Cycloner mdwoicke

Minimal Prompt Chainables

Sequential prompt chaining in one method with context and output back-referencing.

main.py - start here - full example using MinimalChainable from chain.py to build a sequential prompt chain
chain.py - contains zero library minimal prompt chain class
chain_test.py - tests for chain.py, you can ignore this
requirements.py - python requirements

This is not working complete code.

This is strictly a v0, scrapy, proof of concept for the first version of a personal AI Assistant working end to end in just ~322 LOC.

It's only a frame of reference for you to consume the core ideas of how to build a POC of a personal AI Assistant.

To see the high level of how this works check out the explanation video. To follow our agentic journey check out the @IndyDevDan channel.

Stay focused, keep building.

	find: https://order.dominos.com/power/store-locator?s=${line1}&c=${line2}&type=${type}
	method: GET
	s: Street Address
	c: City, State and/or Postal Code
	type: Delivery or Carryout

	store info: https://order.dominos.com/power/store/${storeID}/profile
	method: GET
	storeId: store id number

	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference

	# The previous part of this tutorial is at https://gist.github.com/janakiramm/55d2d8ec5d14dd45c7e9127d81cdafcd

	from vertexai.language_models import TextEmbeddingModel
	from google.cloud import aiplatform
	import vertexai
	from vertexai.preview.generative_models import GenerativeModel, Part
	import json
	import os

	project=”YOUR_GCP_PROJECT”

	###Enable Google Cloud APIs and login with your credentials
	#gcloud services enable compute.googleapis.com aiplatform.googleapis.com storage.googleapis.com
	#gcloud auth application-default login

	###Install required Python modules
	#pip install pypdf2
	#pip install google-cloud-storage
	#pip install google-cloud-aiplatform
	#pip install jupyter

	[
	{
	"model": "gpt-4",
	"api_key": "PASTE_YOUR_API_KEY_HERE"
	},
	{
	"model": "gpt-4-1106-preview",
	"api_key": "PASTE_YOUR_API_KEY_HERE"
	},
	{

	import asyncio
	import contextlib
	import logging
	from telegram import Bot, Update
	from telegram.error import Forbidden, NetworkError

	# Initialize logging
	logging.basicConfig(
	format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.INFO
	)

	//RRF App Backend

	//import node / express dependencies
	import express from "express";
	import path from "path";

	//you'll need a '.dotenv' file in the root with the following variable:
	//OPENAI_API_KEY="[YOUR_OPENAI_API_KEY_HERE]"
	import dotenv from "dotenv";