Open-source embedding models in LangChain - Step-by-Step Execution

Concept Flow - Open-source embedding models

Load Open-source Embedding Model

↓

Input Text Data

↓

Model Processes Text

↓

Generate Vector Embeddings

↓

Use Embeddings for Tasks

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Store or Query Embeddings

This flow shows how open-source embedding models take text input, process it, and produce vector embeddings for use in tasks like search or classification.

Execution Sample

LangChain

from langchain.embeddings import HuggingFaceEmbeddings

model = HuggingFaceEmbeddings(model_name='all-MiniLM-L6-v2')
text = "Hello world"
embedding = model.embed_query(text)

This code loads an open-source embedding model and generates a vector embedding for the text 'Hello world'.

Execution Table

Step	Action	Input	Output	Notes
1	Load model	model_name='all-MiniLM-L6-v2'	Model instance created	Model ready to embed text
2	Input text	"Hello world"	Text accepted	Text prepared for embedding
3	Embed query	"Hello world"	[0.12, -0.05, ...]	Vector embedding generated
4	Use embedding	Vector embedding	Stored or queried	Embedding ready for downstream tasks
5	End	N/A	Process complete	Embedding process finished

💡 Embedding generated and ready for use, process ends.

Variable Tracker

Variable	Start	After Step 1	After Step 3	Final
model	None	Model instance	Model instance	Model instance
text	None	None	"Hello world"	"Hello world"
embedding	None	None	Vector of floats	Vector of floats

Key Moments - 3 Insights

Why do we need to load the model before embedding text?

The model must be loaded first to have the functions and weights ready to convert text into embeddings, as shown in step 1 of the execution_table.

What is the output of the embed_query method?

It outputs a vector (list of numbers) representing the text meaning, as seen in step 3 of the execution_table.

Can we use the embedding directly for tasks?

Yes, embeddings are used for tasks like search or classification, shown in step 4 where embeddings are stored or queried.

Visual Quiz - 3 Questions

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

A The original text unchanged

B An error message

C A vector embedding representing the input text

D A model instance

💡 Hint

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

At which step is the model instance created?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look at the 'Action' and 'Output' columns in the execution_table for model creation.

If the input text changes, which step's output will change?

A Step 2 output

B Step 3 output

C Step 1 output

D Step 4 output

💡 Hint

Consider where the text is converted into a vector embedding in the execution_table.

Concept Snapshot

Open-source embedding models convert text into vectors.
Load the model first.
Call embed_query(text) to get embeddings.
Embeddings are numeric arrays representing meaning.
Use embeddings for search, classification, or storage.

Full Transcript

This visual execution shows how open-source embedding models work in langchain. First, the model is loaded using the model name. Then, input text is given to the model. The model processes the text and generates a vector embedding, which is a list of numbers representing the text's meaning. This embedding can then be used for tasks like searching or storing. Variables like 'model', 'text', and 'embedding' change as the code runs. Key moments include understanding why the model must load first, what the embedding output is, and how embeddings are used. The quiz questions help check understanding of these steps.

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