can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?" , (2)\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 ... Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?'}]    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 ... Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?'}]    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,  6.32it/s]len(docs)    1Run locally‚ÄãSpecify LLM and embeddings that will run locally (e.g., on your laptop).from langchain.llms import LlamaCppfrom langchain.embeddings import GPT4AllEmbeddingsfrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlern_gpu_layers = 1  # Metal set to 1 is enough.n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])llama = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin",
Given a query, this retriever will:
Given a query, this retriever will: ->: can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?" , (2)\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 ... Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?'}]    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 ... Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1." , "What are the subgoals for achieving XYZ?'}]    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,  6.32it/s]len(docs)    1Run locally‚ÄãSpecify LLM and embeddings that will run locally (e.g., on your laptop).from langchain.llms import LlamaCppfrom langchain.embeddings import GPT4AllEmbeddingsfrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlern_gpu_layers = 1  # Metal set to 1 is enough.n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])llama = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin",