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Setup

0. Create a new directory with these three files (requirements.txt, main.py, README.md)
1. python -m venv venv
2. source venv/bin/activate
3. pip install -r requirements.txt
4. python main.py
5. Update main() to run the example prompt chains

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Initial steps

1. Launch Cloud9 IDE with SSM option
2. When it launchers, open the Cloud9 IDE in the AWS Console

With SSM

Install:

• AWS CLI

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from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
import requests
from bs4 import BeautifulSoup
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from langchain.utilities import DuckDuckGoSearchAPIWrapper
import json

RESULTS_PER_QUESTION = 3

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# %%
import os
import openai
# import autogen
from autogen import Agent, ConversableAgent, oai, UserProxyAgent, AssistantAgent
import types
from dotenv import load_dotenv, find_dotenv
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union

_ = load_dotenv(find_dotenv()) # read local .env file

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# Credit 🙏: I just used the example from langchain docs and it works quite well: https://python.langchain.com/en/latest/use_cases/question_answering.html
# Note 2: The Arxiv -> PDF logic is a bit messy, I'm sure it can be done better
# Note 3: Please install the following:

# To run:

# Save this in a `app.py`
# pip install arxiv PyPDF2 langchain chromadb

# The chat feature was shipped in H2O nightly this week, we will need to install from nightly link:

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I'm a [your level] [profession] and I want to learn [topic] so I can [objective]. Follow the RULES below to generate a comprehensive yet concise mini-course for rapid learning. The course should contain chapters that teach me about these SUB_TOPICS. Make sure the chapters fit my level, profession and topic. Ask for clarification if you need more information about my knowledge.

SUB_TOPICS
- [topic 1]
- [topic 2]
- [topic 3]

RULES
- Use concrete examples to explain every concept
- Use emojis to add expression

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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>