text: "Steps for XYZ" and asked to list the subgoals for achieving XYZ.\n\n2. Task-specific instructions: In this approach, task-specific instructions are provided to the agent to guide the decomposition process. For example, if the task is to write a novel, the agent can be instructed to "Write a story outline" as a subgoal.\n\n3. Human inputs: This approach involves incorporating human inputs in the task decomposition process. Humans can provide guidance, feedback, and suggestions to help the agent break down complex tasks into manageable subgoals.\n\nThese approaches aim to enable efficient handling of complex tasks by breaking them down into smaller, more manageable parts.'}3.2 Going Deeperâ€‹3.2.1 Integrationsâ€‹LLMsBrowse the > 55 LLM integrations here.See further documentation on LLMs here. 3.2.2 Running LLMs locallyâ€‹The popularity of PrivateGPT and GPT4All underscore the importance of running LLMs locally.LangChain has integrations with many open source LLMs that can be run locally.Using GPT4All is as simple as downloading the binary and then:from langchain.llms import GPT4Allfrom langchain.chains import RetrievalQAllm = GPT4All(model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin",max_tokens=2048)qa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever())qa_chain({"query": question}){'query': 'What are the approaches to Task Decomposition?', 'result': ' There are three main approaches to task decomposition: (1) using language models like GPT-3 for simple prompting such as "Steps for XYZ.\\n1.", (2) using task-specific instructions, and