Vector databases (Pinecone, ChromaDB, Weaviate) in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize a Pinecone client with your API key.

Prompt Engineering / GenAI

import pinecone
pinecone.init(api_key=[1])

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

A12345

B'your-pinecone-api-key'

Cpinecone_key

Dapi_key

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to put the API key inside quotes.

Using a variable name instead of the actual key string.

You must provide your Pinecone API key as a string to initialize the client.

2fill in blank

medium

Complete the code to create a new index in Pinecone with dimension 128.

Prompt Engineering / GenAI

pinecone.create_index(name='example-index', dimension=[1])

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

A512

B64

C128

D256

Attempts:

3 left

💡 Hint

Common Mistakes

Using a dimension that does not match the vector size.

Using a string instead of a number.

The dimension must match the size of your vector embeddings, here 128.

3fill in blank

hard

Fix the error in the code to query a Pinecone index for the top 3 similar vectors.

Prompt Engineering / GenAI

index = pinecone.Index('example-index')
query_result = index.query(queries=[query_vector], top_k=[1])

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

A3

B'3'

Ctop3

D[3]

Attempts:

3 left

💡 Hint

Common Mistakes

Passing '3' as a string instead of integer 3.

Passing a list like [3] instead of an integer.

The top_k parameter must be an integer, not a string or list.

4fill in blank

hard

Fill both blanks to create a ChromaDB client and add a collection named 'my_collection'.

Prompt Engineering / GenAI

import chromadb
client = chromadb.[1]()
collection = client.[2]('my_collection')

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

AClient

Bcreate_collection

CCollection

Dget_collection

Attempts:

3 left

💡 Hint

Common Mistakes

Using get_collection() instead of create_collection() to add a new collection.

Using Collection() instead of Client() to create the client.

You create a ChromaDB client with Client(), then add a collection with create_collection().

5fill in blank

hard

Fill all three blanks to initialize a Weaviate client, create a schema, and add a data object.

Prompt Engineering / GenAI

import weaviate
client = weaviate.Client([1])
schema = {
  'classes': [{'class': [2], 'properties': [{'name': 'text', 'dataType': ['string']}]}]
}
client.schema.[3](schema)

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

A'http://localhost:8080'

B'Document'

Ccreate

Ddelete

Attempts:

3 left

💡 Hint

Common Mistakes

Using delete() instead of create() to add the schema.

Using wrong URL or missing quotes.

Using wrong class name or missing quotes.

You connect to Weaviate at the URL, define a class named 'Document', and create the schema with create().

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