OpenAI embeddings in LangChain - Mini Project: Build & Apply

Using OpenAI Embeddings with Langchain

📖 Scenario: You want to create a small program that uses OpenAI's text embeddings to convert a list of sentences into vectors. These vectors can help you compare sentence meanings later.

🎯 Goal: Build a Python script using Langchain that creates OpenAI embeddings for a list of sentences.

📋 What You'll Learn

Create a list of sentences called texts with exact values

Create a variable called embedding_model that initializes OpenAIEmbeddings

Use embedding_model.embed_documents to get embeddings for texts

Store the embeddings in a variable called embeddings

💡 Why This Matters

🌍 Real World

Embedding text is useful for search, recommendations, and understanding text meaning in many apps.

💼 Career

Many AI and data jobs require creating and using embeddings to build smart features.

1

Create the list of sentences

Create a list called texts with these exact strings: 'Hello world', 'Langchain is great', and 'OpenAI embeddings are useful'.

LangChain

# Create the list of sentences called texts
# Your code here

Need a hint?

Use square brackets to create a list and include the exact strings separated by commas.

2

Initialize the OpenAI embeddings model

Create a variable called embedding_model and set it to OpenAIEmbeddings() from langchain.embeddings.openai. Assume the import is done.

LangChain

texts = ['Hello world', 'Langchain is great', 'OpenAI embeddings are useful']
# Initialize embedding_model with OpenAIEmbeddings()
# Your code here

Need a hint?

Use embedding_model = OpenAIEmbeddings() to create the embeddings object.

3

Generate embeddings for the sentences

Use embedding_model.embed_documents(texts) to create embeddings for the list texts. Store the result in a variable called embeddings.

LangChain

from langchain.embeddings.openai import OpenAIEmbeddings

texts = ['Hello world', 'Langchain is great', 'OpenAI embeddings are useful']
embedding_model = OpenAIEmbeddings()
# Generate embeddings for texts and store in embeddings
# Your code here

Need a hint?

Call embed_documents on embedding_model with texts as argument.

4

Complete the script with a comment

Add a comment at the end of the script that says # Embeddings are ready to use.

LangChain

from langchain.embeddings.openai import OpenAIEmbeddings

texts = ['Hello world', 'Langchain is great', 'OpenAI embeddings are useful']
embedding_model = OpenAIEmbeddings()
embeddings = embedding_model.embed_documents(texts)
# Add a comment that embeddings are ready
# Your code here

Need a hint?

Just add the exact comment line at the end.