community[minor]: Add solar model chat model (#18556)

Add our solar chat models, available model choices:
* solar-1-mini-chat
* solar-1-mini-translate-enko
* solar-1-mini-translate-koen

More documents and pricing can be found at
https://console.upstage.ai/services/solar.

The references to our solar model can be found at
* https://arxiv.org/abs/2402.17032

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/docs/integrations/chat/solar.ipynb

@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a9667088-04e1-4f67-8221-a0072a2d635f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-06T17:04:59.273702Z",
+     "iopub.status.busy": "2024-03-06T17:04:59.272602Z",
+     "iopub.status.idle": "2024-03-06T17:05:00.129177Z",
+     "shell.execute_reply": "2024-03-06T17:05:00.124594Z",
+     "shell.execute_reply.started": "2024-03-06T17:04:59.273646Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='저는 대형 언어 모델 프로젝트를 구축하고 싶습니다.')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"SOLAR_API_KEY\"] = \"SOLAR_API_KEY\"\n",
+    "\n",
+    "from langchain_community.chat_models.solar import SolarChat\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "\n",
+    "chat = SolarChat(max_tokens=1024)\n",
+    "\n",
+    "messages = [\n",
+    "    SystemMessage(\n",
+    "        content=\"You are a helpful assistant who translates English to Korean.\"\n",
+    "    ),\n",
+    "    HumanMessage(\n",
+    "        content=\"Translate this sentence from English to Korean. I want to build a project of large language model.\"\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "chat.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cb792fe-2844-4969-a9e9-f4c0f97b1699",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/llms/solar.ipynb

@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ff00e23-1a90-4a39-b220-83ebfffd96d6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-06T17:10:57.375714Z",
+     "iopub.status.busy": "2024-03-06T17:10:57.375261Z",
+     "iopub.status.idle": "2024-03-06T17:11:03.473978Z",
+     "shell.execute_reply": "2024-03-06T17:11:03.472875Z",
+     "shell.execute_reply.started": "2024-03-06T17:10:57.375670Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Once upon a time, in a far-off land, there was a young girl named Lily. Lily was a kind and curious girl who loved to explore the world around her. One day, while wandering through the forest, she came across a small, shimmering pond.\\n\\nAs she approached the pond, she saw a beautiful, glowing flower floating on the water's surface. Lily reached out to touch the flower, and as she did, she felt a strange tingling sensation. Suddenly, the flower began to glow even brighter, and Lily was transported to a magical world filled with talking animals and enchanted forests.\\n\\nIn this world, Lily met a wise old owl named Winston who told her that the flower she had touched was a magical one that could grant her any wish she desired. Lily was overjoyed and asked Winston to show her around the magical world.\\n\\nTogether, they explored the enchanted forests, met friendly animals, and discovered hidden treasures. Lily was having the time of her life, but she knew that she couldn't stay in this magical world forever. Eventually, she had to return home.\\n\\nAs she said goodbye to Winston and the magical world, Lily realized that she had learned an important lesson. She had discovered that sometimes, the most magical things in life are the ones that are right in front of us, if we only take the time to look.\\n\\nFrom that day on, Lily always kept her eyes open for the magic in the world around her, and she never forgot the adventure she had in the enchanted forest.\""
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from langchain_community.llms.solar import Solar\n",
+    "\n",
+    "os.environ[\"SOLAR_API_KEY\"] = \"SOLAR_API_KEY\"\n",
+    "llm = Solar()\n",
+    "llm.invoke(\"tell me a story?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "67fa1711-f08f-43fa-a3bd-75ae5bc6b988",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-03-06T17:11:11.359924Z",
+     "iopub.status.busy": "2024-03-06T17:11:11.358357Z",
+     "iopub.status.idle": "2024-03-06T17:11:16.692138Z",
+     "shell.execute_reply": "2024-03-06T17:11:16.686492Z",
+     "shell.execute_reply.started": "2024-03-06T17:11:11.359835Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ary/dev/llm/langchain/libs/core/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `run` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
+      "  warn_deprecated(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'Step 1: Determine the year Justin Bieber was born.\\nJustin Bieber was born on March 1, 1994.\\n\\nStep 2: Determine the Super Bowl held in 1994.\\nSuper Bowl XXVIII was held in 1994.\\n\\nStep 3: Determine the winning team of Super Bowl XXVIII.\\nThe Dallas Cowboys won Super Bowl XXVIII in 1994.\\n\\nFinal Answer: The Dallas Cowboys won the Super Bowl in the year Justin Bieber was born (1994).'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_community.llms.solar import Solar\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(template)\n",
+    "\n",
+    "llm = Solar()\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "\n",
+    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "\n",
+    "llm_chain.run(question)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91961983-d0d5-4901-b854-531e158c0416",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

libs/community/langchain_community/chat_models/solar.py

@@ -0,0 +1,56 @@
+"""Wrapper around Solar chat models."""
+
+from typing import Dict
+
+from langchain_core.pydantic_v1 import root_validator
+from langchain_core.utils import get_from_dict_or_env
+
+from langchain_community.chat_models import ChatOpenAI
+from langchain_community.llms.solar import SOLAR_SERVICE_URL_BASE, SolarCommon
+
+
+class SolarChat(SolarCommon, ChatOpenAI):
+    """Wrapper around Solar large language models.
+    To use, you should have the ``openai`` python package installed, and the
+    environment variable ``SOLAR_API_KEY`` set with your API key.
+    (Solar's chat API is compatible with OpenAI's SDK.)
+    Referenced from https://console.upstage.ai/services/solar
+    Example:
+        .. code-block:: python
+
+            from langchain_community.chat_models.solar import SolarChat
+
+            solar = SolarChat(model="solar-1-mini-chat")
+    """
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that the environment is set up correctly."""
+        values["solar_api_key"] = get_from_dict_or_env(
+            values, "solar_api_key", "SOLAR_API_KEY"
+        )
+
+        try:
+            import openai
+
+        except ImportError:
+            raise ImportError(
+                "Could not import openai python package. "
+                "Please install it with `pip install openai`."
+            )
+
+        client_params = {
+            "api_key": values["solar_api_key"],
+            "base_url": values["base_url"]
+            if "base_url" in values
+            else SOLAR_SERVICE_URL_BASE,
+        }
+
+        if not values.get("client"):
+            values["client"] = openai.OpenAI(**client_params).chat.completions
+        if not values.get("async_client"):
+            values["async_client"] = openai.AsyncOpenAI(
+                **client_params
+            ).chat.completions
+
+        return values

libs/community/langchain_community/llms/solar.py

@@ -0,0 +1,119 @@
+from typing import Any, Dict, List, Optional
+
+import requests
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import LLM
+from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
+from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
+
+from langchain_community.llms.utils import enforce_stop_tokens
+
+SOLAR_SERVICE_URL_BASE = "https://api.upstage.ai/v1/solar"
+SOLAR_SERVICE = "https://api.upstage.ai"
+
+
+class _SolarClient(BaseModel):
+    """An API client that talks to the Solar server."""
+
+    api_key: SecretStr
+    """The API key to use for authentication."""
+    base_url: str = SOLAR_SERVICE_URL_BASE
+
+    def completion(self, request: Any) -> Any:
+        headers = {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
+        response = requests.post(
+            f"{self.base_url}/chat/completions",
+            headers=headers,
+            json=request,
+        )
+        if not response.ok:
+            raise ValueError(f"HTTP {response.status_code} error: {response.text}")
+        return response.json()["choices"][0]["message"]["content"]
+
+
+class SolarCommon(BaseModel):
+    _client: _SolarClient
+    base_url: str = SOLAR_SERVICE_URL_BASE
+    solar_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
+    """Solar API key. Get it here: https://console.upstage.ai/services/solar"""
+    model_name: str = Field(default="solar-1-mini-chat", alias="model")
+    """Model name. Available models listed here: https://console.upstage.ai/services/solar"""
+    max_tokens: int = Field(default=1024, alias="max context")
+    temperature = 0.3
+
+    class Config:
+        allow_population_by_field_name = True
+        arbitrary_types_allowed = True
+        extra = "ignore"
+
+    @property
+    def lc_secrets(self) -> dict:
+        return {"solar_api_key": "SOLAR_API_KEY"}
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        return {
+            "model": self.model_name,
+            "max_tokens": self.max_tokens,
+            "temperature": self.temperature,
+        }
+
+    @property
+    def _invocation_params(self) -> Dict[str, Any]:
+        return {**{"model": self.model_name}, **self._default_params}
+
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        return values
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        api_key = get_from_dict_or_env(values, "solar_api_key", "SOLAR_API_KEY")
+        if api_key is None or len(api_key) == 0:
+            raise ValueError("SOLAR_API_KEY must be configured")
+
+        values["solar_api_key"] = convert_to_secret_str(api_key)
+
+        if "base_url" not in values:
+            values["base_url"] = SOLAR_SERVICE_URL_BASE
+
+        if "base_url" in values and not values["base_url"].startswith(SOLAR_SERVICE):
+            raise ValueError("base_url must match with: " + SOLAR_SERVICE)
+
+        values["_client"] = _SolarClient(
+            api_key=values["solar_api_key"], base_url=values["base_url"]
+        )
+        return values
+
+    @property
+    def _llm_type(self) -> str:
+        return "solar"
+
+
+class Solar(SolarCommon, LLM):
+    """Solar large language models.
+    To use, you should have the environment variable
+    ``SOLAR_API_KEY`` set with your API key.
+    Referenced from https://console.upstage.ai/services/solar
+    """
+
+    class Config:
+        allow_population_by_field_name = True
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        request = self._invocation_params
+        request["messages"] = [{"role": "user", "content": prompt}]
+        request.update(kwargs)
+        text = self._client.completion(request)
+        if stop is not None:
+            # This is required since the stop tokens
+            # are not enforced by the model parameters
+            text = enforce_stop_tokens(text, stop)
+
+        return text