From f6009d1abf9e2c83999de0c9b03a41aa1bf8f2a4 Mon Sep 17 00:00:00 2001 From: PeriniM Date: Mon, 6 Jan 2025 02:01:17 +0100 Subject: [PATCH] fix: better playwright installation handling --- README.md | 89 +++++----- pyproject.toml | 4 - scrapegraphai/docloaders/chromium.py | 76 ++++----- scrapegraphai/nodes/generate_answer_node.py | 4 +- scrapegraphai/utils/llm_callback_manager.py | 3 +- uv.lock | 173 +++++++++++++------- 6 files changed, 205 insertions(+), 144 deletions(-) diff --git a/README.md b/README.md index d2a57877..b97bdf5f 100644 --- a/README.md +++ b/README.md @@ -24,21 +24,6 @@ Just say which information you want to extract and the library will do it for yo ScrapeGraphAI Hero

-## 🔗 ScrapeGraph API & SDKs -If you are looking for a quick solution to integrate ScrapeGraph in your system, check out our powerful API [here!](https://dashboard.scrapegraphai.com/login) - -

- ScrapeGraph API Banner -

- -We offer SDKs in both Python and Node.js, making it easy to integrate into your projects. Check them out below: - -| SDK | Language | GitHub Link | -|-----------|----------|-----------------------------------------------------------------------------| -| Python SDK | Python | [scrapegraph-py](https://github.com/ScrapeGraphAI/scrapegraph-sdk/tree/main/scrapegraph-py) | -| Node.js SDK | Node.js | [scrapegraph-js](https://github.com/ScrapeGraphAI/scrapegraph-sdk/tree/main/scrapegraph-js) | - -The Official API Documentation can be found [here](https://docs.scrapegraphai.com/). ## 🚀 Quick install @@ -47,6 +32,7 @@ The reference page for Scrapegraph-ai is available on the official page of PyPI: ```bash pip install scrapegraphai +# IMPORTANT (to fetch webpage content) playwright install ``` @@ -84,13 +70,12 @@ The most common one is the `SmartScraperGraph`, which extracts information from ```python -import json from scrapegraphai.graphs import SmartScraperGraph # Define the configuration for the scraping pipeline graph_config = { "llm": { - "api_key": "YOUR_OPENAI_APIKEY", + "api_key": "YOUR_OPENAI_API_KEY", "model": "openai/gpt-4o-mini", }, "verbose": True, @@ -99,33 +84,45 @@ graph_config = { # Create the SmartScraperGraph instance smart_scraper_graph = SmartScraperGraph( - prompt="Extract me all the news from the website", - source="https://www.wired.com", + prompt="Extract useful information from the webpage, including a description of what the company does, founders and social media links", + source="https://scrapegraphai.com/", config=graph_config ) # Run the pipeline result = smart_scraper_graph.run() + +import json print(json.dumps(result, indent=4)) ``` The output will be a dictionary like the following: ```python -"result": { - "news": [ - { - "title": "The New Jersey Drone Mystery May Not Actually Be That Mysterious", - "link": "https://www.wired.com/story/new-jersey-drone-mystery-maybe-not-drones/", - "author": "Lily Hay Newman" - }, - { - "title": "Former ByteDance Intern Accused of Sabotage Among Winners of Prestigious AI Award", - "link": "https://www.wired.com/story/bytedance-intern-best-paper-neurips/", - "author": "Louise Matsakis" - }, - ... - ] +{ + "description": "ScrapeGraphAI transforms websites into clean, organized data for AI agents and data analytics. It offers an AI-powered API for effortless and cost-effective data extraction.", + "founders": [ + { + "name": "Marco Perini", + "role": "Founder & Technical Lead", + "linkedin": "https://www.linkedin.com/in/perinim/" + }, + { + "name": "Marco Vinciguerra", + "role": "Founder & Software Engineer", + "linkedin": "https://www.linkedin.com/in/marco-vinciguerra-7ba365242/" + }, + { + "name": "Lorenzo Padoan", + "role": "Founder & Product Engineer", + "linkedin": "https://www.linkedin.com/in/lorenzo-padoan-4521a2154/" + } + ], + "social_media_links": { + "linkedin": "https://www.linkedin.com/company/101881123", + "twitter": "https://x.com/scrapegraphai", + "github": "https://github.com/ScrapeGraphAI/Scrapegraph-ai" + } } ``` There are other pipelines that can be used to extract information from multiple pages, generate Python scripts, or even generate audio files. @@ -145,20 +142,30 @@ It is possible to use different LLM through APIs, such as **OpenAI**, **Groq**, Remember to have [Ollama](https://ollama.com/) installed and download the models using the **ollama pull** command, if you want to use local models. -## 🔍 Demo -Official streamlit demo: - -[![My Skills](https://skillicons.dev/icons?i=react)](https://scrapegraph-demo-demo.streamlit.app) - -Try it directly on the web using Google Colab: - -[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sEZBonBMGP44CtO6GQTwAlL0BGJXjtfd?usp=sharing) ## 📖 Documentation +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sEZBonBMGP44CtO6GQTwAlL0BGJXjtfd?usp=sharing) + The documentation for ScrapeGraphAI can be found [here](https://scrapegraph-ai.readthedocs.io/en/latest/). Check out also the Docusaurus [here](https://docs-oss.scrapegraphai.com/). +## 🔗 ScrapeGraph API & SDKs +If you are looking for a quick solution to integrate ScrapeGraph in your system, check out our powerful API [here!](https://dashboard.scrapegraphai.com/login) + +

+ ScrapeGraph API Banner +

+ +We offer SDKs in both Python and Node.js, making it easy to integrate into your projects. Check them out below: + +| SDK | Language | GitHub Link | +|-----------|----------|-----------------------------------------------------------------------------| +| Python SDK | Python | [scrapegraph-py](https://github.com/ScrapeGraphAI/scrapegraph-sdk/tree/main/scrapegraph-py) | +| Node.js SDK | Node.js | [scrapegraph-js](https://github.com/ScrapeGraphAI/scrapegraph-sdk/tree/main/scrapegraph-js) | + +The Official API Documentation can be found [here](https://docs.scrapegraphai.com/). + ## 🏆 Sponsors
diff --git a/pyproject.toml b/pyproject.toml index 3b65d84a..c2c7bebc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,13 +32,9 @@ dependencies = [ "fastembed>=0.3.6", "semchunk>=2.2.0", "transformers>=4.44.2", - "transformers>=4.44.2", "googlesearch-python>=1.2.5", "async-timeout>=4.0.3", - "transformers>=4.44.2", - "googlesearch-python>=1.2.5", "simpleeval>=1.0.0", - "async_timeout>=4.0.3", "scrapegraph-py>=1.7.0" ] diff --git a/scrapegraphai/docloaders/chromium.py b/scrapegraphai/docloaders/chromium.py index cdbd35e7..1c042561 100644 --- a/scrapegraphai/docloaders/chromium.py +++ b/scrapegraphai/docloaders/chromium.py @@ -23,9 +23,6 @@ class ChromiumLoader(BaseLoader): requires_js_support: Flag to determine if JS rendering is required. """ - RETRY_LIMIT = 3 - TIMEOUT = 10 - def __init__( self, urls: List[str], @@ -37,6 +34,8 @@ class ChromiumLoader(BaseLoader): requires_js_support: bool = False, storage_state: Optional[str] = None, browser_name: str = "chromium", #default chromium + retry_limit: int = 1, + timeout: int = 10, **kwargs: Any, ): """Initialize the loader with a list of URL paths. @@ -47,6 +46,8 @@ class ChromiumLoader(BaseLoader): proxy: A dictionary containing proxy information; None disables protection. urls: A list of URLs to scrape content from. requires_js_support: Whether to use JS rendering for scraping. + retry_limit: Maximum number of retry attempts for scraping. Defaults to 3. + timeout: Maximum time in seconds to wait for scraping. Defaults to 10. kwargs: A dictionary containing additional browser kwargs. Raises: @@ -68,12 +69,17 @@ class ChromiumLoader(BaseLoader): self.requires_js_support = requires_js_support self.storage_state = storage_state self.browser_name = browser_name + self.retry_limit = retry_limit + self.timeout = timeout async def scrape(self, url:str) -> str: if self.backend == "playwright": return await self.ascrape_playwright(url) elif self.backend == "selenium": - return await self.ascrape_undetected_chromedriver(url) + try: + return await self.ascrape_undetected_chromedriver(url) + except Exception as e: + raise ValueError(f"Failed to scrape with undetected chromedriver: {e}") else: raise ValueError(f"Unsupported backend: {self.backend}") @@ -97,9 +103,9 @@ class ChromiumLoader(BaseLoader): results = "" attempt = 0 - while attempt < self.RETRY_LIMIT: + while attempt < self.retry_limit: try: - async with async_timeout.timeout(self.TIMEOUT): + async with async_timeout.timeout(self.timeout): # Handling browser selection if self.backend == "selenium": if self.browser_name == "chromium": @@ -134,9 +140,9 @@ class ChromiumLoader(BaseLoader): except (aiohttp.ClientError, asyncio.TimeoutError) as e: attempt += 1 logger.error(f"Attempt {attempt} failed: {e}") - if attempt == self.RETRY_LIMIT: + if attempt == self.retry_limit: results = ( - f"Error: Network error after {self.RETRY_LIMIT} attempts - {e}" + f"Error: Network error after {self.retry_limit} attempts - {e}" ) finally: driver.quit() @@ -204,7 +210,7 @@ class ChromiumLoader(BaseLoader): results = "" attempt = 0 - while attempt < self.RETRY_LIMIT: + while attempt < self.retry_limit: try: async with async_playwright() as p: browser = None @@ -268,8 +274,8 @@ class ChromiumLoader(BaseLoader): except (aiohttp.ClientError, asyncio.TimeoutError, Exception) as e: attempt += 1 logger.error(f"Attempt {attempt} failed: {e}") - if attempt == self.RETRY_LIMIT: - results = f"Error: Network error after {self.RETRY_LIMIT} attempts - {e}" + if attempt == self.retry_limit: + results = f"Error: Network error after {self.retry_limit} attempts - {e}" finally: await browser.close() @@ -283,7 +289,11 @@ class ChromiumLoader(BaseLoader): url (str): The URL to scrape. Returns: - str: The scraped HTML content or an error message if an exception occurs. + str: The scraped HTML content + + Raises: + RuntimeError: When retry limit is reached without successful scraping + ValueError: When an invalid browser name is provided """ from playwright.async_api import async_playwright from undetected_playwright import Malenia @@ -292,9 +302,9 @@ class ChromiumLoader(BaseLoader): results = "" attempt = 0 - while attempt < self.RETRY_LIMIT: + while attempt < self.retry_limit: try: - async with async_playwright() as p, async_timeout.timeout(self.TIMEOUT): + async with async_playwright() as p, async_timeout.timeout(self.timeout): browser = None if browser_name == "chromium": browser = await p.chromium.launch( @@ -315,22 +325,16 @@ class ChromiumLoader(BaseLoader): await page.wait_for_load_state(self.load_state) results = await page.content() logger.info("Content scraped") - break + return results except (aiohttp.ClientError, asyncio.TimeoutError, Exception) as e: attempt += 1 logger.error(f"Attempt {attempt} failed: {e}") - if attempt == self.RETRY_LIMIT: - results = f"Error: Network error after {self.RETRY_LIMIT} attempts - {e}" + if attempt == self.retry_limit: + raise RuntimeError(f"Failed to scrape after {self.retry_limit} attempts: {str(e)}") finally: - if "browser" in locals(): - await browser.close() + await browser.close() - - return results - - - - async def ascrape_with_js_support(self, url: str , browser_name:str = "chromium") -> str: + async def ascrape_with_js_support(self, url: str, browser_name: str = "chromium") -> str: """ Asynchronously scrape the content of a given URL by rendering JavaScript using Playwright. @@ -338,18 +342,20 @@ class ChromiumLoader(BaseLoader): url (str): The URL to scrape. Returns: - str: The fully rendered HTML content after JavaScript execution, - or an error message if an exception occurs. + str: The fully rendered HTML content after JavaScript execution + + Raises: + RuntimeError: When retry limit is reached without successful scraping + ValueError: When an invalid browser name is provided """ from playwright.async_api import async_playwright logger.info(f"Starting scraping with JavaScript support for {url}...") - results = "" attempt = 0 - while attempt < self.RETRY_LIMIT: + while attempt < self.retry_limit: try: - async with async_playwright() as p, async_timeout.timeout(self.TIMEOUT): + async with async_playwright() as p, async_timeout.timeout(self.timeout): browser = None if browser_name == "chromium": browser = await p.chromium.launch( @@ -368,19 +374,15 @@ class ChromiumLoader(BaseLoader): await page.goto(url, wait_until="networkidle") results = await page.content() logger.info("Content scraped after JavaScript rendering") - break + return results except (aiohttp.ClientError, asyncio.TimeoutError, Exception) as e: attempt += 1 logger.error(f"Attempt {attempt} failed: {e}") - if attempt == self.RETRY_LIMIT: - results = ( - f"Error: Network error after {self.RETRY_LIMIT} attempts - {e}" - ) + if attempt == self.retry_limit: + raise RuntimeError(f"Failed to scrape after {self.retry_limit} attempts: {str(e)}") finally: await browser.close() - return results - def lazy_load(self) -> Iterator[Document]: """ Lazily load text content from the provided URLs. diff --git a/scrapegraphai/nodes/generate_answer_node.py b/scrapegraphai/nodes/generate_answer_node.py index d1a42965..c46ed0b5 100644 --- a/scrapegraphai/nodes/generate_answer_node.py +++ b/scrapegraphai/nodes/generate_answer_node.py @@ -12,10 +12,8 @@ from langchain_aws import ChatBedrock from langchain_community.chat_models import ChatOllama from tqdm import tqdm from .base_node import BaseNode -from ..utils.output_parser import get_structured_output_parser, get_pydantic_output_parser +from ..utils.output_parser import get_pydantic_output_parser from requests.exceptions import Timeout -from langchain.callbacks.manager import CallbackManager -from langchain.callbacks import get_openai_callback from ..prompts import ( TEMPLATE_CHUNKS, TEMPLATE_NO_CHUNKS, TEMPLATE_MERGE, TEMPLATE_CHUNKS_MD, TEMPLATE_NO_CHUNKS_MD, TEMPLATE_MERGE_MD diff --git a/scrapegraphai/utils/llm_callback_manager.py b/scrapegraphai/utils/llm_callback_manager.py index 77e66c02..5bd74e9a 100644 --- a/scrapegraphai/utils/llm_callback_manager.py +++ b/scrapegraphai/utils/llm_callback_manager.py @@ -7,8 +7,7 @@ Classes: import threading from contextlib import contextmanager -from langchain_community.callbacks import get_openai_callback -from langchain_community.callbacks.manager import get_bedrock_anthropic_callback +from langchain_community.callbacks.manager import get_openai_callback, get_bedrock_anthropic_callback from langchain_openai import ChatOpenAI, AzureChatOpenAI from langchain_aws import ChatBedrock from .custom_callback import get_custom_callback diff --git a/uv.lock b/uv.lock index 8e6093b4..1ac1f62f 100644 --- a/uv.lock +++ b/uv.lock @@ -1,21 +1,51 @@ version = 1 requires-python = ">=3.10, <4.0" resolution-markers = [ - "python_full_version < '3.11' and sys_platform == 'darwin'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.11.*' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version >= 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'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, + { name = "nvidia-nvjitlink-cu12", marker = "(platform_machine != 'aarch64' and platform_system != 'Darwin') or (platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')" }, ] wheels = [ { url = "https://files.pythonhosted.org/packages/7a/8a/0e728f749baca3fbeffad762738276e5df60851958be7783af121a7221e7/nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5dad8008fc7f92f5ddfa2101430917ce2ffacd86824914c82e28990ad7f00399", size = 211422548 }, @@ -2707,9 +2767,9 @@ name = "nvidia-cusolver-cu12" version = "11.6.1.9" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" }, - { name = "nvidia-cusparse-cu12", marker = "(platform_machine != 'aarch64' and sys_platform 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"typing-extensions" }, - { name = "watchdog", marker = "sys_platform != 'darwin'" }, + { name = "watchdog", marker = "platform_system != 'Darwin'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/b0/e5/2bf2daa9c98658f1474bb64e7de030cbc4182b5f2b2196536efedaef02cb/streamlit-1.40.2.tar.gz", hash = "sha256:0cc131fc9b18065feaff8f6f241c81164ad37d8d9e3a85499a0240aaaf6a6a61", size = 8265763 } wheels = [ @@ -4771,21 +4830,21 @@ dependencies = [ { name = "fsspec" }, { name = "jinja2" }, { name = "networkx" }, - { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "nvidia-cudnn-cu12", marker = 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