OpenAI embeddings in LangChain - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

What is the output of this LangChain OpenAI embeddings code?

Consider this Python code using LangChain's OpenAIEmbeddings to embed a list of texts. What is the type of the variable embeddings after running this code?

LangChain

from langchain.embeddings import OpenAIEmbeddings
texts = ["Hello world", "LangChain is cool"]
embedder = OpenAIEmbeddings()
embeddings = [embedder.embed_query(text) for text in texts]

A A single list of floats representing the combined embedding of all texts

B A list of lists of floats, each inner list is an embedding vector for a text

C A dictionary mapping each text to its embedding vector

D A string containing the concatenated embeddings as text

💡 Hint

Think about what embed_query returns for each text and how the list comprehension works.

The embed_query method returns a list of floats representing the embedding vector for a single text. Using a list comprehension over multiple texts produces a list of these lists.

📝 Syntax

intermediate

Which option correctly initializes OpenAIEmbeddings with a custom model name?

You want to create an OpenAIEmbeddings instance using the model named "text-embedding-3-large". Which code snippet is correct?

A embedder = OpenAIEmbeddings(model_name="text-embedding-3-large")

B embedder = OpenAIEmbeddings(model="text-embedding-3-large")

C embedder = OpenAIEmbeddings(name="text-embedding-3-large")

D embedder = OpenAIEmbeddings(modelName="text-embedding-3-large")

💡 Hint

Check the official parameter name for specifying the model in OpenAIEmbeddings.

The correct parameter to specify the model name in OpenAIEmbeddings is model_name. Other parameter names will cause errors.

❓ state_output

advanced

What is the length of the embedding vector produced by OpenAIEmbeddings by default?

Using the default OpenAIEmbeddings without specifying a model, what is the typical length of the embedding vector returned by embed_query?

LangChain

from langchain.embeddings import OpenAIEmbeddings
embedder = OpenAIEmbeddings()
vector = embedder.embed_query("test")
length = len(vector)

A 512

B 2048

C 1024

D 1536

💡 Hint

The default OpenAI embedding model returns vectors of a fixed size used widely in practice.

The default OpenAI embedding model returns vectors of length 1536 floats.

🔧 Debug

advanced

Why does this code raise a TypeError?

This code snippet raises a TypeError. What is the cause?

LangChain

from langchain.embeddings import OpenAIEmbeddings
embedder = OpenAIEmbeddings()
result = embedder.embed_query(12345)

A embed_query expects a string input, but an integer was given

B OpenAIEmbeddings must be initialized with an API key before use

C embed_query returns None when input is not a string

D The import statement is incorrect and causes the error

💡 Hint

Check the input type required by embed_query.

The embed_query method requires a string input. Passing an integer causes a TypeError.

🧠 Conceptual

expert

Which statement best describes how OpenAIEmbeddings integrates with LangChain's document search?

How does OpenAIEmbeddings typically work within LangChain to enable semantic search over documents?

A It generates summaries of documents to speed up search

B It directly indexes documents as text strings for keyword matching

C It converts documents and queries into vectors so that similarity can be computed to find relevant documents

D It replaces the need for any vector database by storing embeddings in memory

💡 Hint

Think about what embeddings represent and how they help find related content.

OpenAIEmbeddings converts text into numeric vectors. LangChain uses these vectors to compare similarity between queries and documents, enabling semantic search.