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Add built-in embedding engine into AnythingLLM (#411)
* Implement use of native embedder (all-Mini-L6-v2) stop showing prisma queries during dev * Add native embedder as an available embedder selection * wrap model loader in try/catch * print progress on download * Update to progress output for embedder * move embedder selection options to component * forgot import * add Data privacy alert updates for local embedder
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frontend/src/components/EmbeddingSelection/NativeEmbeddingOptions/index.jsx
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| export default function NativeEmbeddingOptions() { | ||
| return ( | ||
| <div className="w-full h-20 items-center justify-center flex"> | ||
| <p className="text-sm font-base text-white text-opacity-60"> | ||
| There is no set up required when using AnythingLLM's native embedding | ||
| engine. | ||
| </p> | ||
| </div> | ||
| ); | ||
| } |
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| Xenova | ||
| downloaded/* |
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| ## Native models used by AnythingLLM | ||
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| This folder is specifically created as a local cache and storage folder that is used for native models that can run on a CPU. | ||
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| Currently, AnythingLLM uses this folder for the following parts of the application. | ||
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| ### Embedding | ||
| When your embedding engine preference is `native` we will use the ONNX **all-MiniLM-L6-v2** model built by [Xenova on HuggingFace.co](https://huggingface.co/Xenova/all-MiniLM-L6-v2). This model is a quantized and WASM version of the popular [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) which produces a 384-dimension vector. | ||
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| If you are using the `native` embedding engine your vector database should be configured to accept 384-dimension models if that parameter is directly editable (Pinecone only). | ||
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| ### Text generation (LLM selection) | ||
| _in progress_ |
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| const path = require("path"); | ||
| const fs = require("fs"); | ||
| const { toChunks } = require("../../helpers"); | ||
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| class NativeEmbedder { | ||
| constructor() { | ||
| this.model = "Xenova/all-MiniLM-L6-v2"; | ||
| this.cacheDir = path.resolve( | ||
| process.env.STORAGE_DIR | ||
| ? path.resolve(process.env.STORAGE_DIR, `models`) | ||
| : path.resolve(__dirname, `../../../storage/models`) | ||
| ); | ||
| this.modelPath = path.resolve(this.cacheDir, "Xenova", "all-MiniLM-L6-v2"); | ||
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| // Limit the number of chunks to send per loop to not overload compute. | ||
| this.embeddingChunkLimit = 16; | ||
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| // Make directory when it does not exist in existing installations | ||
| if (!fs.existsSync(this.cacheDir)) fs.mkdirSync(this.cacheDir); | ||
| } | ||
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| 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" | ||
| ); | ||
| } | ||
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| 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, { | ||
| cache_dir: this.cacheDir, | ||
| ...(!fs.existsSync(this.modelPath) | ||
| ? { | ||
| // 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 ${ | ||
| data.file | ||
| } ${~~data?.progress}%` | ||
| ); | ||
| }, | ||
| } | ||
| : {}), | ||
| }); | ||
| } catch (error) { | ||
| console.error("Failed to load the native embedding model:", error); | ||
| throw error; | ||
| } | ||
| } | ||
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| async embedTextInput(textInput) { | ||
| const result = await this.embedChunks(textInput); | ||
| return result?.[0] || []; | ||
| } | ||
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| async embedChunks(textChunks = []) { | ||
| const Embedder = await this.embedderClient(); | ||
| const embeddingResults = []; | ||
| for (const chunk of toChunks(textChunks, this.embeddingChunkLimit)) { | ||
| const output = await Embedder(chunk, { | ||
| pooling: "mean", | ||
| normalize: true, | ||
| }); | ||
| if (output.length === 0) continue; | ||
| embeddingResults.push(output.tolist()); | ||
| } | ||
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| return embeddingResults.length > 0 ? embeddingResults.flat() : null; | ||
| } | ||
| } | ||
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| module.exports = { | ||
| NativeEmbedder, | ||
| }; |
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