> ## 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 • Rust AI Agent SDK
> EmbeddingAgent: Agent for generating text embeddings. Provides embedding capabilities for text using AI embedding models, with support for batch processing and...
# EmbeddingAgent
> Defined in the [**embedding**](../modules/embedding) module.
Rust AI Agent SDK
Agent for generating text embeddings. Provides embedding capabilities for text using AI embedding models, with support for batch processing and similarity calculations. # 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` - Voyage: `voyage/voyage-3` - 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
```
## Fields
| Name | Type | Description |
| --------- | ----------------- | ----------------------- |
| `name` | `String` | Agent name |
| `model` | `String` | Model name |
| `config` | `EmbeddingConfig` | Embedding configuration |
| `verbose` | `bool` | Verbose output |
## Methods
### `new`
```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
fn new() -> EmbeddingAgentBuilder
```
Create a new builder
### `simple`
```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
fn simple() -> Result
```
Create a simple embedding agent with default settings
### `embed`
```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
async fn embed(&self, text: &str) -> Result>
```
Generate embedding for a single text. # Arguments \* `text` - Text to embed # Returns Vector of floats representing the embedding
**Parameters:**
| Name | Type |
| ------ | ------ |
| `text` | `&str` |
### `embed_batch`
```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
async fn embed_batch(&self, texts: &[&str]) -> Result
```
Generate embeddings for multiple texts. # Arguments \* `texts` - Slice of texts to embed # Returns EmbeddingResult containing all embeddings
**Parameters:**
| Name | Type |
| ------- | --------- |
| `texts` | `&[&str]` |
### `similarity`
```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
async fn similarity(&self, text1: &str, text2: &str) -> Result
```
Calculate cosine similarity between two texts. # Arguments \* `text1` - First text \* `text2` - Second text # Returns Cosine similarity score (0.0 to 1.0)
**Parameters:**
| Name | Type |
| ------- | ------ |
| `text1` | `&str` |
| `text2` | `&str` |
### `find_most_similar`
```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
async fn find_most_similar(
&self,
query: &str,
candidates: &[&str],
top_k: usize,
) -> Result>
```
Find the most similar texts to a query. # Arguments \* `query` - Query text \* `candidates` - List of candidate texts to compare \* `top_k` - Number of top results to return # Returns List of SimilarityResult sorted by score (descending)
**Parameters:**
| Name | Type |
| ------------ | --------- |
| `query` | `&str` |
| `candidates` | `&[&str]` |
| `top_k` | `usize` |
## Source
`praisonai/src/embedding/mod.rs` at line 229
***
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