OpenAI embeddings API in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this OpenAI embeddings API call?

Consider this Python code snippet that calls the OpenAI embeddings API to get vector embeddings for the text 'Hello world'. What will be the type of the variable embedding after running this code?

Prompt Engineering / GenAI

from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
    model="text-embedding-3-large",
    input="Hello world"
)
embedding = response.data[0].embedding
print(type(embedding))

A <class 'float'>

B <class 'dict'>

C <class 'str'>

D <class 'list'>

💡 Hint

The embedding is a vector of numbers representing the input text.

The OpenAI embeddings API returns a list of floats representing the embedding vector. So the type of embedding is a list.

🧠 Conceptual

intermediate

Which option best describes the purpose of embeddings in AI?

Embeddings are used in AI to convert text or other data into a form that machines can understand. Which of the following best describes what embeddings do?

A They convert input data into fixed-length vectors capturing semantic meaning.

B They generate human-readable summaries of text.

C They translate text from one language to another.

D They classify input data into predefined categories.

💡 Hint

Think about how machines understand text as numbers.

Embeddings transform data into fixed-length numeric vectors that capture meaning, allowing machines to compare and analyze data effectively.

❓ Hyperparameter

advanced

Which parameter controls the model choice in the OpenAI embeddings API call?

In the OpenAI embeddings API, you specify which model to use for generating embeddings. Which parameter in the API call sets this model?

A max_tokens

B model

C input

D temperature

💡 Hint

This parameter names the specific embedding model to use.

The 'model' parameter specifies which embedding model the API should use to generate embeddings.

❓ Metrics

advanced

How can you measure similarity between two embeddings?

After obtaining two embedding vectors from the OpenAI embeddings API, which method is commonly used to measure how similar the two vectors are?

A Cosine similarity

B Mean squared error

C Cross entropy loss

D Euclidean distance squared

💡 Hint

Similarity measures how close two vectors point in the same direction.

Cosine similarity measures the cosine of the angle between two vectors, indicating how similar their directions are, which is ideal for embeddings.

🔧 Debug

expert

What error will this OpenAI embeddings API call raise?

Consider this code snippet calling the OpenAI embeddings API with an empty input list. What error will it raise?

Prompt Engineering / GenAI

from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
    model="text-embedding-3-large",
    input=[]
)

A KeyError

B TypeError

C openai.error.BadRequestError

D No error, returns empty embedding list

💡 Hint

The API expects non-empty input text to generate embeddings.

The OpenAI embeddings API raises a BadRequestError when the input is empty because it requires text to process.

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