---
title: "Understanding Embeddings in Ollama"  
description: "This blog is to understand how the models stores the information in the form of numbers called embedding."  
author: "Yash Srivastava"  
published: 2026-06-10  
updated: 2026-06-10  
canonical: https://answers.mindstick.com/blog/386/understanding-embeddings-in-ollama  
category: "technology"  
tags: ["ollama", "llm", "ai model", "embedding"]  
reading_time: 3 minutes  

---

# Understanding Embeddings in Ollama

## Introduction

when we ask any question from any [AI model](https://www.mindstick.com/news/4386/tencent-unveils-new-ai-model-claims-it-responds-faster-than-deepseek-r1), Model do not understand the words directly, words for any computer is just text. To understand the meaning of the text model use to convert these text into numbers. This work is don by Embeddings. In simple words Embedding are numerical representation of text that helps model to understand its meaning and context.

### Why do we need Embeddings?

Suppose you have an [AI chatbot](https://www.mindstick.com/news/2794/google-ai-chatbot-bard-provides-erroneous-information) and any user ask a question “ How can I [reset my password](https://answers.mindstick.com/qa/116432/sbcglobal-email-login-issues-how-to-reset-my-password)? ” but suppose in company document it is mentioned as “ Steps to change your account credentials ”. Here according to human's perspective both statements have same meanings, but for computer Password is not equals to Credential, and Reset is not equals to change. Model will treat the both sentences differently, which is the main problem.

### What is an Embedding?

Embedding is a technique by using which is used to represent the text into numbers such that computer can understand the text easily. In simple words Embedding is a numerical representation of text that helps AI to understand the meaning and [relationship](https://www.mindstick.com/articles/54917/how-to-keep-her-happy-in-a-long-distance-relationship) between words, sentences and documents. Simply embedding is a vector.

*Example:*

```plaintext
Laptop
↓
[0.21, 0.84, -0.56, 0.34, ...]
```

or

```plaintext
Artificial Intelligence
↓
[0.72, -0.13, 0.91, 0.22, ...]
```

This numbers are not random there are some meaning encoded in the numbers.

## How Embeddings Work?

### Step 1: Input Text

```plaintext
What is AI?
```

suppose this text is given to the embedding model. At this stage it is just a normal human language which can not be understand by the computer daily.

### Step 2: Embedding Model

Now the embedding model will process the text. Firstly the model will break the text into small parts.

*Example:*

```plaintext
What
is
AI
?
```

Now the model will analyze these tokens and during the training process it helps to learn the pattern and tries to understand the meaning of the context. Model not only see the words, other than this it also try to analyze the context and relationship.

example:

“*[Artificial Intelligence](https://www.mindstick.com/articles/328023/artificial-intelligence-the-new-enemy-of-man)*” and “*[Machine Learning](https://www.mindstick.com/articles/324457/what-are-the-different-ways-to-improve-machine-learning-skills)*” are to different words, but model knowns these two are related concept.

### Step 3: Generate Vector

After [processing](https://www.mindstick.com/blog/254/background-processing-in-android) the text embedding model generates the vector with is generally use for training.

*Example:*

```plaintext
Input:
What is AI?

Output:
[0.24, 0.67, -0.11, 0.93, ...]
```

The [collection](https://www.mindstick.com/articles/1718/collections-in-java) of these numbers are called Embedding Vector.

## Flow:

```plaintext
Text → Embedding Model → Vector Representation
```

### Understanding Similarity

suppose any words like “*Laptop*”, “*Computer*” and “*Notebook*” here the embedding of these words will be closer to each other. But if take two words “laptop” and “Banana” there embedding will be different.

## Embeddings in Ollama

Ollama provides some model by using which we can generate embeddings.

*Example:*

```plaintext
ollama pull nomic-embed-text
```

Generate embedding:

```plaintext
ollama embed nomic-embed-text "What is Artificial Intelligence?"
```

Output:

```plaintext
[0.21, 0.34, -0.91, 0.72, ...]
```

These are the vector representation of the text.

read more about ollama:

Previous topic: [building technical customer support model using .net](https://answers.mindstick.com/blog/385/building-technical-customer-support-model-using-dot-net)\
next topic: [How Ollama Generates Responses](https://answers.mindstick.com/blog/387/how-ollama-generates-responses)

---

Original Source: https://answers.mindstick.com/blog/386/understanding-embeddings-in-ollama

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