0.70 ms per token,  1435.25 tokens per second)    llama_print_timings: prompt eval time = 39470.18 ms /  1055 tokens (   37.41 ms per token,    26.73 tokens per second)    llama_print_timings:        eval time =  8090.85 ms /   192 runs   (   42.14 ms per token,    23.73 tokens per second)    llama_print_timings:       total time = 47943.12 ms    '\nBased on the retrieved documents, the main themes are:\n1. 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.\n2. 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.\n3. 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.\n4. 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.'QA Chain‚ÄãWe can use a QA chain to handle our question above.chain_type="stuff" (see here) means that all the docs will be added (stuffed) into a prompt.We can also use the LangChain Prompt Hub to store and fetch prompts that are model-specific.This will work with your LangSmith API key.Let's try with a default RAG prompt, here.pip install langchainhub# Prompt from langchain import hubrag_prompt = hub.pull("rlm/rag-prompt")from langchain.chains.question_answering import load_qa_chain# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True)    Llama.generate: prefix-match hit        Task can be done by down a task into smaller subtasks, using simple prompting like "Steps for
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. ->: 0.70 ms per token,  1435.25 tokens per second)    llama_print_timings: prompt eval time = 39470.18 ms /  1055 tokens (   37.41 ms per token,    26.73 tokens per second)    llama_print_timings:        eval time =  8090.85 ms /   192 runs   (   42.14 ms per token,    23.73 tokens per second)    llama_print_timings:       total time = 47943.12 ms    '\nBased on the retrieved documents, the main themes are:\n1. 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.\n2. 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.\n3. 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.\n4. 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.'QA Chain‚ÄãWe can use a QA chain to handle our question above.chain_type="stuff" (see here) means that all the docs will be added (stuffed) into a prompt.We can also use the LangChain Prompt Hub to store and fetch prompts that are model-specific.This will work with your LangSmith API key.Let's try with a default RAG prompt, here.pip install langchainhub# Prompt from langchain import hubrag_prompt = hub.pull("rlm/rag-prompt")from langchain.chains.question_answering import load_qa_chain# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True)    Llama.generate: prefix-match hit        Task can be done by down a task into smaller subtasks, using simple prompting like "Steps for