plan ahead. Task Decomposition#. Chain of thought (CoT;\xa0...'}]    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Search results: [{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Agent System Overview In a LLM-powered autonomous agent system, ... Task decomposition can be done (1) by LLM with simple prompting like\xa0...'}]    INFO:langchain.retrievers.web_research:New URLs to load: ['https://lilianweng.github.io/posts/2023-06-23-agent/']    INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls ...    Fetching pages: 100%|###################################################################################################################################| 1/1 [00:00<00:00, 10.49it/s]    Llama.generate: prefix-match hit     The content discusses Task Decomposition in LLM Powered Autonomous Agents, which involves breaking down large tasks into smaller, manageable subgoals for efficient handling of complex tasks.    SOURCES:    https://lilianweng.github.io/posts/2023-06-23-agent/        llama_print_timings:        load time =  8585.01 ms    llama_print_timings:      sample time =    52.88 ms /    72 runs   (    0.73 ms per token,  1361.55 tokens per second)    llama_print_timings: prompt eval time = 125925.13 ms /  2358 tokens (   53.40 ms per token,    18.73 tokens per second)    llama_print_timings:        eval time =  3504.16 ms /    71 runs   (   49.35 ms per token,    20.26 tokens per second)    llama_print_timings:       total time = 129584.60 ms    {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?',     'answer': ' The content discusses Task Decomposition in LLM Powered Autonomous Agents, which involves breaking down large tasks into smaller, manageable subgoals for efficient handling of complex tasks.\n',     'sources':
Given a query, this retriever will:
Given a query, this retriever will: ->: plan ahead. Task Decomposition#. Chain of thought (CoT;\xa0...'}]    INFO:langchain.retrievers.web_research:Searching for relevant urls ...    INFO:langchain.retrievers.web_research:Search results: [{'title': "LLM Powered Autonomous Agents | Lil'Log", 'link': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'snippet': 'Jun 23, 2023 ... Agent System Overview In a LLM-powered autonomous agent system, ... Task decomposition can be done (1) by LLM with simple prompting like\xa0...'}]    INFO:langchain.retrievers.web_research:New URLs to load: ['https://lilianweng.github.io/posts/2023-06-23-agent/']    INFO:langchain.retrievers.web_research:Grabbing most relevant splits from urls ...    Fetching pages: 100%|###################################################################################################################################| 1/1 [00:00<00:00, 10.49it/s]    Llama.generate: prefix-match hit     The content discusses Task Decomposition in LLM Powered Autonomous Agents, which involves breaking down large tasks into smaller, manageable subgoals for efficient handling of complex tasks.    SOURCES:    https://lilianweng.github.io/posts/2023-06-23-agent/        llama_print_timings:        load time =  8585.01 ms    llama_print_timings:      sample time =    52.88 ms /    72 runs   (    0.73 ms per token,  1361.55 tokens per second)    llama_print_timings: prompt eval time = 125925.13 ms /  2358 tokens (   53.40 ms per token,    18.73 tokens per second)    llama_print_timings:        eval time =  3504.16 ms /    71 runs   (   49.35 ms per token,    20.26 tokens per second)    llama_print_timings:       total time = 129584.60 ms    {'question': 'What is Task Decomposition in LLM Powered Autonomous Agents?',     'answer': ' The content discusses Task Decomposition in LLM Powered Autonomous Agents, which involves breaking down large tasks into smaller, manageable subgoals for efficient handling of complex tasks.\n',     'sources':