Kenneth Terry dmater01

This is not working complete code.

This is strictly a v0.2, scrapy, proof of concept version of a personal AI Assistant working end to end in just ~726 LOC.

This is the second iteration showcasing the two-way prompt aka multi-step human in the loop. The initial, v0, assistant version is here.

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.

Prompt Chaining with QwQ, Qwen, o1-mini, Ollama, and LLM

Here we explore prompt chaining with local reasoning models in combination with base models. With shockingly powerful local models like QwQ and Qwen, we can build some powerful prompt chains that let us tap into their capabilities in a immediately useful, local, private, AND free way.

Explore the idea of building prompt chains where the first is a powerful reasoning model that generates a response, and then use a base model to extract the response.

Play with the prompts and models to see what works best for your use cases. Use the o1 series to see how qwq compares.

Setup

Bun (to run bun run chain.ts ...)

Chat GPT "DAN" (and other "Jailbreaks")

	import openai

	openai.api_key = "YOUR API KEY HERE"
	model_engine = "text-davinci-003"
	chatbot_prompt = """
	As an advanced chatbot, your primary goal is to assist users to the best of your ability. This may involve answering questions, providing helpful information, or completing tasks based on user input. In order to effectively assist users, it is important to be detailed and thorough in your responses. Use examples and evidence to support your points and justify your recommendations or solutions.

	<conversation history>

	User: <user input>

	import speech_recognition as sr
	import pyttsx3
	import openai

	openai.api_key = "Your API Key"

	engine = pyttsx3.init()
	voices = engine.getProperty('voices')
	engine.setProperty('voices', voices[1].id)

	import speech_recognition as sr
	import pyttsx3
	import openai

	openai.api_key = "Your API Key"

	engine = pyttsx3.init()
	voices = engine.getProperty('voices')
	engine.setProperty('voices', voices[1].id)

	import datetime
	import pandas as pd
	from ib_insync import *

	pd.set_option('display.max_rows', None)

	ib = IB()
	ib.connect('127.0.0.1', 7496, clientId=15)

	contract = Stock('SPY', 'SMART', 'USD')

	import datetime
	import pandas as pd
	from ib_insync import *

	pd.set_option('display.max_rows', None)

	ib = IB()
	ib.connect('127.0.0.1', 7496, clientId=15)

	contract = Stock('SPY', 'SMART', 'USD')