Embedder download - fallback URL (#1056)

* Embedder download - fallback URL

* improve logging for native embedder

server/utils/EmbeddingEngines/native/index.js

@@ -4,6 +4,12 @@ const { toChunks } = require("../../helpers");
 const { v4 } = require("uuid");
 
 class NativeEmbedder {
+  // This is a folder that Mintplex Labs hosts for those who cannot capture the HF model download
+  // endpoint for various reasons. This endpoint is not guaranteed to be active or maintained
+  // and may go offline at any time at Mintplex Labs's discretion.
+  #fallbackHost =
+    "https://s3.us-west-1.amazonaws.com/public.useanything.com/support/models/";
+
   constructor() {
     // Model Card: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
     this.model = "Xenova/all-MiniLM-L6-v2";
@@ -13,6 +19,7 @@ class NativeEmbedder {
         : path.resolve(__dirname, `../../../storage/models`)
     );
     this.modelPath = path.resolve(this.cacheDir, "Xenova", "all-MiniLM-L6-v2");
+    this.modelDownloaded = fs.existsSync(this.modelPath);
 
     // Limit of how many strings we can process in a single pass to stay with resource or network limits
     this.maxConcurrentChunks = 25;
@@ -20,6 +27,11 @@ class NativeEmbedder {
 
     // Make directory when it does not exist in existing installations
     if (!fs.existsSync(this.cacheDir)) fs.mkdirSync(this.cacheDir);
+    this.log("Initialized");
+  }
+
+  log(text, ...args) {
+    console.log(`\x1b[36m[NativeEmbedder]\x1b[0m ${text}`, ...args);
   }
 
   #tempfilePath() {
@@ -39,41 +51,73 @@ class NativeEmbedder {
     }
   }
 
-  async embedderClient() {
-    if (!fs.existsSync(this.modelPath)) {
-      console.log(
-        "\x1b[34m[INFO]\x1b[0m The native embedding model has never been run and will be downloaded right now. Subsequent runs will be faster. (~23MB)\n\n"
-      );
-    }
-
+  async #fetchWithHost(hostOverride = null) {
     try {
       // Convert ESM to CommonJS via import so we can load this library.
       const pipeline = (...args) =>
-        import("@xenova/transformers").then(({ pipeline }) =>
-          pipeline(...args)
-        );
-      return await pipeline("feature-extraction", this.model, {
+        import("@xenova/transformers").then(({ pipeline, env }) => {
+          if (!this.modelDownloaded) {
+            // if model is not downloaded, we will log where we are fetching from.
+            if (hostOverride) {
+              env.remoteHost = hostOverride;
+              env.remotePathTemplate = "{model}/"; // Our S3 fallback url does not support revision File structure.
+            }
+            this.log(`Downloading ${this.model} from ${env.remoteHost}`);
+          }
+          return pipeline(...args);
+        });
+      return {
+        pipeline: await pipeline("feature-extraction", this.model, {
           cache_dir: this.cacheDir,
-        ...(!fs.existsSync(this.modelPath)
+          ...(!this.modelDownloaded
             ? {
                 // Show download progress if we need to download any files
                 progress_callback: (data) => {
                   if (!data.hasOwnProperty("progress")) return;
                   console.log(
-                  `\x1b[34m[Embedding - Downloading Model Files]\x1b[0m ${
+                    `\x1b[36m[NativeEmbedder - Downloading model]\x1b[0m ${
                       data.file
                     } ${~~data?.progress}%`
                   );
                 },
               }
             : {}),
-      });
+        }),
+        retry: false,
+        error: null,
+      };
     } catch (error) {
-      console.error("Failed to load the native embedding model:", error);
-      throw error;
+      return {
+        pipeline: null,
+        retry: hostOverride === null ? this.#fallbackHost : false,
+        error,
+      };
     }
   }
 
+  // This function will do a single fallback attempt (not recursive on purpose) to try to grab the embedder model on first embed
+  // since at time, some clients cannot properly download the model from HF servers due to a number of reasons (IP, VPN, etc).
+  // Given this model is critical and nobody reads the GitHub issues before submitting the bug, we get the same bug
+  // report 20 times a day: https://github.com/Mintplex-Labs/anything-llm/issues/821
+  // So to attempt to monkey-patch this we have a single fallback URL to help alleviate duplicate bug reports.
+  async embedderClient() {
+    if (!this.modelDownloaded)
+      this.log(
+        "The native embedding model has never been run and will be downloaded right now. Subsequent runs will be faster. (~23MB)"
+      );
+
+    let fetchResponse = await this.#fetchWithHost();
+    if (fetchResponse.pipeline !== null) return fetchResponse.pipeline;
+
+    this.log(
+      `Failed to download model from primary URL. Using fallback ${fetchResponse.retry}`
+    );
+    if (!!fetchResponse.retry)
+      fetchResponse = await this.#fetchWithHost(fetchResponse.retry);
+    if (fetchResponse.pipeline !== null) return fetchResponse.pipeline;
+    throw fetchResponse.error;
+  }
+
   async embedTextInput(textInput) {
     const result = await this.embedChunks(textInput);
     return result?.[0] || [];
@@ -89,6 +133,7 @@ class NativeEmbedder {
   // during a very large document (>100K words) but can spike up to 70% before gc.
   // This seems repeatable for all document sizes.
   // While this does take a while, it is zero set up and is 100% free and on-instance.
+  // It still may crash depending on other elements at play - so no promises it works under all conditions.
   async embedChunks(textChunks = []) {
     const tmpFilePath = this.#tempfilePath();
     const chunks = toChunks(textChunks, this.maxConcurrentChunks);
@@ -112,7 +157,7 @@ class NativeEmbedder {
 
       data = JSON.stringify(output.tolist());
       await this.#writeToTempfile(tmpFilePath, data);
-      console.log(`\x1b[34m[Embedded Chunk ${idx + 1} of ${chunkLen}]\x1b[0m`);
+      this.log(`Embedded Chunk ${idx + 1} of ${chunkLen}`);
       if (chunkLen - 1 !== idx) await this.#writeToTempfile(tmpFilePath, ",");
       if (chunkLen - 1 === idx) await this.#writeToTempfile(tmpFilePath, "]");
       pipeline = null;

server/utils/helpers/index.js

@@ -101,7 +101,6 @@ function getEmbeddingEngineSelection() {
       return new OllamaEmbedder();
     case "native":
       const { NativeEmbedder } = require("../EmbeddingEngines/native");
-      console.log("\x1b[34m[INFO]\x1b[0m Using Native Embedder");
       return new NativeEmbedder();
     default:
       return null;