Long-term memory with vector stores in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a vector store from embeddings.

Agentic AI

vector_store = VectorStore.from_texts(texts, [1])

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

Aembeddings

Btexts

Cdocuments

Dvectors

Attempts:

3 left

💡 Hint

Common Mistakes

Using raw texts instead of embeddings

Passing documents instead of embeddings

The vector store needs embeddings to index the texts properly.

2fill in blank

medium

Complete the code to retrieve similar documents from the vector store.

Agentic AI

results = vector_store.similarity_search([1], k=3)

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Adocument_list

Bembedding_vector

Cquery_text

Dsearch_index

Attempts:

3 left

💡 Hint

Common Mistakes

Passing embedding vectors directly

Passing the whole document list

The similarity search takes a query text to find related documents.

3fill in blank

hard

Fix the error in the code to add documents to the vector store.

Agentic AI

vector_store.[1](new_documents)

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Aappend

Bextend

Cinsert

Dadd_texts

Attempts:

3 left

💡 Hint

Common Mistakes

Using append which is not defined

Using extend which is for lists

The method to add new documents as texts to the vector store is add_texts.

4fill in blank

hard

Fill both blanks to create embeddings and initialize the vector store.

Agentic AI

embeddings = [1]()
vector_store = VectorStore.from_texts(texts, [2])

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

AOpenAIEmbeddings

Btexts

Cembeddings

DDocumentLoader

Attempts:

3 left

💡 Hint

Common Mistakes

Passing texts instead of embeddings

Using wrong class names

First, create embeddings using OpenAIEmbeddings(). Then pass these embeddings to from_texts to build the vector store.

5fill in blank

hard

Fill all three blanks to perform a similarity search and print the top result.

Agentic AI

query = [1]
results = vector_store.similarity_search(query, k=[2])
print(results[[3]].page_content)

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

A"What is AI?"

B3

C0

D"Explain machine learning"

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong query strings

Incorrect index for top result

Wrong number of results requested

We set the query to "Explain machine learning", ask for top 3 results, and print the first result's content.

Practice

1. What is the main purpose of using a vector store in long-term memory for AI agents?

easy

A. To replace all databases with text files

B. To store images and videos directly

C. To slow down data retrieval for security

D. To save information as lists of numbers for quick searching

Solution

1. Step 1: Understand vector store role

   Vector stores save data as number lists (vectors) to represent information compactly.

2. Step 2: Identify purpose in AI memory

   This allows fast searching by comparing vector similarity, making recall efficient.

3. Final Answer:

   To save information as lists of numbers for quick searching -> Option D

4. Quick Check:

   Vector store = number lists for fast recall [OK]

Hint: Vector stores = numbers for fast search [OK]

Common Mistakes:

• Thinking vector stores save raw images or videos
• Confusing vector stores with simple text files
• Assuming vector stores slow down retrieval

2. Which of the following is the correct way to add a vector to a vector store in Python-like pseudocode?

easy

A. vector_store.insert('doc1', [0.1, 0.2, 0.3])

B. vector_store.push_vector([0.1, 0.2, 0.3], 'doc1')

C. vector_store.add_vector(id='doc1', vector=[0.1, 0.2, 0.3])

D. vector_store.append_vector('doc1', vector=[0.1, 0.2, 0.3])

Solution

1. Step 1: Identify typical vector store method

   Common vector stores use an add_vector method with id and vector parameters.

2. Step 2: Match correct syntax

   vector_store.add_vector(id='doc1', vector=[0.1, 0.2, 0.3]) matches this pattern: add_vector(id='doc1', vector=[0.1, 0.2, 0.3]). Others use incorrect method names or argument order.

3. Final Answer:

   vector_store.add_vector(id='doc1', vector=[0.1, 0.2, 0.3]) -> Option C

4. Quick Check:

   Correct method and argument names = vector_store.add_vector(id='doc1', vector=[0.1, 0.2, 0.3]) [OK]

Hint: Look for method named add_vector with id and vector [OK]

Common Mistakes:

• Using wrong method names like insert or push_vector
• Swapping argument order
• Missing parameter names

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

query_vector = [0.5, 0.5]
results = vector_store.search(query_vector, top_k=2)
print(results)

Assuming the vector store contains vectors close to [0.5, 0.5] for ids 'docA' and 'docB'.

medium

A. [('docA', 0.98), ('docB', 0.95)]

B. [('docC', 0.50), ('docD', 0.45)]

C. []

D. Error: search method not found

Solution

1. Step 1: Understand search behavior

   The search method returns the top_k closest vectors by similarity score.

2. Step 2: Match expected results

   Since 'docA' and 'docB' are closest to [0.5, 0.5], they appear with high similarity scores like 0.98 and 0.95.

3. Final Answer:

   [('docA', 0.98), ('docB', 0.95)] -> Option A

4. Quick Check:

   Top matches with high similarity = [('docA', 0.98), ('docB', 0.95)] [OK]

Hint: Search returns closest vectors with highest similarity scores [OK]

Common Mistakes:

• Expecting unrelated documents in results
• Assuming empty list if vectors exist
• Thinking search method causes error

4. You run this code but get an error:

vector_store.add_vector([0.1, 0.2, 0.3], id='doc1')

What is the likely cause of the error?

medium

A. The add_vector method requires id first, then vector as keyword arguments

B. The vector argument should be named before id

C. Vectors cannot be lists, must be strings

D. The method add_vector does not exist

Solution

1. Step 1: Check method signature

   add_vector usually expects id first, then vector as keyword arguments.

2. Step 2: Identify argument order error

   Passing vector first without naming causes error; correct call is add_vector(id='doc1', vector=[0.1, 0.2, 0.3]).

3. Final Answer:

   The add_vector method requires id first, then vector as keyword arguments -> Option A

4. Quick Check:

   Correct argument order = The add_vector method requires id first, then vector as keyword arguments [OK]

Hint: Check argument order and names for add_vector [OK]

Common Mistakes:

• Passing vector before id without naming
• Thinking vectors must be strings
• Assuming method does not exist

5. You want your AI agent to remember past conversations using a vector store. Which approach best ensures it retrieves relevant past info quickly and accurately?

hard

A. Store conversation texts as raw strings and search by keyword matching

B. Convert conversation texts into vectors and search by similarity in the vector store

C. Save conversations in a plain text file and read line by line

D. Use a random vector for each conversation and pick one randomly

Solution

1. Step 1: Understand vector store advantage

   Vector stores allow searching by similarity, capturing meaning beyond exact words.

2. Step 2: Compare options for retrieval quality

   Convert conversation texts into vectors and search by similarity in the vector store converts texts to vectors and searches by similarity, enabling fast and accurate recall of related past conversations.

3. Final Answer:

   Convert conversation texts into vectors and search by similarity in the vector store -> Option B

4. Quick Check:

   Similarity search in vector store = best for relevant recall [OK]

Hint: Use vector similarity, not keyword or random picks [OK]

Common Mistakes:

• Relying on keyword matching only
• Using random vectors losing relevance
• Reading plain text files linearly