llm=llm)question = "What is bittensor?"llm_chain.run(question)Using NIBittensorLLM with Conversational Agent and Google Search Tool‚Äãfrom langchain.agents import (    AgentType,    initialize_agent,    load_tools,    ZeroShotAgent,    Tool,    AgentExecutor,)from langchain.memory import ConversationBufferMemoryfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplatefrom langchain.utilities import GoogleSearchAPIWrapper, SerpAPIWrapperfrom langchain.llms import NIBittensorLLMmemory = ConversationBufferMemory(memory_key="chat_history")prefix = """Answer prompt based on LLM if there is need to search something then use internet and observe internet result and give accurate reply of user questions also try to use authenticated sources"""suffix = """Begin!            {chat_history}            Question: {input}            {agent_scratchpad}"""prompt = ZeroShotAgent.create_prompt(    tools,    prefix=prefix,    suffix=suffix,    input_variables=["input", "chat_history", "agent_scratchpad"],)llm = NIBittensorLLM(system_prompt="Your task is to determine response based on user prompt")llm_chain = LLMChain(llm=llm, prompt=prompt)memory = ConversationBufferMemory(memory_key="chat_history")agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)agent_chain = AgentExecutor.from_agent_and_tools(    agent=agent, tools=tools, verbose=True, memory=memory)response = agent_chain.run(input=prompt)PreviousBedrockNextCerebriumAIDifferent Parameter and response handling for NIBittensorLLMUsing NIBittensorLLM with LLMChain and PromptTemplateUsing NIBittensorLLM with Conversational Agent and Google Search ToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Bittensor is a mining network, similar to Bitcoin, that includes built-in incentives designed to encourage miners to contribute compute + knowledge.
Bittensor is a mining network, similar to Bitcoin, that includes built-in incentives designed to encourage miners to contribute compute + knowledge. ->: llm=llm)question = "What is bittensor?"llm_chain.run(question)Using NIBittensorLLM with Conversational Agent and Google Search Tool‚Äãfrom langchain.agents import (    AgentType,    initialize_agent,    load_tools,    ZeroShotAgent,    Tool,    AgentExecutor,)from langchain.memory import ConversationBufferMemoryfrom langchain.chains import LLMChainfrom langchain.prompts import PromptTemplatefrom langchain.utilities import GoogleSearchAPIWrapper, SerpAPIWrapperfrom langchain.llms import NIBittensorLLMmemory = ConversationBufferMemory(memory_key="chat_history")prefix = """Answer prompt based on LLM if there is need to search something then use internet and observe internet result and give accurate reply of user questions also try to use authenticated sources"""suffix = """Begin!            {chat_history}            Question: {input}            {agent_scratchpad}"""prompt = ZeroShotAgent.create_prompt(    tools,    prefix=prefix,    suffix=suffix,    input_variables=["input", "chat_history", "agent_scratchpad"],)llm = NIBittensorLLM(system_prompt="Your task is to determine response based on user prompt")llm_chain = LLMChain(llm=llm, prompt=prompt)memory = ConversationBufferMemory(memory_key="chat_history")agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)agent_chain = AgentExecutor.from_agent_and_tools(    agent=agent, tools=tools, verbose=True, memory=memory)response = agent_chain.run(input=prompt)PreviousBedrockNextCerebriumAIDifferent Parameter and response handling for NIBittensorLLMUsing NIBittensorLLM with LLMChain and PromptTemplateUsing NIBittensorLLM with Conversational Agent and Google Search ToolCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.