Vector database operations (CRUD) in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does CRUD stand for in vector database operations?

CRUD stands for Create, Read, Update, and Delete. These are the four basic operations to manage data in a vector database.

beginner

How is the 'Create' operation performed in a vector database?

Create means adding new vectors (data points) into the database. Each vector represents information like text or images in numeric form.

beginner

What is the purpose of the 'Read' operation in vector databases?

Read means searching or retrieving vectors from the database. This often involves finding vectors similar to a query vector using similarity search.

intermediate

Why is the 'Update' operation important in vector databases?

Update changes existing vectors to keep data accurate or add new information. For example, updating a vector after improving its representation.

beginner

What happens during the 'Delete' operation in a vector database?

Delete removes vectors from the database when they are no longer needed, helping keep the database clean and efficient.

Which operation adds new vectors to a vector database?

A Read

B Delete

C Update

D Create

Create is the operation used to add new vectors to the database.

What does the 'Read' operation usually involve in vector databases?

A Finding similar vectors

B Removing vectors

C Changing vector values

D Adding new vectors

Read involves searching and retrieving vectors, often by finding those similar to a query vector.

Why might you perform an 'Update' on a vector in the database?

A To delete old data

B To add new vectors

C To improve or correct the vector data

D To search for vectors

Update changes existing vectors to improve or correct their data.

Which CRUD operation helps keep the vector database clean by removing unwanted data?

A Create

B Delete

C Read

D Update

Delete removes vectors that are no longer needed.

In vector databases, what form do data points take?

A Vectors of numbers

B Images only

C Text strings

D Audio files

Data points in vector databases are represented as vectors of numbers.

Explain the four CRUD operations in vector databases with simple examples.

Think about adding, finding, changing, and removing data.

Describe why similarity search is important in the 'Read' operation of vector databases.

Imagine finding friends who are most like you.

Practice

1. What does the CRUD acronym stand for in vector database operations?

easy

A. Connect, Run, Undo, Deploy

B. Compute, Retrieve, Upload, Download

C. Create, Read, Update, Delete

D. Cache, Refresh, Use, Drop

Solution

1. Step 1: Understand CRUD basics

   CRUD is a common term in databases meaning the four basic operations you can do with data.

2. Step 2: Match each letter to its meaning

   C stands for Create (add new data), R for Read (get data), U for Update (change data), and D for Delete (remove data).

3. Final Answer:

   Create, Read, Update, Delete -> Option C

4. Quick Check:

   CRUD = Create, Read, Update, Delete [OK]

Hint: Remember CRUD as basic data actions: add, get, change, remove [OK]

Common Mistakes:

• Confusing CRUD with unrelated terms
• Mixing up the order of operations
• Thinking CRUD only applies to files, not vectors

2. Which of the following is the correct syntax to add a vector with ID 'vec1' and values [0.1, 0.2, 0.3] to a vector database named db?

easy

A. db.push_vector(['vec1', 0.1, 0.2, 0.3])

B. db.insert('vec1', [0.1, 0.2, 0.3])

C. db.create_vector('vec1', 0.1, 0.2, 0.3)

D. db.add_vector('vec1', [0.1, 0.2, 0.3])

Solution

1. Step 1: Identify the common method for adding vectors

   Most vector databases use a method like add_vector with an ID and a list of numbers.

2. Step 2: Check method parameters

   The method should take the vector ID as a string and the vector values as a list or array.

3. Final Answer:

   db.add_vector('vec1', [0.1, 0.2, 0.3]) -> Option D

4. Quick Check:

   Add vector syntax = db.add_vector(id, vector) [OK]

Hint: Add vectors with add_vector(id, vector_list) method [OK]

Common Mistakes:

• Using wrong method names like insert or push_vector
• Passing vector values as separate arguments instead of a list
• Mixing ID and vector in one list

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

db = VectorDB()
db.add_vector('v1', [1, 0, 0])
db.add_vector('v2', [0, 1, 0])
results = db.search([0.9, 0.1, 0], top_k=1)
print(results)

medium

A. [('v1', 0.9)]

B. [('v2', 0.9)]

C. [('v1', 0.1)]

D. [('v2', 0.1)]

Solution

1. Step 1: Understand the vectors and query

   Vectors 'v1' = [1,0,0], 'v2' = [0,1,0], query = [0.9,0.1,0].

2. Step 2: Calculate similarity or distance

   Assuming cosine similarity, 'v1' is closer to query (dot product ~0.9), 'v2' is less similar (~0.1).

3. Final Answer:

   [('v1', 0.9)] -> Option A

4. Quick Check:

   Closest vector = v1 with similarity 0.9 [OK]

Hint: Closest vector has highest dot product with query [OK]

Common Mistakes:

• Confusing similarity with distance
• Mixing up vector IDs in output
• Assuming lower score means closer

4. The following code tries to update a vector but throws an error. What is the likely cause?

db = VectorDB()
db.add_vector('v1', [0.5, 0.5, 0.5])
db.update_vector('v2', [0.1, 0.1, 0.1])

medium

A. Vector 'v2' does not exist, so update fails

B. The update_vector method requires 4 arguments

C. Vector values must be integers, not floats

D. The add_vector method was not called before update_vector

Solution

1. Step 1: Check vector existence before update

   Updating a vector requires it to exist in the database first.

2. Step 2: Identify the error cause

   Since 'v2' was never added, trying to update it causes an error.

3. Final Answer:

   Vector 'v2' does not exist, so update fails -> Option A

4. Quick Check:

   Update needs existing vector [OK]

Hint: Update only existing vectors, else error occurs [OK]

Common Mistakes:

• Assuming update creates new vectors
• Thinking data type mismatch causes error
• Ignoring vector existence check

5. You want to delete vectors with similarity less than 0.5 to a query vector [0, 1, 0] from your vector database. Which sequence of operations correctly achieves this?

hard

A. Delete all vectors, then add only those with similarity >= 0.5

B. Search vectors with similarity < 0.5, then delete each by ID

C. Update vectors with similarity < 0.5 to zero vectors

D. Add new vectors with similarity >= 0.5, ignoring deletion

Solution

1. Step 1: Find vectors below similarity threshold

   Use a search or filter operation to get IDs of vectors with similarity less than 0.5.

2. Step 2: Delete vectors by their IDs

   Use the delete operation on each vector ID found to remove them from the database.

3. Final Answer:

   Search vectors with similarity < 0.5, then delete each by ID -> Option B

4. Quick Check:

   Filter then delete unwanted vectors [OK]

Hint: Filter vectors first, then delete by ID [OK]

Common Mistakes:

• Deleting all vectors instead of selective ones
• Trying to update instead of delete
• Ignoring the similarity filter step