Similarity search and retrieval in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to compute cosine similarity between two vectors.

Prompt Engineering / GenAI

from sklearn.metrics.pairwise import [1]

vector_a = [[1, 2, 3]]
vector_b = [[4, 5, 6]]
similarity = [1](vector_a, vector_b)
print(similarity)

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

Apairwise_distances

Beuclidean_distances

Cmanhattan_distances

Dcosine_similarity

Attempts:

3 left

💡 Hint

Common Mistakes

Using distance functions instead of similarity functions.

Confusing cosine similarity with Euclidean distance.

The cosine_similarity function calculates the cosine similarity between vectors, which is commonly used in similarity search.

2fill in blank

medium

Complete the code to find the index of the most similar vector in a list using cosine similarity.

Prompt Engineering / GenAI

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

vectors = np.array([[1, 0], [0, 1], [1, 1]])
query = np.array([[0.9, 0.1]])
similarities = cosine_similarity(query, vectors)
most_similar_index = np.argmax([1])
print(most_similar_index)

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

Asimilarities

Bvectors

Cquery

Dnp.array

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original vectors array instead of similarity scores.

Trying to find max on the query vector.

The similarities array contains similarity scores between the query and each vector. Using np.argmax on it finds the index of the highest similarity.

3fill in blank

hard

Fix the error in the code to correctly compute Euclidean distances between vectors.

Prompt Engineering / GenAI

from sklearn.metrics.pairwise import [1]

vectors = [[1, 2], [3, 4], [5, 6]]
distances = [1](vectors)
print(distances)

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

Acosine_similarity

Beuclidean_distances

Cmanhattan_distances

Dpairwise_kernels

Attempts:

3 left

💡 Hint

Common Mistakes

Using similarity functions instead of distance functions.

Passing a single list instead of a 2D array.

The euclidean_distances function computes the Euclidean distance matrix between vectors. The original code likely used a wrong function causing errors or wrong output.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Prompt Engineering / GenAI

words = ['apple', 'bat', 'carrot', 'dog', 'elephant']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself instead of its length.

Using the wrong comparison operator.

The dictionary comprehension uses len(word) to get word lengths and filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 2.

Prompt Engineering / GenAI

data = {'a': 1, 'b': 3, 'c': 5, 'd': 2}
filtered = [1]: [2] for k, v in data.items() if v [3] 2}
print(filtered)

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Not converting keys to uppercase.

Using wrong comparison operator or filtering condition.

The dictionary comprehension converts keys to uppercase with k.upper(), keeps values as v, and filters for values greater than 2 using >.

Practice

1.

What is the main goal of similarity search in machine learning?

easy

A. To count the number of items in a dataset

B. To sort items alphabetically

C. To find items that are close or alike in a collection

D. To remove duplicate items from a list

Solution

1. Step 1: Understand the purpose of similarity search

   Similarity search is used to find items that are similar or close to each other in a dataset.

2. Step 2: Compare options with the definition

   Only To find items that are close or alike in a collection describes finding similar or close items, which matches the goal of similarity search.

3. Final Answer:

   To find items that are close or alike in a collection -> Option C

4. Quick Check:

   Similarity search = find similar items [OK]

Hint: Similarity search finds close or alike items [OK]

Common Mistakes:

• Confusing similarity search with sorting
• Thinking similarity search counts items
• Assuming it removes duplicates

2.

Which of the following is the correct way to compute cosine similarity between two vectors A and B in Python using numpy?

import numpy as np
A = np.array([1, 2, 3])
B = np.array([4, 5, 6])
# What code computes cosine similarity?

easy

A. np.dot(A, B) * (np.linalg.norm(A) + np.linalg.norm(B))

B. np.dot(A, B) / (np.linalg.norm(A) - np.linalg.norm(B))

C. np.sum(A * B) / (np.linalg.norm(A) - np.linalg.norm(B))

D. np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B))

Solution

1. Step 1: Recall cosine similarity formula

   Cosine similarity = dot product of A and B divided by product of their norms.

2. Step 2: Match formula to code options

   np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) matches the formula exactly: np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)).

3. Final Answer:

   np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) -> Option D

4. Quick Check:

   Cosine similarity = dot / (norm A * norm B) [OK]

Hint: Cosine similarity = dot product divided by norms product [OK]

Common Mistakes:

• Adding norms instead of multiplying
• Subtracting norms in denominator
• Multiplying dot product by sum of norms

3.

Given the following vectors, what is the cosine similarity between vec1 and vec2?

import numpy as np
vec1 = np.array([1, 0, 0])
vec2 = np.array([0, 1, 0])
cos_sim = np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
print("{:.2f}".format(cos_sim))

medium

A. 0.00

B. 0.50

C. -1.00

D. 1.00

Solution

1. Step 1: Calculate dot product of vec1 and vec2

   Dot product = 1*0 + 0*1 + 0*0 = 0.

2. Step 2: Calculate norms and cosine similarity

   Norm of vec1 = 1, norm of vec2 = 1, so cosine similarity = 0 / (1*1) = 0.

3. Final Answer:

   0.00 -> Option A

4. Quick Check:

   Orthogonal vectors have cosine similarity 0 [OK]

Hint: Orthogonal vectors have cosine similarity zero [OK]

Common Mistakes:

• Confusing dot product with cosine similarity
• Forgetting to divide by norms
• Rounding errors causing wrong answer

4.

Consider this code snippet for similarity search. What is the error?

import numpy as np
vectors = [np.array([1, 2]), np.array([3, 4])]
query = np.array([1, 0])
scores = []
for v in vectors:
    score = np.dot(query, v) / np.linalg.norm(query) * np.linalg.norm(v)
    scores.append(score)
print(scores)

medium

A. Missing parentheses causing wrong order of operations

B. Using np.dot instead of np.cross

C. Vectors have different lengths

D. Query vector is not normalized

Solution

1. Step 1: Analyze the cosine similarity formula in code

   The formula should divide dot product by product of norms: dot(query, v) / (norm(query) * norm(v)).

2. Step 2: Identify missing parentheses

   Code does np.dot(query, v) / np.linalg.norm(query) * np.linalg.norm(v), which computes division then multiplication separately, causing wrong result.

3. Final Answer:

   Missing parentheses causing wrong order of operations -> Option A

4. Quick Check:

   Use parentheses to group denominator multiplication [OK]

Hint: Use parentheses to group denominator in cosine similarity [OK]

Common Mistakes:

• Forgetting parentheses around denominator
• Using cross product instead of dot product
• Ignoring vector length mismatch

5.

You have a collection of text documents converted into vectors. You want to find the top 2 most similar documents to a new query vector using cosine similarity. Which approach is best?

1. Compute cosine similarity between query and each document vector.
2. Sort documents by similarity score descending.
3. Return top 2 documents.

Which code snippet correctly implements this?

import numpy as np

docs = [np.array([1, 0]), np.array([0, 1]), np.array([1, 1])]
query = np.array([1, 0])

# Choose the correct code:

hard

A. scores = [np.dot(query, d) * np.linalg.norm(query) * np.linalg.norm(d) for d in docs]
top2 = sorted(scores)[:2]
print(top2)

B. scores = [np.dot(query, d) / (np.linalg.norm(query) * np.linalg.norm(d)) for d in docs]
top2 = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:2]
print(top2)

C. scores = [np.dot(query, d) / (np.linalg.norm(query) - np.linalg.norm(d)) for d in docs]
top2 = sorted(range(len(scores)), key=lambda i: scores[i])[:2]
print(top2)

D. scores = [np.cross(query, d) / (np.linalg.norm(query) * np.linalg.norm(d)) for d in docs]
top2 = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:2]
print(top2)

Solution

1. Step 1: Compute cosine similarity correctly

   scores = [np.dot(query, d) / (np.linalg.norm(query) * np.linalg.norm(d)) for d in docs] top2 = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:2] print(top2) computes cosine similarity as dot product divided by product of norms, which is correct.

2. Step 2: Sort indices by similarity descending and select top 2

   scores = [np.dot(query, d) / (np.linalg.norm(query) * np.linalg.norm(d)) for d in docs] top2 = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:2] print(top2) sorts indices by scores descending and selects top 2, matching the requirement.

3. Final Answer:

   scores = [np.dot(query, d) / (np.linalg.norm(query) * np.linalg.norm(d)) for d in docs] top2 = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:2] print(top2) -> Option B

4. Quick Check:

   Cosine similarity + sort descending + top 2 = scores = [np.dot(query, d) / (np.linalg.norm(query) * np.linalg.norm(d)) for d in docs] top2 = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:2] print(top2) [OK]

Hint: Compute cosine similarity, sort descending, pick top results [OK]

Common Mistakes:

• Multiplying norms instead of dividing
• Using cross product instead of dot product
• Sorting ascending instead of descending