Коrd Campbell kordless

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.

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:

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

	import sys
	import os
	import logging
	import requests
	import re
	import json
	from typing import Dict, Any, Optional, List, Tuple
	from mcp.server.fastmcp import FastMCP, Context
	from urllib.parse import urlparse, urljoin
	import asyncio

	"""
	Adaptive Connector Framework (ACF)

	A self-bootstrapping alternative to MCP that dynamically builds and tests
	connectors based on current needs. The system evolves its own capabilities
	through iterative learning and testing.

	Key components:
	1. Registry - Manages available connectors and their capabilities
	2. Connector Builder - Dynamically creates new connectors

	import os
	import sys
	from dotenv import load_dotenv, set_key
	from langchain_openai import ChatOpenAI
	from langchain.prompts import ChatPromptTemplate, PromptTemplate
	from langchain_core.runnables import RunnableSequence
	from langchain.tools import Tool
	from langchain.agents import create_react_agent, AgentExecutor
	from langchain.schema import HumanMessage
	import getpass

	# 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>"

	"""
	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

	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

	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