Merge branch 'main' into feature/Credential

# Conflicts:
#	README.md
#	docker/.env.example
#	packages/components/nodes/documentloaders/Notion/NotionDB.ts
#	packages/components/nodes/memory/DynamoDb/DynamoDb.ts
#	packages/components/nodes/memory/MotorheadMemory/MotorheadMemory.ts
#	packages/components/nodes/memory/ZepMemory/ZepMemory.ts
#	packages/components/package.json
#	packages/components/src/utils.ts
#	packages/server/.env.example
#	packages/server/README.md
#	packages/server/marketplaces/chatflows/Conversational Retrieval QA Chain.json
#	packages/server/src/ChildProcess.ts
#	packages/server/src/DataSource.ts
#	packages/server/src/commands/start.ts
#	packages/server/src/index.ts
#	packages/server/src/utils/index.ts
#	packages/server/src/utils/logger.ts

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

@@ -32,6 +32,8 @@ class HuggingFaceInferenceEmbedding_Embeddings implements INode {
                 label: 'Model',
                 name: 'modelName',
                 type: 'string',
+                description: 'If using own inference endpoint, leave this blank',
+                placeholder: 'sentence-transformers/distilbert-base-nli-mean-tokens',
                 optional: true
             },
             {

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

@@ -30,18 +30,26 @@ export class HuggingFaceInferenceEmbeddings extends Embeddings implements Huggin
     async _embed(texts: string[]): Promise<number[][]> {
         // replace newlines, which can negatively affect performance.
         const clean = texts.map((text) => text.replace(/\n/g, ' '))
+        const hf = new HfInference(this.apiKey)
         const obj: any = {
             inputs: clean
         }
-        if (!this.endpoint) obj.model = this.model
-        return this.caller.call(() => this.client.featureExtraction(obj)) as Promise<number[][]>
+        if (this.endpoint) {
+            hf.endpoint(this.endpoint)
+        } else {
+            obj.model = this.model
+        }
+
+        const res = await this.caller.callWithOptions({}, hf.featureExtraction.bind(hf), obj)
+        return res as number[][]
     }
 
-    embedQuery(document: string): Promise<number[]> {
-        return this._embed([document]).then((embeddings) => embeddings[0])
+    async embedQuery(document: string): Promise<number[]> {
+        const res = await this._embed([document])
+        return res[0]
     }
 
-    embedDocuments(documents: string[]): Promise<number[][]> {
+    async embedDocuments(documents: string[]): Promise<number[][]> {
         return this._embed(documents)
     }
 }