multiple reasoning possibilities at each step, creating a tree structure. The search process can be BFS or DFS with each state evaluated by a classifier or majority vote.    3. Using task-specific instructions: For example, "Write a story outline." for writing a novel.    4. Human inputs: The agent can receive input from a human operator to perform tasks that require creativity and domain expertise.        These approaches allow the agent to break down complex tasks into manageable subgoals, enabling efficient handling of tasks and improving the quality of final results through self-reflection and refinement.You can also get logging for tokens.from langchain.schema import LLMResultfrom langchain.callbacks.base import BaseCallbackHandlerclass GenerationStatisticsCallback(BaseCallbackHandler):    def on_llm_end(self, response: LLMResult, **kwargs) -> None:        print(response.generations[0][0].generation_info)        callback_manager = CallbackManager([StreamingStdOutCallbackHandler(), GenerationStatisticsCallback()])llm = Ollama(base_url="http://localhost:11434",             model="llama2",             verbose=True,             callback_manager=callback_manager)qa_chain = RetrievalQA.from_chain_type(    llm,    retriever=vectorstore.as_retriever(),    chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},)question = "What are the approaches to Task Decomposition?"result = qa_chain({"query": question})eval_count / (eval_duration/10e9)  gets tok / s62 / (1313002000/1000/1000/1000)    47.22003469910937Using the Hub for prompt management‚ÄãOpen-source models often benefit from specific prompts. For example, Mistral 7b was fine-tuned for chat using the prompt format shown here.Get the model: ollama pull mistral:7b-instruct# LLMfrom langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Ollama(model="mistral:7b-instruct",             verbose=True,
Ollama allows you to run open-source large language models, such as Llama 2, locally.
Ollama allows you to run open-source large language models, such as Llama 2, locally. ->: multiple reasoning possibilities at each step, creating a tree structure. The search process can be BFS or DFS with each state evaluated by a classifier or majority vote.    3. Using task-specific instructions: For example, "Write a story outline." for writing a novel.    4. Human inputs: The agent can receive input from a human operator to perform tasks that require creativity and domain expertise.        These approaches allow the agent to break down complex tasks into manageable subgoals, enabling efficient handling of tasks and improving the quality of final results through self-reflection and refinement.You can also get logging for tokens.from langchain.schema import LLMResultfrom langchain.callbacks.base import BaseCallbackHandlerclass GenerationStatisticsCallback(BaseCallbackHandler):    def on_llm_end(self, response: LLMResult, **kwargs) -> None:        print(response.generations[0][0].generation_info)        callback_manager = CallbackManager([StreamingStdOutCallbackHandler(), GenerationStatisticsCallback()])llm = Ollama(base_url="http://localhost:11434",             model="llama2",             verbose=True,             callback_manager=callback_manager)qa_chain = RetrievalQA.from_chain_type(    llm,    retriever=vectorstore.as_retriever(),    chain_type_kwargs={"prompt": QA_CHAIN_PROMPT},)question = "What are the approaches to Task Decomposition?"result = qa_chain({"query": question})eval_count / (eval_duration/10e9)  gets tok / s62 / (1313002000/1000/1000/1000)    47.22003469910937Using the Hub for prompt management‚ÄãOpen-source models often benefit from specific prompts. For example, Mistral 7b was fine-tuned for chat using the prompt format shown here.Get the model: ollama pull mistral:7b-instruct# LLMfrom langchain.llms import Ollamafrom langchain.callbacks.manager import CallbackManagerfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Ollama(model="mistral:7b-instruct",             verbose=True,