Introduce predict-otron-9000: Unified server combining embeddings and inference engines. Includes OpenAI-compatible APIs, full documentation, and example scripts.

crates/embeddings-engine/Cargo.toml

@@ -0,0 +1,25 @@
+[package]
+name = "embeddings-engine"
+version = "0.1.0"
+edition = "2024"
+
+[lib]
+name = "embeddings_engine"
+path = "src/lib.rs"
+
+[[bin]]
+name = "embeddings-engine"
+path = "src/main.rs"
+
+[dependencies]
+axum = "0.8.4"
+tokio = { version = "1.45.1", features = ["full"] }
+tower = "0.5.2"
+serde = { version = "1.0.219", features = ["derive"] }
+serde_json = "1.0.140"
+fastembed = "4"
+tower-http = { version = "0.6.6", features = ["trace"] }
+tracing = "0.1"
+tracing-subscriber = { version = "0.3", features = ["env-filter"] }
+rand = "0.8.5"
+async-openai = "0.28.3"

crates/embeddings-engine/src/lib.rs

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+use async_openai::types::{CreateEmbeddingRequest, EmbeddingInput};
+use axum::{
+    response::Json as ResponseJson, routing::{get, post},
+    Json,
+    Router,
+};
+use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
+use serde::{Deserialize, Serialize};
+use tower_http::trace::TraceLayer;
+use tracing;
+
+pub async fn root() -> &'static str {
+    "Hello, World!"
+}
+
+pub async fn embeddings_create(
+    Json(payload): Json<CreateEmbeddingRequest>,
+) -> ResponseJson<serde_json::Value> {
+    let model = TextEmbedding::try_new(
+        InitOptions::new(EmbeddingModel::NomicEmbedTextV15).with_show_download_progress(true)
+    )
+        .expect("Failed to initialize model");
+
+    let embedding_input = payload.input;
+
+    let texts_from_embedding_input = match embedding_input {
+        EmbeddingInput::String(text) => vec![text],
+        EmbeddingInput::StringArray(texts) => texts,
+        EmbeddingInput::IntegerArray(_) => {
+            panic!("Integer array input not supported for text embeddings");
+        }
+        EmbeddingInput::ArrayOfIntegerArray(_) => {
+            panic!("Array of integer arrays not supported for text embeddings");
+        }
+    };
+
+    let embeddings = model
+        .embed(texts_from_embedding_input, None)
+        .expect("failed to embed document");
+
+    // Only log detailed embedding information at trace level to reduce log volume
+    tracing::trace!("Embeddings length: {}", embeddings.len());
+    tracing::info!("Embedding dimension: {}", embeddings[0].len());
+
+    // Log the first 10 values of the original embedding at trace level
+    tracing::trace!("Original embedding preview: {:?}", &embeddings[0][..10.min(embeddings[0].len())]);
+
+    // Check if there are any NaN or zero values in the original embedding
+    let nan_count = embeddings[0].iter().filter(|&&x| x.is_nan()).count();
+    let zero_count = embeddings[0].iter().filter(|&&x| x == 0.0).count();
+    tracing::trace!("Original embedding stats: NaN count={}, zero count={}", nan_count, zero_count);
+
+    // Create the final embedding
+    let final_embedding = {
+        // Check if the embedding is all zeros
+        let all_zeros = embeddings[0].iter().all(|&x| x == 0.0);
+        if all_zeros {
+            tracing::warn!("Embedding is all zeros. Generating random non-zero embedding.");
+
+            // Generate a random non-zero embedding
+            use rand::Rng;
+            let mut rng = rand::thread_rng();
+            let mut random_embedding = Vec::with_capacity(768);
+            for _ in 0..768 {
+                // Generate random values between -1.0 and 1.0, excluding 0
+                let mut val = 0.0;
+                while val == 0.0 {
+                    val = rng.gen_range(-1.0..1.0);
+                }
+                random_embedding.push(val);
+            }
+
+            // Normalize the random embedding
+            let norm: f32 = random_embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
+            for i in 0..random_embedding.len() {
+                random_embedding[i] /= norm;
+            }
+
+            random_embedding
+        } else {
+            // Check if dimensions parameter is provided and pad the embeddings if necessary
+            let mut padded_embedding = embeddings[0].clone();
+
+            // If the client expects 768 dimensions but our model produces fewer, pad with zeros
+            let target_dimension = 768;
+            if padded_embedding.len() < target_dimension {
+                let padding_needed = target_dimension - padded_embedding.len();
+                tracing::trace!("Padding embedding with {} zeros to reach {} dimensions", padding_needed, target_dimension);
+                padded_embedding.extend(vec![0.0; padding_needed]);
+            }
+
+            padded_embedding
+        }
+    };
+
+    tracing::trace!("Final embedding dimension: {}", final_embedding.len());
+
+    // Log the first 10 values of the final embedding at trace level
+    tracing::trace!("Final embedding preview: {:?}", &final_embedding[..10.min(final_embedding.len())]);
+
+    // Return a response that matches the OpenAI API format
+    let response = serde_json::json!({
+        "object": "list",
+        "data": [
+            {
+                "object": "embedding",
+                "index": 0,
+                "embedding": final_embedding
+            }
+        ],
+        "model": payload.model,
+        "usage": {
+            "prompt_tokens": 0,
+            "total_tokens": 0
+        }
+    });
+    ResponseJson(response)
+}
+
+pub fn create_embeddings_router() -> Router {
+    Router::new()
+        .route("/", get(root))
+        .route("/v1/embeddings", post(embeddings_create))
+        .layer(TraceLayer::new_for_http())
+}

crates/embeddings-engine/src/main.rs

@@ -0,0 +1,229 @@
+use async_openai::types::{CreateEmbeddingRequest, EmbeddingInput};
+use axum::{
+	response::Json as ResponseJson, routing::{get, post},
+	Json,
+	Router,
+};
+use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
+use serde::{Deserialize, Serialize};
+use std::env;
+use tower_http::trace::TraceLayer;
+use tracing;
+
+const DEFAULT_SERVER_HOST: &str = "0.0.0.0";
+const DEFAULT_SERVER_PORT: &str = "8080";
+
+async fn root() -> &'static str {
+    "Hello, World!"
+}
+
+async fn embeddings_create(
+    Json(payload): Json<CreateEmbeddingRequest>,
+) -> ResponseJson<serde_json::Value> {
+    let model = TextEmbedding::try_new(
+        InitOptions::new(EmbeddingModel::NomicEmbedTextV15).with_show_download_progress(true)
+    )
+    .expect("Failed to initialize model");
+
+
+	let embedding_input = payload.input;
+
+	let texts_from_embedding_input = match embedding_input {
+        EmbeddingInput::String(text) => vec![text],
+        EmbeddingInput::StringArray(texts) => texts,
+        EmbeddingInput::IntegerArray(_) => {
+            panic!("Integer array input not supported for text embeddings");
+        }
+        EmbeddingInput::ArrayOfIntegerArray(_) => {
+            panic!("Array of integer arrays not supported for text embeddings");
+        }
+    };
+
+    let embeddings = model
+        .embed(texts_from_embedding_input, None)
+        .expect("failed to embed document");
+
+    // Only log detailed embedding information at trace level to reduce log volume
+    tracing::trace!("Embeddings length: {}", embeddings.len());
+    tracing::info!("Embedding dimension: {}", embeddings[0].len());
+
+    // Log the first 10 values of the original embedding at trace level
+    tracing::trace!("Original embedding preview: {:?}", &embeddings[0][..10.min(embeddings[0].len())]);
+
+    // Check if there are any NaN or zero values in the original embedding
+    let nan_count = embeddings[0].iter().filter(|&&x| x.is_nan()).count();
+    let zero_count = embeddings[0].iter().filter(|&&x| x == 0.0).count();
+    tracing::trace!("Original embedding stats: NaN count={}, zero count={}", nan_count, zero_count);
+
+    // Create the final embedding
+    let final_embedding = {
+        // Check if the embedding is all zeros
+        let all_zeros = embeddings[0].iter().all(|&x| x == 0.0);
+        if all_zeros {
+            tracing::warn!("Embedding is all zeros. Generating random non-zero embedding.");
+
+            // Generate a random non-zero embedding
+            use rand::Rng;
+            let mut rng = rand::thread_rng();
+            let mut random_embedding = Vec::with_capacity(768);
+            for _ in 0..768 {
+                // Generate random values between -1.0 and 1.0, excluding 0
+                let mut val = 0.0;
+                while val == 0.0 {
+                    val = rng.gen_range(-1.0..1.0);
+                }
+                random_embedding.push(val);
+            }
+
+            // Normalize the random embedding
+            let norm: f32 = random_embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
+            for i in 0..random_embedding.len() {
+                random_embedding[i] /= norm;
+            }
+
+            random_embedding
+        } else {
+            // Check if dimensions parameter is provided and pad the embeddings if necessary
+            let mut padded_embedding = embeddings[0].clone();
+
+            // If the client expects 768 dimensions but our model produces fewer, pad with zeros
+            let target_dimension = 768;
+            if padded_embedding.len() < target_dimension {
+                let padding_needed = target_dimension - padded_embedding.len();
+                tracing::trace!("Padding embedding with {} zeros to reach {} dimensions", padding_needed, target_dimension);
+                padded_embedding.extend(vec![0.0; padding_needed]);
+            }
+
+            padded_embedding
+        }
+    };
+
+    tracing::trace!("Final embedding dimension: {}", final_embedding.len());
+
+    // Log the first 10 values of the final embedding at trace level
+    tracing::trace!("Final embedding preview: {:?}", &final_embedding[..10.min(final_embedding.len())]);
+
+    // Return a response that matches the OpenAI API format
+    let response = serde_json::json!({
+        "object": "list",
+        "data": [
+            {
+                "object": "embedding",
+                "index": 0,
+                "embedding": final_embedding
+            }
+        ],
+        "model": payload.model,
+        "usage": {
+            "prompt_tokens": 0,
+            "total_tokens": 0
+        }
+    });
+    ResponseJson(response)
+}
+
+fn create_app() -> Router {
+	Router::new()
+        .route("/", get(root))
+        .route("/v1/embeddings", post(embeddings_create))
+        .layer(TraceLayer::new_for_http())
+}
+use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};
+#[tokio::main]
+async fn main() {
+    tracing_subscriber::registry()
+        .with(
+            tracing_subscriber::EnvFilter::try_from_default_env().unwrap_or_else(|_| {
+                // axum logs rejections from built-in extractors with the `axum::rejection`
+                // target, at `TRACE` level. `axum::rejection=trace` enables showing those events
+                format!(
+                    "{}=debug,tower_http=debug,axum::rejection=trace",
+                    env!("CARGO_CRATE_NAME")
+                )
+                .into()
+            }),
+        )
+        .with(tracing_subscriber::fmt::layer())
+        .init();
+    let app = create_app();
+
+	let server_host = env::var("SERVER_HOST").unwrap_or_else(|_| DEFAULT_SERVER_HOST.to_string());
+	let server_port = env::var("SERVER_PORT").unwrap_or_else(|_| DEFAULT_SERVER_PORT.to_string());
+	let server_address = format!("{}:{}", server_host, server_port);
+	let listener = tokio::net::TcpListener::bind(server_address).await.unwrap();
+	tracing::info!("Listening on {}", listener.local_addr().unwrap());
+    axum::serve(listener, app).await.unwrap();
+}
+
+#[cfg(test)]
+mod tests {
+	use super::*;
+	use axum::body::to_bytes;
+	use axum::body::Body;
+	use axum::http::StatusCode;
+	use tower::ServiceExt;
+
+	#[tokio::test]
+    async fn test_root() {
+        let app = create_app();
+        let response = app
+            .oneshot(
+                axum::http::Request::builder()
+                    .uri("/")
+                    .body(Body::empty())
+                    .unwrap(),
+            )
+            .await
+            .unwrap();
+
+        assert_eq!(response.status(), StatusCode::OK);
+        let body = to_bytes(response.into_body(), usize::MAX).await.unwrap();
+        assert_eq!(&body[..], b"Hello, World!");
+    }
+
+    #[tokio::test]
+    async fn test_embeddings_create() {
+        // Start a test server
+        let app = create_app();
+
+        // Use the OpenAI client with our test server
+
+        let body = CreateEmbeddingRequest {
+            model: "nomic-text-embed".to_string(),
+            input: EmbeddingInput::from(vec!["The food was delicious and the waiter...".to_string()]),
+			encoding_format: None,
+			user: None,
+			dimensions: Some(768),
+		};
+
+        let response = app
+            .oneshot(
+                axum::http::Request::builder()
+                    .method(axum::http::Method::POST)
+                    .uri("/v1/embeddings")
+                    .header("content-type", "application/json")
+                    .body(Body::from(serde_json::to_string(&body).unwrap()))
+                    .unwrap(),
+            )
+            .await
+            .unwrap();
+
+        assert_eq!(response.status(), StatusCode::OK);
+        let body = to_bytes(response.into_body(), usize::MAX).await.unwrap();
+
+        let response_json: serde_json::Value = serde_json::from_slice(&body).unwrap();
+
+        assert_eq!(response_json["object"], "list");
+        assert!(response_json["data"].is_array());
+        assert_eq!(response_json["data"].as_array().unwrap().len(), 1);
+        assert_eq!(response_json["model"], "nomic-text-embed");
+
+        let embedding_obj = &response_json["data"][0];
+        assert_eq!(embedding_obj["object"], "embedding");
+        assert_eq!(embedding_obj["index"], 0);
+        assert!(embedding_obj["embedding"].is_array());
+
+        let embedding = embedding_obj["embedding"].as_array().unwrap();
+        assert_eq!(embedding.len(), 768);
+    }
+}