OpenAI embeddings in LangChain - Step-by-Step Execution

Concept Flow - OpenAI embeddings

Initialize OpenAI Embeddings

↓

Input text to embed

↓

Call OpenAI API with text

↓

Receive vector embedding

↓

Use embedding for search or ML tasks

↓

End

The flow starts by setting up OpenAI embeddings, then input text is sent to the API, which returns a vector used for tasks like search or classification.

Execution Sample

LangChain

from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vector = embeddings.embed_query("Hello world")

This code creates an OpenAI embeddings object and generates a vector for the text 'Hello world'.

Execution Table

Step	Action	Input	API Call	Output
1	Initialize embeddings object	None	No	OpenAIEmbeddings instance created
2	Call embed_query	"Hello world"	Yes	API request sent with 'Hello world'
3	Receive embedding	None	No	Vector of floats representing 'Hello world'
4	Use embedding	Vector	No	Embedding ready for search or ML tasks
5	End	None	No	Process complete

💡 Embedding vector received and ready for use, no further steps.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
embeddings	None	OpenAIEmbeddings instance	OpenAIEmbeddings instance	OpenAIEmbeddings instance	OpenAIEmbeddings instance
vector	None	None	Vector<float>	Vector<float>	Vector<float>

Key Moments - 3 Insights

Why do we need to initialize OpenAIEmbeddings before calling embed_query?

Because the embeddings object holds configuration and API keys needed to make the call, as shown in step 1 of the execution_table.

What does the vector output represent?

It is a list of numbers (floats) that capture the meaning of the input text, as shown in step 3 of the execution_table.

Is the API call synchronous or asynchronous in this example?

It is synchronous here, meaning the code waits for the API to return the vector before continuing, as seen in step 2 and 3.

Visual Quiz - 3 Questions

Look at the execution_table, what is the output after step 3?

A An error message from the API

B A vector of floats representing the input text

C The original input text

D An uninitialized embeddings object

💡 Hint

Check the 'Output' column for step 3 in the execution_table.

At which step is the OpenAI API called?

A Step 2

B Step 1

C Step 4

D Step 5

💡 Hint

Look at the 'API Call' column in the execution_table.

If you change the input text to 'Goodbye world', which variable changes in variable_tracker?

A embeddings

B Both embeddings and vector

C vector

D Neither embeddings nor vector

💡 Hint

The embeddings object stays the same, but the vector output changes with input text.

Concept Snapshot

OpenAI embeddings convert text into numeric vectors.
Initialize OpenAIEmbeddings object first.
Call embed_query(text) to get vector.
Vector captures text meaning for search or ML.
API call happens during embed_query.
Use vectors for similarity or classification.

Full Transcript

This visual execution shows how OpenAI embeddings work in Langchain. First, you create an embeddings object that sets up the connection to OpenAI. Then you send your text, like 'Hello world', to the embed_query method. This triggers an API call to OpenAI, which returns a vector of numbers representing the text's meaning. This vector can then be used for tasks like searching or machine learning. The variable tracker shows the embeddings object stays the same, while the vector changes with each input. Key moments clarify why initialization is needed, what the vector means, and that the API call is synchronous. The quiz tests understanding of these steps and variable changes.

Practice

1. What is the main purpose of OpenAI embeddings in Langchain?

easy

A. To convert text into numerical vectors that represent meaning

B. To generate images from text descriptions

C. To translate text from one language to another

D. To create user interfaces automatically

Solution

1. Step 1: Understand what embeddings do

   Embeddings turn text into lists of numbers that capture the meaning of the text.

2. Step 2: Identify the purpose in Langchain

   Langchain uses embeddings to help computers find similar or related texts easily by comparing these number lists.

3. Final Answer:

   To convert text into numerical vectors that represent meaning -> Option A

4. Quick Check:

   Embeddings = numerical meaning vectors [OK]

Hint: Embeddings = text to numbers showing meaning [OK]

Common Mistakes:

• Confusing embeddings with image generation
• Thinking embeddings translate languages
• Assuming embeddings create UI elements

2. Which of the following is the correct way to create OpenAI embeddings using Langchain in Python?

easy

A. import OpenAI from langchain
embeddings = OpenAI.create_embeddings()

B. import langchain
embeddings = langchain.OpenAIEmbeddings()

C. from langchain import embeddings
embeddings = embeddings.OpenAI()

D. from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()

Solution

1. Step 1: Check the import statement

   The correct import is from langchain.embeddings import OpenAIEmbeddings.

2. Step 2: Check the instantiation

   Creating embeddings is done by calling OpenAIEmbeddings() with parentheses.

3. Final Answer:

   from langchain.embeddings import OpenAIEmbeddings\nembeddings = OpenAIEmbeddings() -> Option D

4. Quick Check:

   Correct import and instantiation syntax [OK]

Hint: Import from langchain.embeddings, then call OpenAIEmbeddings() [OK]

Common Mistakes:

• Wrong import path or module
• Missing parentheses when creating embeddings
• Using incorrect method names

3. Given the code snippet:

from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vector = embeddings.embed_query('Hello world')

What is the type of vector?

medium

A. A dictionary with text keys and values

B. A string containing the original text

C. A list of numbers representing the text meaning

D. An integer representing text length

Solution

1. Step 1: Understand embed_query output

   The embed_query method returns a list of numbers (a vector) representing the meaning of the input text.

2. Step 2: Identify the type of vector

   Since it encodes meaning, the vector is a list of floats or numbers, not a string or dictionary.

3. Final Answer:

   A list of numbers representing the text meaning -> Option C

4. Quick Check:

   embed_query output = number list [OK]

Hint: embed_query returns a number list vector [OK]

Common Mistakes:

• Expecting a string instead of numbers
• Thinking it returns a dictionary
• Assuming it returns text length as integer

4. What is wrong with this code snippet for creating embeddings?

from langchain.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings
vector = embeddings.embed_query('Test')

medium

A. embed_query method does not exist

B. Missing parentheses when creating OpenAIEmbeddings instance

C. Incorrect import statement

D. The input text must be a list, not a string

Solution

1. Step 1: Check how embeddings instance is created

   The code assigns OpenAIEmbeddings without parentheses, so embeddings is a class, not an instance.

2. Step 2: Understand the impact on embed_query call

   Calling embed_query on the class (not instance) will cause an error because methods need an instance.

3. Final Answer:

   Missing parentheses when creating OpenAIEmbeddings instance -> Option B

4. Quick Check:

   Instantiate with () to call methods [OK]

Hint: Always add () to create instance before method calls [OK]

Common Mistakes:

• Forgetting parentheses when creating instances
• Assuming methods work on classes directly
• Misreading import statements

5. You want to find the most similar text from a list using OpenAI embeddings in Langchain. Which approach correctly uses embeddings to compare similarity?

hard

A. Create embeddings for all texts, then compute cosine similarity between vectors

B. Compare texts directly as strings using == operator

C. Use embed_query only on the first text and compare lengths

D. Generate embeddings but do not compare vectors

Solution

1. Step 1: Understand how similarity is found with embeddings

   Embeddings convert texts into vectors; similarity is measured by comparing these vectors, often with cosine similarity.

2. Step 2: Identify correct approach to find most similar text

   Create embeddings for all texts, then compute similarity scores between vectors to find the closest match.

3. Final Answer:

   Create embeddings for all texts, then compute cosine similarity between vectors -> Option A

4. Quick Check:

   Similarity = compare embedding vectors [OK]

Hint: Compare vector embeddings with cosine similarity [OK]

Common Mistakes:

• Comparing raw strings instead of vectors
• Using embed_query on only one text
• Not comparing embeddings at all