disler

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

adrienbrault

Here is how to use @MistralAI experimental Mistral-medium using @simonw's https://llm.datasette.io CLI:

1. Edit the OpenAI-compatible models configuration file:

vi "$(dirname "$(llm logs path)")"/extra-openai-models.yaml

2. Add the following and save:

- model_id: mistral-tiny

hwchase17

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

nikogamulin

nikogamulin

nikogamulin

bonadio

# %%
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

disler

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

lmammino

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

init27

# 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: