anything-llm/server/utils/EmbeddingEngines/native/index.js

134 lines
4.7 KiB
JavaScript

const path = require("path");
const fs = require("fs");
const { toChunks } = require("../../helpers");
const { v4 } = require("uuid");
class NativeEmbedder {
constructor() {
// Model Card: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
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");
// Limit of how many strings we can process in a single pass to stay with resource or network limits
this.maxConcurrentChunks = 25;
this.embeddingMaxChunkLength = 1_000;
// Make directory when it does not exist in existing installations
if (!fs.existsSync(this.cacheDir)) fs.mkdirSync(this.cacheDir);
}
#tempfilePath() {
const filename = `${v4()}.tmp`;
const tmpPath = process.env.STORAGE_DIR
? path.resolve(process.env.STORAGE_DIR, "tmp")
: path.resolve(__dirname, `../../../storage/tmp`);
if (!fs.existsSync(tmpPath)) fs.mkdirSync(tmpPath, { recursive: true });
return path.resolve(tmpPath, filename);
}
async #writeToTempfile(filePath, data) {
try {
await fs.promises.appendFile(filePath, data, { encoding: "utf8" });
} catch (e) {
console.error(`Error writing to tempfile: ${e}`);
}
}
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"
);
}
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;
}
}
async embedTextInput(textInput) {
const result = await this.embedChunks(textInput);
return result?.[0] || [];
}
// If you are thinking you want to edit this function - you probably don't.
// This process was benchmarked heavily on a t3.small (2GB RAM 1vCPU)
// and without careful memory management for the V8 garbage collector
// this function will likely result in an OOM on any resource-constrained deployment.
// To help manage very large documents we run a concurrent write-log each iteration
// to keep the embedding result out of memory. The `maxConcurrentChunk` is set to 25,
// as 50 seems to overflow no matter what. Given the above, memory use hovers around ~30%
// 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.
async embedChunks(textChunks = []) {
const tmpFilePath = this.#tempfilePath();
const chunks = toChunks(textChunks, this.maxConcurrentChunks);
const chunkLen = chunks.length;
for (let [idx, chunk] of chunks.entries()) {
if (idx === 0) await this.#writeToTempfile(tmpFilePath, "[");
let data;
let pipeline = await this.embedderClient();
let output = await pipeline(chunk, {
pooling: "mean",
normalize: true,
});
if (output.length === 0) {
pipeline = null;
output = null;
data = null;
continue;
}
data = JSON.stringify(output.tolist());
await this.#writeToTempfile(tmpFilePath, data);
console.log(`\x1b[34m[Embedded Chunk ${idx + 1} of ${chunkLen}]\x1b[0m`);
if (chunkLen - 1 !== idx) await this.#writeToTempfile(tmpFilePath, ",");
if (chunkLen - 1 === idx) await this.#writeToTempfile(tmpFilePath, "]");
pipeline = null;
output = null;
data = null;
}
const embeddingResults = JSON.parse(
fs.readFileSync(tmpFilePath, { encoding: "utf-8" })
);
fs.rmSync(tmpFilePath, { force: true });
return embeddingResults.length > 0 ? embeddingResults.flat() : null;
}
}
module.exports = {
NativeEmbedder,
};