Open-source embedding models in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the OpenAIEmbeddings class from langchain.embeddings.

LangChain

from langchain.embeddings import [1]

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

AOpenAIEmbedder

BOpenAIEmbedding

COpenAIEmbed

DOpenAIEmbeddings

Attempts:

3 left

💡 Hint

Common Mistakes

Using singular form like OpenAIEmbedding

Using incomplete or incorrect class names

The correct class name to import is OpenAIEmbeddings.

2fill in blank

medium

Complete the code to create an instance of OpenAIEmbeddings with default settings.

LangChain

embedding_model = [1]()

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

AOpenAIEmbeddings

BOpenAIEmbed

COpenAIEmbedder

DOpenAIEmbedding

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong class names

Forgetting parentheses when creating an instance

The correct class to instantiate is OpenAIEmbeddings. It creates the embedding model object.

3fill in blank

hard

Fix the error in the code to generate embeddings for a list of texts.

LangChain

texts = ["hello", "world"]
embeddings = embedding_model.[1](texts)

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

Agenerate_embeddings

Bembed_text

Cembed_documents

Dcreate_embedding

Attempts:

3 left

💡 Hint

Common Mistakes

Using singular method names that expect one text

Using non-existent method names

The method to generate embeddings for multiple texts is embed_documents.

4fill in blank

hard

Fill both blanks to create embeddings for a list and print the shape of the first embedding.

LangChain

texts = ["apple", "banana", "cherry"]
embeddings = embedding_model.[1](texts)
print(len(embeddings[[2]]))

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

Aembed_documents

Bembed_texts

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using embed_texts which may not exist

Using index 1 instead of 0 for the first element

Use embed_documents to embed the list of texts. The first embedding is at index 0.

5fill in blank

hard

Fill the blanks to create embeddings for texts, filter embeddings longer than 100, and print count.

LangChain

texts = ["dog", "cat", "mouse"]
embeddings = embedding_model.[1](texts)
filtered = [e for e in embeddings if len(e) [2] 100]
print(len(filtered))  # Number of embeddings longer than 100

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

Aembed_documents

B>

C<

Dembed_texts

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong method names

Using wrong comparison operators

Use embed_documents to embed texts. Filter embeddings with length greater than 100 using >. The print statement shows how many embeddings are longer than 100.

Practice

1. What is the main purpose of open-source embedding models in Langchain?

easy

A. To generate images from text

B. To create user interfaces for apps

C. To store large databases efficiently

D. To convert text into numbers that computers can understand

Solution

1. Step 1: Understand embedding models

   Embedding models convert text into numerical vectors so computers can process meaning.

2. Step 2: Identify Langchain's role

   Langchain uses these models to help build language apps that understand text better.

3. Final Answer:

   To convert text into numbers that computers can understand -> Option D

4. Quick Check:

   Embedding models = convert text to numbers [OK]

Hint: Remember embeddings turn words into numbers [OK]

Common Mistakes:

• Confusing embeddings with UI design
• Thinking embeddings store data
• Mixing embeddings with image generation

2. Which code snippet correctly creates an embedding model instance using Langchain's open-source model?

easy

A. embedding = create_embedding('all-MiniLM-L6-v2')

B. embedding = OpenAIEmbeddings()

C. embedding = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')

D. embedding = EmbeddingModel('OpenAI')

Solution

1. Step 1: Recall Langchain embedding syntax

   Langchain uses classes like HuggingFaceEmbeddings with a model_name parameter for open-source models.

2. Step 2: Check each option

   embedding = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2') matches the correct syntax for creating a HuggingFace embedding instance.

3. Final Answer:

   embedding = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2') -> Option C

4. Quick Check:

   Correct class and parameter = embedding = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2') [OK]

Hint: Look for class name and model_name parameter [OK]

Common Mistakes:

• Using OpenAIEmbeddings for open-source models
• Calling a non-existent create_embedding function
• Passing wrong arguments to constructors

3. What will be the output type when you run this code snippet in Langchain?

from langchain.embeddings import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')
vector = embedding.embed_query('Hello world')

medium

A. A dictionary with word counts

B. A list of numbers representing the text

C. A string with the original text

D. An error because embed_query is invalid

Solution

1. Step 1: Understand embed_query method

   The embed_query method converts input text into a vector, which is a list of numbers.

2. Step 2: Check output type

   The variable 'vector' will hold a list (array) of floats representing the text embedding.

3. Final Answer:

   A list of numbers representing the text -> Option B

4. Quick Check:

   embed_query output = list of numbers [OK]

Hint: embed_query returns numeric vectors, not strings [OK]

Common Mistakes:

• Expecting a string instead of numbers
• Thinking output is a dictionary
• Assuming embed_query method does not exist

4. Identify the error in this Langchain code using an open-source embedding model:

from langchain.embeddings import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings('all-MiniLM-L6-v2')
vector = embedding.embed_query('Test')

medium

A. Missing model_name keyword argument in HuggingFaceEmbeddings constructor

B. embed_query method does not exist in HuggingFaceEmbeddings

C. The import statement is incorrect

D. The string 'Test' should be a list

Solution

1. Step 1: Check constructor usage

   HuggingFaceEmbeddings requires model_name as a keyword argument, not a positional argument.

2. Step 2: Verify method and import

   embed_query exists and import is correct; input can be a string.

3. Final Answer:

   Missing model_name keyword argument in HuggingFaceEmbeddings constructor -> Option A

4. Quick Check:

   Constructor needs model_name=... [OK]

Hint: Use model_name= when creating HuggingFaceEmbeddings [OK]

Common Mistakes:

• Passing model name as positional argument
• Thinking embed_query is missing
• Changing input type unnecessarily

5. You want to build a semantic search using Langchain with an open-source embedding model. Which approach correctly combines embeddings and a vector store?

hard

A. Create embeddings with HuggingFaceEmbeddings, then store vectors in FAISS for fast similarity search

B. Use OpenAIEmbeddings and store raw text in a SQL database

C. Generate embeddings with HuggingFaceEmbeddings and store them as plain text files

D. Use HuggingFaceEmbeddings without any vector store for search

Solution

1. Step 1: Understand semantic search components

   Semantic search needs embeddings to represent text and a vector store to find similar vectors quickly.

2. Step 2: Match correct tools

   HuggingFaceEmbeddings creates vectors; FAISS is a popular vector store for fast similarity search.

3. Step 3: Evaluate other options

   OpenAIEmbeddings is not open-source; storing raw text or plain files won't enable vector search; no vector store means no fast search.

4. Final Answer:

   Create embeddings with HuggingFaceEmbeddings, then store vectors in FAISS for fast similarity search -> Option A

5. Quick Check:

   Embedding + vector store = semantic search [OK]

Hint: Combine embeddings with vector store like FAISS [OK]

Common Mistakes:

• Using non-open-source embeddings
• Storing embeddings as plain text
• Skipping vector store for search