```yaml
product: AlterLab
title: "Embeddings — Web Scraping Glossary | AlterLab"
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
source_url: https://alterlab.io/glossary/embeddings
```

ai-agent

# Embeddings

Embeddings are dense vector representations of text (or other data) produced by a neural network, where semantically similar content maps to nearby points in vector space.

An embedding model transforms a piece of text — a word, sentence, paragraph, or document — into a fixed-length vector of floating-point numbers. The model is trained so that semantically related texts produce vectors that are geometrically close (high cosine similarity), while unrelated texts produce distant vectors. This property allows semantic search: a query vector finds documents by meaning rather than exact keyword match.

Embeddings are the foundation of modern retrieval systems, clustering algorithms, recommendation engines, and RAG pipelines. Common embedding models include OpenAI's `text-embedding-3-large`, Cohere Embed, and open-source alternatives like `all-MiniLM-L6-v2` from Sentence Transformers.

In a web scraping context, scraped text is typically chunked into overlapping segments of 200–500 tokens, embedded, and stored in a vector database. At query time, the user's question is embedded and the nearest chunks are retrieved by approximate nearest-neighbour search.

### What is Embeddings?

Embeddings are dense vector representations of text (or other data) produced by a neural network, where semantically similar content maps to nearby points in vector space.

### How does AlterLab handle Embeddings?

In a web scraping context, scraped text is typically chunked into overlapping segments of 200–500 tokens, embedded, and stored in a vector database. At query time, the user's question is embedded and the nearest chunks are retrieved by approximate nearest-neighbour search.

## Examples

```
from openai import OpenAI

client = OpenAI()
response = client.embeddings.create(
    model="text-embedding-3-small",
    input="What is the price of AlterLab's Pro plan?"
)
vector = response.data[0].embedding  # 1536-dim float list
```

## Related Terms

[RAG (Retrieval-Augmented Generation)RAG is an AI architecture that grounds LLM responses in retrieved documents, combining a vector search step with language model generation to reduce hallucinations.](https://alterlab.io/glossary/rag)[Vector StoreA vector store is a database optimised for storing and querying high-dimensional embedding vectors by semantic similarity, enabling fast nearest-neighbour search at scale.](https://alterlab.io/glossary/vector-store)[LLM (Large Language Model)A Large Language Model is a neural network trained on vast text corpora that can generate, summarise, translate, and reason over natural language at human level.](https://alterlab.io/glossary/llm)[Data ExtractionThe process of identifying and pulling specific fields from raw web content, converting unstructured HTML into typed JSON records.](https://alterlab.io/glossary/data-extraction)[JSONJavaScript Object Notation — the universal text format for structured data exchange between APIs and web services.](https://alterlab.io/glossary/json)

## Extract Embeddings data from any website

AlterLab returns clean, structured data from any public URL — no scraper infrastructure needed. Start free, no credit card required.

[View API docs](https://alterlab.io/docs/api/rest)

[Back to Glossary](https://alterlab.io/glossary)