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 module.

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

Fields

Methods

new

fn new() -> EmbeddingAgentBuilder

Create a new builder

simple

fn simple() -> Result<Self>

Create a simple embedding agent with default settings

embed

async fn embed(&self, text: &str) -> Result<Vec<f32>>

Generate embedding for a single text. # Arguments * text – Text to embed # Returns Vector of floats representing the embedding

Parameters:

embed_batch

async fn embed_batch(&self, texts: &[&str]) -> Result<EmbeddingResult>

Generate embeddings for multiple texts. # Arguments * texts – Slice of texts to embed # Returns EmbeddingResult containing all embeddings

Parameters:

similarity

async fn similarity(&self, text1: &str, text2: &str) -> Result<f32>

Calculate cosine similarity between two texts. # Arguments text1 – First text text2 – Second text # Returns Cosine similarity score (0.0 to 1.0)

Parameters:

find_most_similar

async fn find_most_similar(
        &self,
        query: &str,
        candidates: &[&str],
        top_k: usize,
    ) -> Result<Vec<SimilarityResult>>

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:

Source

Related Documentation