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In this tutorial, we’ll create a dynamic context for a chatbot by enriching prompts with custom data. We’re using the “Oscar Award, 1927 – 2023” dataset from Kaggle, which includes every nominee and winner by year, ceremony number, category, nominee name, film, and winner status.
The image shows a Kaggle dataset page titled "The Oscar Award, 1927 - 2023," displaying a CSV file with columns for year, ceremony, category, name, film, and winner status.

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1. Environment Setup

First, install dependencies and configure your API key. 
pip install pandas numpy openai
Make sure your OPENAI_API_KEY is set in the environment:
export OPENAI_API_KEY="your_api_key_here"
Then, load the Python modules and define helper functions: 
import os
import pandas as pd
import numpy as np
import openai

# Load API key
openai.api_key = os.getenv("OPENAI_API_KEY")

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Helper Functions

def text_embedding(text) -> list[float]:
    """
    Generate embeddings using OpenAI.
    """
    response = openai.Embedding.create(
        model="text-embedding-ada-002",
        input=text
    )
    return response["data"][0]["embedding"]

def get_word_completion(prompt: str) -> str:
    """
    Query GPT-3.5 Turbo for Oscar-related questions.
    """
    messages = [
        {"role": "system", "content": "You answer questions about the 95th Oscar awards."},
        {"role": "user",   "content": prompt},
    ]
    response = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=messages,
        max_tokens=3000,
        n=1
    )
    return response.choices[0].message.content

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2. Loading and Preprocessing the Oscar Dataset

The lab environment already includes data/oscars.csv. Let’s read and trim it: 
# Load the dataset
df = pd.read_csv("data/oscars.csv")
df.head()
We focus only on the 2023 ceremony, remove missing film entries, and standardize categories to lowercase.
StepOperationCode Example
Filter by yearKeep only 2023 ceremonydf = df[df["year_ceremony"] == 2023]
Drop missing filmsRemove rows where film is nulldf = df.dropna(subset=["film"])
Normalize categoryLowercase all category namesdf["category"] = df["category"].str.lower()
# 1. Keep only the 2023 ceremony
df = df[df["year_ceremony"] == 2023]

# 2. Remove entries with no film listed
df = df.dropna(subset=["film"])

# 3. Convert category names to lowercase
df["category"] = df["category"].str.lower()

print(df.shape)  # Expect (121, 7)

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3. Creating Dynamic Context Sentences

Next, we generate a self-contained sentence for each nomination, combining name, category, film title, and win status. 
def make_context(row) -> str:
    status = "and won the award" if row["winner"] else "but did not win"
    return (
        f"{row['name']} got nominated under the category {row['category']} "
        f"for the film {row['film']} {status}"
    )

df["text"] = df.apply(make_context, axis=1)
df.head(3)
The image shows a table with rows and columns containing data about film awards, including categories, nominees, and films. Below the table, there are sections for generating word embeddings and defining a function for the dot product.

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Example Context Sentences

# Example entry #12
print(df["text"].iloc[11])
# Ana de Armas got nominated under the category actress in a leading role
# Example entry #101
print(df["text"].iloc[100])
# Viktor Prášil, Frank Kruse, Markus Stemler, Lars Ginzel and Stefan Korte
# got nominated under the category sound for the film All Quiet on the Western Front
# but did not win

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4. Next Steps

With the text column in place, you can now:
  • Generate embeddings for each sentence
  • Compute similarity scores via dot-product matching
  • Build dynamic prompts by selecting the most relevant contexts
In Part 2, we’ll dive into OpenAI Embeddings and similarity matching to power intelligent chatbot responses.

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