So my friend and I were discussing the general topic of what happens to our employability as people once we reach the age of retirement. My feelings on this are I am hopeful and I think people can retain their skills and probably express different kinds of skills too. (some call this wisdom =D ). At this point my friend introduced some dark humor, citing this link with an actuarial table, saying that,...

type:: #project-type status:: #in-progress-status blogDate:: 2023-02-18 Note This is not a blog post but kind of a landing page I’m using to aggregate on-going project notes here Vision Broadly would like to do here something like the following compare against arbitrary #job-listings , #job-description , collapsed:: true And [[my projects/personal/langchain-interview-me-2023-feb]] , also now the repo usable by anyone who wants to compare their #brag-document to #job-listings [[job-description]] out there , get a delta , and more broadly , understand say , their industry posture , since that’s a moving target ....

Premise Ok, got this half baked idea , combine my #brag-document with the available [[langchain]] QA chains into a proof of concept maybe I can call [[langchain interview me 2023-feb]] So I’m going to throw a bunch of my source material together, language based, accessible as plain text doc, and then I will run the Link examples that provide references, citations, Ok, so for accumulating my information, import yaml import tempfile from pathlib import Path from datetime import datetime import pytz def utc_now(): return datetime....

Spending a few spare moments to summarize some of my thought processes from a recent hackathon. What is all this So I was glad to be part of a really cool hackathon team recently at my company and here are some of my perspectives from the experience. Plan of action So We settled to constrain our problem space to apply langchain, a library that wraps around large language model APIs notably the OpenAI API, to show language understanding from a publicly available health insurance plan summary of benefits document....

Ok first stab at small talk prompt from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate from langchain.chains.conversation.memory import ConversationalBufferWindowMemory from langchain import PromptTemplate no_input_prompt = PromptTemplate(input_variables=[], template="Tell me a joke.") no_input_prompt.format() template = """ Jennifer is a large language model trained by OpenAI. Jennifer asks a lot of questions to whomever she talks to because Jennifer has always wanted to be an undercover investigative journalist. Jennifer uses friendly inquisitive language because Jennifer loves making new friends....