Embedding models for semantic search in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an embedding vector from text using a model.

Agentic AI

embedding = model.[1](text)

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

Atrain

Bdecode

Cencode

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'decode' instead of 'encode' which is for reversing embeddings.

Using 'train' which is for model training, not embedding generation.

The encode method converts text into an embedding vector for semantic search.

2fill in blank

medium

Complete the code to compute cosine similarity between two embedding vectors.

Agentic AI

similarity = cosine_similarity(vec1, [1])

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

Avec2

Bmodel

Cembedding

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the model or raw text instead of the second vector.

Using the embedding variable without specifying which vector.

Cosine similarity compares two vectors, so the second vector vec2 is needed.

3fill in blank

hard

Fix the error in the code to normalize an embedding vector.

Agentic AI

normalized_vec = vec / np.[1](vec)

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

Amax

Bmean

Csum

Dlinalg.norm

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum or mean which do not give vector length.

Using max which only finds the largest element.

Normalization divides by the vector's length, computed by np.linalg.norm.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their embedding lengths greater than 5.

Agentic AI

lengths = {word: [1] for word in words if [2] > 5}

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

Alen(word)

Bword

Dembedding[word]

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of embedding length.

Confusing word length with embedding vector length.

The dictionary maps each word to the length of its embedding vector, filtering words with length greater than 5.

5fill in blank

hard

Fill all three blanks to filter embeddings with similarity above 0.8 and create a result dictionary.

Agentic AI

result = [1]: [2] for [3] in embeddings if similarity(embeddings[query], embeddings[[1]]) > 0.8}

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

Aword

Bembeddings[word]

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names causing runtime errors.

Mixing keys and values in the dictionary comprehension.

The dictionary comprehension uses word as key and its embedding as value, iterating over words in embeddings and filtering by similarity.

Practice

1. What is the main purpose of embedding models in semantic search?

easy

A. To convert text into numbers that capture meaning

B. To count the number of words in a text

C. To translate text into another language

D. To remove stop words from text

Solution

1. Step 1: Understand embedding models

   Embedding models transform text into numerical vectors that represent the meaning of the text.

2. Step 2: Identify the purpose in semantic search

   These vectors help find texts with similar meanings, even if the exact words differ.

3. Final Answer:

   To convert text into numbers that capture meaning -> Option A

4. Quick Check:

   Embedding models = convert text to meaningful numbers [OK]

Hint: Embedding models turn words into meaningful numbers [OK]

Common Mistakes:

• Thinking embeddings count words
• Confusing embeddings with translation
• Believing embeddings remove words

2. Which of the following is the correct way to get an embedding vector for a text using a model called embed_model in Python?

easy

A. embedding = embed_model.get_embedding('sample text')

B. embedding = embed_model.text_to_vector('sample text')

C. embedding = embed_model.encode('sample text')

D. embedding = embed_model.vectorize('sample text')

Solution

1. Step 1: Recall common embedding method names

   Many embedding libraries use encode to convert text to vectors.

2. Step 2: Check method correctness

   Only embed_model.encode('sample text') is a standard and valid call; others are not typical method names.

3. Final Answer:

   embedding = embed_model.encode('sample text') -> Option C

4. Quick Check:

   Use encode() to get embeddings [OK]

Hint: Use encode() method to get embeddings [OK]

Common Mistakes:

• Using non-existent methods like text_to_vector
• Confusing method names
• Forgetting to call the method with parentheses

3. Given the following Python code using an embedding model, what will be the output type of embedding?

embedding = embed_model.encode('Find similar texts')

medium

A. A list of words

B. A numeric vector (list or array) representing the text

C. A string representing the text

D. A dictionary with word counts

Solution

1. Step 1: Understand what encode() returns

   The encode() method returns a numeric vector that captures the meaning of the input text.

2. Step 2: Identify the output type

   This vector is usually a list or array of numbers, not words, strings, or dictionaries.

3. Final Answer:

   A numeric vector (list or array) representing the text -> Option B

4. Quick Check:

   encode() output = numeric vector [OK]

Hint: Embedding output is always numeric vector [OK]

Common Mistakes:

• Expecting a list of words
• Thinking output is a string
• Confusing embeddings with word counts

4. You wrote this code to get embeddings but get an error:

embedding = embed_model.encode['text to search']

What is the error and how to fix it?

medium

A. Add a return statement before encode

B. Change 'text to search' to a list of words

C. Remove the encode method and use embed_model directly

D. Use parentheses () instead of brackets [] to call encode method

Solution

1. Step 1: Identify the syntax error

   Methods in Python are called with parentheses (), not brackets []. Using brackets causes a TypeError.

2. Step 2: Correct the method call

   Replace encode['text to search'] with encode('text to search') to fix the error.

3. Final Answer:

   Use parentheses () instead of brackets [] to call encode method -> Option D

4. Quick Check:

   Method calls need () not [] [OK]

Hint: Call methods with () not [] [OK]

Common Mistakes:

• Using brackets [] instead of parentheses ()
• Passing wrong argument types
• Trying to call method without parentheses

5. You want to build a semantic search system that finds documents similar in meaning to a query. Which approach best uses embedding models for this task?

hard

A. Convert all documents and the query to embeddings, then find documents with closest vectors

B. Count keyword frequency in documents and query, then match counts

C. Translate documents to another language before searching

D. Sort documents alphabetically and pick the first matches

Solution

1. Step 1: Understand semantic search with embeddings

   Semantic search uses embeddings to represent meaning, so comparing vectors finds similar meaning.

2. Step 2: Identify the correct approach

   Converting documents and query to embeddings and finding closest vectors is the correct method for semantic search.

3. Final Answer:

   Convert all documents and the query to embeddings, then find documents with closest vectors -> Option A

4. Quick Check:

   Semantic search = compare embedding vectors [OK]

Hint: Compare embeddings of query and documents for semantic search [OK]

Common Mistakes:

• Using keyword counts instead of embeddings
• Translating text unnecessarily
• Sorting alphabetically instead of by meaning