have logging on, so we seem them there too.from langchain.chains import RetrievalQAWithSourcesChain# Initializeweb_research_retriever = WebResearchRetriever.from_llm(    vectorstore=vectorstore_llama,    llm=llama,     search=search, )# Runuser_input = "What is Task Decomposition in LLM Powered Autonomous Agents?"qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llama,retriever=web_research_retriever)result = qa_chain({"question": user_input})result    INFO:langchain.retrievers.web_research:Generating questions for Google Search ...      Sure, here are five Google search queries that are similar to "What is Task Decomposition in LLM Powered Autonomous Agents?":        1. How does Task Decomposition work in LLM Powered Autonomous Agents?     2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents?     3. Can you provide examples of Task Decomposition in LLM Powered Autonomous Agents?     4. How does Task Decomposition improve the performance of LLM Powered Autonomous Agents?     5. What are some common challenges or limitations of using Task Decomposition in LLM Powered Autonomous Agents, and how can they be addressed?        llama_print_timings:        load time =  8585.01 ms    llama_print_timings:      sample time =   124.24 ms /   164 runs   (    0.76 ms per token,  1320.04 tokens per second)    llama_print_timings: prompt eval time =  8584.83 ms /   101 tokens (   85.00 ms per token,    11.76 tokens per second)    llama_print_timings:        eval time =  7268.55 ms /   163 runs   (   44.59 ms per token,    22.43 tokens per second)    llama_print_timings:       total time = 16236.13 ms    INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How does Task Decomposition work in LLM Powered Autonomous Agents? \n', '2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents? \n',
Given a query, this retriever will:
Given a query, this retriever will: ->: have logging on, so we seem them there too.from langchain.chains import RetrievalQAWithSourcesChain# Initializeweb_research_retriever = WebResearchRetriever.from_llm(    vectorstore=vectorstore_llama,    llm=llama,     search=search, )# Runuser_input = "What is Task Decomposition in LLM Powered Autonomous Agents?"qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llama,retriever=web_research_retriever)result = qa_chain({"question": user_input})result    INFO:langchain.retrievers.web_research:Generating questions for Google Search ...      Sure, here are five Google search queries that are similar to "What is Task Decomposition in LLM Powered Autonomous Agents?":        1. How does Task Decomposition work in LLM Powered Autonomous Agents?     2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents?     3. Can you provide examples of Task Decomposition in LLM Powered Autonomous Agents?     4. How does Task Decomposition improve the performance of LLM Powered Autonomous Agents?     5. What are some common challenges or limitations of using Task Decomposition in LLM Powered Autonomous Agents, and how can they be addressed?        llama_print_timings:        load time =  8585.01 ms    llama_print_timings:      sample time =   124.24 ms /   164 runs   (    0.76 ms per token,  1320.04 tokens per second)    llama_print_timings: prompt eval time =  8584.83 ms /   101 tokens (   85.00 ms per token,    11.76 tokens per second)    llama_print_timings:        eval time =  7268.55 ms /   163 runs   (   44.59 ms per token,    22.43 tokens per second)    llama_print_timings:       total time = 16236.13 ms    INFO:langchain.retrievers.web_research:Questions for Google Search (raw): {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?', 'text': LineList(lines=['1. How does Task Decomposition work in LLM Powered Autonomous Agents? \n', '2. What are the benefits of using Task Decomposition in LLM Powered Autonomous Agents? \n',