Add Baidu Qianfan endpoint for LLM (#10496)

- Description：
* Baidu AI Cloud's [Qianfan
Platform](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) is an
all-in-one platform for large model development and service deployment,
catering to enterprise developers in China. Qianfan Platform offers a
wide range of resources, including the Wenxin Yiyan model (ERNIE-Bot)
and various third-party open-source models.
- Issue: none
- Dependencies: 
    * qianfan
- Tag maintainer: @baskaryan
- Twitter handle:

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/extras/integrations/text_embedding/baidu_qianfan_endpoint.ipynb

@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Baidu Qianfan\n",
+    "\n",
+    "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
+    "\n",
+    "Basically, those model are split into the following type:\n",
+    "\n",
+    "- Embedding\n",
+    "- Chat\n",
+    "- Completion\n",
+    "\n",
+    "In this notebook, we will introduce how to use langchain with [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) mainly in `Embedding` corresponding\n",
+    " to the package `langchain/embeddings` in langchain:\n",
+    "\n",
+    "\n",
+    "\n",
+    "## API Initialization\n",
+    "\n",
+    "To use the LLM services based on Baidu Qianfan, you have to initialize these parameters:\n",
+    "\n",
+    "You could either choose to init the AK,SK in enviroment variables or init params:\n",
+    "\n",
+    "```base\n",
+    "export QIANFAN_AK=XXX\n",
+    "export QIANFAN_SK=XXX\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"For basic init and call\"\"\"\n",
+    "from langchain.embeddings.baidu_qianfan_endpoint import QianfanEmbeddingsEndpoint \n",
+    "\n",
+    "import os\n",
+    "os.environ[\"QIANFAN_AK\"] = \"xx\"\n",
+    "os.environ[\"QIANFAN_SK\"] = \"xx\"\n",
+    "\n",
+    "embed = QianfanEmbeddingsEndpoint(qianfan_ak='xxx', \n",
+    "                            qianfan_sk='xxx')\n",
+    "res = embed.embed_documents([\"hi\", \"world\"])\n",
+    "\n",
+    "import asyncio\n",
+    "\n",
+    "async def aioEmbed():\n",
+    "    res = await embed.aembed_query(\"qianfan\")\n",
+    "    print(res)\n",
+    "await aioEmbed()\n",
+    "\n",
+    "import asyncio\n",
+    "async def aioEmbedDocs():\n",
+    "    res = await embed.aembed_documents([\"hi\", \"world\"])\n",
+    "    for r in res:\n",
+    "        print(\"\", r[:8])\n",
+    "await aioEmbedDocs()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use different models in Qianfan\n",
+    "\n",
+    "In the case you want to deploy your own model based on Ernie Bot or third-party open sources model, you could follow these steps:\n",
+    "\n",
+    "- 1. （Optional, if the model are included in the default models, skip it）Deploy your model in Qianfan Console, get your own customized deploy endpoint.\n",
+    "- 2. Set up the field called `endpoint` in the initlization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embed = QianfanEmbeddingsEndpoint(qianfan_ak='xxx', \n",
+    "                            qianfan_sk='xxx',\n",
+    "                            model=\"bge_large_zh\",\n",
+    "                            endpoint=\"bge_large_zh\")\n",
+    "\n",
+    "res = embed.embed_documents([\"hi\", \"world\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "6fa70026b407ae751a5c9e6bd7f7d482379da8ad616f98512780b705c84ee157"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}