Why embeddings capture semantic meaning in LangChain - See It in Action

Why embeddings capture semantic meaning

📖 Scenario: You want to understand how text embeddings can capture the meaning of words and sentences. Imagine you have a list of simple sentences, and you want to convert them into numbers that show how similar their meanings are.

🎯 Goal: Build a small Python program using Langchain to create embeddings for sentences and compare their similarity scores. This will help you see how embeddings capture semantic meaning.

📋 What You'll Learn

Create a list of sentences called sentences with exact values

Create a variable called embedding_model to hold the embedding model name

Use Langchain's OpenAIEmbeddings to generate embeddings for the sentences

Calculate cosine similarity between the first sentence embedding and the others

💡 Why This Matters

🌍 Real World

Embeddings help computers understand the meaning of text, which is useful in search engines, chatbots, and recommendation systems.

💼 Career

Knowing how to generate and compare embeddings is important for roles in AI, data science, and software development involving natural language processing.

1

Create the sentences list

Create a list called sentences with these exact strings: 'I love apples', 'Apples are my favorite fruit', 'I enjoy eating bananas', 'Bananas are yellow'.

LangChain

# Your code here

Hint

Use square brackets to create a list and include the exact sentences as strings.

2

Set the embedding model

Create a variable called embedding_model and set it to the string 'text-embedding-ada-002' which is the OpenAI embedding model name.

LangChain

sentences = ['I love apples', 'Apples are my favorite fruit', 'I enjoy eating bananas', 'Bananas are yellow']
# Your code here

Hint

Assign the exact string to the variable embedding_model.

3

Generate embeddings for sentences

Import OpenAIEmbeddings from langchain.embeddings. Create an instance called embedder using embedding_model. Then create a list called embeddings by applying embedder.embed_documents(sentences).

LangChain

sentences = ['I love apples', 'Apples are my favorite fruit', 'I enjoy eating bananas', 'Bananas are yellow']
embedding_model = 'text-embedding-ada-002'
# Your code here

Hint

Remember to import the class first, then create the embedder instance, and finally generate embeddings for the sentences list.

4

Calculate similarity with first sentence

Import cosine_similarity from sklearn.metrics.pairwise. Create a list called similarities by calculating the cosine similarity between the first embedding and each embedding in embeddings. Use a list comprehension with cosine_similarity([embeddings[0]], [e])[0][0] for each e in embeddings.

LangChain

from langchain.embeddings import OpenAIEmbeddings

sentences = ['I love apples', 'Apples are my favorite fruit', 'I enjoy eating bananas', 'Bananas are yellow']
embedding_model = 'text-embedding-ada-002'

embedder = OpenAIEmbeddings(model=embedding_model)
embeddings = embedder.embed_documents(sentences)
# Your code here

Hint

Use cosine similarity to compare the first sentence embedding with each embedding in the list.

Practice

1. What is the main reason embeddings capture semantic meaning in Langchain?

easy

A. They convert text into number vectors that represent meaning.

B. They store the original text exactly as it is.

C. They translate text into another language.

D. They count the number of words in the text.

Solution

1. Step 1: Understand what embeddings do

   Embeddings transform text into vectors of numbers that capture the meaning behind the words.

2. Step 2: Compare options based on this understanding

   Only They convert text into number vectors that represent meaning. correctly states that embeddings convert text into number vectors representing meaning.

3. Final Answer:

   They convert text into number vectors that represent meaning. -> Option A

4. Quick Check:

   Embeddings = number vectors of meaning [OK]

Hint: Remember embeddings turn words into numbers showing meaning [OK]

Common Mistakes:

• Thinking embeddings store original text
• Confusing embeddings with translation
• Believing embeddings just count words

2. Which of the following is the correct way to create an embedding in Langchain?

easy

A. embedding = Embeddings.create(text)

B. embedding = Embeddings.generate(text)

C. embedding = Embeddings.embed(text)

D. embedding = Embeddings.toVector(text)

Solution

1. Step 1: Recall Langchain embedding method

   The standard method to get embeddings is usually called embed in Langchain libraries.

2. Step 2: Match method names with options

   Only embedding = Embeddings.embed(text) uses embed, which is the correct method name.

3. Final Answer:

   embedding = Embeddings.embed(text) -> Option C

4. Quick Check:

   Embedding method = embed() [OK]

Hint: Look for method named 'embed' to create embeddings [OK]

Common Mistakes:

• Using create() instead of embed()
• Confusing generate() with embed()
• Assuming toVector() is a valid method

3. Given two texts: text1 = 'I love apples' and text2 = 'I enjoy oranges', which statement about their embeddings is true?

medium

A. Their embeddings will be close because both express liking fruit.

B. Their embeddings will be very different because the words differ.

C. Their embeddings will be identical because both mention fruits.

D. Their embeddings will be random and unrelated.

Solution

1. Step 1: Understand semantic similarity in embeddings

   Embeddings capture meaning, so texts with similar ideas have embeddings close to each other.

2. Step 2: Analyze the texts' meaning

   Both texts express liking fruit, so their embeddings should be close, not identical or random.

3. Final Answer:

   Their embeddings will be close because both express liking fruit. -> Option A

4. Quick Check:

   Similar meaning = close embeddings [OK]

Hint: Focus on meaning similarity, not exact words [OK]

Common Mistakes:

• Assuming embeddings must be identical for similar topics
• Thinking embeddings depend only on exact words
• Believing embeddings are random vectors

4. You wrote this code to get embeddings but get an error:

embedding = Embeddings.embed('Hello world')

What is the likely cause?

medium

A. You must pass a list of texts, not a single string.

B. The embed method requires an instance, not called on the class.

C. Embeddings.embed does not exist; use Embeddings.create instead.

D. The text 'Hello world' is too short for embeddings.

Solution

1. Step 1: Check how embed method is called

   In Langchain, embedding methods are usually instance methods, not static class methods.

2. Step 2: Identify error cause

   Calling embed directly on the class without creating an instance causes an error.

3. Final Answer:

   The embed method requires an instance, not called on the class. -> Option B

4. Quick Check:

   Instance method call needed [OK]

Hint: Create an instance before calling embed() [OK]

Common Mistakes:

• Calling embed as a static method
• Thinking text length causes error
• Using wrong method name

5. You want to find documents related to the query 'best hiking trails' using embeddings. Which approach best uses embeddings to capture semantic meaning?

hard

A. Use embeddings only on the query, not on documents.

B. Search documents by counting how many times 'hiking' appears.

C. Translate documents to another language before embedding.

D. Convert the query and documents to embeddings, then find documents with closest vectors.

Solution

1. Step 1: Understand semantic search with embeddings

   Semantic search compares embeddings of query and documents to find close matches in meaning.

2. Step 2: Evaluate options for semantic search

   Convert the query and documents to embeddings, then find documents with closest vectors. correctly describes converting both query and documents to embeddings and comparing vectors.

3. Final Answer:

   Convert the query and documents to embeddings, then find documents with closest vectors. -> Option D

4. Quick Check:

   Semantic search = compare embeddings [OK]

Hint: Embed both query and documents for best match [OK]

Common Mistakes:

• Searching by word count only
• Embedding only query or only documents
• Unnecessary translation before embedding