add endpoint to HF

packages/components/nodes/embeddings/HuggingFaceInferenceEmbedding/core.ts

@@ -0,0 +1,48 @@
+import { HfInference } from '@huggingface/inference'
+import { Embeddings, EmbeddingsParams } from 'langchain/embeddings/base'
+import { getEnvironmentVariable } from '../../../src/utils'
+
+export interface HuggingFaceInferenceEmbeddingsParams extends EmbeddingsParams {
+    apiKey?: string
+    model?: string
+    endpoint?: string
+}
+
+export class HuggingFaceInferenceEmbeddings extends Embeddings implements HuggingFaceInferenceEmbeddingsParams {
+    apiKey?: string
+
+    endpoint?: string
+
+    model: string
+
+    client: HfInference
+
+    constructor(fields?: HuggingFaceInferenceEmbeddingsParams) {
+        super(fields ?? {})
+
+        this.model = fields?.model ?? 'sentence-transformers/distilbert-base-nli-mean-tokens'
+        this.apiKey = fields?.apiKey ?? getEnvironmentVariable('HUGGINGFACEHUB_API_KEY')
+        this.endpoint = fields?.endpoint ?? ''
+        this.client = new HfInference(this.apiKey)
+        if (this.endpoint) this.client.endpoint(this.endpoint)
+    }
+
+    async _embed(texts: string[]): Promise<number[][]> {
+        // replace newlines, which can negatively affect performance.
+        const clean = texts.map((text) => text.replace(/\n/g, ' '))
+        return this.caller.call(() =>
+            this.client.featureExtraction({
+                model: this.model,
+                inputs: clean
+            })
+        ) as Promise<number[][]>
+    }
+
+    embedQuery(document: string): Promise<number[]> {
+        return this._embed([document]).then((embeddings) => embeddings[0])
+    }
+
+    embedDocuments(documents: string[]): Promise<number[][]> {
+        return this._embed(documents)
+    }
+}