modified the mission

README.md

@@ -30,14 +30,14 @@
 
 The agent can produce detailed, factual and unbiased research reports, with customization options for focusing on relevant resources, outlines, and lessons. Inspired by the recent [Plan-and-Solve](https://arxiv.org/abs/2305.04091) and [RAG](https://arxiv.org/abs/2005.11401) papers, GPT Researcher addresses issues of speed, determinism and reliability, offering a more stable performance and increased speed through parallelized agent work, as opposed to synchronous operations.
 
-**Our mission is to empower individuals and organizations with accurate, unbiased, and factual information by leveraging the power of AI.**
+**Our mission is to combat misinformation and empower individuals and organizations with accurate, unbiased, and factual information through advanced AI.**
 
 ## Why GPT Researcher?
 
 - To form objective conclusions for manual research tasks can take time, sometimes weeks to find the right resources and information.
 - Current LLMs are trained on past and outdated information, with heavy risks of hallucinations, making them almost irrelevant for research tasks.
 - Current LLMs are limited to short token outputs which are not sufficient for long detailed research reports (2k+ words).
-- Services that enable web search (such as ChatGPT + Web Plugin), only consider limited sources and content that in some cases result in superficial and biased answers.
+- Services that enable web search (such as ChatGPT + Web Plugin), only consider limited sources and content that in some cases result in misinformation and shallow results.
 - Using only a selection of web sources can create bias in determining the right conclusions for research tasks.
 
 ## Demo

gpt_researcher/scraper/web_base_loader/web_base_loader.py

@@ -1,5 +1,3 @@
-from langchain_community.document_loaders import WebBaseLoader
-
 
 class WebBaseLoaderScraper:
 
@@ -18,6 +16,7 @@ def scrape(self) -> str:
         occurs during the process, an error message is printed and an empty string is returned.
         """
         try:
+            from langchain_community.document_loaders import WebBaseLoader
             loader = WebBaseLoader(self.link)
             loader.requests_kwargs = {"verify": False}
             docs = loader.load()