Embeddings

This guide shows how to convert text into vector representations using Synaptic's Embeddings trait and its built-in providers.

Overview

All embedding providers implement the Embeddings trait from synaptic_embeddings:

#[async_trait]
pub trait Embeddings: Send + Sync {
    async fn embed_documents(&self, texts: &[&str]) -> Result<Vec<Vec<f32>>, SynapticError>;
    async fn embed_query(&self, text: &str) -> Result<Vec<f32>, SynapticError>;
}

embed_documents() embeds multiple texts in a single batch -- use this for indexing.
embed_query() embeds a single query text -- use this at retrieval time.

FakeEmbeddings

Generates deterministic vectors based on a simple hash of the input text. Useful for testing and development without API calls.

use synaptic::embeddings::FakeEmbeddings;
use synaptic::embeddings::Embeddings;

// Specify the number of dimensions (default is 4)
let embeddings = FakeEmbeddings::new(4);

let doc_vectors = embeddings.embed_documents(&["doc one", "doc two"]).await?;
let query_vector = embeddings.embed_query("search query").await?;

// Vectors are normalized to unit length
// Similar texts produce similar vectors

OpenAiEmbeddings

Uses the OpenAI embeddings API. Requires an API key and a ProviderBackend.

use std::sync::Arc;
use synaptic::embeddings::{OpenAiEmbeddings, OpenAiEmbeddingsConfig};
use synaptic::embeddings::Embeddings;
use synaptic::models::backend::HttpBackend;

let config = OpenAiEmbeddingsConfig::new("sk-...")
    .with_model("text-embedding-3-small");  // default model

let backend = Arc::new(HttpBackend::new());
let embeddings = OpenAiEmbeddings::new(config, backend);

let vectors = embeddings.embed_documents(&["hello world"]).await?;

You can customize the base URL for compatible APIs:

let config = OpenAiEmbeddingsConfig::new("sk-...")
    .with_base_url("https://my-proxy.example.com/v1");

OllamaEmbeddings

Uses a local Ollama instance for embedding. No API key required -- just specify the model name.

use std::sync::Arc;
use synaptic::embeddings::{OllamaEmbeddings, OllamaEmbeddingsConfig};
use synaptic::embeddings::Embeddings;
use synaptic::models::backend::HttpBackend;

let config = OllamaEmbeddingsConfig::new("nomic-embed-text");
// Default base_url: http://localhost:11434

let backend = Arc::new(HttpBackend::new());
let embeddings = OllamaEmbeddings::new(config, backend);

let vector = embeddings.embed_query("search query").await?;

Custom Ollama endpoint:

let config = OllamaEmbeddingsConfig::new("nomic-embed-text")
    .with_base_url("http://my-ollama:11434");

CacheBackedEmbeddings

Wraps any Embeddings provider with an in-memory cache. Previously computed embeddings are returned from cache; only uncached texts are sent to the underlying provider.

use std::sync::Arc;
use synaptic::embeddings::{CacheBackedEmbeddings, FakeEmbeddings, Embeddings};

let inner = Arc::new(FakeEmbeddings::new(128));
let cached = CacheBackedEmbeddings::new(inner);

// First call computes the embedding
let v1 = cached.embed_query("hello").await?;

// Second call returns the cached result -- no recomputation
let v2 = cached.embed_query("hello").await?;

assert_eq!(v1, v2);

This is especially useful when adding documents to a vector store and then querying, since the same text may be embedded multiple times across operations.

Using embeddings with vector stores

Embeddings are passed to vector store methods rather than stored inside the vector store. This lets you swap embedding providers without rebuilding the store.

use synaptic::vectorstores::{InMemoryVectorStore, VectorStore};
use synaptic::embeddings::FakeEmbeddings;
use synaptic::retrieval::Document;

let embeddings = FakeEmbeddings::new(128);
let store = InMemoryVectorStore::new();

let docs = vec![Document::new("1", "Rust is fast")];
store.add_documents(docs, &embeddings).await?;

let results = store.similarity_search("fast language", 5, &embeddings).await?;

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