Below you will find some suggested materials on these topics, which we believe may help you complete the final activity in the lesson. Please note that using these resources is entirely optional, and you are welcome to explore any other sources or courses at your own discretion.

Introduction (2 Minutes)

Opening Statement:

• Welcome back to Module 3! In this lesson, we’re diving into the exciting world of embedding generation.

• You’ll learn how to generate embeddings using APIs like OpenAI and HuggingFace Transformers, and we’ll cover best practices to prepare your data for embedding generation.

Agenda:

1. What are embeddings, and why generate them with APIs?

2. Step-by-step process for embedding text data.

3. Best practices for preparing data before embedding generation.

Part 1: What Are Embeddings and Why Use APIs? (5 Minutes)

Recap of Embeddings:

• Embeddings are numerical representations of text, capturing its meaning in a multidimensional vector space.

• Similar meanings or contexts result in closer embeddings, enabling tasks like similarity search and clustering.

Why Use APIs for Embedding Generation?

• OpenAI API: Offers state-of-the-art embeddings with minimal setup. Great for general-purpose tasks.

• HuggingFace Transformers: Provides flexibility with models like BERT and RoBERTa, ideal for contextual embeddings.

• Advantages of APIs:

  • Easy to use with pre-trained models.

  • High accuracy and quality without requiring expensive training resources.

  • Scalable for large datasets.

Part 2: Step-by-Step Process for Embedding Text Data (10 Minutes)

Step 1: Set Up the Environment

Install required libraries:
bash
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pip install openai transformers

1. 
   

2. Obtain API keys (e.g., OpenAI API key).

Step 2: Choose a Pre-Trained Model

• Decide based on your use case:

  • OpenAI’s text-embedding-ada-002: General-purpose embedding generation.

  • HuggingFace’s all-MiniLM-L6-v2: Optimized for sentence-level tasks.

Step 3: Load the Data

Use a dataset like employees_cleaned.csv:
python
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import pandas as pd

df = pd.read_csv("employees_cleaned.csv")

print(df.head())

Step 4: Generate Embeddings

Option 1: Using OpenAI API

python

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import openai

# Set your OpenAI API key

openai.api_key = "your_api_key"

# Generate embeddings for a sample text

response = openai.Embedding.create(

    input="Software Engineer in AI Department",

    model="text-embedding-ada-002"

)

embedding = response['data'][0]['embedding']

print(embedding[:10])  # Show the first 10 dimensions

Option 2: Using HuggingFace Transformers

python

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from transformers import AutoTokenizer, AutoModel

import torch

# Load model and tokenizer

model_name = "sentence-transformers/all-MiniLM-L6-v2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)

# Tokenize text and generate embeddings

text = "Software Engineer in AI Department"

tokens = tokenizer(text, return_tensors="pt")

with torch.no_grad():

    output = model(**tokens)

    embedding = output.last_hidden_state.mean(dim=1).squeeze()

print(embedding[:10])  # Show the first 10 dimensions

Step 5: Save the Embeddings

Save the embeddings for future use:
python
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import numpy as np

np.savetxt("embeddings.csv", embedding.numpy(), delimiter=",")

Part 3: Best Practices for Preparing Data (5 Minutes)

1. Clean and Normalize Text Data

    Convert text to lowercase to maintain consistency.

Remove punctuation and special characters.
python
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import string

df['cleaned_text'] = df['text_column'].apply(

    lambda x: x.lower().translate(str.maketrans('', '', string.punctuation))

)

2. Handle Missing Values

Fill missing entries with a placeholder or drop rows with insufficient data.
python
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df['text_column'].fillna("unknown", inplace=True)

3. Tokenize Sentences

Break large paragraphs into smaller, meaningful sentences to improve embedding relevance.
python
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from nltk.tokenize import sent_tokenize

df['sentences'] = df['text_column'].apply(sent_tokenize)

4. Batch Process Large Datasets

For scalability, process text data in batches, especially when using APIs with rate limits.
python
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batch_size = 100

for i in range(0, len(df), batch_size):

    batch = df[i:i+batch_size]

    # Process embeddings for the batch

5. Evaluate and Validate Embeddings

• Test embeddings with small datasets before processing large ones.

• Use similarity checks to ensure embeddings align with expectations.

Conclusion and Key Takeaways (3 Minutes)

Recap:

1. APIs like OpenAI and HuggingFace provide powerful tools for embedding generation.

2. Following a structured process ensures consistency and accuracy.

3. Preparing your data is critical to generating high-quality embeddings.

Closing Statement:

• Embeddings are the foundation for many AI applications. With clean data and the right tools, you can unlock the potential of your dataset.

• In the next lesson, we’ll learn how to use these embeddings to build a vector database for similarity searches.

Objective

In this activity, you will:

1. Generate embeddings for the employees_cleaned.csv dataset using a pre-trained model.

2. Save the generated embeddings to a local file for future use.

Prerequisites

Install the necessary libraries:
bash
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pip install openai transformers pandas numpy

1. 
   

2. Obtain an API key for OpenAI if using the OpenAI API, or ensure you have downloaded a HuggingFace model locally.

3. Ensure the cleaned dataset employees_cleaned.csv is available in the working directory.

Step-by-Step Instructions

Step 1: Load the Dataset

Load the cleaned dataset and preview its structure.

Code Example:

python

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import pandas as pd

# Load the cleaned dataset

file_path = "employees_cleaned.csv"

df = pd.read_csv(file_path)

# Display the first few rows of the dataset

print("Dataset Preview:")

print(df.head())

# Ensure there are no missing values in the 'Role' or 'Department' columns

assert not df[['Role', 'Department']].isnull().any().any(), "Dataset contains missing values."

Step 2: Generate Embeddings

Option 1: Using OpenAI API

• Generate embeddings for the Role and Department columns.

Code Example:

python

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import openai

# Set OpenAI API key

export OPENAI_API_KEY="your_api_key_here"

# Define a function to generate embeddings

def generate_embedding(text):

   client = openai.OpenAI()

   response = client.embeddings.create(

        input=text,

        model="text-embedding-ada-002"

    )

    return response.data[0].embedding

# Generate embeddings for each row

df['Role_embedding'] = df['Role'].apply(generate_embedding)

df['Department_embedding'] = df['Department'].apply(generate_embedding)

print("Embeddings generated successfully!")

Option 2: Using HuggingFace Transformers

• Generate embeddings locally using a model like sentence-transformers/all-MiniLM-L6-v2.

Code Example:

python

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from transformers import AutoTokenizer, AutoModel

import torch

# Load model and tokenizer

model_name = "sentence-transformers/all-MiniLM-L6-v2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)

# Define a function to generate embeddings

def generate_embedding(text):

    tokens = tokenizer(text, return_tensors="pt")

    with torch.no_grad():

        output = model(**tokens)

    return output.last_hidden_state.mean(dim=1).squeeze().numpy()

# Generate embeddings for each row

df['Role_embedding'] = df['Role'].apply(generate_embedding)

df['Department_embedding'] = df['Department'].apply(generate_embedding)

print("Embeddings generated successfully!")

Step 3: Save the Embeddings

Combine the embeddings into a structured format and save them to a file.

Code Example:

python

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import numpy as np

# Combine embeddings into a single array

embeddings = np.array([

    np.concatenate((Role, Department))

    for Role, Department in zip(df['Role_embedding'], df['Department_embedding'])

])

# Save embeddings to a local file

np.savetxt("embeddings.csv", embeddings, delimiter=",")

print("Embeddings saved to 'embeddings.csv' successfully!")

Step 4: Verify the Results

• Check the shape of the saved embeddings to ensure correctness.

• Reload the embeddings to confirm they were saved properly.

Code Example:

python

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# Load the saved embeddings

loaded_embeddings = np.loadtxt("embeddings.csv", delimiter=",")

print("Loaded Embeddings Shape:", loaded_embeddings.shape)

Expected Outcomes

1. Generated Embeddings:

   • Each row in the dataset has an embedding that captures semantic information from the Role and Department columns.

2. Saved File:

   • A file embeddings.csv containing the concatenated embeddings for each row.

3. Ready Data:

   • The embeddings are ready for use in building a vector database or for similarity search.

Objective

This activity involves writing a Python script that:

1. Loads the cleaned dataset.

2. Generates embeddings using an API (e.g., OpenAI or HuggingFace).

3. Saves the embeddings in a format compatible with vector databases like FAISS.

Prerequisites

Install the required libraries:
bash
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pip install openai pandas numpy

1. 
   

2. Obtain the cleaned dataset file (employees_cleaned.csv).

3. If using the OpenAI API, ensure you have an API key.

Step-by-Step Implementation

Step 1: Load the Cleaned Dataset

Code Block:

python

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import pandas as pd

# Load the cleaned dataset

file_path = "employees_cleaned.csv"

df = pd.read_csv(file_path)

# Display the first few rows to inspect the dataset

print("Dataset Preview:")

print(df.head())

# Ensure the dataset does not have missing values in required columns

assert not df[['role', 'department']].isnull().any().any(), "Dataset contains missing values."

Step 2: Generate Embeddings Using an API

Option 1: Using OpenAI API

python

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import openai

# Set OpenAI API key

openai.api_key = "your_openai_api_key"

# Function to generate embeddings using OpenAI API

def generate_embedding(text):

    response = openai.Embedding.create(

        input=text,

        model="text-embedding-ada-002"

    )

    return response['data'][0]['embedding']

# Apply the function to the 'role' and 'department' columns

df['role_embedding'] = df['role'].apply(generate_embedding)

df['department_embedding'] = df['department'].apply(generate_embedding)

print("Embeddings generated successfully using OpenAI API!")

Option 2: Using HuggingFace Transformers

python

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from transformers import AutoTokenizer, AutoModel

import torch

# Load the HuggingFace model and tokenizer

model_name = "sentence-transformers/all-MiniLM-L6-v2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModel.from_pretrained(model_name)

# Function to generate embeddings using HuggingFace Transformers

def generate_embedding(text):

    tokens = tokenizer(text, return_tensors="pt")

    with torch.no_grad():

        output = model(**tokens)

    return output.last_hidden_state.mean(dim=1).squeeze().numpy()

# Apply the function to the 'role' and 'department' columns

df['role_embedding'] = df['role'].apply(generate_embedding)

df['department_embedding'] = df['department'].apply(generate_embedding)

print("Embeddings generated successfully using HuggingFace Transformers!")

Step 3: Save the Embeddings for Vector Database Integration

Code Block:

python

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import numpy as np

# Combine embeddings for each row

combined_embeddings = np.array([

    np.concatenate((role, department))

    for role, department in zip(df['role_embedding'], df['department_embedding'])

])

# Save embeddings to a NumPy file for compatibility with FAISS

np.save("embeddings.npy", combined_embeddings)

# Save metadata (e.g., employee names) to a separate CSV for reference

df[['name']].to_csv("metadata.csv", index=False)

print("Embeddings saved to 'embeddings.npy' and metadata saved to 'metadata.csv'")

Step 4: Verify the Saved Embeddings

Code Block:

python

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# Reload embeddings to verify they were saved correctly

loaded_embeddings = np.load("embeddings.npy")

print("Loaded Embeddings Shape:", loaded_embeddings.shape)

print("Sample Embedding (First Row):", loaded_embeddings[0])

Expected Output:

• • The shape of the loaded embeddings (e.g., (n_rows, embedding_dim)).

  • A preview of the first embedding vector.

Final Script

Here’s the complete script combining all steps:

python

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import pandas as pd

import numpy as np

import openai  # Replace with HuggingFace code if preferred

# Load dataset

file_path = "employees_cleaned.csv"

df = pd.read_csv(file_path)

assert not df[['role', 'department']].isnull().any().any(), "Dataset contains missing values."

# Set OpenAI API key

openai.api_key = "your_openai_api_key"

# Function to generate embeddings

def generate_embedding(text):

    response = openai.Embedding.create(

        input=text,

        model="text-embedding-ada-002"

    )

    return response['data'][0]['embedding']

# Generate embeddings

df['role_embedding'] = df['role'].apply(generate_embedding)

df['department_embedding'] = df['department'].apply(generate_embedding)

# Combine embeddings

combined_embeddings = np.array([

    np.concatenate((role, department))

    for role, department in zip(df['role_embedding'], df['department_embedding'])

])

# Save embeddings and metadata

np.save("embeddings.npy", combined_embeddings)

df[['name']].to_csv("metadata.csv", index=False)

print("Embeddings and metadata saved successfully!")

Expected Outcomes

1. Generated Embeddings:

   • Combined embeddings for each row, stored in a file ready for vector database integration.

2. Metadata File:

   • A CSV file containing employee names or other relevant identifiers.

3. Verification:

   • Successful validation of the saved embeddings and metadata.