> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# Embedding Agent • AI Agent SDK
> EmbeddingAgent: A specialized agent for generating text embeddings.
# EmbeddingAgent
> Defined in the [**Embedding Agent**](../modules/embedding_agent) module.
AI Agent
A specialized agent for generating text embeddings.
Provides:
* Single text embedding
* Batch text embedding
* Similarity calculation between texts
Supported Providers:
* OpenAI: `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`
* Azure: `azure/text-embedding-3-small`
* Cohere: `cohere/embed-english-v3.0`, `cohere/embed-multilingual-v3.0`
* Voyage: `voyage/voyage-3`, `voyage/voyage-3-lite`
* Mistral: `mistral/mistral-embed`
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
%%{init: {'theme': 'base', 'themeVariables': { 'primaryColor': '#8B0000', 'primaryTextColor': '#fff', 'primaryBorderColor': '#710101', 'lineColor': '#189AB4', 'secondaryColor': '#189AB4', 'tertiaryColor': '#fff' }}}%%
graph LR
input["Input Data"] --> agent["Agent: EmbeddingAgent"]
agent --> output["Output Result"]
style agent fill:#8B0000,color:#fff
style input fill:#8B0000,color:#fff
style output fill:#8B0000,color:#fff
```
## Constructor
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## Methods
Lazily initialize Rich Console.
Lazy load litellm module when needed.
Generate embedding for a single text.
Generate embeddings for multiple texts.
Calculate cosine similarity between two texts.
Find the most similar texts to a query.
Async version of embed().
Async version of embed\_batch().
Async version of similarity().
## Usage
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import EmbeddingAgent
# Simple usage
agent = EmbeddingAgent()
embedding = agent.embed("Hello world")
print(f"Embedding dimension: {len(embedding)}")
# Batch embedding
embeddings = agent.embed_batch([
"First text",
"Second text",
"Third text"
])
# Calculate similarity
score = agent.similarity("Hello", "Hi there")
print(f"Similarity: {score:.2f}")
```
## Source
`praisonaiagents/agent/embedding_agent.py` at line 48
***
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