Vector store selection (Pinecone, Chroma, FAISS) in Agentic AI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a vector store in the context of AI?

A vector store is a system that saves and organizes data as vectors (lists of numbers) so that AI can quickly find similar items by comparing these vectors.

intermediate

Name one key difference between Pinecone and FAISS.

Pinecone is a cloud-based managed service that handles scaling and maintenance for you, while FAISS is an open-source library you run on your own machine or server.

beginner

Why might someone choose Chroma as their vector store?

Chroma is easy to set up and use locally, supports fast similarity search, and is good for small to medium projects without needing cloud services.

intermediate

What is a common use case for FAISS?

FAISS is often used for fast similarity search in large datasets when you want full control over your data and infrastructure.

intermediate

How does Pinecone help with scaling vector search?

Pinecone automatically manages the storage and computing resources needed to handle large amounts of vector data, so you don’t have to worry about technical details.

Which vector store is a fully managed cloud service?

A Pinecone

B Chroma

C FAISS

D None of the above

Pinecone is a cloud-based managed vector store service.

Which vector store is best if you want to run everything locally without cloud dependencies?

A FAISS

B Pinecone

C Chroma

D All of the above

FAISS is designed for easy local use without cloud.

FAISS is primarily known as:

A A cloud service

B A database for text storage

C A visualization tool

D An open-source library for similarity search

FAISS is an open-source library for fast similarity search.

Which vector store automatically handles scaling and maintenance?

A FAISS

B Chroma

C Pinecone

D None

Pinecone manages scaling and maintenance automatically.

If you have a very large dataset and want full control over your infrastructure, which vector store is suitable?

A Pinecone

B FAISS

C None

D Chroma

FAISS lets you run similarity search on your own infrastructure.

Explain the main differences between Pinecone, Chroma, and FAISS as vector stores.

Think about where and how each vector store runs and what kind of projects they suit.

Describe a scenario where you would choose Pinecone over FAISS or Chroma.

Consider the benefits of cloud-managed services.

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