Vector store selection (Pinecone, Chroma, FAISS) in Agentic AI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Choosing a vector store for large-scale real-time search

You want to build a system that handles millions of vectors and requires fast, real-time similarity search with automatic scaling. Which vector store is the best choice?

A Chroma, because it is an open-source vector database designed for small datasets and local use only.

B Pinecone, because it is a managed cloud service that automatically scales and supports real-time search at large scale.

C FAISS, because it is optimized for CPU and GPU and supports large datasets but requires manual scaling.

D Any of the three will perform equally well for large-scale real-time search without extra setup.

💡 Hint

Think about which option offers managed scaling and cloud support for millions of vectors.

Pinecone is a managed vector database service designed for large-scale, real-time similarity search with automatic scaling. FAISS is powerful but requires manual setup and scaling. Chroma is better suited for smaller, local datasets.

❓ Metrics

intermediate

Evaluating vector store retrieval accuracy

You run a similarity search on a vector store and get a list of retrieved items. Which metric best measures how many relevant items are retrieved among the top results?

A Recall, because it measures the fraction of relevant items retrieved out of all relevant items.

B Precision, because it measures the fraction of retrieved items that are relevant.

C Mean Squared Error, because it measures the average squared difference between predicted and true vectors.

D Accuracy, because it measures the overall correct predictions over all queries.

💡 Hint

Recall focuses on how many relevant items you find, not how many you retrieved.

Recall measures the fraction of relevant items retrieved among all relevant items, which is important for evaluating retrieval completeness in vector search.

❓ Predict Output

advanced

Output of FAISS index search code snippet

What is the output of this Python code using FAISS for a simple vector search?

Agentic AI

import numpy as np
import faiss

# Create 5 vectors of dimension 3
vectors = np.array([[1,0,0],[0,1,0],[0,0,1],[1,1,0],[0,1,1]], dtype='float32')

# Build index
index = faiss.IndexFlatL2(3)
index.add(vectors)

# Query vector
query = np.array([[1,0,0]], dtype='float32')

# Search top 2 nearest neighbors
D, I = index.search(query, 2)
print(I[0].tolist())

A [0, 3]

B [0, 1]

C [1, 4]

D [3, 0]

💡 Hint

Remember FAISS returns nearest neighbors by distance. The query is [1,0,0].

The closest vector to [1,0,0] is itself at index 0, then [1,1,0] at index 3 is the next closest by Euclidean distance.

❓ Model Choice

advanced

Selecting vector store for offline batch processing

You want to perform offline batch similarity searches on a dataset of 100,000 vectors without needing real-time responses. Which vector store is most suitable?

A Pinecone, because it is optimized for real-time cloud queries and automatic scaling.

B None of these, you should use a relational database for vector search.

C FAISS, because it is optimized for fast batch processing on large datasets locally or on GPU.

D Chroma, because it is designed for small datasets and local use only.

💡 Hint

Consider which tool is best for offline batch processing and large datasets without cloud dependency.

FAISS is designed for efficient similarity search on large datasets locally or on GPU, making it ideal for offline batch processing.

🔧 Debug

expert

Identifying error in Pinecone vector insertion code

What error will this Pinecone vector insertion code raise?

Agentic AI

import pinecone
pinecone.init(api_key='fake_key', environment='us-west1-gcp')
index = pinecone.Index('example-index')
vectors = [(1, [0.1, 0.2, 0.3]), (2, [0.4, 0.5])]  # Note second vector has length 2
index.upsert(vectors)

A No error; vectors will be inserted successfully.

B KeyError because the index name 'example-index' does not exist.

C TypeError because vectors must be a dictionary, not a list of tuples.

D ValueError due to inconsistent vector dimensions in the upsert list.

💡 Hint

Check if all vectors have the same dimension length.

Pinecone requires all vectors to have the same dimension length. The second vector has length 2 instead of 3, causing a ValueError.

Practice

1.

Which vector store is best known for easy cloud-based deployment and scalability?

easy

A. Pinecone

B. Chroma

C. FAISS

D. Local file system

Solution

1. Step 1: Understand cloud-based vector stores

   Pinecone is designed as a managed cloud service, making deployment and scaling easy.

2. Step 2: Compare with other options

   Chroma and FAISS are typically used locally or self-hosted, not primarily cloud services.

3. Final Answer:

   Pinecone -> Option A

4. Quick Check:

   Cloud deployment = Pinecone [OK]

Hint: Cloud + scalability? Think Pinecone first [OK]

Common Mistakes:

• Confusing FAISS as cloud service
• Assuming Chroma is cloud-only
• Choosing local file system as vector store

2.

Which of the following is the correct way to initialize a FAISS index for 128-dimensional vectors in Python?

import faiss
index = faiss.IndexFlatL2(____)

easy

A. '128'

B. IndexFlatL2(128)

C. faiss.IndexFlatL2(128)

D. 128

Solution

1. Step 1: Understand FAISS index initialization

   The IndexFlatL2 constructor expects an integer dimension, not a string or nested call.

2. Step 2: Check the correct argument type

   Passing 128 as an integer is correct; quotes or extra calls cause errors.

3. Final Answer:

   128 -> Option D

4. Quick Check:

   Dimension as int = 128 [OK]

Hint: Dimension must be integer, no quotes [OK]

Common Mistakes:

• Passing dimension as string
• Calling constructor inside argument
• Using undefined names without import

3.

Given this code snippet using Chroma vector store, what will be the output?

from chromadb import Client
client = Client()
collection = client.create_collection('test')
collection.add(ids=['1'], embeddings=[[0.1, 0.2]], metadatas=[{'name': 'item1'}], documents=['doc1'])
results = collection.query(query_embeddings=[[0.1, 0.2]], n_results=1)
print(results['documents'])

medium

A. [['doc1']]

B. ['doc1']

C. [{'name': 'item1'}]

D. Error: missing parameters

Solution

1. Step 1: Understand Chroma query output format

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

2. Step 2: Check the printed output

   Printing results['documents'] shows a list containing a list with 'doc1', so output is [['doc1']].

3. Final Answer:

   [['doc1']] -> Option A

4. Quick Check:

   Chroma query docs = [['doc1']] [OK]

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

Common Mistakes:

• Expecting flat list instead of nested list
• Confusing metadata with documents
• Assuming query returns error without reason

4.

What is the main error in this FAISS usage code snippet?

import faiss
index = faiss.IndexFlatL2(64)
vectors = [[0.1]*64, [0.2]*64]
index.add(vectors)
print(index.ntotal)

medium

A. Vectors length must be 63, not 64

B. Vectors must be a numpy array of type float32

C. ntotal is not a valid attribute

D. Index dimension should be 128, not 64

Solution

1. Step 1: Check vector data type for FAISS

   FAISS requires vectors as numpy arrays with dtype float32, not Python lists.

2. Step 2: Identify the error cause

   Passing a list causes a type error; converting to numpy float32 fixes it.

3. Final Answer:

   Vectors must be a numpy array of type float32 -> Option B

4. Quick Check:

   FAISS vectors = numpy float32 array [OK]

Hint: FAISS needs numpy float32 arrays, not lists [OK]

Common Mistakes:

• Using Python lists instead of numpy arrays
• Wrong dimension assumption
• Misunderstanding ntotal attribute

5.

You have a large dataset of 10 million vectors and want fast similarity search on your local machine without internet. Which vector store is the best choice?

hard

A. Pinecone

B. Chroma

C. FAISS

D. SQLite database

Solution

1. Step 1: Consider dataset size and environment

   10 million vectors is large; local machine without internet means no cloud services.

2. Step 2: Match vector store to requirements

   FAISS is optimized for large-scale local similarity search and does not require internet.

3. Step 3: Exclude other options

   Pinecone is cloud-based, Chroma is less optimized for huge local datasets, SQLite is not a vector store.

4. Final Answer:

   FAISS -> Option C

5. Quick Check:

   Large local dataset = FAISS [OK]

Hint: Big local data? Choose FAISS for speed [OK]

Common Mistakes:

• Choosing cloud-based Pinecone for offline use
• Assuming Chroma handles huge data best locally
• Using SQLite as vector store