OpenAI embeddings in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an OpenAI embeddings object using LangChain.

LangChain

from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings([1]="text-embedding-ada-002")

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

Aapi_key

Bengine

Cmodel

Dembedding_model

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'api_key' instead of 'model' causes an error.

Using 'engine' is outdated for this library.

The model parameter specifies which OpenAI embedding model to use.

2fill in blank

medium

Complete the code to get the embedding vector for a text string.

LangChain

text = "Hello world"
vector = embeddings.[1](text)

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

Aencode

Bembed_query

Cget_vector

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'encode' causes attribute errors.

Using 'get_vector' is not a valid method here.

The method embed_query returns the embedding vector for a single text input.

3fill in blank

hard

Fix the error in the code to create embeddings with an API key.

LangChain

embeddings = OpenAIEmbeddings(model="text-embedding-ada-002", [1]="my_api_key")

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

Aapi_key

Bkey

Ctoken

Dauth

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'key' or 'token' causes authentication errors.

Using 'auth' is not recognized by the constructor.

The correct parameter to pass the API key is api_key.

4fill in blank

hard

Fill both blanks to create embeddings and get vector for a list of texts.

LangChain

texts = ["apple", "banana"]
embeddings = OpenAIEmbeddings([1]="text-embedding-ada-002")
vectors = embeddings.[2](texts)

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

Amodel

Bembed_documents

Capi_key

Dembed_query

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'embed_query' for multiple texts returns errors.

Using 'api_key' as the first blank is incorrect here.

Use model to specify the model and embed_documents to get embeddings for multiple texts.

5fill in blank

hard

Fill all three blanks to create embeddings with API key and get vector for a single text.

LangChain

embeddings = OpenAIEmbeddings([1]="text-embedding-ada-002", [2]="my_api_key")
text = "OpenAI is great"
vector = embeddings.[3](text)

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

Amodel

Bapi_key

Cembed_query

Dembed_documents

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'embed_documents' for a single text causes confusion.

Mixing up 'api_key' parameter name causes errors.

Use model and api_key to create the embeddings object, then embed_query to get the vector for a single text.

Practice

1. What is the main purpose of OpenAI embeddings in Langchain?

easy

A. To convert text into numerical vectors that represent meaning

B. To generate images from text descriptions

C. To translate text from one language to another

D. To create user interfaces automatically

Solution

1. Step 1: Understand what embeddings do

   Embeddings turn text into lists of numbers that capture the meaning of the text.

2. Step 2: Identify the purpose in Langchain

   Langchain uses embeddings to help computers find similar or related texts easily by comparing these number lists.

3. Final Answer:

   To convert text into numerical vectors that represent meaning -> Option A

4. Quick Check:

   Embeddings = numerical meaning vectors [OK]

Hint: Embeddings = text to numbers showing meaning [OK]

Common Mistakes:

• Confusing embeddings with image generation
• Thinking embeddings translate languages
• Assuming embeddings create UI elements

2. Which of the following is the correct way to create OpenAI embeddings using Langchain in Python?

easy

A. import OpenAI from langchain
embeddings = OpenAI.create_embeddings()

B. import langchain
embeddings = langchain.OpenAIEmbeddings()

C. from langchain import embeddings
embeddings = embeddings.OpenAI()

D. from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()

Solution

1. Step 1: Check the import statement

   The correct import is from langchain.embeddings import OpenAIEmbeddings.

2. Step 2: Check the instantiation

   Creating embeddings is done by calling OpenAIEmbeddings() with parentheses.

3. Final Answer:

   from langchain.embeddings import OpenAIEmbeddings\nembeddings = OpenAIEmbeddings() -> Option D

4. Quick Check:

   Correct import and instantiation syntax [OK]

Hint: Import from langchain.embeddings, then call OpenAIEmbeddings() [OK]

Common Mistakes:

• Wrong import path or module
• Missing parentheses when creating embeddings
• Using incorrect method names

3. Given the code snippet:

from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vector = embeddings.embed_query('Hello world')

What is the type of vector?

medium

A. A dictionary with text keys and values

B. A string containing the original text

C. A list of numbers representing the text meaning

D. An integer representing text length

Solution

1. Step 1: Understand embed_query output

   The embed_query method returns a list of numbers (a vector) representing the meaning of the input text.

2. Step 2: Identify the type of vector

   Since it encodes meaning, the vector is a list of floats or numbers, not a string or dictionary.

3. Final Answer:

   A list of numbers representing the text meaning -> Option C

4. Quick Check:

   embed_query output = number list [OK]

Hint: embed_query returns a number list vector [OK]

Common Mistakes:

• Expecting a string instead of numbers
• Thinking it returns a dictionary
• Assuming it returns text length as integer

4. What is wrong with this code snippet for creating embeddings?

from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings
vector = embeddings.embed_query('Test')

medium

A. embed_query method does not exist

B. Missing parentheses when creating OpenAIEmbeddings instance

C. Incorrect import statement

D. The input text must be a list, not a string

Solution

1. Step 1: Check how embeddings instance is created

   The code assigns OpenAIEmbeddings without parentheses, so embeddings is a class, not an instance.

2. Step 2: Understand the impact on embed_query call

   Calling embed_query on the class (not instance) will cause an error because methods need an instance.

3. Final Answer:

   Missing parentheses when creating OpenAIEmbeddings instance -> Option B

4. Quick Check:

   Instantiate with () to call methods [OK]

Hint: Always add () to create instance before method calls [OK]

Common Mistakes:

• Forgetting parentheses when creating instances
• Assuming methods work on classes directly
• Misreading import statements

5. You want to find the most similar text from a list using OpenAI embeddings in Langchain. Which approach correctly uses embeddings to compare similarity?

hard

A. Create embeddings for all texts, then compute cosine similarity between vectors

B. Compare texts directly as strings using == operator

C. Use embed_query only on the first text and compare lengths

D. Generate embeddings but do not compare vectors

Solution

1. Step 1: Understand how similarity is found with embeddings

   Embeddings convert texts into vectors; similarity is measured by comparing these vectors, often with cosine similarity.

2. Step 2: Identify correct approach to find most similar text

   Create embeddings for all texts, then compute similarity scores between vectors to find the closest match.

3. Final Answer:

   Create embeddings for all texts, then compute cosine similarity between vectors -> Option A

4. Quick Check:

   Similarity = compare embedding vectors [OK]

Hint: Compare vector embeddings with cosine similarity [OK]

Common Mistakes:

• Comparing raw strings instead of vectors
• Using embed_query on only one text
• Not comparing embeddings at all