Embedding models for semantic search in Agentic AI

Introduction

Embedding models turn words or sentences into numbers that computers can understand. This helps find similar meanings in text, even if the exact words are different.

When you want to find documents that mean the same thing as a search query.

When you need to group similar customer reviews or feedback.

When building chatbots that understand user questions better.

When organizing large collections of articles by topic.

When matching job descriptions with candidate resumes.

Syntax

Agentic AI

embedding = model.encode(texts)
# texts is a list of sentences or documents
# embedding is a list of number arrays representing each text

The model.encode() function converts text into vectors (lists of numbers).

These vectors capture the meaning of the text, not just the words.

Examples

This creates embeddings for two sentences to compare their meanings.

Agentic AI

embedding = model.encode(["I love apples", "Apples are tasty"])

Embedding a single search query to find similar texts.

Agentic AI

query_embedding = model.encode([query])
# query is a single search sentence

Embedding many documents efficiently by processing in batches.

Agentic AI

embeddings = model.encode(documents, batch_size=32)

Sample Model

This program uses an embedding model to find which document best matches a search query by meaning.

Agentic AI

from sentence_transformers import SentenceTransformer, util

# Load a pre-trained embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Sample documents
documents = [
    "Machine learning helps computers learn from data.",
    "Artificial intelligence is a broad field.",
    "Deep learning is a part of machine learning.",
    "I love reading about AI advancements."
]

# Create embeddings for documents
doc_embeddings = model.encode(documents, convert_to_tensor=True)

# Query to search
query = "What is machine learning?"
query_embedding = model.encode([query], convert_to_tensor=True)

# Find the most similar document
hits = util.semantic_search(query_embedding, doc_embeddings, top_k=1)

# Get index of best match
best_match_idx = hits[0][0]['corpus_id']

print(f"Query: {query}")
print(f"Best matching document: {documents[best_match_idx]}")

Important Notes

Embedding models work well even if the words in the query and documents are different but the meaning is similar.

Using pre-trained models saves time and works well for many languages and topics.

Embedding vectors can be compared using cosine similarity to find how close meanings are.

Summary

Embedding models convert text into numbers that capture meaning.

They help find similar texts even if words differ.

Useful for search, grouping, and understanding text better.

Practice

1. What is the main purpose of embedding models in semantic search?

easy

A. To convert text into numbers that capture meaning

B. To count the number of words in a text

C. To translate text into another language

D. To remove stop words from text

Solution

1. Step 1: Understand embedding models

   Embedding models transform text into numerical vectors that represent the meaning of the text.

2. Step 2: Identify the purpose in semantic search

   These vectors help find texts with similar meanings, even if the exact words differ.

3. Final Answer:

   To convert text into numbers that capture meaning -> Option A

4. Quick Check:

   Embedding models = convert text to meaningful numbers [OK]

Hint: Embedding models turn words into meaningful numbers [OK]

Common Mistakes:

• Thinking embeddings count words
• Confusing embeddings with translation
• Believing embeddings remove words

2. Which of the following is the correct way to get an embedding vector for a text using a model called embed_model in Python?

easy

A. embedding = embed_model.get_embedding('sample text')

B. embedding = embed_model.text_to_vector('sample text')

C. embedding = embed_model.encode('sample text')

D. embedding = embed_model.vectorize('sample text')

Solution

1. Step 1: Recall common embedding method names

   Many embedding libraries use encode to convert text to vectors.

2. Step 2: Check method correctness

   Only embed_model.encode('sample text') is a standard and valid call; others are not typical method names.

3. Final Answer:

   embedding = embed_model.encode('sample text') -> Option C

4. Quick Check:

   Use encode() to get embeddings [OK]

Hint: Use encode() method to get embeddings [OK]

Common Mistakes:

• Using non-existent methods like text_to_vector
• Confusing method names
• Forgetting to call the method with parentheses

3. Given the following Python code using an embedding model, what will be the output type of embedding?

embedding = embed_model.encode('Find similar texts')

medium

A. A list of words

B. A numeric vector (list or array) representing the text

C. A string representing the text

D. A dictionary with word counts

Solution

1. Step 1: Understand what encode() returns

   The encode() method returns a numeric vector that captures the meaning of the input text.

2. Step 2: Identify the output type

   This vector is usually a list or array of numbers, not words, strings, or dictionaries.

3. Final Answer:

   A numeric vector (list or array) representing the text -> Option B

4. Quick Check:

   encode() output = numeric vector [OK]

Hint: Embedding output is always numeric vector [OK]

Common Mistakes:

• Expecting a list of words
• Thinking output is a string
• Confusing embeddings with word counts

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

embedding = embed_model.encode['text to search']

What is the error and how to fix it?

medium

A. Add a return statement before encode

B. Change 'text to search' to a list of words

C. Remove the encode method and use embed_model directly

D. Use parentheses () instead of brackets [] to call encode method

Solution

1. Step 1: Identify the syntax error

   Methods in Python are called with parentheses (), not brackets []. Using brackets causes a TypeError.

2. Step 2: Correct the method call

   Replace encode['text to search'] with encode('text to search') to fix the error.

3. Final Answer:

   Use parentheses () instead of brackets [] to call encode method -> Option D

4. Quick Check:

   Method calls need () not [] [OK]

Hint: Call methods with () not [] [OK]

Common Mistakes:

• Using brackets [] instead of parentheses ()
• Passing wrong argument types
• Trying to call method without parentheses

5. You want to build a semantic search system that finds documents similar in meaning to a query. Which approach best uses embedding models for this task?

hard

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

B. Count keyword frequency in documents and query, then match counts

C. Translate documents to another language before searching

D. Sort documents alphabetically and pick the first matches

Solution

1. Step 1: Understand semantic search with embeddings

   Semantic search uses embeddings to represent meaning, so comparing vectors finds similar meaning.

2. Step 2: Identify the correct approach

   Converting documents and query to embeddings and finding closest vectors is the correct method for semantic search.

3. Final Answer:

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

4. Quick Check:

   Semantic search = compare embedding vectors [OK]

Hint: Compare embeddings of query and documents for semantic search [OK]

Common Mistakes:

• Using keyword counts instead of embeddings
• Translating text unnecessarily
• Sorting alphabetically instead of by meaning