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359//! Embedding driver for vector-based semantic memory.
//!
//! Provides an `EmbeddingDriver` trait and an OpenAI-compatible implementation
//! that works with any provider offering a `/v1/embeddings` endpoint (OpenAI,
//! Groq, Together, Fireworks, Ollama, etc.).
use async_trait::async_trait;
use openfang_types::model_catalog::{
FIREWORKS_BASE_URL, GROQ_BASE_URL, LMSTUDIO_BASE_URL, MISTRAL_BASE_URL, OLLAMA_BASE_URL,
OPENAI_BASE_URL, TOGETHER_BASE_URL, VLLM_BASE_URL,
};
use serde::{Deserialize, Serialize};
use tracing::{debug, warn};
use zeroize::Zeroizing;
/// Error type for embedding operations.
#[derive(Debug, thiserror::Error)]
pub enum EmbeddingError {
#[error("HTTP error: {0}")]
Http(String),
#[error("API error (status {status}): {message}")]
Api { status: u16, message: String },
#[error("Parse error: {0}")]
Parse(String),
#[error("Missing API key: {0}")]
MissingApiKey(String),
}
/// Configuration for creating an embedding driver.
#[derive(Debug, Clone)]
pub struct EmbeddingConfig {
/// Provider name (openai, groq, together, ollama, etc.).
pub provider: String,
/// Model name (e.g., "text-embedding-3-small", "all-MiniLM-L6-v2").
pub model: String,
/// API key (resolved from env var).
pub api_key: String,
/// Base URL for the API.
pub base_url: String,
}
/// Trait for computing text embeddings.
#[async_trait]
pub trait EmbeddingDriver: Send + Sync {
/// Compute embedding vectors for a batch of texts.
async fn embed(&self, texts: &[&str]) -> Result<Vec<Vec<f32>>, EmbeddingError>;
/// Compute embedding for a single text.
async fn embed_one(&self, text: &str) -> Result<Vec<f32>, EmbeddingError> {
let results = self.embed(&[text]).await?;
results
.into_iter()
.next()
.ok_or_else(|| EmbeddingError::Parse("Empty embedding response".to_string()))
}
/// Return the dimensionality of embeddings produced by this driver.
fn dimensions(&self) -> usize;
}
/// OpenAI-compatible embedding driver.
///
/// Works with any provider that implements the `/v1/embeddings` endpoint:
/// OpenAI, Groq, Together, Fireworks, Ollama, vLLM, LM Studio, etc.
pub struct OpenAIEmbeddingDriver {
api_key: Zeroizing<String>,
base_url: String,
model: String,
client: reqwest::Client,
dims: usize,
}
#[derive(Serialize)]
struct EmbedRequest<'a> {
model: &'a str,
input: &'a [&'a str],
}
#[derive(Deserialize)]
struct EmbedResponse {
data: Vec<EmbedData>,
}
#[derive(Deserialize)]
struct EmbedData {
embedding: Vec<f32>,
}
impl OpenAIEmbeddingDriver {
/// Create a new OpenAI-compatible embedding driver.
pub fn new(config: EmbeddingConfig) -> Result<Self, EmbeddingError> {
// Infer dimensions from model name (common models)
let dims = infer_dimensions(&config.model);
Ok(Self {
api_key: Zeroizing::new(config.api_key),
base_url: config.base_url,
model: config.model,
client: reqwest::Client::new(),
dims,
})
}
}
/// Infer embedding dimensions from model name.
fn infer_dimensions(model: &str) -> usize {
match model {
// OpenAI
"text-embedding-3-small" => 1536,
"text-embedding-3-large" => 3072,
"text-embedding-ada-002" => 1536,
// Sentence Transformers / local models
"all-MiniLM-L6-v2" => 384,
"all-MiniLM-L12-v2" => 384,
"all-mpnet-base-v2" => 768,
"nomic-embed-text" => 768,
"mxbai-embed-large" => 1024,
// Default to 1536 (most common)
_ => 1536,
}
}
#[async_trait]
impl EmbeddingDriver for OpenAIEmbeddingDriver {
async fn embed(&self, texts: &[&str]) -> Result<Vec<Vec<f32>>, EmbeddingError> {
if texts.is_empty() {
return Ok(vec![]);
}
let url = format!("{}/embeddings", self.base_url);
let body = EmbedRequest {
model: &self.model,
input: texts,
};
let mut req = self.client.post(&url).json(&body);
if !self.api_key.as_str().is_empty() {
req = req.header("Authorization", format!("Bearer {}", self.api_key.as_str()));
}
let resp = req
.send()
.await
.map_err(|e| EmbeddingError::Http(e.to_string()))?;
let status = resp.status().as_u16();
if status != 200 {
let body_text = resp.text().await.unwrap_or_default();
return Err(EmbeddingError::Api {
status,
message: body_text,
});
}
let data: EmbedResponse = resp
.json()
.await
.map_err(|e| EmbeddingError::Parse(e.to_string()))?;
// Update dimensions from actual response if available
let embeddings: Vec<Vec<f32>> = data.data.into_iter().map(|d| d.embedding).collect();
debug!(
"Embedded {} texts (dims={})",
embeddings.len(),
embeddings.first().map(|e| e.len()).unwrap_or(0)
);
Ok(embeddings)
}
fn dimensions(&self) -> usize {
self.dims
}
}
/// Create an embedding driver from kernel config.
pub fn create_embedding_driver(
provider: &str,
model: &str,
api_key_env: &str,
) -> Result<Box<dyn EmbeddingDriver + Send + Sync>, EmbeddingError> {
let api_key = if api_key_env.is_empty() {
String::new()
} else {
std::env::var(api_key_env).unwrap_or_default()
};
let base_url = match provider {
"openai" => OPENAI_BASE_URL.to_string(),
"groq" => GROQ_BASE_URL.to_string(),
"together" => TOGETHER_BASE_URL.to_string(),
"fireworks" => FIREWORKS_BASE_URL.to_string(),
"mistral" => MISTRAL_BASE_URL.to_string(),
"ollama" => OLLAMA_BASE_URL.to_string(),
"vllm" => VLLM_BASE_URL.to_string(),
"lmstudio" => LMSTUDIO_BASE_URL.to_string(),
other => {
warn!("Unknown embedding provider '{other}', using OpenAI-compatible format");
format!("https://{other}/v1")
}
};
// SECURITY: Warn when embedding requests will be sent to an external API
let is_local = base_url.contains("localhost")
|| base_url.contains("127.0.0.1")
|| base_url.contains("[::1]");
if !is_local {
warn!(
provider = %provider,
base_url = %base_url,
"Embedding driver configured to send data to external API — text content will leave this machine"
);
}
let config = EmbeddingConfig {
provider: provider.to_string(),
model: model.to_string(),
api_key,
base_url,
};
let driver = OpenAIEmbeddingDriver::new(config)?;
Ok(Box::new(driver))
}
/// Compute cosine similarity between two vectors.
///
/// Returns a value in [-1.0, 1.0] where 1.0 = identical direction.
pub fn cosine_similarity(a: &[f32], b: &[f32]) -> f32 {
if a.len() != b.len() || a.is_empty() {
return 0.0;
}
let mut dot = 0.0f32;
let mut norm_a = 0.0f32;
let mut norm_b = 0.0f32;
for i in 0..a.len() {
dot += a[i] * b[i];
norm_a += a[i] * a[i];
norm_b += b[i] * b[i];
}
let denom = norm_a.sqrt() * norm_b.sqrt();
if denom < f32::EPSILON {
0.0
} else {
dot / denom
}
}
/// Serialize an embedding vector to bytes (for SQLite BLOB storage).
pub fn embedding_to_bytes(embedding: &[f32]) -> Vec<u8> {
let mut bytes = Vec::with_capacity(embedding.len() * 4);
for &val in embedding {
bytes.extend_from_slice(&val.to_le_bytes());
}
bytes
}
/// Deserialize an embedding vector from bytes.
pub fn embedding_from_bytes(bytes: &[u8]) -> Vec<f32> {
bytes
.chunks_exact(4)
.map(|chunk| f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]))
.collect()
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_cosine_similarity_identical() {
let a = vec![1.0, 0.0, 0.0];
let b = vec![1.0, 0.0, 0.0];
let sim = cosine_similarity(&a, &b);
assert!((sim - 1.0).abs() < 1e-6);
}
#[test]
fn test_cosine_similarity_orthogonal() {
let a = vec![1.0, 0.0];
let b = vec![0.0, 1.0];
let sim = cosine_similarity(&a, &b);
assert!(sim.abs() < 1e-6);
}
#[test]
fn test_cosine_similarity_opposite() {
let a = vec![1.0, 0.0];
let b = vec![-1.0, 0.0];
let sim = cosine_similarity(&a, &b);
assert!((sim + 1.0).abs() < 1e-6);
}
#[test]
fn test_cosine_similarity_real_vectors() {
let a = vec![0.1, 0.2, 0.3, 0.4];
let b = vec![0.1, 0.2, 0.3, 0.4];
let sim = cosine_similarity(&a, &b);
assert!((sim - 1.0).abs() < 1e-5);
let c = vec![0.4, 0.3, 0.2, 0.1];
let sim2 = cosine_similarity(&a, &c);
assert!(sim2 > 0.0 && sim2 < 1.0); // Similar but not identical
}
#[test]
fn test_cosine_similarity_empty() {
let sim = cosine_similarity(&[], &[]);
assert_eq!(sim, 0.0);
}
#[test]
fn test_cosine_similarity_length_mismatch() {
let a = vec![1.0, 2.0];
let b = vec![1.0, 2.0, 3.0];
let sim = cosine_similarity(&a, &b);
assert_eq!(sim, 0.0);
}
#[test]
fn test_embedding_roundtrip() {
let embedding = vec![0.1, -0.5, 1.23456, 0.0, -1e10, 1e10];
let bytes = embedding_to_bytes(&embedding);
let recovered = embedding_from_bytes(&bytes);
assert_eq!(embedding.len(), recovered.len());
for (a, b) in embedding.iter().zip(recovered.iter()) {
assert!((a - b).abs() < f32::EPSILON);
}
}
#[test]
fn test_embedding_bytes_empty() {
let bytes = embedding_to_bytes(&[]);
assert!(bytes.is_empty());
let recovered = embedding_from_bytes(&bytes);
assert!(recovered.is_empty());
}
#[test]
fn test_infer_dimensions() {
assert_eq!(infer_dimensions("text-embedding-3-small"), 1536);
assert_eq!(infer_dimensions("all-MiniLM-L6-v2"), 384);
assert_eq!(infer_dimensions("nomic-embed-text"), 768);
assert_eq!(infer_dimensions("unknown-model"), 1536); // default
}
#[test]
fn test_create_embedding_driver_ollama() {
// Should succeed even without API key (ollama is local)
let driver = create_embedding_driver("ollama", "all-MiniLM-L6-v2", "");
assert!(driver.is_ok());
assert_eq!(driver.unwrap().dimensions(), 384);
}
}