community: Add SparkLLM to community

docs/docs/integrations/llms/sparkllm.ipynb

@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SparkLLM\n",
+    "[SparkLLM](https://xinghuo.xfyun.cn/spark) is a large-scale cognitive model independently developed by iFLYTEK.\n",
+    "It has cross-domain knowledge and language understanding ability by learning a large amount of texts, codes and images.\n",
+    "It can understand and perform tasks based on natural dialogue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisite\n",
+    "- Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API Console](https://console.xfyun.cn/services/bm3) (for more info, see [iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY` and `IFLYTEK_SPARK_API_SECRET` or pass parameters when creating `ChatSparkLLM` as the demo above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use SparkLLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"IFLYTEK_SPARK_APP_ID\"] = \"app_id\"\n",
+    "os.environ[\"IFLYTEK_SPARK_API_KEY\"] = \"api_key\"\n",
+    "os.environ[\"IFLYTEK_SPARK_API_SECRET\"] = \"api_secret\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/liugddx/code/langchain/libs/core/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.7 and will be removed in 0.2.0. Use invoke instead.\n",
+      "  warn_deprecated(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My name is iFLYTEK Spark. How can I assist you today?\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_community.llms import SparkLLM\n",
+    "\n",
+    "# Load the model\n",
+    "llm = SparkLLM()\n",
+    "\n",
+    "res = llm(\"What's your name?\")\n",
+    "print(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-18T13:04:29.305856Z",
+     "start_time": "2024-02-18T13:04:28.085715Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "LLMResult(generations=[[Generation(text='Hello! How can I assist you today?')]], llm_output=None, run=[RunInfo(run_id=UUID('d8cdcd41-a698-4cbf-a28d-e74f9cd2037b'))])"
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = llm.generate(prompts=[\"hello!\"])\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-18T13:05:44.640035Z",
+     "start_time": "2024-02-18T13:05:43.244126Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello! How can I assist you today?\n"
+     ]
+    }
+   ],
+   "source": [
+    "for res in llm.stream(\"foo:\"):\n",
+    "    print(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

libs/community/langchain_community/llms/__init__.py

@@ -582,6 +582,12 @@ def _import_volcengine_maas() -> Any:
     return VolcEngineMaasLLM
 
 
+def _import_sparkllm() -> Any:
+    from langchain_community.llms.sparkllm import SparkLLM
+
+    return SparkLLM
+
+
 def __getattr__(name: str) -> Any:
     if name == "AI21":
         return _import_ai21()
@@ -769,6 +775,8 @@ def __getattr__(name: str) -> Any:
             k: v() for k, v in get_type_to_cls_dict().items()
         }
         return type_to_cls_dict
+    elif name == "SparkLLM":
+        return _import_sparkllm()
     else:
         raise AttributeError(f"Could not find: {name}")
 
@@ -861,6 +869,7 @@ def __getattr__(name: str) -> Any:
     "YandexGPT",
     "Yuan2",
     "VolcEngineMaasLLM",
+    "SparkLLM",
 ]
 
 
@@ -950,4 +959,5 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
         "yandex_gpt": _import_yandex_gpt,
         "yuan2": _import_yuan2,
         "VolcEngineMaasLLM": _import_volcengine_maas,
+        "SparkLLM": _import_sparkllm(),
     }