model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Token sales model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from radcad import Model, Simulation, Experiment\n",
    "from radcad.engine import Engine, Backend\n",
    "from dataclasses import dataclass\n",
    "from typing import TypedDict, List, Callable, NamedTuple\n",
    "import copy\n",
    "from dataclasses import field\n",
    "from functools import partial\n",
    "from enum import Enum\n",
    "\n",
    "from langchain import PromptTemplate\n",
    "from IPython.display import display, Markdown\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.prompts import PromptTemplate\n",
    "# from langchain.llms import OpenAI\n",
    "from langchain.agents import initialize_agent, Tool\n",
    "from langchain.agents import AgentType\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.agents import initialize_agent, Tool\n",
    "from langchain.agents import AgentType\n",
    "from langchain import PromptTemplate\n",
    "from langchain.chains import LLMChain\n",
    "# from langchain.llms import OpenAI\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "from langchain import PromptTemplate\n",
    "from IPython.display import display, Markdown\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.llms import HuggingFaceHub\n",
    "from dotenv import load_dotenv\n",
    "import os\n",
    "# from model.types import * \n",
    "\n",
    "\n",
    "#types \n",
    "\n",
    "PERCENTAGE = float\n",
    "USD = float\n",
    "USD_PER_MONTH = float\n",
    "MONTHS = int\n",
    "MULTIPLIER = float\n",
    "USD_PER_TOKEN = float\n",
    "TOKENS = int\n",
    "\n",
    "\n",
    "DELTA_TIME=1\n",
    "Run = 1\n",
    "Timestep = 100\n",
    "\n",
    "def default(obj):\n",
    "    return field(default_factory=lambda: copy.copy(obj))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class Parameters:\n",
    "    \"\"\"Params for the model\"\"\"\n",
    "\n",
    "    # Fundraise and runway calcs\n",
    "    raise_amount : USD = default([5_000_000])\n",
    "    \n",
    "\n",
    "    # Token Generation and sale calcs\n",
    "    total_token_supply : TOKENS = default([180_000_000])\n",
    "\n",
    "    private_price : USD_PER_TOKEN = default([0.1])\n",
    "    public_price : USD_PER_TOKEN = default([0.12])\n",
    "\n",
    "    private_raise : USD = default([4_800_000])\n",
    "    public_raise : USD = default([200_000])\n",
    "\n",
    "# Initialize Parameters instance with default values\n",
    "system_params = Parameters().__dict__\n",
    "\n",
    "@dataclass\n",
    "class Agents:\n",
    "    type: str = ''\n",
    "    InvestorBool: bool = False\n",
    "    unlocked_at_listing: PERCENTAGE = 0.0\n",
    "    lock_term_after_listing: MONTHS = 0\n",
    "    release_term: MONTHS = 0\n",
    "    perc_total_token_supply: PERCENTAGE = 0.0\n",
    "    tokens: TOKENS = 0\n",
    "\n",
    "\n",
    "# state variables\n",
    "\n",
    "\n",
    "\n",
    "private_round_size = system_params['private_raise'][0]/system_params['private_price'][0]\n",
    "public_round_size = system_params['public_raise'][0]/system_params['public_price'][0]\n",
    "\n",
    "\n",
    "\n",
    "Investor_list = [Agents(type='Private', InvestorBool=True, unlocked_at_listing=25.0, lock_term_after_listing=3, release_term=24, \n",
    "                        perc_total_token_supply= np.round(public_round_size/system_params['total_token_supply'][0],3), tokens=0).__dict__,\n",
    "                 Agents(type='Public', InvestorBool=True, unlocked_at_listing=0.0, lock_term_after_listing=0, release_term=6, \n",
    "                        perc_total_token_supply= np.round(private_round_size/system_params['total_token_supply'][0],3), tokens=0).__dict__,\n",
    "                 Agents(type='Team', InvestorBool=False, unlocked_at_listing=0.0, lock_term_after_listing=9, release_term=36, \n",
    "                        perc_total_token_supply= 0.15, tokens=0).__dict__,\n",
    "                 Agents(type='Liquidity', InvestorBool=False, unlocked_at_listing=100.0, lock_term_after_listing=0, release_term=12, \n",
    "                        perc_total_token_supply= 0.05, tokens=0).__dict__,\n",
    "                 Agents(type='Ecosystem', InvestorBool=False, unlocked_at_listing=0.0, lock_term_after_listing=0, release_term=36, \n",
    "                        perc_total_token_supply= 0.10, tokens=0).__dict__,]\n",
    "\n",
    "\n",
    "\n",
    "def correct_perc_total_token_supply(Investor_list):\n",
    "    total = 0\n",
    "    for agent in Investor_list:\n",
    "        if agent['type']!='Ecosystem':\n",
    "            total += agent['perc_total_token_supply']\n",
    "    for agent in Investor_list:\n",
    "        if agent['type']=='Ecosystem':\n",
    "            agent['perc_total_token_supply'] = np.round(1-total,3)\n",
    "    return None\n",
    "\n",
    "correct_perc_total_token_supply(Investor_list)\n",
    "\n",
    "@dataclass\n",
    "class StateVariables:\n",
    "    # Investors\n",
    "    Investors: List[Agents] = default(Investor_list)\n",
    "    # Total: int= 0\n",
    "    # Total_as_perc_of_total_supply: PERCENTAGE = 0.0\n",
    "    # FLAG: bool = False\n",
    "    # MarketCap_at_highest_round_price: USD = 0.0\n",
    "    # Market_cap_at_1x_highest_round_valuation: USD = 0.0\n",
    "\n",
    "\n",
    "initial_state = StateVariables().__dict__\n",
    "\n",
    "Investor_df = pd.DataFrame(Investor_list)\n",
    "\n",
    "\n",
    "def p_monthly_unlocks(params, substep, state_history, prev_state, **kwargs):\n",
    "    agents = prev_state['Investors']\n",
    "    new_tokens = []\n",
    "    for agent in agents:\n",
    "        if prev_state['timestep'] == agent['lock_term_after_listing']:\n",
    "            new_tokens.append(agent['unlocked_at_listing']*params['total_token_supply']*agent['perc_total_token_supply']/100)\n",
    "        elif (prev_state['timestep'] > agent['lock_term_after_listing'] and (prev_state['timestep'] <= (agent['lock_term_after_listing']+agent['release_term']))):\n",
    "            # new_tokens.append(((agent['perc_total_token_supply'])*params['total_token_supply'])/agent['release_term'])\n",
    "            new_tokens.append((agent['perc_total_token_supply']*(1-(agent['unlocked_at_listing']/100)))*params['total_token_supply']/agent['release_term'])\n",
    "\n",
    "        else:\n",
    "            new_tokens.append(0)\n",
    "    \n",
    "    return {'new_unlocks': new_tokens}\n",
    "\n",
    "def s_update_tokens(params, substep, state_history, prev_state, policy_input, **kwargs):\n",
    "    agents=prev_state['Investors']\n",
    "    new_tokens = policy_input['new_unlocks']\n",
    "    for i, agent in enumerate(agents):\n",
    "        agent['tokens'] += new_tokens[i]\n",
    "    return ('Investors', agents)\n",
    "\n",
    "# def s_update_total(params, substep, state_history, prev_state, policy_input, **kwargs):\n",
    "#     new_tokens = policy_input['new_unlocks']\n",
    "#     total = prev_state['Total'] + sum(new_tokens)\n",
    "#     return ('Total', total)\n",
    "\n",
    "# def s_update_total_perc(params, substep, state_history, prev_state, policy_input, **kwargs):\n",
    "#     total = prev_state['Total'] + sum(policy_input['new_unlocks'])\n",
    "#     total_as_perc_of_total_supply = total/params['total_token_supply']\n",
    "#     return ('Total_as_perc_of_total_supply', total_as_perc_of_total_supply)\n",
    "\n",
    "\n",
    "state_update_blocks = [\n",
    "    {\n",
    "        'policies': {\n",
    "            'p_monthly_unlocks': p_monthly_unlocks,\n",
    "        },\n",
    "        'variables': {\n",
    "            # 'Total': s_update_total,\n",
    "            'Investors': s_update_tokens,\n",
    "            # 'Total_as_perc_of_total_supply': s_update_total_perc,\n",
    "\n",
    "        }\n",
    "        \n",
    "    },\n",
    "\n",
    "]\n",
    "\n",
    "\n",
    "# config and run\n",
    "\n",
    "TIMESTEPS = 120\n",
    "RUNS = 1\n",
    "\n",
    "\n",
    "model = Model(initial_state=initial_state, state_update_blocks=state_update_blocks, params=system_params)\n",
    "simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)\n",
    "\n",
    "# simulation.model.params.update({\n",
    "#     '': [\n",
    "        # 5\n",
    "#     ]\n",
    "# })\n",
    "\n",
    "experiment = Experiment(simulation)\n",
    "# Select the Pathos backend to avoid issues with multiprocessing and Jupyter Notebooks\n",
    "experiment.engine = Engine(backend=Backend.PATHOS)\n",
    "\n",
    "result = experiment.run()\n",
    "\n",
    "\n",
    "df = pd.DataFrame(result)\n",
    "\n",
    "\n",
    "# post processing \n",
    "#extract types of investors from first row\n",
    "def post_processing(df):\n",
    "    df2 = df.copy()\n",
    "    investor_types=[]\n",
    "    for investor in df2['Investors'][0]:\n",
    "        investor_types.append(investor['type'])\n",
    "    #make a new dataframe called df2 with the column names as the investor types and the values as the tokens\n",
    "    df3 = pd.DataFrame(columns=investor_types)\n",
    "    for i in range(df2.shape[0]):\n",
    "        for j in range(Investor_list.__len__()):\n",
    "            df3.loc[i, df['Investors'][i][j]['type']] = df2['Investors'][i][j]['tokens']\n",
    "\n",
    "    # remove the investor column from df and add df2 into df)\n",
    "    df2 = pd.concat([df3, df2['timestep']], axis=1)\n",
    "    # df2.set_index('timestep', inplace=True)\n",
    "    df2.rename(columns={'timestep': 'month'}, inplace=True)\n",
    "    return df2\n",
    "\n",
    "\n",
    "\n",
    "clean_df = post_processing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Private</th>\n",
       "      <th>Public</th>\n",
       "      <th>Team</th>\n",
       "      <th>Liquidity</th>\n",
       "      <th>Ecosystem</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>8010000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>2620000.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>16020000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>5240000.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>405000.0</td>\n",
       "      <td>24030000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>7860000.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1620000.0</td>\n",
       "      <td>48060000.0</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>94320000.0</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>1620000.0</td>\n",
       "      <td>48060000.0</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>94320000.0</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>1620000.0</td>\n",
       "      <td>48060000.0</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>94320000.0</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1620000.0</td>\n",
       "      <td>48060000.0</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>94320000.0</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>1620000.0</td>\n",
       "      <td>48060000.0</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>9000000.0</td>\n",
       "      <td>94320000.0</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Private      Public        Team  Liquidity   Ecosystem  month\n",
       "0            0           0           0          0           0      0\n",
       "1            0         0.0           0  9000000.0         0.0      1\n",
       "2            0   8010000.0           0  9000000.0   2620000.0      2\n",
       "3            0  16020000.0           0  9000000.0   5240000.0      3\n",
       "4     405000.0  24030000.0           0  9000000.0   7860000.0      4\n",
       "..         ...         ...         ...        ...         ...    ...\n",
       "116  1620000.0  48060000.0  27000000.0  9000000.0  94320000.0    116\n",
       "117  1620000.0  48060000.0  27000000.0  9000000.0  94320000.0    117\n",
       "118  1620000.0  48060000.0  27000000.0  9000000.0  94320000.0    118\n",
       "119  1620000.0  48060000.0  27000000.0  9000000.0  94320000.0    119\n",
       "120  1620000.0  48060000.0  27000000.0  9000000.0  94320000.0    120\n",
       "\n",
       "[121 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ai Integration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the openaikey from keys.env file\n",
    "\n",
    "\n",
    "# Load environment variables from .env file\n",
    "load_dotenv()\n",
    "\n",
    "# Access the value of the environment variable\n",
    "OPENAI_API_KEY = os.getenv('OPENAI_KEY')\n",
    "HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hugging face integration to use google flan-t5-large language model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "# use the hugging face llm\n",
    "llm=HuggingFaceHub(repo_id=\"google/flan-t5-large\", model_kwargs={\"temperature\":1e-10})\n",
    "llm_demo1 = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Langchain framework for prompt engineering and Build the Plan and Execute agents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def create_agent1():\n",
    "    \n",
    "    temp1 = f\"\"\"\n",
    "    You are answering questions about a token supply model. If information isnt there then say \"I dont know\". The model has the following parameters:\n",
    "    system_params = {system_params.items()}\"\"\"\n",
    "    temp2 = \"\"\"Input: {input}\n",
    "    Output: According to the system parameters, \"\"\"\n",
    "\n",
    "    template = temp1 + temp2\n",
    "\n",
    "    prompt = PromptTemplate.from_template(template)\n",
    "    # prompt.format(input=\"if my token supply was 60 million, what would the private round investors have at month 24\")\n",
    "\n",
    "\n",
    "\n",
    "    llm2 = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n",
    "    prompt = PromptTemplate(\n",
    "        input_variables=[\"input\"],\n",
    "        template=template,\n",
    "    )\n",
    "\n",
    "    from langchain.chains import LLMChain\n",
    "    return LLMChain(llm=llm, prompt=prompt)\n",
    "\n",
    "def agent1(input):\n",
    "    agent1 = create_agent1()\n",
    "    return agent1.run(input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def prompt_cleaner():\n",
    "        \n",
    "    template = \"\"\"/\n",
    "    You are cleaning the input for another large language model agent. Follow these instructions:\n",
    "    1) If the input is about of these ['total token supply', 'raise_amount', 'private_price', 'public_price', 'private_raise', 'public_raise'] then output should be 'find about the input in my system parameters'\n",
    "    2) If the input is about average allocations or average token prices in our sector then output should be 'find about the input in the benchmarked data'\n",
    "    3) If the input is about showing the full model then the output should be 'I should print the full model'\n",
    "\n",
    "    Input: 'What is the total token supply?'\n",
    "    Output: 'find the total token supply in my system parameters'\n",
    "    Input: 'What is the average private vs public round size in my sector?'\n",
    "    Output: 'find the average private vs public size in the benchmarked data'\n",
    "    Input: {input}\n",
    "    Output:\n",
    "    \"\"\"\n",
    "\n",
    "    prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "    # llm_demo1 = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n",
    "    prompt = PromptTemplate(\n",
    "        input_variables=[\"input\"],\n",
    "        template=template,\n",
    "    )\n",
    "\n",
    "\n",
    "    return LLMChain(llm=llm, prompt=prompt)\n",
    "\n",
    "chain_demo1 = prompt_cleaner()\n",
    "\n",
    "# Run the chain only specifying the input variable.\n",
    "# print(chain_demo1.run(\"What's the avg liquidity allocation in my sector?\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def model_info(input):\n",
    "    response = agent1(input)\n",
    "    return response\n",
    "\n",
    "def memory_database(input):\n",
    "    tempdata = f\"\"\"\n",
    "    Based on the similarity search on the benchmarked documents, closest match I found was on page 30 of pdf #322\n",
    "    It is observed that in the sector of DePIN projects the standard allocation schemes look like:\n",
    "    - private around 20%-35%\n",
    "    - public around 2%\n",
    "    - team less than 15%\n",
    "    - liquidity around 5%\n",
    "    - ecosystem+community (rest of the total allocation)\n",
    "    \"\"\"\n",
    "    return tempdata\n",
    "\n",
    "def parsing_model_info(string):\n",
    "    # a, b = string.split(\",\")\n",
    "    return model_info(string)\n",
    "\n",
    "def parsing_memory(string):\n",
    "    # r,a,b,c = string.split(\",\")\n",
    "    return memory_database(string)\n",
    "\n",
    "# llm_demo1 = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n",
    "tools_demo1 = [\n",
    "    Tool(\n",
    "        name=\"model info\",\n",
    "        func=parsing_model_info,\n",
    "        description=\"Use this tool when asked about system parameters like total token supply, raise amount\",\n",
    "    ),\n",
    "    Tool(\n",
    "        name=\"memory vector database\",\n",
    "        func=parsing_memory,\n",
    "        description=\"Use this tool only when asked about benchmarked data. \",\n",
    "    )\n",
    "\n",
    "]\n",
    "\n",
    "mrkl_demo1 = initialize_agent(\n",
    "    tools_demo1, llm_demo1, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
    ")\n",
    "\n",
    "def agent_demo_1(input):\n",
    "    return mrkl_demo1.run(chain_demo1.run(input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m I need to find the total token supply\n",
      "Action: model info\n",
      "Action Input: total token supply\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m180000000\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the total token supply\n",
      "Final Answer: The total token supply is 180000000.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The total token supply is 180000000.'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_demo_1('total token supply?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def rel_finder(a, b):\n",
    "    return f\"we can either reduce total supply or reduce Token Price to reduce FDV by 15%.\"\n",
    "\n",
    "def optimizer(r,a,b,c):\n",
    "    return \"We can either reduce Token price down to 0.08 or reduce token supply to 150 million\"\n",
    "\n",
    "def parsing_multiplier(string):\n",
    "    a, b = string.split(\",\")\n",
    "    return rel_finder(str(a), str(b))\n",
    "\n",
    "def parising_optimizer(string):\n",
    "    r,a,b,c = string.split(\",\")\n",
    "    return optimizer(str(r),str(a),str(b),str(c))\n",
    "\n",
    "# llm_demo2 = OpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)\n",
    "tools_demo2 = [\n",
    "    Tool(\n",
    "        name=\"relationship_finder\",\n",
    "        func=parsing_multiplier,\n",
    "        description=\"useful as first step when you need to reduce FDV. The input to this tool should be a comma separated list of a string and a number of length two, representing the variable to change and percentage you want to reduce it by. For example, 'FDV,5' would be the input if you wanted to reduce FDV by 5%\",\n",
    "    ),\n",
    "    Tool(\n",
    "        name=\"optimizer\",\n",
    "        func=parising_optimizer,\n",
    "        description=\"useful as second step, when we know we can either reduce total supply or reduce Token Price to reduce FDV by 15%. The input to this tool should be a comma separated list of two strings and a number, representing the constant FDV, then the other 2 variales to change and percentage you want to reduce it by. For example, 'FDV, token_supply, token_price,15' would be the input if you wanted to reduce FDV by 15perc and knew token supply and token price affet FDV\",\n",
    "    )\n",
    "\n",
    "]\n",
    "mrkl_demo2 = initialize_agent(\n",
    "    tools_demo2, llm_demo1, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n",
    ")\n",
    "\n",
    "def agent_demo_2(input):\n",
    "    return mrkl_demo2.run('first use relationship finder and then use optimizer'+input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m I need to reduce FDV by 15%, so I should use the tools to help me.\n",
      "Action: relationship_finder\n",
      "Action Input: FDV,5\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mwe can either reduce total supply or reduce Token Price to reduce FDV by 15%.\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I need to use the optimizer to reduce FDV by 15%.\n",
      "Action: optimizer\n",
      "Action Input: FDV, token_supply, token_price,15\u001b[0m\n",
      "Observation: \u001b[33;1m\u001b[1;3mWe can either reduce Token price down to 0.08 or reduce token supply to 150 million\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
      "Final Answer: Yes, we can reduce FDV by 15% by either reducing Token price down to 0.08 or reducing token supply to 150 million.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Yes, we can reduce FDV by 15% by either reducing Token price down to 0.08 or reducing token supply to 150 million.'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_demo_2('can you reduce FDV by 15%?')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEMO"
   ]
  },
  {
   "attachments": {
    "image.png": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m I need to find the total token supply\n",
      "Action: model info\n",
      "Action Input: total token supply\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m180000000\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the total token supply\n",
      "Final Answer: The total token supply is 180000000.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The total token supply is 180000000.'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# agent has access to the system parameters and documentation of the entire cadcad based token supply model\n",
    "\n",
    "\n",
    "agent_demo_1('total token supply?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m I need to reduce FDV by 15%, so I should use the tools to help me.\n",
      "Action: relationship_finder\n",
      "Action Input: FDV,5\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mwe can either reduce total supply or reduce Token Price to reduce FDV by 15%.\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I need to use the optimizer to reduce FDV by 15%.\n",
      "Action: optimizer\n",
      "Action Input: FDV, token_supply, token_price,15\u001b[0m\n",
      "Observation: \u001b[33;1m\u001b[1;3mWe can either reduce Token price down to 0.08 or reduce token supply to 150 million\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
      "Final Answer: Yes, we can reduce FDV by 15% by either reducing Token price down to 0.08 or reducing token supply to 150 million.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Yes, we can reduce FDV by 15% by either reducing Token price down to 0.08 or reducing token supply to 150 million.'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it can run scenarios and give you potential changes to the system parameters to achieve a certain metric or goal\n",
    "\n",
    "\n",
    "agent_demo_2('can you reduce FDV by 15%?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m I need to use the memory vector database\n",
      "Action: memory vector database\n",
      "Action Input: benchmarked data\u001b[0m\n",
      "Observation: \u001b[33;1m\u001b[1;3m\n",
      "    Based on the similarity search on the benchmarked documents, closest match I found was on page 30 of pdf #322\n",
      "    It is observed that in the sector of DePIN projects the standard allocation schemes look like:\n",
      "    - private around 20%-35%\n",
      "    - public around 2%\n",
      "    - team less than 15%\n",
      "    - liquidity around 5%\n",
      "    - ecosystem+community (rest of the total allocation)\n",
      "    \u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
      "Final Answer: The standard allocation scheme for DePIN projects is private around 20%-35%, public around 2%, team less than 15%, liquidity around 5%, and ecosystem+community (rest of the total allocation).\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'The standard allocation scheme for DePIN projects is private around 20%-35%, public around 2%, team less than 15%, liquidity around 5%, and ecosystem+community (rest of the total allocation).'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it can also answer questions about the model and the benchmarked industry which we upload as a data asset on ocean \n",
    "\n",
    "agent_demo_1('avg allocation in my sector')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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