Bittensor

Bittensor 是一个类似于比特币的挖掘网络，其中包含旨在鼓励矿工贡献计算能力和知识的内置激励机制。

NIBittensorLLM 由 Neural Internet 开发，由 Bittensor 提供支持。

这个 LLM 通过向您提供来自 Bittensor 协议 的最佳响应展示了去中心化人工智能的真正潜力，该协议包含各种人工智能模型，如 OpenAI、LLaMA2 等。

用户可以在 验证器端点前端 查看他们的日志、请求和 API 密钥。但是，目前禁止对配置进行更改；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，请随时联系我们的开发人员，联系方式为 GitHub、Discord，或加入我们的 Discord 服务器以获取最新更新和查询 Neural Internet。

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 retrive 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)

API 参考：set_debug | NIBittensorLLM

使用 NIBittensorLLM 与 LLMChain 和 PromptTemplate

from langchain.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)

API 参考：LLMChain | set_debug | NIBittensorLLM | PromptTemplate

使用 NIBittensorLLM 与对话式代理和 Google 搜索工具

from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_core.tools import Tool

search = GoogleSearchAPIWrapper()

tool = Tool(
    name="Google Search",
    description="Search Google for recent results.",
    func=search.run,
)

API 参考：GoogleSearchAPIWrapper | Tool

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

tools = [tool]

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

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

API 参考：AgentExecutor | create_react_agent | ConversationBufferMemory | NIBittensorLLM

相关

• LLM 概念指南
• LLM 操作指南