Vector store selection (Pinecone, Chroma, FAISS) in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize a FAISS vector store.

Agentic AI

from langchain.vectorstores import FAISS
vector_store = FAISS.from_texts(texts, embeddings, index_factory=[1])

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

A"Pinecone"

B"Flat"

C"Chroma"

D"Annoy"

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Pinecone' or 'Chroma' as FAISS index factory names.

Leaving the index_factory parameter empty.

FAISS uses index factories like "Flat" to create a simple vector index. "Pinecone" and "Chroma" are different vector store types, not FAISS index factories.

2fill in blank

medium

Complete the code to create a Chroma vector store with a persist directory.

Agentic AI

from langchain.vectorstores import Chroma
vector_store = Chroma.from_texts(texts, embeddings, persist_directory=[1])

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

A"./chroma_db"

BNone

C"/tmp/faiss"

D"pinecone_index"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing None or invalid paths to persist_directory.

Using paths meant for other vector stores like FAISS or Pinecone.

Chroma vector store uses a directory path string for persist_directory to save the index. './chroma_db' is a valid local folder path.

3fill in blank

hard

Fix the error in the Pinecone initialization code by completing the missing parameter.

Agentic AI

import pinecone
pinecone.init(api_key=[1], environment="us-west1-gcp")

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

A12345

BNone

C"your-pinecone-api-key"

D"chroma"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing None or numeric values as api_key.

Confusing environment names with API keys.

Pinecone requires a valid API key string to initialize. The placeholder 'your-pinecone-api-key' should be replaced with your actual key.

4fill in blank

hard

Fill both blanks to create a FAISS vector store and save it to disk.

Agentic AI

from langchain.vectorstores import FAISS
vector_store = FAISS.from_texts(texts, embeddings, index_factory=[1])
vector_store.[2]("faiss_index")

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

A"Flat"

Bsave_local

Csave_local_index

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect method names like 'save' or 'save_local_index'.

Using wrong index factory names.

FAISS uses "Flat" as an index factory for exact search. The method to save the index locally is 'save_local'.

5fill in blank

hard

Fill all three blanks to create a Pinecone vector store client with index name and namespace.

Agentic AI

import pinecone
pinecone.init(api_key=[1], environment=[2])
index = pinecone.Index(name=[3])

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

A"your-pinecone-api-key"

B"us-west1-gcp"

C"my-index"

D"chroma"

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up environment and index name strings.

Using invalid API key formats.

To use Pinecone, you initialize with your API key and environment string, then create an Index object with the index name.

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