OpenAI embeddings API in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of the OpenAI embeddings API?

The OpenAI embeddings API converts text into numerical vectors that capture the meaning of the text, allowing machines to understand and compare text based on meaning.

beginner

How does the OpenAI embeddings API help in search or recommendation systems?

It transforms text into vectors so that similar texts have vectors close to each other. This helps find relevant documents or recommend items by comparing vector distances.

beginner

Which type of data can you send to the OpenAI embeddings API?

You send text data, such as sentences, paragraphs, or documents, to get back vector representations.

beginner

What is a vector in the context of embeddings?

A vector is a list of numbers that represents the meaning of text in a way that computers can understand and compare.

intermediate

Name one common use case of OpenAI embeddings API besides search.

One common use case is clustering similar texts together, like grouping customer feedback by topic.

What does the OpenAI embeddings API output for a given text?

A A vector of numbers representing the text meaning

B A summary of the text

C A translated version of the text

D A classification label

The API outputs a vector that captures the meaning of the input text.

Which of these is NOT a typical use of embeddings?

A Generating images from text

B Improving search results

C Clustering text by topic

D Finding similar documents

Generating images from text is not done by embeddings; it requires different models.

What kind of input does the OpenAI embeddings API accept?

A Images

B Text data

C Audio files

D Video clips

The embeddings API takes text as input to create vector representations.

Why are vectors useful in machine learning for text?

A They generate new text automatically

B They translate text into other languages

C They compress text into smaller files

D They allow computers to compare and understand text meaning

Vectors represent text meaning numerically so machines can compare and analyze text.

Which metric is commonly used to compare embedding vectors?

A Word count

B Text length

C Cosine similarity

D Character frequency

Cosine similarity measures how close two vectors are in meaning.

Explain how the OpenAI embeddings API transforms text and why this is useful.

Think about turning words into numbers that show what the text means.

Describe a real-life example where using embeddings from the OpenAI API can improve a product or service.

Imagine helping someone find similar books or customer reviews.

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