Adding website ingestion!

dags/hivemind_etl_helpers/src/db/website/crawlee_client.py

@@ -0,0 +1,116 @@
+import asyncio
+from typing import Any
+
+from crawlee.playwright_crawler import PlaywrightCrawler, PlaywrightCrawlingContext
+from defusedxml import ElementTree as ET
+
+
+class CrawleeClient:
+    def __init__(
+        self,
+        max_requests: int = 20,
+        headless: bool = True,
+        browser_type: str = "chromium",
+    ) -> None:
+        self.crawler = PlaywrightCrawler(
+            max_requests_per_crawl=max_requests,
+            headless=headless,
+            browser_type=browser_type,
+        )
+
+        # do not persist crawled data to local storage
+        self.crawler._configuration.persist_storage = False
+        self.crawler._configuration.write_metadata = False
+
+        @self.crawler.router.default_handler
+        async def request_handler(context: PlaywrightCrawlingContext) -> None:
+            context.log.info(f"Processing {context.request.url} ...")
+
+            inner_text = await context.page.inner_text(selector="body")
+
+            if "sitemap.xml" in context.request.url:
+                links = self._extract_links_from_sitemap(inner_text)
+                await context.add_requests(requests=list(set(links)))
+            else:
+                await context.enqueue_links()
+
+            data = {
+                "url": context.request.url,
+                "title": await context.page.title(),
+                "inner_text": inner_text,
+            }
+
+            await context.push_data(data)
+
+    def _extract_links_from_sitemap(self, sitemap_content: str) -> list[str]:
+        """
+
+        Extract URLs from a sitemap XML content.
+
+        Parameters
+        ----------
+        sitemap_content : str
+            The XML content of the sitemap
+
+        Raises
+        ------
+        ET.ParseError
+        If the XML content is malformed
+
+        Returns
+        -------
+        links : list[str]
+            list of valid URLs extracted from the sitemap
+        """
+        links = []
+        try:
+            root = ET.fromstring(sitemap_content)
+            namespace = {"ns": "http://www.sitemaps.org/schemas/sitemap/0.9"}
+            for element in root.findall("ns:url/ns:loc", namespace):
+                url = element.text.strip() if element.text else None
+                if url and url.startswith(("http://", "https://")):
+                    links.append(url)
+        except ET.ParseError as e:
+            raise ValueError(f"Invalid sitemap XML: {str(e)}")
+
+        return links
+
+    async def crawl(self, links: list[str]) -> list[dict[str, Any]]:
+        """
+        Crawl websites and extract data from all inner links under the domain routes.
+
+        Parameters
+        ----------
+        links : list[str]
+            List of valid URLs to crawl
+
+        Returns
+        -------
+        crawled_data : list[dict[str, Any]]
+            List of dictionaries containing crawled data with keys:
+            - url: str
+            - title: str
+            - inner_text: str
+
+        Raises
+        ------
+        ValueError
+            If any of the input URLs is invalid (not starting with http or https)
+        TimeoutError
+            If the crawl operation times out
+        """
+        # Validate input URLs
+        valid_links = []
+        for url in links:
+            if url and isinstance(url, str) and url.startswith(("http://", "https://")):
+                valid_links.append(url)
+            else:
+                raise ValueError(f"Invalid URL: {url}")
+
+        try:
+            await self.crawler.add_requests(requests=valid_links)
+            await asyncio.wait_for(self.crawler.run(), timeout=3600)  # 1 hour timeout
+            crawled_data = await self.crawler.get_data()
+            return crawled_data.items
+        except asyncio.TimeoutError:
+            raise TimeoutError("Crawl operation timed out")

dags/hivemind_etl_helpers/src/utils/modules/__init__.py

@@ -5,3 +5,4 @@
 from .github import ModulesGitHub
 from .mediawiki import ModulesMediaWiki
 from .notion import ModulesNotion
+from .website import ModulesWebsite

dags/hivemind_etl_helpers/src/utils/modules/modules_base.py

@@ -98,7 +98,7 @@ def get_token(self, platform_id: ObjectId, token_type: str) -> str:
 
     def get_platform_metadata(
         self, platform_id: ObjectId, metadata_name: str
-    ) -> str | dict:
+    ) -> str | dict | list:
         """
         get the userid that belongs to a platform
 
@@ -111,8 +111,8 @@ def get_platform_metadata(
 
         Returns
         ---------
-        user_id : str
-            the user id that the platform belongs to
+        metadata_value : Any
+            the values that the metadata belongs to
         """
         client = MongoSingleton.get_instance().get_client()
 

dags/hivemind_etl_helpers/src/utils/modules/website.py

@@ -0,0 +1,63 @@
+import logging
+
+from .modules_base import ModulesBase
+
+
+class ModulesWebsite(ModulesBase):
+    def __init__(self) -> None:
+        self.platform_name = "website"
+        super().__init__()
+
+    def get_learning_platforms(
+        self,
+    ) -> list[dict[str, str | list[str]]]:
+        """
+        Get all the website communities with their page titles.
+
+        Returns
+        ---------
+        community_orgs : list[dict[str, str | list[str]]] = []
+            a list of website data information
+
+            example data output:
+            ```
+            [{
+                "community_id": "6579c364f1120850414e0dc5",
+                "platform_id": "6579c364f1120850414e0dc6",
+                "urls": ["link1", "link2"],
+            }]
+            ```
+        """
+        modules = self.query(platform=self.platform_name, projection={"name": 0})
+        communities_data: list[dict[str, str | list[str]]] = []
+
+        for module in modules:
+            community = module["community"]
+
+            # each platform of the community
+            for platform in module["options"]["platforms"]:
+                if platform["name"] != self.platform_name:
+                    continue
+
+                platform_id = platform["platform"]
+
+                try:
+                    website_links = self.get_platform_metadata(
+                        platform_id=platform_id,
+                        metadata_name="resources",
+                    )
+
+                    communities_data.append(
+                        {
+                            "community_id": str(community),
+                            "platform_id": platform_id,
+                            "urls": website_links,
+                        }
+                    )
+                except Exception as exp:
+                    logging.error(
+                        "Exception while fetching website modules "
+                        f"for platform: {platform_id} | exception: {exp}"
+                    )
+
+        return communities_data

dags/hivemind_etl_helpers/tests/unit/test_website_etl.py

@@ -0,0 +1,81 @@
+from unittest import IsolatedAsyncioTestCase
+from unittest.mock import AsyncMock, MagicMock
+
+from dotenv import load_dotenv
+from hivemind_etl_helpers.website_etl import WebsiteETL
+from llama_index.core import Document
+
+
+class TestWebsiteETL(IsolatedAsyncioTestCase):
+    def setUp(self):
+        """
+        Setup for the test cases. Initializes a WebsiteETL instance with mocked dependencies.
+        """
+        load_dotenv()
+        self.community_id = "test_community"
+        self.website_etl = WebsiteETL(self.community_id)
+        self.website_etl.crawlee_client = AsyncMock()
+        self.website_etl.ingestion_pipeline = MagicMock()
+
+    async def test_extract(self):
+        """
+        Test the extract method.
+        """
+        urls = ["https://example.com"]
+        mocked_data = [
+            {
+                "url": "https://example.com",
+                "inner_text": "Example text",
+                "title": "Example",
+            }
+        ]
+        self.website_etl.crawlee_client.crawl.return_value = mocked_data
+
+        extracted_data = await self.website_etl.extract(urls)
+
+        self.assertEqual(extracted_data, mocked_data)
+        self.website_etl.crawlee_client.crawl.assert_awaited_once_with(urls)
+
+    def test_transform(self):
+        """
+        Test the transform method.
+        """
+        raw_data = [
+            {
+                "url": "https://example.com",
+                "inner_text": "Example text",
+                "title": "Example",
+            }
+        ]
+        expected_documents = [
+            Document(
+                doc_id="https://example.com",
+                text="Example text",
+                metadata={"title": "Example", "url": "https://example.com"},
+            )
+        ]
+
+        documents = self.website_etl.transform(raw_data)
+
+        self.assertEqual(len(documents), len(expected_documents))
+        self.assertEqual(documents[0].doc_id, expected_documents[0].doc_id)
+        self.assertEqual(documents[0].text, expected_documents[0].text)
+        self.assertEqual(documents[0].metadata, expected_documents[0].metadata)
+
+    def test_load(self):
+        """
+        Test the load method.
+        """
+        documents = [
+            Document(
+                doc_id="https://example.com",
+                text="Example text",
+                metadata={"title": "Example", "url": "https://example.com"},
+            )
+        ]
+
+        self.website_etl.load(documents)
+
+        self.website_etl.ingestion_pipeline.run_pipeline.assert_called_once_with(
+            docs=documents
+        )

dags/hivemind_etl_helpers/website_etl.py

@@ -0,0 +1,94 @@
+from typing import Any
+
+from hivemind_etl_helpers.ingestion_pipeline import CustomIngestionPipeline
+from hivemind_etl_helpers.src.db.website.crawlee_client import CrawleeClient
+from llama_index.core import Document
+
+
+class WebsiteETL:
+    def __init__(
+        self,
+        community_id: str,
+    ) -> None:
+        """
+        Parameters
+        -----------
+        community_id : str
+            the community to save its data
+        """
+        self.community_id = community_id
+        collection_name = "website"
+
+        # preparing the data extractor and ingestion pipelines
+        self.crawlee_client = CrawleeClient()
+        self.ingestion_pipeline = CustomIngestionPipeline(
+            self.community_id, collection_name=collection_name
+        )
+
+    async def extract(
+        self,
+        urls: list[str],
+    ) -> list[dict[str, Any]]:
+        """
+        Extract given urls
+
+        Parameters
+        -----------
+        urls : list[str]
+            a list of urls
+
+        Returns
+        ---------
+        extracted_data : list[dict[str, Any]]
+            The crawled data from urls
+        """
+        if not urls:
+            raise ValueError("No URLs provided for crawling")
+        extracted_data = await self.crawlee_client.crawl(urls)
+
+        if not extracted_data:
+            raise ValueError(f"No data extracted from URLs: {urls}")
+
+        return extracted_data
+
+    def transform(self, raw_data: list[dict[str, Any]]) -> list[Document]:
+        """
+        transform raw data to llama-index documents
+
+        Parameters
+        ------------
+        raw_data : list[dict[str, Any]]
+            crawled data
+
+        Returns
+        ---------
+        documents : list[llama_index.Document]
+            list of llama-index documents
+        """
+        documents: list[Document] = []
+
+        for data in raw_data:
+            doc_id = data["url"]
+            doc = Document(
+                doc_id=doc_id,
+                text=data["inner_text"],
+                metadata={
+                    "title": data["title"],
+                    "url": data["url"],
+                },
+            )
+            documents.append(doc)
+
+        return documents
+
+    def load(self, documents: list[Document]) -> None:
+        """
+        load the documents into the vector db
+
+        Parameters
+        -------------
+        documents: list[llama_index.Document]
+            the llama-index documents to be ingested
+        """
+        # loading data into db
+        self.ingestion_pipeline.run_pipeline(docs=documents)