Chroma vector store setup in LangChain - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ component_behavior

intermediate

Chroma vector store initialization output

What will be the output behavior when initializing a Chroma vector store with the following code snippet?

LangChain

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Chroma(collection_name="test_collection", embedding_function=embeddings)
print(vectorstore._collection_name)

A Raises a TypeError because embedding_function expects a list, not an object

B Raises a NameError because OpenAIEmbeddings is not imported

C Prints the memory address of the vectorstore object

D Prints 'test_collection' indicating the collection name used

💡 Hint

Check how the collection_name attribute is stored and accessed in Chroma vector store.

The Chroma vector store stores the collection name in the _collection_name attribute. Printing it outputs the string 'test_collection'.

📝 Syntax

intermediate

Correct syntax for adding documents to Chroma vector store

Which option correctly adds a list of documents to an existing Chroma vector store instance named 'vectorstore'?

LangChain

docs = ["Hello world", "Langchain is great"]

A vectorstore.add_documents(docs)

B vectorstore.add(docs)

C vectorstore.append_documents(docs)

D vectorstore.insert_documents(docs)

💡 Hint

Check the official method name for adding documents in Chroma vector store.

The correct method to add documents to a Chroma vector store is add_documents. Other method names do not exist and cause AttributeError.

🔧 Debug

advanced

Identify the error in Chroma vector store setup

What error will this code raise when trying to create a Chroma vector store without specifying the embedding function?

LangChain

from langchain.vectorstores import Chroma

vectorstore = Chroma(collection_name="my_collection")

A TypeError: __init__() missing 1 required positional argument: 'embedding_function'

B ValueError: collection_name cannot be empty

C RuntimeError: Chroma client not found

D No error, vector store initializes with default embedding function

💡 Hint

Check the required parameters for Chroma vector store initialization.

The embedding_function parameter is required when creating a Chroma vector store. Omitting it causes a TypeError.

❓ state_output

advanced

Number of documents after adding to Chroma vector store

Given this code, what is the number of documents stored in the vector store after execution?

LangChain

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Chroma(collection_name="col1", embedding_function=embeddings)
docs = ["doc1", "doc2", "doc3"]
vectorstore.add_documents(docs)
count = vectorstore._collection.count()
print(count)

A Raises AttributeError because _collection has no count method

B 0

C 3

D Raises RuntimeError because documents are not indexed yet

💡 Hint

Check how add_documents affects the internal collection count.

After adding 3 documents, the internal collection count method returns 3.

🧠 Conceptual

expert

Effect of persistent directory in Chroma vector store

What is the effect of specifying the 'persist_directory' parameter when initializing a Chroma vector store?

A It encrypts the vector store data in memory for security

B It saves the vector store data to disk, allowing data to persist across program runs

C It limits the vector store size to the specified directory's free space

D It disables in-memory caching, forcing all queries to read from disk

💡 Hint

Think about why you would want to save vector data to a folder.

The persist_directory parameter tells Chroma where to save data on disk so it can be reused later without rebuilding the index.

Practice

1. What is the main purpose of setting up a Chroma vector store in Langchain?

easy

A. To generate text summaries automatically

B. To store text as vectors for fast similarity search

C. To create a relational database for text storage

D. To visualize text data in charts

Solution

1. Step 1: Understand Chroma vector store role

   Chroma stores text as vectors to quickly find similar meanings, not for relational data or visualization.

2. Step 2: Identify main use case

   Its main use is fast similarity search using vector embeddings.

3. Final Answer:

   To store text as vectors for fast similarity search -> Option B

4. Quick Check:

   Chroma = vector storage for similarity [OK]

Hint: Chroma = vector storage for quick similarity search [OK]

Common Mistakes:

• Thinking Chroma is a database for tables
• Confusing it with text summarization tools
• Assuming it creates visual charts

2. Which of the following is the correct way to initialize a Chroma vector store with an embedding function named embed and collection name my_collection?

easy

A. store = Chroma(embedding=embed, collection='my_collection')

B. store = Chroma(collection='my_collection', embed=embedding_function)

C. store = Chroma('my_collection', embed_function=embed)

D. store = Chroma(embedding_function=embed, collection_name='my_collection')

Solution

1. Step 1: Check parameter names for Chroma initialization

   The correct parameters are embedding_function and collection_name.

2. Step 2: Match correct syntax

   store = Chroma(embedding_function=embed, collection_name='my_collection') uses embedding_function=embed and collection_name='my_collection', which is correct.

3. Final Answer:

   store = Chroma(embedding_function=embed, collection_name='my_collection') -> Option D

4. Quick Check:

   embedding_function + collection_name = store = Chroma(embedding_function=embed, collection_name='my_collection') [OK]

Hint: Use embedding_function and collection_name keys exactly [OK]

Common Mistakes:

• Using wrong parameter names like embed or collection
• Passing parameters in wrong order without keywords
• Confusing embedding_function with embed_function

3. Given the following code snippet, what will results contain after running store.similarity_search('Hello world')?

embed = SomeEmbeddingFunction()
store = Chroma(embedding_function=embed, collection_name='texts')
store.add_texts(['Hello world', 'Goodbye world'])
results = store.similarity_search('Hello world')

medium

A. A list containing 'Hello world' as the most similar text

B. An empty list because no texts were added

C. A list containing 'Goodbye world' only

D. A syntax error due to missing parameters

Solution

1. Step 1: Analyze texts added to the store

   The store has two texts: 'Hello world' and 'Goodbye world'.

2. Step 2: Understand similarity_search behavior

   Searching 'Hello world' will return texts most similar, so 'Hello world' itself is the closest match.

3. Final Answer:

   A list containing 'Hello world' as the most similar text -> Option A

4. Quick Check:

   Added texts include 'Hello world' = A list containing 'Hello world' as the most similar text [OK]

Hint: Similarity search returns closest added text matches [OK]

Common Mistakes:

• Assuming no texts were added so result is empty
• Thinking it returns unrelated texts
• Confusing syntax error with runtime behavior

4. Identify the error in this Chroma vector store setup code:

embed = SomeEmbeddingFunction()
store = Chroma(embedding_function=embed)
store.add_texts(['Sample text'])

medium

A. Missing collection_name parameter in Chroma initialization

B. Embedding function is not assigned correctly

C. add_texts method does not exist

D. Chroma requires a database URL parameter

Solution

1. Step 1: Check required parameters for Chroma

   Chroma requires both embedding_function and collection_name to be set.

2. Step 2: Identify missing parameter

   The code misses collection_name, which is mandatory.

3. Final Answer:

   Missing collection_name parameter in Chroma initialization -> Option A

4. Quick Check:

   collection_name missing = Missing collection_name parameter in Chroma initialization [OK]

Hint: Always include collection_name when creating Chroma store [OK]

Common Mistakes:

• Assuming embedding_function alone is enough
• Thinking add_texts method is invalid
• Believing database URL is mandatory

5. You want to store multiple documents in a Chroma vector store and then find the top 2 most similar documents to a query. Which code snippet correctly sets this up and performs the search?

hard

A. embed = Embedding()
store = Chroma(collection_name='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text', top=2)

B. embed = Embedding()
store = Chroma(embedding_function=embed, collection='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text', k=2)

C. embed = Embedding()
store = Chroma(embedding_function=embed, collection_name='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text', k=2)

D. embed = Embedding()
store = Chroma(embedding_function=embed, collection_name='docs')
docs = ['Doc1', 'Doc2', 'Doc3']
store.add_texts(docs)
results = store.similarity_search('query text')

Solution

1. Step 1: Verify Chroma initialization parameters

   embedding_function and collection_name must be set correctly; embed = Embedding() store = Chroma(embedding_function=embed, collection_name='docs') docs = ['Doc1', 'Doc2', 'Doc3'] store.add_texts(docs) results = store.similarity_search('query text', k=2) does this properly.

2. Step 2: Check similarity_search usage for top results

   embed = Embedding() store = Chroma(embedding_function=embed, collection_name='docs') docs = ['Doc1', 'Doc2', 'Doc3'] store.add_texts(docs) results = store.similarity_search('query text', k=2) uses k=2 to get top 2 results, which is the correct parameter.

3. Final Answer:

   Correct setup with embedding_function, collection_name, and k=2 in similarity_search -> Option C

4. Quick Check:

   embedding_function + collection_name + k=2 = embed = Embedding() store = Chroma(embedding_function=embed, collection_name='docs') docs = ['Doc1', 'Doc2', 'Doc3'] store.add_texts(docs) results = store.similarity_search('query text', k=2) [OK]

Hint: Use k=2 in similarity_search to get top 2 results [OK]

Common Mistakes:

• Using wrong parameter name like top instead of k
• Missing embedding_function or collection_name
• Not specifying number of results to return