[HuggingFace Pipeline] add streaming support (#23852)

docs/docs/integrations/llms/huggingface_pipelines.ipynb

@@ -143,6 +143,25 @@
     "print(chain.invoke({\"question\": question}))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "5141dc4d",
+   "metadata": {},
+   "source": [
+    "Streaming repsonse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1819250-2db9-4143-b88a-12e92d4e2386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for chunk in chain.stream(question):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "dbbc3a37",

docs/docs/integrations/llms/openvino.ipynb

@@ -245,7 +245,7 @@
    "source": [
     "### Streaming\n",
     "\n",
-    "To get streaming of LLM output, you can create a Huggingface `TextIteratorStreamer` for `_forward_params`."
+    "You can use `stream` method to get a streaming of LLM output, "
    ]
   },
   {
@@ -255,24 +255,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from threading import Thread\n",
+    "generation_config = {\"skip_prompt\": True, \"pipeline_kwargs\": {\"max_new_tokens\": 100}}\n",
+    "chain = prompt | ov_llm.bind(**generation_config)\n",
     "\n",
-    "from transformers import TextIteratorStreamer\n",
-    "\n",
-    "streamer = TextIteratorStreamer(\n",
-    "    ov_llm.pipeline.tokenizer,\n",
-    "    timeout=30.0,\n",
-    "    skip_prompt=True,\n",
-    "    skip_special_tokens=True,\n",
-    ")\n",
-    "pipeline_kwargs = {\"pipeline_kwargs\": {\"streamer\": streamer, \"max_new_tokens\": 100}}\n",
-    "chain = prompt | ov_llm.bind(**pipeline_kwargs)\n",
-    "\n",
-    "t1 = Thread(target=chain.invoke, args=({\"question\": question},))\n",
-    "t1.start()\n",
-    "\n",
-    "for new_text in streamer:\n",
-    "    print(new_text, end=\"\", flush=True)"
+    "for chunk in chain.stream(question):\n",
+    "    print(chunk, end=\"\", flush=True)"
    ]
   },
   {