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To run the Python script for splitting a PDF into segments of just under 25MB each, you'll need to follow these steps:

Prerequisites

Python Installation: Ensure that Python is installed on your system. If not, you can download and install it from python.org.

PyPDF2 Library: The script uses the PyPDF2 library. You can install it using pip, Python's package installer. If pip is not already installed, it comes bundled with Python 3.4 and later versions.

Installation Steps

Open Terminal or Command Prompt: On Windows, you can open Command Prompt by searching for cmd in the Start menu.

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# tokens from https://cloud.featurebase.com/configuration/api-keys
featurebase_token = "<token>"

# featurebase ($300 free credit on signup)
# https://query.featurebase.com/v2/databases/bc355-t-t-t-362c1416/query/sql (but remove /query/sql)
featurebase_endpoint = "https://query.featurebase.com/v2/databases/<uuid-only-no-query-sql>"

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Example of using OpenAI functions in completions with Python decorators

This example illustrates a way to utilize a function dynamically while querying an OpenAI GPT model. It uses the newly released functions support in the completion endpoints OpenAI provides.

The general concept is based on using a decorator to extract information from a function so it can be presented to the language model for use, and then pass the result of that function back to the completion endpoint for language augmentation.

In general, a wide variety of functions can be swapped in for use by the model. By changing the get_top_stories function, plus the prompt in run_conversation, you should be able to get the model to run your function without changing any of the other code.

Configuration

To use this, create a config.py file and add a variable with your OpenAI token:

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"""
Hacker News Top Stories
Author: 
Date: June 12, 2023

Description:
This script fetches the top 10 stories from Hacker News using Algolia's search API. It retrieves the stories posted within the last 24 hours and prints their titles and URLs.

Dependencies:
- requests: HTTP library for sending API requests

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import openai
import numpy as np
from openai.embeddings_utils import get_embedding

openai.api_key = "TOKEN"

def gpt3_embedding(content, engine='text-similarity-ada-001'):
		content = content.encode(encoding='ASCII',errors='ignore').decode()
		response = openai.Embedding.create(input=content,engine=engine)
		vector = response['data'][0]['embedding']  # this is a normal list

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State of AI Report
October 11, 2022
#stateofai
stateof.ai
Ian Hogarth
Nathan Benaich

About the authors
Nathan is the General Partner of Air Street Capital, a venture capital firm investing in AI-first technology and life science companies. He founded RAAIS and London.AI (AI community for industry and research), the RAAIS Foundation (funding open-source AI projects), and Spinout.fyi (improving university spinout creation). He studied biology at Williams College and earned a PhD from Cambridge in cancer research. 
Nathan Benaich

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Analyzing 5 Billion Games of Set with FeatureBase

FeatureBase is a binary-tree database built on Roaring Bitmaps. This makes it suitable for running analytics on massive data sets in real time. If you've never used FeatureBase before, you can get it running locally in about 5 minutes.

Today, we're going to take a look at using FeatureBase to simulate and analyze a very large number of Set games in real-time.

Set (the game)

Set is a card game designed by Marsha Falco in 1974 and published by Set Enterprises in 1991. The deck consists of 81 unique cards that vary in four features across three possibilities for each kind of feature: number of shapes (one, two, or three), shape (diamond, squiggle, oval), shading (solid, striped, or open), and color (red, green, or purple).

In a game of Set, the cards are shuffled and then 12 cards are drawn from the top of the deck and placed on the table. Game play then com

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FeatureBase in 5 Minutes

FeatureBase is a B-tree database built on Roaring Bitmaps. This makes it suitable for running analytics on massive data sets in real time. It's also perfect for use in machine learning applications, where a fast feature store is desired during training or inference. FeatureBase itself may be considered a type of machine learning model which is trained on large amounts of other model's inference data, over time.

This guide is designed to get you started using FeatureBase on a Mac or Linux/UNIX based system. It covers downloading and starting the FeatureBase server as well as ingesting and querying a small amount of data.

You may also want to reference the documentation for FeatureBase after you finish running through this guide.

Install

Start by heading over to the downloads on the Github repo and select the buil

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import openai
import numpy as np
from openai.embeddings_utils import get_embedding

openai.api_key = ""

def gpt3_embedding(content, engine='text-similarity-ada-001'):
    content = content.encode(encoding='ASCII',errors='ignore').decode()
    response = openai.Embedding.create(input=content,engine=engine)
    vector = response['data'][0]['embedding']  # this is a normal list

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// google docs app
var line = "{{line}}";

if ("{{text}}" == "" && "{{view}}" == "") {
	// just nothing
	url = "https://docs.google.com/";
} else if ("{{text}}" == "" && "{{view}}" != ""){
	// view
	var view = "{{view}}".substring(1); // trim the | off (fix this)
	if (view == "sheets") {