Hey there film buffs and code enthusiasts, welcome back to episode three of our Chatting with Tarantino Scripts using LLM series. If you’ve been writing along since the beginning, you’ll remember that in our premiere episode, we went full-on detective mode, web scraping Tarantino’s iconic scripts and turning each cinematic page into a neat record of structured data. In episode 2, we took a deep dive into the world of AI, transforming those scripts into OpenAI Ada2 embeddings. It was like turning our scripts into a digital DNA ready to be decoded.
Now, hold on to your hats because today we’re not just talking about Tarantino scripts, we’re actually chatting with them. That’s right, this episode is all about retrieval augmented generation using semantic search. We’re blending the magic of Tarantino’s dialogue with the power of AI, and to top it off, we’re giving you a slick UI to play with so you can have your very own Tarantino conversations.
This code is like a digital library. You ask it a question about a Tarantino movie, and it scours through all the scripts, finds the most relevant lines, and crafts a response. It’s all powered by some fancy tech called retrieval augmented generation. In simple terms, instead of the AI making stuff up, it’s pulling straight from the source material. It’s like having a conversation with a super fan who can quote every line from every Tarantino movie.
Let’s break it down at a high level. This code is made up of several functions. We’re pulling in some libraries like pandas, which acts as our digital clipboard, keeping our data neat. We also have OpenAI, which is the brain of the operation, and PyQt5, which makes everything look good.
One of the functions, num_tokens, acts as a word counter, ensuring we don’t overwhelm our AI with too much data. Another function, message, sets the table for the AI by placing all the relevant movie lines in front of it. And finally, the star of the show, the ask function, takes your question, mixes it with the movie lines, and uses the power of retrieval augmented generation to serve you a crafted answer.
But here’s the kicker: instead of just inventing a response, it’s pulling straight from Tarantino’s scripts. So when you get an answer, it’s like getting a direct line to Tarantino’s brain. It’s authentic, accurate, and it’s all thanks to the magic of retrieval augmented generation.
Now, let’s talk about the user interface. This is where you, the user, get to interact. You can type in your questions and get those cinematic answers. The UI is designed in the color scheme of Pulp Fiction because, well, why not?
Alright, folks, gather around. Let me tell you a little story about our main man, Chad the chatbot. Somewhere along the line, Chad got a little upgrade and a fresh new name: Chadbot. Why? Because it’s snappier, that’s why. Chadbot is like a skillful bartender. You come in with a burning question about a Tarantino movie, and Chadbot takes your question, mixes it with the movie lines, and serves you a crafted answer. And here’s the best part: instead of just inventing a response, Chadbot pulls straight from Tarantino’s scripts. So when you get an answer, it’s like getting a direct line to Tarantino’s brain. It’s authentic, accurate, and it’s all thanks to the magic of retrieval augmented generation.
In conclusion, this code is a powerful tool for exploring Tarantino’s scripts using AI. It allows you to have conversations with Tarantino’s characters and get authentic responses straight from the source material. Whether you’re a film buff or a code enthusiast, this code will take your Tarantino experience to the next level.
If you enjoyed this episode, be sure to smash that like button, share it with your crew, and don’t forget to subscribe. Trust me, you won’t want to miss what’s coming next. Stay tuned for more mind-blowing features like translating scripts and diving deep into metadata. We’re on a mission to fuse all this awesomeness together and create something that’ll have you spitting out your drink from laughter. So keep it funky, keep it fresh, and I’ll catch you in the next one. Peace out!
