text: / 21845.34)LLMChainâ€‹Run an LLMChain (see here) with either model by passing in the retrieved docs and a simple prompt.It formats the prompt template using the input key values provided and passes the formatted string to GPT4All, LLama-V2, or another specified LLM.In this case, the list of retrieved documents (docs) above are pass into {context}.from langchain import PromptTemplate, LLMChain# Promptprompt = PromptTemplate.from_template(    "Summarize the main themes in these retrieved docs: {docs}")# Chainllm_chain = LLMChain(llm=llm, prompt=prompt)# Runquestion = "What are the approaches to Task Decomposition?"docs = vectorstore.similarity_search(question)result = llm_chain(docs)# Outputresult["text"]    Llama.generate: prefix-match hit        Based on the retrieved documents, the main themes are:    1. Task decomposition: The ability to break down complex tasks into smaller subtasks, which can be handled by an LLM or other components of the agent system.    2. LLM as the core controller: The use of a large language model (LLM) as the primary controller of an autonomous agent system, complemented by other key components such as a knowledge graph and a planner.    3. Potentiality of LLM: The idea that LLMs have the potential to be used as powerful general problem solvers, not just for generating well-written copies but also for solving complex tasks and achieving human-like intelligence.    4. Challenges in long-term planning: The challenges in planning over a lengthy history and effectively exploring the solution space, which are important limitations of current LLM-based autonomous agent systems.        llama_print_timings: