Why embeddings capture semantic meaning in LangChain - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an embedding for the text using LangChain.

LangChain

from langchain.embeddings import OpenAIEmbeddings

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

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

Acreate_embedding

Bgenerate_vector

Cembed_documents

Dembed_query

Attempts:

3 left

💡 Hint

Common Mistakes

Using embed_documents instead of embed_query for single text.

The embed_query method creates an embedding vector for a single text query, capturing its semantic meaning.

2fill in blank

medium

Complete the code to embed multiple documents at once.

LangChain

from langchain.embeddings import OpenAIEmbeddings
texts = ["Hello world", "Goodbye world"]
embeddings = OpenAIEmbeddings()
vectors = embeddings.[1](texts)

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

Aembed_query

Bgenerate_vector

Cembed_documents

Dcreate_embedding

Attempts:

3 left

💡 Hint

Common Mistakes

Using embed_query for a list of texts.

The embed_documents method creates embeddings for a list of texts, capturing semantic meaning for each.

3fill in blank

hard

Fix the error in the code to correctly create embeddings for a list of texts.

LangChain

from langchain.embeddings import OpenAIEmbeddings
texts = ["apple", "banana"]
embeddings = OpenAIEmbeddings()
vectors = embeddings.[1](texts)

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

Aembed_documents

Bembed_query

Cembed_text

Dcreate_vector

Attempts:

3 left

💡 Hint

Common Mistakes

Using embed_query causes errors with list input.

To embed multiple texts, use embed_documents. embed_query is for single texts only.

4fill in blank

hard

Fill both blanks to create embeddings and compare similarity between two texts.

LangChain

from langchain.embeddings import OpenAIEmbeddings
from sklearn.metrics.pairwise import [1]

embeddings = OpenAIEmbeddings()
vec1 = embeddings.embed_query("I love apples")
vec2 = embeddings.embed_query("I enjoy oranges")
similarity = [2]([vec1], [vec2])[0][0]

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Acosine_similarity

Beuclidean_distance

Cmanhattan_distance

Ddot_product

Attempts:

3 left

💡 Hint

Common Mistakes

Using distance metrics instead of similarity.

Cosine similarity measures how close two vectors are in direction, capturing semantic similarity.

5fill in blank

hard

Fill all three blanks to create a dictionary of word embeddings filtered by length and similarity threshold.

LangChain

from langchain.embeddings import OpenAIEmbeddings
texts = ["cat", "dog", "elephant"]
embeddings = OpenAIEmbeddings()
vectors = {word: embeddings.embed_query(word) for word in texts if len(word) [1] 3 and sum(a * b for a, b in zip(embeddings.embed_query(word), embeddings.embed_query("animal"))) [2] 0.5}
filtered = {k: v for k, v in vectors.items() if sum(v) [3] 0}

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

A>

B<

C>=

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators causing empty results.

We filter words longer than 3, with similarity at least 0.5, and embeddings with positive sum.