langchain.llms import GPT4Allllm = GPT4All(    model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin",    max_tokens=2048,)LLMChain‚ÄãRun 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.prompts import PromptTemplatefrom langchain.chains import 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:        load time =  1191.88 ms    llama_print_timings:      sample time =   134.47 ms /   193 runs   (    0.70 ms per token,  1435.25 tokens per second)
The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.
The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: langchain.llms import GPT4Allllm = GPT4All(    model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin",    max_tokens=2048,)LLMChain‚ÄãRun 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.prompts import PromptTemplatefrom langchain.chains import 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:        load time =  1191.88 ms    llama_print_timings:      sample time =   134.47 ms /   193 runs   (    0.70 ms per token,  1435.25 tokens per second)