In 2024, there is no shortage of LLM-based applications. Major tech companies like Microsoft and Google are pushing forward ever-more-powerful versions of their flagship ChatGPT and Gemini models, and specialist players like Anthropic are pushing forward competing offerings with additional integrations. In the applied LLM world, companies and even governments are experimenting with chat applications for a variety of contexts.
Ms LLMWare Today we will be using the Ms models for sentiment analysis, SQL generation, and multi-step agents. Now we understand the technology, let’s implement the application! Implementation Data Downloading and Processing Let’s begin by downloading the CB Insights articles. Let’s import the dependencies: import requests from bs4 import BeautifulSoup import os import pandas as pd import json import re And now the code to download the newsletter archive: res=requests.get soup=BeautifulSoup article_links=] for i in soup.
Ms model. Question-Answering with DRAGON Now that the entities have been detected, let’s build a question-answering workflow that demonstrates the power of this technique. For our example, we will use the test question: What role does OpenAI play in Microsoft's AI strategy? Let’s begin by importing the proper packages for running DRAGON: from llmware.