Download tools_langchain_browser.py from JPM34/final_assignment_agentic_course: direct link, hf CLI and curl.
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https://huggingface.co/spaces/JPM34/final_assignment_agentic_course/resolve/main/tools_langchain_browser.py
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hf download hf://spaces/JPM34/final_assignment_agentic_course/tools_langchain_browser.py
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curl -L -o tools_langchain_browser.py https://huggingface.co/spaces/JPM34/final_assignment_agentic_course/resolve/main/tools_langchain_browser.py
2.55 kB
| from bs4 import BeautifulSoup | |
| from langchain_community.document_loaders import WikipediaLoader | |
| from langchain_community.document_loaders import ArxivLoader | |
| from langchain_community.tools.tavily_search import TavilySearchResults | |
| from langchain_core.tools import tool | |
| # from smolagents import tool | |
| from playwright.sync_api import sync_playwright | |
| def wiki_search(query: str) -> str: | |
| """Search Wikipedia for a query and return maximum 2 results. | |
| Args: | |
| query: The search query.""" | |
| search_docs = WikipediaLoader(query=query, load_max_docs=2).load() | |
| formatted_search_docs = "\n\n---\n\n".join( | |
| [ | |
| f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>' | |
| for doc in search_docs | |
| ] | |
| ) | |
| return {"wiki_results": formatted_search_docs} | |
| def web_search(query: str) -> str: | |
| """Search Tavily for a query and return maximum 3 results. | |
| Args: | |
| query: The search query.""" | |
| search_docs = TavilySearchResults(max_results=3).invoke(query=query) | |
| formatted_search_docs = "\n\n---\n\n".join( | |
| [ | |
| f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>' | |
| for doc in search_docs | |
| ] | |
| ) | |
| return {"web_results": formatted_search_docs} | |
| def arxiv_search(query: str) -> str: | |
| """Search Arxiv for a query and return maximum 3 result. | |
| Args: | |
| query: The search query.""" | |
| search_docs = ArxivLoader(query=query, load_max_docs=3).load() | |
| formatted_search_docs = "\n\n---\n\n".join( | |
| [ | |
| f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>' | |
| for doc in search_docs | |
| ] | |
| ) | |
| return {"arxiv_results": formatted_search_docs} | |
| def website_scrape(url: str, question: str) -> str: | |
| """Scrapes a website and returns the text. | |
| Args: | |
| url: the URL to the website to scrape. | |
| question: the question to answer when searching for the answer in the website. | |
| Returns: | |
| str: The text of the website. | |
| """ | |
| with sync_playwright() as p: | |
| browser = p.chromium.launch(headless=True) | |
| page = browser.new_page() | |
| page.goto(url) | |
| html_content = page.content() | |
| browser.close() | |
| soup = BeautifulSoup(html_content, "html.parser") | |
| # Extract text from the website | |
| text = soup.get_text() | |
| return text | |