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Bittensor 是一个类似于 Bitcoin 的挖矿网络，内置激励机制以鼓励矿工贡献算力和知识。 NIBittensorLLM 由 Neural Internet 开发，基于 Bittensor 提供支持。
该 LLM 通过提供来自 Bittensor 协议 的最佳响应，展示了去中心化 AI 的真正潜力。该协议由 OpenAI、LLaMA2 等多种 AI 模型组成。
用户可以在验证器端点前端查看其日志、请求和 API 密钥。但目前不允许更改配置，否则用户的查询将被阻止。 如果您遇到任何困难或有任何问题，请随时通过 GitHub、Discord 联系我们的开发者，或加入我们的 Discord 服务器以获取最新更新和答疑 Neural Internet。

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NIBittensorLLM 的不同参数和响应处理

import json
from pprint import pprint

from langchain.globals import set_debug
from langchain_community.llms import NIBittensorLLM

set_debug(True)

# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with model
llm_sys = NIBittensorLLM(
    system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project"
)
sys_resp = llm_sys(
    "What is bittensor and What are the potential benefits of decentralized AI?"
)
print(f"Response provided by LLM with system prompt set is : {sys_resp}")

# The top_responses parameter can give multiple responses based on its parameter value
# This below code retrieve top 10 miner's response all the response are in format of json

# Json response structure is
""" {
    "choices":  [
                    {"index": Bittensor's Metagraph index number,
                    "uid": Unique Identifier of a miner,
                    "responder_hotkey": Hotkey of a miner,
                    "message":{"role":"assistant","content": Contains actual response},
                    "response_ms": Time in millisecond required to fetch response from a miner}
                ]
    } """

multi_response_llm = NIBittensorLLM(top_responses=10)
multi_resp = multi_response_llm.invoke("What is Neural Network Feeding Mechanism?")
json_multi_resp = json.loads(multi_resp)
pprint(json_multi_resp)

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将 NIBittensorLLM 与 LLMChain 和 PromptTemplate 结合使用

from langchain_classic.chains import LLMChain
from langchain.globals import set_debug
from langchain_community.llms import NIBittensorLLM
from langchain_core.prompts import PromptTemplate

set_debug(True)

template = """Question: {question}

Answer: Let's think step by step."""


prompt = PromptTemplate.from_template(template)

# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with model
llm = NIBittensorLLM(
    system_prompt="Your task is to determine response based on user prompt."
)

llm_chain = LLMChain(prompt=prompt, llm=llm)
question = "What is bittensor?"

llm_chain.run(question)

​
将 NIBittensorLLM 与对话代理和 Google 搜索工具结合使用

from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain.tools import tool

search = GoogleSearchAPIWrapper()


@tool
def google_search(query: str) -> str:
    """Search Google for recent results."""
    return search.run(query)

from langchain_classic import hub
from langchain.agents import (
    AgentExecutor,
    create_agent,
)
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import NIBittensorLLM

tools = [google_search]

prompt = hub.pull("hwchase17/react")


llm = NIBittensorLLM(
    system_prompt="Your task is to determine a response based on user prompt"
)

memory = ConversationBufferMemory(memory_key="chat_history")

agent = create_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory)

response = agent_executor.invoke({"input": prompt})
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