Peter WD piclez

How to add ChatGPT streaming to your Ruby on Rails 7 app!

This guide will walk you through adding a ChatGPT-like messaging stream to your Ruby on Rails 7 app using ruby-openai, Rails 7, Hotwire, Turbostream, Sidekiq and Tailwind. All code included below!

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Released under the MIT License - use as you wish :)

How to?

This is a prompt that will make ChatGPT play a RPG adventure with you. Just paste the prompt into the input. You can also provide information like your name

To pause a game, save the JSON and paste it in the second file and paste that as the prompt. Optionally provide more information for the model like setting

Why?

To play original and very adaptive RPG games without friends. Obviously, I'm a redditor.

Logging setup for FastAPI

This logging setup configures Structlog to output pretty logs in development, and JSON log lines in production.

Then, you can use Structlog loggers or standard logging loggers, and they both will be processed by the Structlog pipeline (see the hello() endpoint for reference). That way any log generated by your dependencies will also be processed and enriched, even if they know nothing about Structlog!

Requests are assigned a correlation ID with the asgi-correlation-id middleware (either captured from incoming request or generated on the fly). All logs are linked to the correlation ID, and to the Datadog trace/span if instrumented. This data "global to the request" is stored in context vars, and automatically added to all logs produced during the request thanks to Structlog. You can add to these "global local variables" at any point in an endpoint with `structlog.contextvars.bind_contextvars(custom

	import sys
	import logging

	from google.cloud import aiplatform

	logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(name)s - %(levelname)s: %(message)s")
	logger = logging.getLogger(__name__)

	model_id = sys.argv[1]
	artifact_uri = sys.argv[2]

	// Chart.tsx example

	'use client'

	import { AreaChart, Card, Title } from '@tremor/react'

	const generateData = () => {
	let dataset = []
	const dates = [
	'Jun 30',

	# Example of using SQLite VSS with OpenAI's text embedding API
	# from Ruby.

	# Note: Install/bundle the sqlite3, sqlite_vss, and ruby-openai gems first
	# OPENAI_API_KEY must also be set in the environment
	# Other embeddings can be used, but this is the easiest for a quick demo

	# More on the topic at
	# https://observablehq.com/@asg017/introducing-sqlite-vss
	# https://observablehq.com/@asg017/making-sqlite-extension-gem-installable

	require "weaviate"

	client = Weaviate::Client.new(
	url: ENV['WEAVIATE_URL'],
	api_key: ENV['WEAVIATE_API_KEY'],

	# Configure Weaviate to use OpenAI to create vectors and use it for querying
	# You can also use Cohere or Hugging Face and pass their API key here instead
	model_service: :openai,
	model_service_api_key: ENV['OPENAI_API_KEY']

	{
	"name": "setup-chat",
	"description": "the gist to setup Build your own AI-Powered chat app with Next.js and LangChain from blog.experienced.dev",
	"version": "0.3.0",
	"engines": {
	"node": ">=18"
	},
	"bin": "./setup.js"
	}

	# Basic key operators to query the JSON objects :

	# #> : Get the JSON object at that path (if you need to do something fancy)
	# -> : Get the JSON object at that path (if you don't)
	# ->> : Get the JSON object at that path as text
	# {obj, n} : Get the nth item in that object

	# https://www.postgresql.org/docs/9.4/functions-json.html#FUNCTIONS-JSONB-OP-TABLE
	# Date
	# date before today

	import { decode } from "blurhash"

	export function blurHashToDataURL(hash: string \| undefined): string \| undefined {
	if (!hash) return undefined

	const pixels = decode(hash, 32, 32)
	const dataURL = parsePixels(pixels, 32, 32)
	return dataURL
	}