: If True, crawl pages on subdomains of the base domain.
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"""
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self.limit = limit
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self.allow_subdomains = allow_subdomains
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def load_data(self, inputs):
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url = inputs
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if isinstance(url, list) and url:
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url = url[0]
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# Validate URL to prevent SSRF attacks
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try:
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url = validate_url(url)
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except SSRFError as e:
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print(f"URL validation failed: {e}")
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return []
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# Keep track of visited URLs to avoid revisiting the same page
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visited_urls = set()
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# Determine the base domain for link filtering using tldextract
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base_domain = self._get_base_domain(url)
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urls_to_visit = {url}
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documents = []
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while urls_to_visit:
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current_url = urls_to_visit.pop()
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# Skip if already visited
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if current_url in visited_urls:
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continue
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visited_urls.add(current_url)
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# Fetch the page content
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html_content = self._fetch_page(current_url)
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if html_content is None:
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continue
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# Convert the HTML to Markdown for cleaner text formatting
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title, language, processed_markdown = self._process_html_to_markdown(html_content, current_url)
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if processed_markdown:
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# Generate virtual file path from URL for consistent file-like matching
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virtual_path = self._url_to_virtual_path(current_url)
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# Create a Document for each visited page
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documents.append(
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Document(
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processed_markdown, # content
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None, # doc_id
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None, # embedding
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{
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"source": current_url,
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"title": title,
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"language": language,
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"file_path": virtual_path,
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}, # extra_info
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)
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)
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# Extract links and filter them according to domain rules
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new_links = self._extract_links(html_content, current_url)
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filtered_links = self._filter_links(new_links, base_domain)
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# Add any new, not-yet-visited links to the queue
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urls_to_visit.update(link for link in filtered_links if link not in visited_urls)
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# If we've reached the limit, stop crawling
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if self.limit is not None and len(visited_urls) >= self.limit:
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break
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return documents
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def _fetch_page(self, url):
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try:
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response = pinned_request("GET", url, timeout=10)
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response.raise_for_status()
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return response.text
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except UnsafeUserUrlError as e:
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print(f"URL validation failed for {url}: {e}")
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return None
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except Exception as e:
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print(f"Error fetching URL {url}: {e}")
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return None
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def _process_html_to_markdown(self, html_content, current_url):
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soup = BeautifulSoup(html_content, 'html.parser')
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title_tag = soup.find('title')
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title = title_tag.text.strip() if title_tag else "No Title"
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# Extract language
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language_tag = soup.find('html')
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language = language_tag.get('lang', 'en') if language_tag else "en"
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markdownified = markdownify(html_content, heading_style="ATX", newline_style="BACKSLASH")
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# Collapse runs of blank lines to a single one — the same shape
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# ``html_to_markdown`` gives uploaded HTML files.
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markdownified = re.sub(r'\n{3,}', '\n\n', markdownified)
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return title, language, markdownified
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def _extract_links(self, html_content, current_url):
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soup = BeautifulSoup(html_content, 'html.parser')
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links = []
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for a in soup.find_all('a', href=True):
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full_url = urljoin(current_url, a['href'])
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links.append((full_url, a.text.strip()))
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return links
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def _get_base_domain(self, url):
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extracted = _extract(url)
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# Reconstruct the domain as domain.suffix
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base_domain = f"{extracted.domain}.{extracted.suffix}"
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return base_domain
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def _filter_links(self, links, base_domain):
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"""
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Filter the extracted links to only include those that match the crawling criteria:
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- If allow_subdomains is True, allow any link whose domain ends with the base_domain.
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- If allow_subdomains is False, only allow exact matches of the base_domain.
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"""
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filtered = []
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for link, _ in links:
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parsed_link = urlparse(link)
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if not parsed_link.netloc:
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continue
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extracted = _extract(parsed_link.netloc)
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link_base = f"{extracted.domain}.{extracted.suffix}"
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if self.allow_subdomains:
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# For subdomains: sub.example.com ends with example.com
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if link_base == base_domain or link_base.endswith("." + base_domain):
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filtered.append(link)
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else:
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# Exact domain match
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if link_base == base_domain:
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filtered.append(link)
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return filtered
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def _url_to_virtual_path(self, url):
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"""
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Convert a URL to a virtual file path ending with .md.
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Examples:
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https://docs.docsgpt.cloud/ -> index.md
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https://docs.docsgpt.cloud/guides/setup -> guides/setup.md
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https://docs.docsgpt.cloud/guides/setup/ -> guides/setup.md
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https://example.com/page.html -> page.md
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"""
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parsed = urlparse(url)
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path = parsed.path.strip("/")
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if not path:
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return "index.md"
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# Remove common file extensions and add .md
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base, ext = os.path.splitext(path)
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if ext.lower() in [".html", ".htm", ".php", ".asp", ".aspx", ".jsp"]:
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path = base
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# Ensure path ends with .md
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if not path.endswith(".md"):
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path = path + ".md"
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return path
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