├── .gitignore ├── .vscode └── settings.json ├── README.md ├── ai_extractor.py ├── main.py ├── poetry.lock ├── pyproject.toml ├── schemas.py └── scrape.py /.gitignore: -------------------------------------------------------------------------------- 1 | virtual-env 2 | .env 3 | __pycache__ -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "[python]": { 3 | "editor.defaultFormatter": "ms-python.autopep8" 4 | }, 5 | "python.formatting.provider": "none" 6 | } 7 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Scrape the Web with entities extraction using OpenAI Function 2 | 3 | ## What is this? 4 | 5 | This codebase allows you to scrape any website and extract relevant data points easily using [OpenAI Functions](https://openai.com/blog/function-calling-and-other-api-updates) and [LangChain](https://python.langchain.com/docs/get_started/introduction). 6 | Create a schema in `schemas.py`, pick a url, and use them with `scrape_with_playwright()` in `main.py` to start scraping. 7 | 8 | Tip: each website has the bulk of content either in `

`, `` or `` tags. For best performance, choose a combination of tags that work for you. 9 | 10 | ### Example 11 | 12 | 1. Define the schema of the website you want to scrape in `schemas.py` (Pydantic class or dictionary are both fine): 13 | 14 | ```python 15 | class SchemaNewsWebsites(BaseModel): 16 | news_headline: str 17 | news_short_summary: str 18 | ``` 19 | 20 | 2. To start scraping, in `main.py`, run something like this: 21 | 22 | ```python 23 | asyncio.run(scrape_with_playwright( 24 | url="https://www.bbc.com", 25 | tags=["span"], 26 | schema_pydantic=SchemaNewsWebsites 27 | )) 28 | ``` 29 | 30 | ## Setup 31 | 32 | ### 1. Create a new Python virtual environment 33 | 34 | `python -m venv virtual-env` or `python3 -m venv virtual-env` (Mac) 35 | 36 | `py -m venv virtual-env` (Windows 11) 37 | 38 | ### 2. Activate virtual environment 39 | 40 | `.\virtual-env\Scripts\activate` (Windows) 41 | 42 | `source virtual-env/bin/activate` (Mac) 43 | 44 | ### 3. Install dependencies using Poetry 45 | 46 | Run `poetry install --sync` or `poetry install` 47 | 48 | ### 4. Install playwright 49 | 50 | ```bash 51 | playwright install 52 | ``` 53 | 54 | ### 5. Create a new `.env` file to store OpenAI's API key 55 | 56 | ```text 57 | OPENAI_API_KEY=XXXXXX 58 | ``` 59 | 60 | ## Usage 61 | 62 | ### Run locally 63 | 64 | ```bash 65 | python main.py 66 | ``` 67 | 68 | ## Additional Information 69 | 70 | - Add onto this a FastAPI server to serve this as an API endpoint for ease of use. 71 | 72 | - Use caution when scraping. Don't do anything I wouldn't do (illegal) 73 | 74 | - P.S I've added this functionality to LangChain [in this PR](https://github.com/langchain-ai/langchain/pull/8732). You can read [the official docs here.](https://python.langchain.com/docs/use_cases/web_scraping#quickstart) 75 | -------------------------------------------------------------------------------- /ai_extractor.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | from dotenv import load_dotenv 4 | from langchain.chains import (create_extraction_chain, 5 | create_extraction_chain_pydantic) 6 | from langchain.chat_models import ChatOpenAI 7 | 8 | load_dotenv() 9 | 10 | openai_api_key = os.getenv('OPENAI_API_KEY') 11 | 12 | llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613", 13 | openai_api_key=openai_api_key) 14 | 15 | 16 | def extract(content: str, **kwargs): 17 | """ 18 | The `extract` function takes in a string `content` and additional keyword arguments, and returns the 19 | extracted data based on the provided schema. 20 | """ 21 | 22 | # This part just formats the output from a Pydantic class to a Python dictionary for easier reading. Feel free to remove or tweak this. 23 | if 'schema_pydantic' in kwargs: 24 | response = create_extraction_chain_pydantic( 25 | pydantic_schema=kwargs["schema_pydantic"], llm=llm).run(content) 26 | response_as_dict = [item.dict() for item in response] 27 | 28 | return response_as_dict 29 | else: 30 | return create_extraction_chain(schema=kwargs["schema"], llm=llm).run(content) 31 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import pprint 3 | 4 | from ai_extractor import extract 5 | from schemas import SchemaNewsWebsites, ecommerce_schema 6 | from scrape import ascrape_playwright 7 | 8 | # TESTING 9 | if __name__ == "__main__": 10 | token_limit = 4000 11 | 12 | # News sites mostly have tags to scrape 13 | cnn_url = "https://www.cnn.com" 14 | wsj_url = "https://www.wsj.com" 15 | nyt_url = "https://www.nytimes.com/ca/" 16 | 17 | amazon_url = "https://www.amazon.ca/s?k=computers&crid=1LUXGQOD2ULFD&sprefix=%2Caps%2C94&ref=nb_sb_ss_recent_1_0_recent" 18 | 19 | async def scrape_with_playwright(url: str, tags, **kwargs): 20 | html_content = await ascrape_playwright(url, tags) 21 | 22 | print("Extracting content with LLM") 23 | 24 | html_content_fits_context_window_llm = html_content[:token_limit] 25 | 26 | extracted_content = extract(**kwargs, 27 | content=html_content_fits_context_window_llm) 28 | 29 | pprint.pprint(extracted_content) 30 | 31 | # Scrape and Extract with LLM 32 | asyncio.run(scrape_with_playwright( 33 | url=wsj_url, 34 | tags=["span"], 35 | schema_pydantic=SchemaNewsWebsites 36 | )) 37 | -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Poetry 1.4.0 and should not be changed by hand. 2 | 3 | [[package]] 4 | name = "aiohttp" 5 | version = 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[build-system] 20 | requires = ["poetry-core"] 21 | build-backend = "poetry.core.masonry.api" 22 | -------------------------------------------------------------------------------- /schemas.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | 3 | ecommerce_schema = { 4 | "properties": { 5 | "item_title": {"type": "string"}, 6 | "item_price": {"type": "number"}, 7 | "item_extra_info": {"type": "string"} 8 | }, 9 | "required": ["item_name", "price", "item_extra_info"], 10 | } 11 | 12 | 13 | class SchemaNewsWebsites(BaseModel): 14 | news_headline: str 15 | news_short_summary: str 16 | -------------------------------------------------------------------------------- /scrape.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | import pprint 3 | 4 | from bs4 import BeautifulSoup 5 | from playwright.async_api import async_playwright 6 | 7 | 8 | def remove_unwanted_tags(html_content, unwanted_tags=["script", "style"]): 9 | """ 10 | This removes unwanted HTML tags from the given HTML content. 11 | """ 12 | soup = BeautifulSoup(html_content, 'html.parser') 13 | 14 | for tag in unwanted_tags: 15 | for element in soup.find_all(tag): 16 | element.decompose() 17 | 18 | return str(soup) 19 | 20 | 21 | def extract_tags(html_content, tags: list[str]): 22 | """ 23 | This takes in HTML content and a list of tags, and returns a string 24 | containing the text content of all elements with those tags, along with their href attribute if the 25 | tag is an "a" tag. 26 | """ 27 | soup = BeautifulSoup(html_content, 'html.parser') 28 | text_parts = [] 29 | 30 | for tag in tags: 31 | elements = soup.find_all(tag) 32 | for element in elements: 33 | # If the tag is a link (a tag), append its href as well 34 | if tag == "a": 35 | href = element.get('href') 36 | if href: 37 | text_parts.append(f"{element.get_text()} ({href})") 38 | else: 39 | text_parts.append(element.get_text()) 40 | else: 41 | text_parts.append(element.get_text()) 42 | 43 | return ' '.join(text_parts) 44 | 45 | 46 | def remove_unessesary_lines(content): 47 | # Split content into lines 48 | lines = content.split("\n") 49 | 50 | # Strip whitespace for each line 51 | stripped_lines = [line.strip() for line in lines] 52 | 53 | # Filter out empty lines 54 | non_empty_lines = [line for line in stripped_lines if line] 55 | 56 | # Remove duplicated lines (while preserving order) 57 | seen = set() 58 | deduped_lines = [line for line in non_empty_lines if not ( 59 | line in seen or seen.add(line))] 60 | 61 | # Join the cleaned lines without any separators (remove newlines) 62 | cleaned_content = "".join(deduped_lines) 63 | 64 | return cleaned_content 65 | 66 | 67 | async def ascrape_playwright(url, tags: list[str] = ["h1", "h2", "h3", "span"]) -> str: 68 | """ 69 | An asynchronous Python function that uses Playwright to scrape 70 | content from a given URL, extracting specified HTML tags and removing unwanted tags and unnecessary 71 | lines. 72 | """ 73 | print("Started scraping...") 74 | results = "" 75 | async with async_playwright() as p: 76 | browser = await p.chromium.launch(headless=True) 77 | try: 78 | page = await browser.new_page() 79 | await page.goto(url) 80 | 81 | page_source = await page.content() 82 | 83 | results = remove_unessesary_lines(extract_tags(remove_unwanted_tags( 84 | page_source), tags)) 85 | print("Content scraped") 86 | except Exception as e: 87 | results = f"Error: {e}" 88 | await browser.close() 89 | return results 90 | 91 | 92 | # TESTING 93 | if __name__ == "__main__": 94 | url = "https://www.patagonia.ca/shop/new-arrivals" 95 | 96 | async def scrape_playwright(): 97 | results = await ascrape_playwright(url) 98 | print(results) 99 | 100 | pprint.pprint(asyncio.run(scrape_playwright())) 101 | --------------------------------------------------------------------------------