OpenAI embeddings API in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the OpenAI library.

Prompt Engineering / GenAI

import [1]

Drag options to blanks, or click blank then click option'

Aopenai

Btensorflow

Cnumpy

Dpandas

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated libraries like tensorflow or numpy.

Using uppercase letters in the import statement.

The OpenAI Python library is imported using import openai.

2fill in blank

medium

Complete the code to set your OpenAI API key.

Prompt Engineering / GenAI

openai.api_key = [1]

Drag options to blanks, or click blank then click option'

Aos.getenv('OPENAI_API_KEY')

B12345

C'my_api_key'

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Hardcoding the API key as a string.

Using an invalid key format.

Using os.getenv('OPENAI_API_KEY') safely loads the API key from environment variables.

3fill in blank

hard

Fix the error in the code to create an embedding for the text.

Prompt Engineering / GenAI

response = openai.Embedding.create(input=[1], model='text-embedding-3-small')

Drag options to blanks, or click blank then click option'

A'Hello world'

BNone

C123

D['Hello world']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string instead of a list.

Passing a number or None as input.

The input parameter must be a list of strings, so ['Hello world'] is correct.

4fill in blank

hard

Fill both blanks to extract the embedding vector from the response.

Prompt Engineering / GenAI

embedding_vector = response['data'][[1]]['[2]']

Drag options to blanks, or click blank then click option'

A0

B1

Cembedding

Dvector

Attempts:

3 left

💡 Hint

Common Mistakes

Using index 1 instead of 0.

Using the wrong key like 'vector'.

The embedding is stored in response['data'][0]['embedding'].

5fill in blank

hard

Fill all three blanks to create an embedding and print its length.

Prompt Engineering / GenAI

response = openai.Embedding.create(input=[1], model=[2])
embedding = response['data'][0][[3]]
print(len(embedding))

Drag options to blanks, or click blank then click option'

A['OpenAI is great']

B'text-embedding-3-large'

C'embedding'

D'data'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing input as a string instead of a list.

Using an incorrect model name.

Accessing the wrong key in the response.

We pass a list of strings as input, specify the correct model name, and access the 'embedding' key to get the vector.

Practice

1. What does the OpenAI embeddings API primarily do?

easy

A. Translates text from one language to another

B. Generates images from text descriptions

C. Converts text into number vectors to capture meaning

D. Summarizes long documents into short paragraphs

Solution

1. Step 1: Understand the purpose of embeddings

   Embeddings are numeric representations of text that capture its meaning.

2. Step 2: Match the API function

   The OpenAI embeddings API converts text into these numeric vectors.

3. Final Answer:

   Converts text into number vectors to capture meaning -> Option C

4. Quick Check:

   Embeddings = numeric text vectors [OK]

Hint: Embeddings turn words into numbers for computers [OK]

Common Mistakes:

• Confusing embeddings with image generation
• Thinking embeddings translate languages
• Assuming embeddings summarize text

2. Which of the following is the correct way to call the OpenAI embeddings API in Python?

easy

A. openai.Embeddings.generate(text='text', model='embedding-3')

B. openai.Embedding.create(input=['text'], model='text-embedding-3-large')

C. openai.embedding.create(text='text', model='text-embedding-3-large')

D. openai.Embedding.create(input='text', model='text-embedding-3-small')

Solution

1. Step 1: Recall correct method and parameters

   The correct method is openai.Embedding.create with 'input' as a list of texts and a valid model name.

2. Step 2: Check each option

   openai.Embedding.create(input=['text'], model='text-embedding-3-large') uses correct method, parameter name 'input' as a list, and a valid model name.

3. Final Answer:

   openai.Embedding.create(input=['text'], model='text-embedding-3-large') -> Option B

4. Quick Check:

   Correct method and input list = A [OK]

Hint: Use 'Embedding.create' with input list and model name [OK]

Common Mistakes:

• Using wrong method name like Embeddings.generate
• Passing input as string instead of list
• Incorrect parameter names like 'text' instead of 'input'

3. What will be the output type of the following Python code snippet using OpenAI embeddings API?

response = openai.Embedding.create(input=['hello world'], model='text-embedding-3-large')
embedding_vector = response['data'][0]['embedding']
print(type(embedding_vector))

medium

A. <class 'list'>

B. <class 'dict'>

C. <class 'float'>

D. <class 'str'>

Solution

1. Step 1: Understand the API response structure

   The 'embedding' field contains a list of floats representing the vector.

2. Step 2: Check the type of 'embedding_vector'

   Extracting response['data'][0]['embedding'] returns a list of numbers.

3. Final Answer:

   <class 'list'> -> Option A

4. Quick Check:

   Embedding vector is a list of floats [OK]

Hint: Embedding is a list of numbers, not a single value [OK]

Common Mistakes:

• Assuming embedding is a dict or string
• Thinking embedding is a single float
• Confusing API response with raw text

4. Identify the error in this code snippet using OpenAI embeddings API:

response = openai.Embedding.create(input='hello world', model='text-embedding-3-large')
embedding = response['data'][0]['embedding']
print(len(embedding))

medium

A. The print statement should be print(embedding.length)

B. The model name 'text-embedding-3-large' is invalid

C. The 'embedding' key does not exist in the response

D. The 'input' parameter should be a list, not a string

Solution

1. Step 1: Check the 'input' parameter type

   The API expects 'input' as a list of strings, not a single string.

2. Step 2: Identify the error cause

   Passing a string causes the API to error or behave unexpectedly.

3. Final Answer:

   The 'input' parameter should be a list, not a string -> Option D

4. Quick Check:

   Input must be list, not string [OK]

Hint: Always pass input as a list of texts [OK]

Common Mistakes:

• Passing input as a single string
• Using wrong model names
• Incorrect print syntax for length

5. You want to find the similarity between two sentences using OpenAI embeddings API. Which approach is correct?

hard

A. Get embeddings for both sentences, then compute cosine similarity between vectors

B. Send both sentences as one string to embeddings API and compare output length

C. Use embeddings API to translate sentences, then compare translated texts

D. Get embeddings for one sentence only and compare with raw text of the other

Solution

1. Step 1: Understand similarity calculation with embeddings

   Similarity is measured by comparing numeric vectors, usually with cosine similarity.

2. Step 2: Apply correct method

   Get embeddings separately for each sentence, then compute cosine similarity between their vectors.

3. Final Answer:

   Get embeddings for both sentences, then compute cosine similarity between vectors -> Option A

4. Quick Check:

   Similarity = cosine of embedding vectors [OK]

Hint: Compare vectors with cosine similarity after embedding [OK]

Common Mistakes:

• Combining sentences into one string before embedding
• Comparing raw text lengths instead of vectors
• Using embeddings for only one sentence