Step 5: NLP and Generative AI

Generative AI uses Large Language Models (LLMs) to understand and generate human-like text. Modern development focus on using these models via APIs and frameworks like LangChain.

🛠️ Code Example: Simple Chatbot with LangChain

This example shows how to use an LLM (like GPT-4) to perform a task.

from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate

# 1. Setup the Model
llm = ChatOpenAI(model="gpt-4o")

# 2. Define a Prompt Template
prompt = ChatPromptTemplate.from_template(
    "You are a helpful assistant that explains {topic} to a 5-year old."
)

# 3. Create a Chain
chain = prompt | llm

# 4. Invoke
response = chain.invoke({"topic": "Quantum Computing"})
print(response.content)

🏗️ Core Concepts

Tokenization: Converting text into numbers the model can understand.
Embeddings: High-dimensional vectors that represent the “Meaning” of words.
RAG (Retrieval Augmented Generation): Connecting an LLM to your own data (PDFs, SQL).
Prompt Engineering: The art of crafting instructions to get better model output.

🥅 Your Goal

Build a script that summarizes a YouTube transcript using LangChain.
Create a local LLM setup using Ollama.