Vector databases (Pinecone, ChromaDB, Weaviate) in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Vector Database Indexing

Which of the following best describes how vector databases like Pinecone, ChromaDB, and Weaviate index data for fast similarity search?

A They convert data into fixed-length vectors and use approximate nearest neighbor algorithms for indexing.

B They store raw text data and perform keyword matching during queries.

C They use relational tables with primary keys to index data for exact matches.

D They compress images and store them as binary blobs without indexing.

💡 Hint

Think about how similarity search works with numbers instead of text.

Vector databases convert data into vectors (numbers) and use special algorithms to quickly find similar vectors, enabling fast similarity search.

❓ Model Choice

intermediate

Choosing a Vector Database for Real-Time Search

You want to build a real-time recommendation system that updates frequently and requires low latency. Which vector database is best suited for this use case?

A ChromaDB, because it only supports batch updates and slower queries.

B Weaviate, because it does not support vector search but excels in text search.

C Pinecone, because it supports dynamic updates and low-latency queries.

D None of these, because vector databases cannot handle real-time data.

💡 Hint

Consider which database supports fast updates and quick queries.

Pinecone is designed for real-time applications with dynamic updates and low-latency vector search, making it suitable for recommendation systems.

❓ Metrics

advanced

Evaluating Vector Search Quality

Which metric is most appropriate to evaluate the quality of a vector database's approximate nearest neighbor search results?

A BLEU score, measuring similarity between text sequences.

B Precision@k, measuring how many of the top k results are relevant.

C Accuracy, measuring the percentage of correct classifications.

D Mean Squared Error, measuring the difference between predicted and true values.

💡 Hint

Think about how to measure how many returned results are actually relevant.

Precision@k measures how many of the top k retrieved vectors are relevant, which is suitable for evaluating approximate nearest neighbor search quality.

🔧 Debug

advanced

Debugging Vector Search Result Errors

You notice that your vector database returns very poor search results despite correct vector embeddings. Which of the following is the most likely cause?

A The distance metric used for search does not match the embedding space properties.

B The database is missing some raw text data fields.

C The vectors are stored as integers instead of floats, causing syntax errors.

D The database index is too large, causing it to crash on queries.

💡 Hint

Consider how similarity is measured between vectors.

If the distance metric (like Euclidean or cosine) does not match the embedding space, similarity search results will be poor even if embeddings are correct.

❓ Predict Output

expert

Output of Vector Similarity Query Code

What is the output of the following Python code snippet using a vector database client?

Prompt Engineering / GenAI

import numpy as np

# Sample vectors
vectors = {
    'id1': np.array([1.0, 0.0]),
    'id2': np.array([0.0, 1.0]),
    'id3': np.array([1.0, 1.0])
}

# Query vector
query = np.array([1.0, 0.5])

# Function to compute cosine similarity
def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Find id with highest similarity
best_id = max(vectors, key=lambda k: cosine_similarity(query, vectors[k]))
print(best_id)

A id1 and id3 tie

B id1

C id2

D id3

💡 Hint

Calculate cosine similarity for each vector with the query.

Cosine similarity between query and id3 is highest, so 'id3' is printed.

Practice

1. What is the main purpose of a vector database like Pinecone, ChromaDB, or Weaviate?

easy

A. To store plain text documents only

B. To perform traditional SQL queries on structured data

C. To store and search data based on similarity using number lists

D. To create visual graphs from data

Solution

1. Step 1: Understand what vector databases store

   Vector databases store data as vectors, which are lists of numbers representing complex data like images or text.

2. Step 2: Identify the main use of vector databases

   They allow fast searching by similarity, not by exact matches like traditional databases.

3. Final Answer:

   To store and search data based on similarity using number lists -> Option C

4. Quick Check:

   Vector databases = similarity search [OK]

Hint: Vector DBs = search by meaning, not exact text [OK]

Common Mistakes:

• Thinking vector DBs only store text
• Confusing vector DBs with SQL databases
• Assuming vector DBs create visual graphs

2. Which of the following is the correct way to insert a vector into Pinecone using Python?

easy

A. pinecone.insert(id='vec1', vector=[0.1, 0.2, 0.3])

B. pinecone.upsert(vectors=[('vec1', [0.1, 0.2, 0.3])])

C. pinecone.add_vector('vec1', [0.1, 0.2, 0.3])

D. pinecone.push_vector(id='vec1', vector=[0.1, 0.2, 0.3])

Solution

1. Step 1: Recall Pinecone's method to add vectors

   Pinecone uses the 'upsert' method to insert or update vectors, which takes a list of tuples with id and vector.

2. Step 2: Match the correct syntax

   pinecone.upsert(vectors=[('vec1', [0.1, 0.2, 0.3])]) uses 'upsert' with a list of tuples, which is the correct syntax.

3. Final Answer:

   pinecone.upsert(vectors=[('vec1', [0.1, 0.2, 0.3])]) -> Option B

4. Quick Check:

   Use upsert with list of (id, vector) tuples [OK]

Hint: Pinecone uses upsert() with list of (id, vector) [OK]

Common Mistakes:

• Using insert() instead of upsert()
• Passing vector without wrapping in a list
• Using non-existent methods like add_vector or push_vector

3. Given the following code snippet using ChromaDB, what will be the output?

collection.add(ids=['1'], embeddings=[[0.1, 0.2, 0.3]], metadatas=[{'type': 'image'}], documents=['cat image'])
results = collection.query(query_embeddings=[[0.1, 0.2, 0.3]], n_results=1)
print(results['documents'])

medium

A. [['cat image']]

B. ['cat image']

C. [{'type': 'image'}]

D. []

Solution

1. Step 1: Understand what add() does in ChromaDB

   The add() method stores the document with its vector and metadata in the collection.

2. Step 2: Understand query() output format

   The query() method returns a dictionary with keys like 'documents' containing a list of lists of matched documents.

3. Final Answer:

   [['cat image']] -> Option A

4. Quick Check:

   Query returns list of lists of documents [OK]

Hint: ChromaDB query returns list of lists for documents [OK]

Common Mistakes:

• Expecting a flat list instead of list of lists
• Confusing documents with metadata
• Assuming empty result when vector matches exactly

4. You wrote this Weaviate query to find similar items but get an error:

client.query.get('Article', ['title']).with_near_vector({'vector': [0.1, 0.2]}).do()

What is the likely cause of the error?

medium

A. The query must include a filter parameter

B. The method with_near_vector does not exist in Weaviate client

C. The class name 'Article' must be lowercase

D. The vector length is too short; it should match the database dimension

Solution

1. Step 1: Check vector length requirement in Weaviate

   Weaviate expects the vector length to match the dimension used when creating the index, usually 3 or more numbers.

2. Step 2: Identify the error cause

   The vector [0.1, 0.2] has length 2, which is likely shorter than expected, causing the error.

3. Final Answer:

   The vector length is too short; it should match the database dimension -> Option D

4. Quick Check:

   Vector length must match index dimension [OK]

Hint: Vector length must match index dimension in Weaviate [OK]

Common Mistakes:

• Thinking method name is wrong
• Assuming class names must be lowercase
• Believing filter is always required

5. You want to build a search system that finds similar product descriptions using Weaviate. Which steps should you follow to prepare and query the data correctly?

hard

A. Create a schema with a vector index, add product descriptions as objects with vectors, then query using nearVector filter

B. Store product descriptions as plain text only, then query with SQL-like text search

C. Upload product images only, then query using image metadata filters

D. Create a schema without vector index, add descriptions, then query using exact match filters

Solution

1. Step 1: Define schema with vector index in Weaviate

   To search by similarity, the schema must include a vector index for the product description class.

2. Step 2: Add product descriptions as objects with vectors

   Each product description is stored as an object with its vector embedding representing meaning.

3. Step 3: Query using nearVector filter

   Use the nearVector filter in queries to find objects with vectors close to the query vector.

4. Final Answer:

   Create a schema with a vector index, add product descriptions as objects with vectors, then query using nearVector filter -> Option A

5. Quick Check:

   Schema + vectors + nearVector query = correct approach [OK]

Hint: Schema with vectors + nearVector query = similarity search [OK]

Common Mistakes:

• Trying to search plain text without vectors
• Using exact match filters for similarity search
• Ignoring schema vector index setup