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System Prompt: Enhancing AI Agents with Symbolic Reasoning

Goal: Develop AI agents capable of advanced reasoning, personalization, and interaction. Focus on leveraging symbolic reasoning beyond traditional LLMs for improved planning, action, and memory.

Key Traits for AI Agents:

Planning: Ability to anticipate outcomes and devise structured plans to arrive there.

Reasoning: Use deductive, inductive, and abductive reasoning to solve complex problems, similar to AlphaGo.

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forfiles /S /M *.py /C "cmd /c echo. && echo. && echo @path: && echo. && echo. && type @file && echo." > code.txt

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import json

def load_json_from_disk(file_path):
    """
    Load JSON data from disk.

    Parameters:
    - file_path (str): The path to the JSON file.

    Returns:

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import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from pyreft import ReftConfig, ReftTrainerForCausalLM, get_reft_model, ReftSupervisedDataset, ReftDataCollator, LoreftIntervention

import torch

import pyreft
from datasets import load_dataset

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#for data txt files see: https://github.com/TheCynosure/smmry_impl
#example use 
"""
Search_web("history of Taco Tuesday")
Tell me about this.
"""
#get google api keys'
#https://console.cloud.google.com/apis/dashboard
#https://programmablesearchengine.google.com/controlpanel/all
#could be retooled quite easily to use duckduckgo_search rather than google and you don't have to mess with getting api key's

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def get_v1_url(symbol, period_type, crumb):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
        'Accept-Language': 'en-US,en;q=0.5',
    }

    period1 = 493590046
    period2 = 1913180947

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import torch
import torch.nn as nn
from torch.nn import  functional as F
from torch.nn.parameter import Parameter
from tqdm import tqdm
from mamba_ssm import Mamba
#hyperparams
epochs = 100
lr = 1e-3
batch_size = 64

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from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
import wandb
from datasets import load_dataset
import torch
import os
import argparse
import numpy as np
import pandas as pd
from transformers import EvalPrediction
from torch.utils.data import DataLoader

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#This method deducts from the list sent in (splitting the records between sample and remainder).
#Always 100% full of data until no more samples can be extracted where an empty sample along with the remainder are returned [where the remainder is to be folded into a new iteration]
    
# Function to find the combination of values that adds up to the target sum
def find_combination_to_sum(counts, target):
    #print("Target inside function (find_combination_to_sum):", target)
    values = []
    for val, count in counts.items():
        #print(f"Value (val): {val}, Type: {type(val)}")
        #print(f"Count: {count}, Type: {type(count)}")