diff --git a/docs/docs/integrations/chat/friendli.ipynb b/docs/docs/integrations/chat/friendli.ipynb
new file mode 100644
index 0000000000..4af9f0049d
--- /dev/null
+++ b/docs/docs/integrations/chat/friendli.ipynb
@@ -0,0 +1,286 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Friendli\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ChatFriendli\n",
+    "\n",
+    "> [Friendli](https://friendli.ai/) enhances AI application performance and optimizes cost savings with scalable, efficient deployment options, tailored for high-demand AI workloads.\n",
+    "\n",
+    "This tutorial guides you through integrating `ChatFriendli` for chat applications using LangChain. `ChatFriendli` offers a flexible approach to generating conversational AI responses, supporting both synchronous and asynchronous calls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Ensure the `langchain_community` and `friendli-client` are installed.\n",
+    "\n",
+    "```sh\n",
+    "pip install -U langchain-comminity friendli-client.\n",
+    "```\n",
+    "\n",
+    "Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can initialize a Friendli chat model with selecting the model you want to use. The default model is `mixtral-8x7b-instruct-v0-1`. You can check the available models at [docs.friendli.ai](https://docs.periflow.ai/guides/serverless_endpoints/pricing#text-generation-models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models.friendli import ChatFriendli\n",
+    "\n",
+    "chat = ChatFriendli(model=\"llama-2-13b-chat\", max_tokens=100, temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "`FrienliChat` supports all methods of [`ChatModel`](/docs/modules/model_io/chat/) including async APIs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use functionality of  `invoke`, `batch`, `generate`, and `stream`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages.human import HumanMessage\n",
+    "from langchain_core.messages.system import SystemMessage\n",
+    "\n",
+    "system_message = SystemMessage(content=\"Answer questions as short as you can.\")\n",
+    "human_message = HumanMessage(content=\"Tell me a joke.\")\n",
+    "messages = [system_message, human_message]\n",
+    "\n",
+    "chat.invoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
+       " AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.batch([messages, messages])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('a0c2d733-6971-4ae7-beea-653856f4e57c')), RunInfo(run_id=UUID('f3d35e44-ac9a-459a-9e4b-b8e3a73a91e1'))])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.generate([messages, messages])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Knock, knock!\n",
+      "Who's there?\n",
+      "Cows go.\n",
+      "Cows go who?\n",
+      "MOO!"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in chat.stream(messages):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use all functionality of async APIs: `ainvoke`, `abatch`, `agenerate`, and `astream`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chat.ainvoke(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"),\n",
+       " AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\")]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chat.abatch([messages, messages])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))], [ChatGeneration(text=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\", message=AIMessage(content=\" Knock, knock!\\nWho's there?\\nCows go.\\nCows go who?\\nMOO!\"))]], llm_output={}, run=[RunInfo(run_id=UUID('f2255321-2d8e-41cc-adbd-3f4facec7573')), RunInfo(run_id=UUID('fcc297d0-6ca9-48cb-9d86-e6f78cade8ee'))])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chat.agenerate([messages, messages])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Knock, knock!\n",
+      "Who's there?\n",
+      "Cows go.\n",
+      "Cows go who?\n",
+      "MOO!"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in chat.astream(messages):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/docs/integrations/llms/friendli.ipynb b/docs/docs/integrations/llms/friendli.ipynb
new file mode 100644
index 0000000000..90d5d491c4
--- /dev/null
+++ b/docs/docs/integrations/llms/friendli.ipynb
@@ -0,0 +1,277 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Friendli\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Friendli\n",
+    "\n",
+    "> [Friendli](https://friendli.ai/) enhances AI application performance and optimizes cost savings with scalable, efficient deployment options, tailored for high-demand AI workloads.\n",
+    "\n",
+    "This tutorial guides you through integrating `Friendli` with LangChain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Ensure the `langchain_community` and `friendli-client` are installed.\n",
+    "\n",
+    "```sh\n",
+    "pip install -U langchain-comminity friendli-client.\n",
+    "```\n",
+    "\n",
+    "Sign in to [Friendli Suite](https://suite.friendli.ai/) to create a Personal Access Token, and set it as the `FRIENDLI_TOKEN` environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"FRIENDLI_TOKEN\"] = getpass.getpass(\"Friendi Personal Access Token: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can initialize a Friendli chat model with selecting the model you want to use. The default model is `mixtral-8x7b-instruct-v0-1`. You can check the available models at [docs.friendli.ai](https://docs.periflow.ai/guides/serverless_endpoints/pricing#text-generation-models)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms.friendli import Friendli\n",
+    "\n",
+    "llm = Friendli(model=\"mixtral-8x7b-instruct-v0-1\", max_tokens=100, temperature=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage\n",
+    "\n",
+    "`Frienli` supports all methods of [`LLM`](/docs/modules/model_io/llms/) including async APIs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use functionality of `invoke`, `batch`, `generate`, and `stream`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm.invoke(\"Tell me a joke.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"',\n",
+       " 'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm.batch([\"Tell me a joke.\", \"Tell me a joke.\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[Generation(text='Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"')], [Generation(text='Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"')]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('a2009600-baae-4f5a-9f69-23b2bc916e4c')), RunInfo(run_id=UUID('acaf0838-242c-4255-85aa-8a62b675d046'))])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm.generate([\"Tell me a joke.\", \"Tell me a joke.\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Username checks out.\n",
+      "User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.\n",
+      "User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.\n",
+      "User 1: Oh, I thought you were saying that I'm a \"dumbass\" because I'm a \"dumbass\" who \"checks out\""
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in llm.stream(\"Tell me a joke.\"):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use all functionality of async APIs: `ainvoke`, `abatch`, `agenerate`, and `astream`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await llm.ainvoke(\"Tell me a joke.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"',\n",
+       " 'Username checks out.\\nUser 1: I\\'m not sure if you\\'re being sarcastic or not, but I\\'ll take it as a compliment.\\nUser 0: I\\'m not being sarcastic. I\\'m just saying that your username is very fitting.\\nUser 1: Oh, I thought you were saying that I\\'m a \"dumbass\" because I\\'m a \"dumbass\" who \"checks out\"']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await llm.abatch([\"Tell me a joke.\", \"Tell me a joke.\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[Generation(text=\"Username checks out.\\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\\nUser 0: I'm being serious. I'm not sure\")], [Generation(text=\"Username checks out.\\nUser 1: I'm not sure if you're being serious or not, but I'll take it as a compliment.\\nUser 0: I'm being serious. I'm not sure if you're being serious or not.\\nUser 1: I'm being serious. I'm not sure if you're being serious or not.\\nUser 0: I'm being serious. I'm not sure\")]], llm_output={'model': 'mixtral-8x7b-instruct-v0-1'}, run=[RunInfo(run_id=UUID('46144905-7350-4531-a4db-22e6a827c6e3')), RunInfo(run_id=UUID('e2b06c30-ffff-48cf-b792-be91f2144aa6'))])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await llm.agenerate([\"Tell me a joke.\", \"Tell me a joke.\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Username checks out.\n",
+      "User 1: I'm not sure if you're being sarcastic or not, but I'll take it as a compliment.\n",
+      "User 0: I'm not being sarcastic. I'm just saying that your username is very fitting.\n",
+      "User 1: Oh, I thought you were saying that I'm a \"dumbass\" because I'm a \"dumbass\" who \"checks out\""
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in llm.astream(\"Tell me a joke.\"):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "langchain",
+   "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/libs/community/langchain_community/chat_models/__init__.py b/libs/community/langchain_community/chat_models/__init__.py
index 9c013930fa..fb3495ef17 100644
--- a/libs/community/langchain_community/chat_models/__init__.py
+++ b/libs/community/langchain_community/chat_models/__init__.py
@@ -30,6 +30,7 @@ from langchain_community.chat_models.ernie import ErnieBotChat
 from langchain_community.chat_models.everlyai import ChatEverlyAI
 from langchain_community.chat_models.fake import FakeListChatModel
 from langchain_community.chat_models.fireworks import ChatFireworks
+from langchain_community.chat_models.friendli import ChatFriendli
 from langchain_community.chat_models.gigachat import GigaChat
 from langchain_community.chat_models.google_palm import ChatGooglePalm
 from langchain_community.chat_models.gpt_router import GPTRouter
@@ -94,6 +95,7 @@ __all__ = [
     "ChatYandexGPT",
     "ChatBaichuan",
     "ChatHunyuan",
+    "ChatFriendli",
     "GigaChat",
     "ChatSparkLLM",
     "VolcEngineMaasChat",
diff --git a/libs/community/langchain_community/chat_models/friendli.py b/libs/community/langchain_community/chat_models/friendli.py
new file mode 100644
index 0000000000..593c51ea9e
--- /dev/null
+++ b/libs/community/langchain_community/chat_models/friendli.py
@@ -0,0 +1,217 @@
+from __future__ import annotations
+
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
+
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.chat_models import (
+    BaseChatModel,
+    agenerate_from_stream,
+    generate_from_stream,
+)
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    ChatMessage,
+    HumanMessage,
+    SystemMessage,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+
+from langchain_community.llms.friendli import BaseFriendli
+
+
+def get_role(message: BaseMessage) -> str:
+    """Get role of the message.
+
+    Args:
+        message (BaseMessage): The message object.
+
+    Raises:
+        ValueError: Raised when the message is of an unknown type.
+
+    Returns:
+        str: The role of the message.
+    """
+    if isinstance(message, ChatMessage) or isinstance(message, HumanMessage):
+        return "user"
+    if isinstance(message, AIMessage):
+        return "assistant"
+    if isinstance(message, SystemMessage):
+        return "system"
+    raise ValueError(f"Got unknown type {message}")
+
+
+def get_chat_request(messages: List[BaseMessage]) -> Dict[str, Any]:
+    """Get a request of the Friendli chat API.
+
+    Args:
+        messages (List[BaseMessage]): Messages comprising the conversation so far.
+
+    Returns:
+        Dict[str, Any]: The request for the Friendli chat API.
+    """
+    return {
+        "messages": [
+            {"role": get_role(message), "content": message.content}
+            for message in messages
+        ]
+    }
+
+
+class ChatFriendli(BaseChatModel, BaseFriendli):
+    """Friendli LLM for chat.
+
+    ``friendli-client`` package should be installed with `pip install friendli-client`.
+    You must set ``FRIENDLI_TOKEN`` environment variable or provide the value of your
+    personal access token for the ``friendli_token`` argument.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.chat_models import FriendliChat
+
+            chat = Friendli(
+                model="llama-2-13b-chat", friendli_token="YOUR FRIENDLI TOKEN"
+            )
+            chat.invoke("What is generative AI?")
+    """
+
+    model: str = "llama-2-13b-chat"
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {"friendli_token": "FRIENDLI_TOKEN"}
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Friendli completions API."""
+        return {
+            "frequency_penalty": self.frequency_penalty,
+            "presence_penalty": self.presence_penalty,
+            "max_tokens": self.max_tokens,
+            "stop": self.stop,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+        }
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {"model": self.model, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        return "friendli-chat"
+
+    def _get_invocation_params(
+        self, stop: Optional[List[str]] = None, **kwargs: Any
+    ) -> Dict[str, Any]:
+        """Get the parameters used to invoke the model."""
+        params = self._default_params
+        if self.stop is not None and stop is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+        elif self.stop is not None:
+            params["stop"] = self.stop
+        else:
+            params["stop"] = stop
+        return {**params, **kwargs}
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        stream = self.client.chat.completions.create(
+            **get_chat_request(messages), stream=True, model=self.model, **params
+        )
+        for chunk in stream:
+            delta = chunk.choices[0].delta.content
+            if delta:
+                yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
+                if run_manager:
+                    run_manager.on_llm_new_token(delta)
+
+    async def _astream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        stream = await self.async_client.chat.completions.create(
+            **get_chat_request(messages), stream=True, model=self.model, **params
+        )
+        async for chunk in stream:
+            delta = chunk.choices[0].delta.content
+            if delta:
+                yield ChatGenerationChunk(message=AIMessageChunk(content=delta))
+                if run_manager:
+                    await run_manager.on_llm_new_token(delta)
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            stream_iter = self._stream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return generate_from_stream(stream_iter)
+
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        response = self.client.chat.completions.create(
+            messages=[
+                {
+                    "role": get_role(message),
+                    "content": message.content,
+                }
+                for message in messages
+            ],
+            stream=False,
+            model=self.model,
+            **params,
+        )
+
+        message = AIMessage(content=response.choices[0].message.content)
+        return ChatResult(generations=[ChatGeneration(message=message)])
+
+    async def _agenerate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            stream_iter = self._astream(
+                messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return await agenerate_from_stream(stream_iter)
+
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        response = await self.async_client.chat.completions.create(
+            messages=[
+                {
+                    "role": get_role(message),
+                    "content": message.content,
+                }
+                for message in messages
+            ],
+            stream=False,
+            model=self.model,
+            **params,
+        )
+
+        message = AIMessage(content=response.choices[0].message.content)
+        return ChatResult(generations=[ChatGeneration(message=message)])
diff --git a/libs/community/langchain_community/llms/__init__.py b/libs/community/langchain_community/llms/__init__.py
index 4d29156af0..810f7390b6 100644
--- a/libs/community/langchain_community/llms/__init__.py
+++ b/libs/community/langchain_community/llms/__init__.py
@@ -209,6 +209,12 @@ def _import_forefrontai() -> Type[BaseLLM]:
     return ForefrontAI
 
 
+def _import_friendli() -> Type[BaseLLM]:
+    from langchain_community.llms.friendli import Friendli
+
+    return Friendli
+
+
 def _import_gigachat() -> Type[BaseLLM]:
     from langchain_community.llms.gigachat import GigaChat
 
@@ -665,6 +671,8 @@ def __getattr__(name: str) -> Any:
         return _import_fireworks()
     elif name == "ForefrontAI":
         return _import_forefrontai()
+    elif name == "Friendli":
+        return _import_friendli()
     elif name == "GigaChat":
         return _import_gigachat()
     elif name == "GooglePalm":
@@ -827,6 +835,7 @@ __all__ = [
     "FakeListLLM",
     "Fireworks",
     "ForefrontAI",
+    "Friendli",
     "GigaChat",
     "GPT4All",
     "GooglePalm",
@@ -919,6 +928,7 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
         "edenai": _import_edenai,
         "fake-list": _import_fake,
         "forefrontai": _import_forefrontai,
+        "friendli": _import_friendli,
         "giga-chat-model": _import_gigachat,
         "google_palm": _import_google_palm,
         "gooseai": _import_gooseai,
diff --git a/libs/community/langchain_community/llms/friendli.py b/libs/community/langchain_community/llms/friendli.py
new file mode 100644
index 0000000000..88e23e4ac8
--- /dev/null
+++ b/libs/community/langchain_community/llms/friendli.py
@@ -0,0 +1,350 @@
+from __future__ import annotations
+
+import os
+from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
+
+from langchain_core.callbacks.manager import (
+    AsyncCallbackManagerForLLMRun,
+    CallbackManagerForLLMRun,
+)
+from langchain_core.language_models.llms import LLM
+from langchain_core.load.serializable import Serializable
+from langchain_core.outputs import GenerationChunk, LLMResult
+from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
+from langchain_core.utils.env import get_from_dict_or_env
+from langchain_core.utils.utils import convert_to_secret_str
+
+
+def _stream_response_to_generation_chunk(stream_response: Any) -> GenerationChunk:
+    """Convert a stream response to a generation chunk."""
+    if stream_response.event == "token_sampled":
+        return GenerationChunk(
+            text=stream_response.text,
+            generation_info={"token": str(stream_response.token)},
+        )
+    return GenerationChunk(text="")
+
+
+class BaseFriendli(Serializable):
+    """Base class of Friendli."""
+
+    # Friendli client.
+    client: Any = Field(default=None, exclude=True)
+    # Friendli Async client.
+    async_client: Any = Field(default=None, exclude=True)
+    # Model name to use.
+    model: str = "mixtral-8x7b-instruct-v0-1"
+    # Friendli personal access token to run as.
+    friendli_token: Optional[SecretStr] = None
+    # Friendli team ID to run as.
+    friendli_team: Optional[str] = None
+    # Whether to enable streaming mode.
+    streaming: bool = False
+    # Number between -2.0 and 2.0. Positive values penalizes tokens that have been
+    # sampled, taking into account their frequency in the preceding text. This
+    # penalization diminishes the model's tendency to reproduce identical lines
+    # verbatim.
+    frequency_penalty: Optional[float] = None
+    # Number between -2.0 and 2.0. Positive values penalizes tokens that have been
+    # sampled at least once in the existing text.
+    presence_penalty: Optional[float] = None
+    # The maximum number of tokens to generate. The length of your input tokens plus
+    # `max_tokens` should not exceed the model's maximum length (e.g., 2048 for OpenAI
+    # GPT-3)
+    max_tokens: Optional[int] = None
+    # When one of the stop phrases appears in the generation result, the API will stop
+    # generation. The phrase is included in the generated result. If you are using
+    # beam search, all of the active beams should contain the stop phrase to terminate
+    # generation. Before checking whether a stop phrase is included in the result, the
+    # phrase is converted into tokens.
+    stop: Optional[List[str]] = None
+    # Sampling temperature. Smaller temperature makes the generation result closer to
+    # greedy, argmax (i.e., `top_k = 1`) sampling. If it is `None`, then 1.0 is used.
+    temperature: Optional[float] = None
+    # Tokens comprising the top `top_p` probability mass are kept for sampling. Numbers
+    # between 0.0 (exclusive) and 1.0 (inclusive) are allowed. If it is `None`, then 1.0
+    # is used by default.
+    top_p: Optional[float] = None
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate if personal access token is provided in environment."""
+        try:
+            import friendli
+        except ImportError as e:
+            raise ImportError(
+                "Could not import friendli-client python package. "
+                "Please install it with `pip install friendli-client`."
+            ) from e
+
+        friendli_token = convert_to_secret_str(
+            get_from_dict_or_env(values, "friendli_token", "FRIENDLI_TOKEN")
+        )
+        values["friendli_token"] = friendli_token
+        friendli_token_str = friendli_token.get_secret_value()
+        friendli_team = values["friendli_team"] or os.getenv("FRIENDLI_TEAM")
+        values["friendli_team"] = friendli_team
+        values["client"] = values["client"] or friendli.Friendli(
+            token=friendli_token_str, team_id=friendli_team
+        )
+        values["async_client"] = values["async_client"] or friendli.AsyncFriendli(
+            token=friendli_token_str, team_id=friendli_team
+        )
+        return values
+
+
+class Friendli(LLM, BaseFriendli):
+    """Friendli LLM.
+
+    ``friendli-client`` package should be installed with `pip install friendli-client`.
+    You must set ``FRIENDLI_TOKEN`` environment variable or provide the value of your
+    personal access token for the ``friendli_token`` argument.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.llms import Friendli
+
+            friendli = Friendli(
+                model="mixtral-8x7b-instruct-v0-1", friendli_token="YOUR FRIENDLI TOKEN"
+            )
+    """
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {"friendli_token": "FRIENDLI_TOKEN"}
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Friendli completions API."""
+        return {
+            "frequency_penalty": self.frequency_penalty,
+            "presence_penalty": self.presence_penalty,
+            "max_tokens": self.max_tokens,
+            "stop": self.stop,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+        }
+
+    @property
+    def _identifying_params(self) -> Dict[str, Any]:
+        """Get the identifying parameters."""
+        return {"model": self.model, **self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of llm."""
+        return "friendli"
+
+    def _get_invocation_params(
+        self, stop: Optional[List[str]] = None, **kwargs: Any
+    ) -> Dict[str, Any]:
+        """Get the parameters used to invoke the model."""
+        params = self._default_params
+        if self.stop is not None and stop is not None:
+            raise ValueError("`stop` found in both the input and default params.")
+        elif self.stop is not None:
+            params["stop"] = self.stop
+        else:
+            params["stop"] = stop
+        return {**params, **kwargs}
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out Friendli's completions API.
+
+        Args:
+            prompt (str): The text prompt to generate completion for.
+            stop (Optional[List[str]], optional): When one of the stop phrases appears
+                in the generation result, the API will stop generation. The stop phrases
+                are excluded from the result. If beam search is enabled, all of the
+                active beams should contain the stop phrase to terminate generation.
+                Before checking whether a stop phrase is included in the result, the
+                phrase is converted into tokens. We recommend using stop_tokens because
+                it is clearer. For example, after tokenization, phrases "clear" and
+                " clear" can result in different token sequences due to the prepended
+                space character. Defaults to None.
+
+        Returns:
+            str: The generated text output.
+
+        Example:
+            .. code-block:: python
+
+                response = frienldi("Give me a recipe for the Old Fashioned cocktail.")
+        """
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        completion = self.client.completions.create(
+            model=self.model, prompt=prompt, stream=False, **params
+        )
+        return completion.choices[0].text
+
+    async def _acall(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Call out Friendli's completions API Asynchronously.
+
+        Args:
+            prompt (str): The text prompt to generate completion for.
+            stop (Optional[List[str]], optional): When one of the stop phrases appears
+                in the generation result, the API will stop generation. The stop phrases
+                are excluded from the result. If beam search is enabled, all of the
+                active beams should contain the stop phrase to terminate generation.
+                Before checking whether a stop phrase is included in the result, the
+                phrase is converted into tokens. We recommend using stop_tokens because
+                it is clearer. For example, after tokenization, phrases "clear" and
+                " clear" can result in different token sequences due to the prepended
+                space character. Defaults to None.
+
+        Returns:
+            str: The generated text output.
+
+        Example:
+            .. code-block:: python
+
+                response = await frienldi("Tell me a joke.")
+        """
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        completion = await self.async_client.completions.create(
+            model=self.model, prompt=prompt, stream=False, **params
+        )
+        return completion.choices[0].text
+
+    def _stream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[GenerationChunk]:
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        stream = self.client.completions.create(
+            model=self.model, prompt=prompt, stream=True, **params
+        )
+        for line in stream:
+            chunk = _stream_response_to_generation_chunk(line)
+            yield chunk
+            if run_manager:
+                run_manager.on_llm_new_token(line.text, chunk=chunk)
+
+    async def _astream(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[GenerationChunk]:
+        params = self._get_invocation_params(stop=stop, **kwargs)
+        stream = await self.async_client.completions.create(
+            model=self.model, prompt=prompt, stream=True, **params
+        )
+        async for line in stream:
+            chunk = _stream_response_to_generation_chunk(line)
+            yield chunk
+            if run_manager:
+                await run_manager.on_llm_new_token(line.text, chunk=chunk)
+
+    def _generate(
+        self,
+        prompts: list[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        """Call out Friendli's completions API with k unique prompts.
+
+        Args:
+            prompt (str): The text prompt to generate completion for.
+            stop (Optional[List[str]], optional): When one of the stop phrases appears
+                in the generation result, the API will stop generation. The stop phrases
+                are excluded from the result. If beam search is enabled, all of the
+                active beams should contain the stop phrase to terminate generation.
+                Before checking whether a stop phrase is included in the result, the
+                phrase is converted into tokens. We recommend using stop_tokens because
+                it is clearer. For example, after tokenization, phrases "clear" and
+                " clear" can result in different token sequences due to the prepended
+                space character. Defaults to None.
+
+        Returns:
+            str: The generated text output.
+
+        Example:
+            .. code-block:: python
+
+                response = frienldi.generate(["Tell me a joke."])
+        """
+        llm_output = {"model": self.model}
+        if self.streaming:
+            if len(prompts) > 1:
+                raise ValueError("Cannot stream results with multiple prompts.")
+
+            generation: Optional[GenerationChunk] = None
+            for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
+                if generation is None:
+                    generation = chunk
+                else:
+                    generation += chunk
+            assert generation is not None
+            return LLMResult(generations=[[generation]], llm_output=llm_output)
+
+        llm_result = super()._generate(prompts, stop, run_manager, **kwargs)
+        llm_result.llm_output = llm_output
+        return llm_result
+
+    async def _agenerate(
+        self,
+        prompts: list[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> LLMResult:
+        """Call out Friendli's completions API asynchronously with k unique prompts.
+
+        Args:
+            prompt (str): The text prompt to generate completion for.
+            stop (Optional[List[str]], optional): When one of the stop phrases appears
+                in the generation result, the API will stop generation. The stop phrases
+                are excluded from the result. If beam search is enabled, all of the
+                active beams should contain the stop phrase to terminate generation.
+                Before checking whether a stop phrase is included in the result, the
+                phrase is converted into tokens. We recommend using stop_tokens because
+                it is clearer. For example, after tokenization, phrases "clear" and
+                " clear" can result in different token sequences due to the prepended
+                space character. Defaults to None.
+
+        Returns:
+            str: The generated text output.
+
+        Example:
+            .. code-block:: python
+
+                response = await frienldi.agenerate(
+                    ["Give me a recipe for the Old Fashioned cocktail."]
+                )
+        """
+        llm_output = {"model": self.model}
+        if self.streaming:
+            if len(prompts) > 1:
+                raise ValueError("Cannot stream results with multiple prompts.")
+
+            generation = None
+            async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
+                if generation is None:
+                    generation = chunk
+                else:
+                    generation += chunk
+            assert generation is not None
+            return LLMResult(generations=[[generation]], llm_output=llm_output)
+
+        llm_result = await super()._agenerate(prompts, stop, run_manager, **kwargs)
+        llm_result.llm_output = llm_output
+        return llm_result
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index c7f2d87c28..276c8412c3 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -379,13 +379,13 @@ extras = ["pyaudio (>=0.2.13)"]
 
 [[package]]
 name = "asteval"
-version = "0.9.31"
+version = "0.9.32"
 description = "Safe, minimalistic evaluator of python expression using ast module"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "asteval-0.9.31-py3-none-any.whl", hash = "sha256:2761750c184d97707c292b62df3b10e330a809a2201721acc435a2b89a114263"},
-    {file = "asteval-0.9.31.tar.gz", hash = "sha256:a2da066b6696dba9835c5f7dec63e0ffb5bd2b4e3bb5f0b9a604aeafb17d833d"},
+    {file = "asteval-0.9.32-py3-none-any.whl", hash = "sha256:4d0da45a15f15eeb88bb53cf4c352591ccb00f00f81f74649fd7084519adc3fe"},
+    {file = "asteval-0.9.32.tar.gz", hash = "sha256:3bef25a973d378fda21c83a38c6292c4d0d94773f49f42073e69dbb19932bb74"},
 ]
 
 [package.extras]
@@ -496,13 +496,13 @@ test = ["flake8 (>=6.1,<7.0)", "uvloop (>=0.15.3)"]
 
 [[package]]
 name = "atlassian-python-api"
-version = "3.41.9"
+version = "3.41.11"
 description = "Python Atlassian REST API Wrapper"
 optional = true
 python-versions = "*"
 files = [
-    {file = "atlassian-python-api-3.41.9.tar.gz", hash = "sha256:3682b9539c1e31fe53b020bdcbce532ca5655fc2cc98cc0e7f9572d92afcc13b"},
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@@ -551,15 +551,26 @@ files = [
 azure-core = ">=1.28.0,<2.0.0"
 isodate = ">=0.6.1,<1.0.0"
 
+[[package]]
+name = "azure-common"
+version = "1.1.28"
+description = "Microsoft Azure Client Library for Python (Common)"
+optional = true
+python-versions = "*"
+files = [
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+    {file = "azure_common-1.1.28-py2.py3-none-any.whl", hash = "sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad"},
+]
+
 [[package]]
 name = "azure-core"
-version = "1.30.0"
+version = "1.30.1"
 description = "Microsoft Azure Core Library for Python"
 optional = true
 python-versions = ">=3.7"
 files = [
-    {file = "azure-core-1.30.0.tar.gz", hash = "sha256:6f3a7883ef184722f6bd997262eddaf80cfe7e5b3e0caaaf8db1695695893d35"},
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+    {file = "azure_core-1.30.1-py3-none-any.whl", hash = "sha256:7c5ee397e48f281ec4dd773d67a0a47a0962ed6fa833036057f9ea067f688e74"},
 ]
 
 [package.dependencies]
@@ -570,6 +581,56 @@ typing-extensions = ">=4.6.0"
 [package.extras]
 aio = ["aiohttp (>=3.0)"]
 
+[[package]]
+name = "azure-mgmt-core"
+version = "1.4.0"
+description = "Microsoft Azure Management Core Library for Python"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "azure-mgmt-core-1.4.0.zip", hash = "sha256:d195208340094f98e5a6661b781cde6f6a051e79ce317caabd8ff97030a9b3ae"},
+    {file = "azure_mgmt_core-1.4.0-py3-none-any.whl", hash = "sha256:81071675f186a585555ef01816f2774d49c1c9024cb76e5720c3c0f6b337bb7d"},
+]
+
+[package.dependencies]
+azure-core = ">=1.26.2,<2.0.0"
+
+[[package]]
+name = "azure-mgmt-storage"
+version = "20.1.0"
+description = "Microsoft Azure Storage Management Client Library for Python"
+optional = true
+python-versions = ">=3.7"
+files = [
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+    {file = "azure_mgmt_storage-20.1.0-py3-none-any.whl", hash = "sha256:afdc830329c674d96a91c963fa03ac81a4e387dfbf9f5a4e823950dc1fe95659"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1,<2.0"
+azure-mgmt-core = ">=1.3.1,<2.0.0"
+msrest = ">=0.6.21"
+
+[[package]]
+name = "azure-storage-blob"
+version = "12.19.1"
+description = "Microsoft Azure Blob Storage Client Library for Python"
+optional = true
+python-versions = ">=3.7"
+files = [
+    {file = "azure-storage-blob-12.19.1.tar.gz", hash = "sha256:13e16ba42fc54ac2c7e8f976062173a5c82b9ec0594728e134aac372965a11b0"},
+    {file = "azure_storage_blob-12.19.1-py3-none-any.whl", hash = "sha256:c5530dc51c21c9564e4eb706cd499befca8819b10dd89716d3fc90d747556243"},
+]
+
+[package.dependencies]
+azure-core = ">=1.28.0,<2.0.0"
+cryptography = ">=2.1.4"
+isodate = ">=0.6.1"
+typing-extensions = ">=4.3.0"
+
+[package.extras]
+aio = ["azure-core[aio] (>=1.28.0,<2.0.0)"]
+
 [[package]]
 name = "babel"
 version = "2.14.0"
@@ -609,34 +670,6 @@ files = [
     {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
 ]
 
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-version = "0.2.1"
-description = "Backport of the standard library zoneinfo module"
-optional = true
-python-versions = ">=3.6"
-files = [
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-]
-
-[package.extras]
-tzdata = ["tzdata"]
-
 [[package]]
 name = "beautifulsoup4"
 version = "4.12.3"
@@ -700,15 +733,474 @@ files = [
     {file = "blinker-1.7.0.tar.gz", hash = "sha256:e6820ff6fa4e4d1d8e2747c2283749c3f547e4fee112b98555cdcdae32996182"},
 ]
 
+[[package]]
+name = "boto3"
+version = "1.34.58"
+description = "The AWS SDK for Python"
+optional = true
+python-versions = ">= 3.8"
+files = [
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+    {file = "boto3-1.34.58.tar.gz", hash = "sha256:09e3d17c718bc938a76774f31bc557b20733c0f5f9135a3e7782b55f3459cbdd"},
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+
+[package.dependencies]
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+jmespath = ">=0.7.1,<2.0.0"
+s3transfer = ">=0.10.0,<0.11.0"
+
+[package.extras]
+crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
+
+[[package]]
+name = "boto3-stubs"
+version = "1.34.58"
+description = "Type annotations for boto3 1.34.58 generated with mypy-boto3-builder 7.23.2"
+optional = true
+python-versions = ">=3.8"
+files = [
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+
+[package.dependencies]
+botocore-stubs = "*"
+types-s3transfer = "*"
+typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.12\""}
+
+[package.extras]
+accessanalyzer = ["mypy-boto3-accessanalyzer (>=1.34.0,<1.35.0)"]
+account = ["mypy-boto3-account (>=1.34.0,<1.35.0)"]
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+acm-pca = ["mypy-boto3-acm-pca (>=1.34.0,<1.35.0)"]
+alexaforbusiness = ["mypy-boto3-alexaforbusiness (>=1.34.0,<1.35.0)"]
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 [[package]]
@@ -765,13 +1257,13 @@ graph = ["gremlinpython (==3.4.6)"]
 
 [[package]]
 name = "cassio"
-version = "0.1.4"
+version = "0.1.5"
 description = "A framework-agnostic Python library to seamlessly integrate Apache Cassandra(R) with ML/LLM/genAI workloads."
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@@ -1063,13 +1555,13 @@ types = ["chardet (>=5.1.0)", "mypy", "pytest", "pytest-cov", "pytest-dependency
 
 [[package]]
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@@ -1125,6 +1617,20 @@ traitlets = ">=4"
 [package.extras]
 test = ["pytest"]
 
+[[package]]
+name = "commonmark"
+version = "0.9.1"
+description = "Python parser for the CommonMark Markdown spec"
+optional = true
+python-versions = "*"
+files = [
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+[package.extras]
+test = ["flake8 (==3.7.8)", "hypothesis (==3.55.3)"]
+
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 name = "contourpy"
 version = "1.1.0"
@@ -1260,63 +1766,63 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "coverage"
-version = "7.4.1"
+version = "7.4.3"
 description = "Code coverage measurement for Python"
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 [package.dependencies]
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 [[package]]
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+]
+
+[package.dependencies]
+cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}
 
 [package.extras]
 docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
-docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
+docstest = ["pyenchant (>=1.6.11)", "readme-renderer", "sphinxcontrib-spelling (>=4.0.1)"]
 nox = ["nox"]
-pep8test = ["black", "check-sdist", "mypy", "ruff"]
+pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1396,26 +1911,6 @@ files = [
 docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
 tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
-[[package]]
-name = "databricks-cli"
-version = "0.18.0"
-description = "A command line interface for Databricks"
-optional = true
-python-versions = ">=3.7"
-files = [
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-]
-
-[package.dependencies]
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-oauthlib = ">=3.1.0"
-pyjwt = ">=1.7.0"
-requests = ">=2.17.3"
-six = ">=1.10.0"
-tabulate = ">=0.7.7"
-urllib3 = ">=1.26.7,<3"
-
 [[package]]
 name = "databricks-vectorsearch"
 version = "0.21"
@@ -1459,26 +1954,26 @@ typing-inspect = ">=0.4.0,<1"
 
 [[package]]
 name = "datasets"
-version = "2.16.1"
+version = "2.18.0"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = true
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
 aiohttp = "*"
-dill = ">=0.3.0,<0.3.8"
+dill = ">=0.3.0,<0.3.9"
 filelock = "*"
-fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]}
+fsspec = {version = ">=2023.1.0,<=2024.2.0", extras = ["http"]}
 huggingface-hub = ">=0.19.4"
 multiprocess = "*"
 numpy = ">=1.17"
 packaging = "*"
 pandas = "*"
-pyarrow = ">=8.0.0"
+pyarrow = ">=12.0.0"
 pyarrow-hotfix = "*"
 pyyaml = ">=5.1"
 requests = ">=2.19.0"
@@ -1486,46 +1981,50 @@ tqdm = ">=4.62.1"
 xxhash = "*"
 
 [package.extras]
-apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
+apache-beam = ["apache-beam (>=2.26.0)"]
 audio = ["librosa", "soundfile (>=0.12.1)"]
 benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
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+dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
 jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
 metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
-quality = ["ruff (>=0.1.5)"]
+quality = ["ruff (>=0.3.0)"]
 s3 = ["s3fs"]
 tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
 tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
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+tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
 torch = ["torch"]
 vision = ["Pillow (>=6.2.1)"]
 
 [[package]]
 name = "debugpy"
-version = "1.8.0"
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 [[package]]
@@ -1583,13 +2082,13 @@ packaging = "*"
 
 [[package]]
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-version = "0.3.1"
+version = "0.3.2"
 description = "Python utilities to work with the Docugami Markup Language (DGML) format."
 optional = true
 python-versions = ">=3.8.1,<4.0"
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 [package.dependencies]
@@ -1598,17 +2097,18 @@ tabulate = ">=0.9.0,<0.10.0"
 
 [[package]]
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-version = "0.3.7"
+version = "0.3.8"
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 [package.extras]
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 [[package]]
 name = "distro"
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-doq = ["aioquic (>=0.9.20)"]
-idna = ["idna (>=2.1)"]
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+idna = ["idna (>=3.6)"]
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 wmi = ["wmi (>=1.5.1)"]
 
 [[package]]
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 [[package]]
@@ -1734,13 +2242,13 @@ develop = ["aiohttp", "furo", "mock", "pytest", "pytest-asyncio", "pytest-cov",
 
 [[package]]
 name = "elasticsearch"
-version = "8.12.0"
+version = "8.12.1"
 description = "Python client for Elasticsearch"
 optional = true
 python-versions = ">=3.7"
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@@ -1750,6 +2258,21 @@ elastic-transport = ">=8,<9"
 async = ["aiohttp (>=3,<4)"]
 requests = ["requests (>=2.4.0,<3.0.0)"]
 
+[[package]]
+name = "email-validator"
+version = "2.1.1"
+description = "A robust email address syntax and deliverability validation library."
+optional = true
+python-versions = ">=3.8"
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+idna = ">=2.0.0"
+
 [[package]]
 name = "entrypoints"
 version = "0.4"
@@ -1801,76 +2324,100 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
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 [package.extras]
@@ -2105,6 +2652,43 @@ files = [
 [package.dependencies]
 python-dateutil = ">=2.7"
 
+[[package]]
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+mllib = ["accelerate (==0.21.0)", "datasets (==2.16.0)", "einops (>=0.6.1,<0.7.0)", "h5py (>=3.9.0,<4.0.0)", "peft (==0.6.0)", "transformers (==4.36.2)"]
+
 [[package]]
 name = "frozenlist"
 version = "1.4.1"
@@ -2193,18 +2777,17 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2023.10.0"
+version = "2024.2.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.8"
 files = [
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 aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
-requests = {version = "*", optional = true, markers = "extra == \"http\""}
 
 [package.extras]
 abfs = ["adlfs"]
@@ -2221,7 +2804,7 @@ github = ["requests"]
 gs = ["gcsfs"]
 gui = ["panel"]
 hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
 libarchive = ["libarchive-c"]
 oci = ["ocifs"]
 s3 = ["s3fs"]
@@ -2279,30 +2862,30 @@ smmap = ">=3.0.1,<6"
 
 [[package]]
 name = "gitpython"
-version = "3.1.41"
+version = "3.1.42"
 description = "GitPython is a Python library used to interact with Git repositories"
 optional = true
 python-versions = ">=3.7"
 files = [
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 gitdb = ">=4.0.1,<5"
 
 [package.extras]
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+test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar"]
 
 [[package]]
 name = "google-api-core"
-version = "2.16.2"
+version = "2.17.1"
 description = "Google API client core library"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2326,13 +2909,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
 
 [[package]]
 name = "google-auth"
-version = "2.27.0"
+version = "2.28.1"
 description = "Google Authentication Library"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -2349,18 +2932,18 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
 
 [[package]]
 name = "google-cloud-documentai"
-version = "2.23.0"
+version = "2.24.1"
 description = "Google Cloud Documentai API client library"
 optional = true
 python-versions = ">=3.7"
 files = [
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 [package.dependencies]
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-google-auth = ">=2.14.1,<3.0.0dev"
+google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
+google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
 
@@ -2411,13 +2994,13 @@ websockets = ["websockets (>=10,<12)"]
 
 [[package]]
 name = "gradientai"
-version = "1.7.0"
+version = "1.8.0"
 description = "Gradient AI API"
 optional = true
 python-versions = ">=3.8.1,<4.0.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2510,151 +3093,151 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "grpcio"
-version = "1.60.1"
+version = "1.62.0"
 description = "HTTP/2-based RPC framework"
 optional = true
 python-versions = ">=3.7"
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+
+[package.dependencies]
+grpcio = ">=1.62.0"
 protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
@@ -2776,13 +3359,13 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.20.3"
+version = "0.21.4"
 description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 ]
 
 [package.dependencies]
@@ -2799,11 +3382,12 @@ all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi",
 cli = ["InquirerPy (==0.3.4)"]
 dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
 fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
 inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
 quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
 tensorflow = ["graphviz", "pydot", "tensorflow"]
 testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["torch"]
+torch = ["safetensors", "torch"]
 typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
 
 [[package]]
@@ -2852,13 +3436,13 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.1"
+version = "6.1.3"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2866,7 +3450,7 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
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+testing = ["jaraco.collections", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
 
 [[package]]
 name = "inflection"
@@ -2890,15 +3474,32 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "injector"
+version = "0.21.0"
+description = "Injector - Python dependency injection framework, inspired by Guice"
+optional = true
+python-versions = "*"
+files = [
+    {file = "injector-0.21.0-py2.py3-none-any.whl", hash = "sha256:3942c5e4c501d390d5ad1b8d7d486ef93b844934afeeb17211acb6c4ca29eeb4"},
+    {file = "injector-0.21.0.tar.gz", hash = "sha256:919eb6b9f96f40bf98fda34c79762b217bd1544d9adc35805ff2948e92356c9c"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=3.7.4", markers = "python_version < \"3.9\""}
+
+[package.extras]
+dev = ["black (==23.3.0)", "build (==0.10.0)", "check-manifest (==0.49)", "click (==8.1.3)", "coverage (==7.2.7)", "exceptiongroup (==1.1.1)", "iniconfig (==2.0.0)", "mypy (==1.4.1)", "mypy-extensions (==1.0.0)", "packaging (==23.1)", "pathspec (==0.11.1)", "platformdirs (==3.8.0)", "pluggy (==1.2.0)", "pyproject-hooks (==1.0.0)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "tomli (==2.0.1)", "typing-extensions (==4.7.0)"]
+
 [[package]]
 name = "ipykernel"
-version = "6.29.2"
+version = "6.29.3"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ipykernel-6.29.2-py3-none-any.whl", hash = "sha256:50384f5c577a260a1d53f1f59a828c7266d321c9b7d00d345693783f66616055"},
-    {file = "ipykernel-6.29.2.tar.gz", hash = "sha256:3bade28004e3ff624ed57974948116670604ac5f676d12339693f3142176d3f0"},
+    {file = "ipykernel-6.29.3-py3-none-any.whl", hash = "sha256:5aa086a4175b0229d4eca211e181fb473ea78ffd9869af36ba7694c947302a21"},
+    {file = "ipykernel-6.29.3.tar.gz", hash = "sha256:e14c250d1f9ea3989490225cc1a542781b095a18a19447fcf2b5eaf7d0ac5bd2"},
 ]
 
 [package.dependencies]
@@ -2921,7 +3522,7 @@ cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
 docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.4)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
@@ -2964,21 +3565,21 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa
 
 [[package]]
 name = "ipywidgets"
-version = "8.1.1"
+version = "8.1.2"
 description = "Jupyter interactive widgets"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"},
-    {file = "ipywidgets-8.1.1.tar.gz", hash = "sha256:40211efb556adec6fa450ccc2a77d59ca44a060f4f9f136833df59c9f538e6e8"},
+    {file = "ipywidgets-8.1.2-py3-none-any.whl", hash = "sha256:bbe43850d79fb5e906b14801d6c01402857996864d1e5b6fa62dd2ee35559f60"},
+    {file = "ipywidgets-8.1.2.tar.gz", hash = "sha256:d0b9b41e49bae926a866e613a39b0f0097745d2b9f1f3dd406641b4a57ec42c9"},
 ]
 
 [package.dependencies]
 comm = ">=0.1.3"
 ipython = ">=6.1.0"
-jupyterlab-widgets = ">=3.0.9,<3.1.0"
+jupyterlab-widgets = ">=3.0.10,<3.1.0"
 traitlets = ">=4.3.1"
-widgetsnbextension = ">=4.0.9,<4.1.0"
+widgetsnbextension = ">=4.0.10,<4.1.0"
 
 [package.extras]
 test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
@@ -3178,13 +3779,13 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.9.14"
+version = "0.9.22"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
 files = [
-    {file = "json5-0.9.14-py2.py3-none-any.whl", hash = "sha256:740c7f1b9e584a468dbb2939d8d458db3427f2c93ae2139d05f47e453eae964f"},
-    {file = "json5-0.9.14.tar.gz", hash = "sha256:9ed66c3a6ca3510a976a9ef9b8c0787de24802724ab1860bc0153c7fdd589b02"},
+    {file = "json5-0.9.22-py3-none-any.whl", hash = "sha256:6621007c70897652f8b5d03885f732771c48d1925591ad989aa80c7e0e5ad32f"},
+    {file = "json5-0.9.22.tar.gz", hash = "sha256:b729bde7650b2196a35903a597d2b704b8fdf8648bfb67368cfb79f1174a17bd"},
 ]
 
 [package.extras]
@@ -3386,13 +3987,13 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.2"
+version = "2.2.4"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter-lsp-2.2.2.tar.gz", hash = "sha256:256d24620542ae4bba04a50fc1f6ffe208093a07d8e697fea0a8d1b8ca1b7e5b"},
-    {file = "jupyter_lsp-2.2.2-py3-none-any.whl", hash = "sha256:3b95229e4168355a8c91928057c1621ac3510ba98b2a925e82ebd77f078b1aa5"},
+    {file = "jupyter-lsp-2.2.4.tar.gz", hash = "sha256:5e50033149344065348e688608f3c6d654ef06d9856b67655bd7b6bac9ee2d59"},
+    {file = "jupyter_lsp-2.2.4-py3-none-any.whl", hash = "sha256:da61cb63a16b6dff5eac55c2699cc36eac975645adee02c41bdfc03bf4802e77"},
 ]
 
 [package.dependencies]
@@ -3401,13 +4002,13 @@ jupyter-server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.12.5"
+version = "2.13.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_server-2.12.5-py3-none-any.whl", hash = "sha256:184a0f82809a8522777cfb6b760ab6f4b1bb398664c5860a27cec696cb884923"},
-    {file = "jupyter_server-2.12.5.tar.gz", hash = "sha256:0edb626c94baa22809be1323f9770cf1c00a952b17097592e40d03e6a3951689"},
+    {file = "jupyter_server-2.13.0-py3-none-any.whl", hash = "sha256:77b2b49c3831fbbfbdb5048cef4350d12946191f833a24e5f83e5f8f4803e97b"},
+    {file = "jupyter_server-2.13.0.tar.gz", hash = "sha256:c80bfb049ea20053c3d9641c2add4848b38073bf79f1729cea1faed32fc1c78e"},
 ]
 
 [package.dependencies]
@@ -3433,7 +4034,7 @@ websocket-client = "*"
 
 [package.extras]
 docs = ["ipykernel", "jinja2", "jupyter-client", "jupyter-server", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"]
-test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.4)", "pytest-timeout", "requests"]
+test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"]
 
 [[package]]
 name = "jupyter-server-terminals"
@@ -3456,13 +4057,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.0.12"
+version = "4.0.13"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.0.12-py3-none-any.whl", hash = "sha256:53f132480e5f6564f4e20d1b5ed4e8b7945952a2decd5bdfa43760b1b536c99d"},
-    {file = "jupyterlab-4.0.12.tar.gz", hash = "sha256:965d92efa82a538ed70ccb3968d9aabba788840da882e13d7b061780cdedc3b7"},
+    {file = "jupyterlab-4.0.13-py3-none-any.whl", hash = "sha256:3aa81c364d50cc715f6c2935674c7cca8936bd74b5898d6ad6598aef08c43808"},
+    {file = "jupyterlab-4.0.13.tar.gz", hash = "sha256:e8950f94e0d8ab8aa7d8166b19db27f4d4fea5000ee04ba372c50116e98fb733"},
 ]
 
 [package.dependencies]
@@ -3482,9 +4083,9 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.1.6)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.2.0)"]
 docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-tornasync", "sphinx (>=1.8,<7.2.0)", "sphinx-copybutton"]
-docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.0.2)", "scipy (==1.10.1)", "vega-datasets (==0.9.0)"]
+docs-screenshots = ["altair (==5.0.1)", "ipython (==8.14.0)", "ipywidgets (==8.0.6)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.0.post0)", "matplotlib (==3.7.1)", "nbconvert (>=7.0.0)", "pandas (==2.2.0)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
 
 [[package]]
@@ -3500,13 +4101,13 @@ files = [
 
 [[package]]
 name = "jupyterlab-server"
-version = "2.25.2"
+version = "2.25.3"
 description = "A set of server components for JupyterLab and JupyterLab like applications."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab_server-2.25.2-py3-none-any.whl", hash = "sha256:5b1798c9cc6a44f65c757de9f97fc06fc3d42535afbf47d2ace5e964ab447aaf"},
-    {file = "jupyterlab_server-2.25.2.tar.gz", hash = "sha256:bd0ec7a99ebcedc8bcff939ef86e52c378e44c2707e053fcd81d046ce979ee63"},
+    {file = "jupyterlab_server-2.25.3-py3-none-any.whl", hash = "sha256:c48862519fded9b418c71645d85a49b2f0ec50d032ba8316738e9276046088c1"},
+    {file = "jupyterlab_server-2.25.3.tar.gz", hash = "sha256:846f125a8a19656611df5b03e5912c8393cea6900859baa64fa515eb64a8dc40"},
 ]
 
 [package.dependencies]
@@ -3526,13 +4127,13 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
 
 [[package]]
 name = "jupyterlab-widgets"
-version = "3.0.9"
+version = "3.0.10"
 description = "Jupyter interactive widgets for JupyterLab"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "jupyterlab_widgets-3.0.9-py3-none-any.whl", hash = "sha256:3cf5bdf5b897bf3bccf1c11873aa4afd776d7430200f765e0686bd352487b58d"},
-    {file = "jupyterlab_widgets-3.0.9.tar.gz", hash = "sha256:6005a4e974c7beee84060fdfba341a3218495046de8ae3ec64888e5fe19fdb4c"},
+    {file = "jupyterlab_widgets-3.0.10-py3-none-any.whl", hash = "sha256:dd61f3ae7a5a7f80299e14585ce6cf3d6925a96c9103c978eda293197730cb64"},
+    {file = "jupyterlab_widgets-3.0.10.tar.gz", hash = "sha256:04f2ac04976727e4f9d0fa91cdc2f1ab860f965e504c29dbd6a65c882c9d04c0"},
 ]
 
 [[package]]
@@ -3695,16 +4296,17 @@ url = "../text-splitters"
 
 [[package]]
 name = "langsmith"
-version = "0.1.1"
+version = "0.1.22"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = ">=3.8.1,<4.0"
 files = [
-    {file = "langsmith-0.1.1-py3-none-any.whl", hash = "sha256:10ff2b977a41e3f6351d1a4239d9bd57af0547aa909e839d2791e16cc197a6f9"},
-    {file = "langsmith-0.1.1.tar.gz", hash = "sha256:09df0c2ca9085105f97a4e4f281b083e312c99d162f3fe2b2d5eefd5c3692e60"},
+    {file = "langsmith-0.1.22-py3-none-any.whl", hash = "sha256:b877d302bd4cf7c79e9e6e24bedf669132abf0659143390a29350eda0945544f"},
+    {file = "langsmith-0.1.22.tar.gz", hash = "sha256:2921ae2297c2fb23baa2641b9cf416914ac7fd65f4a9dd5a573bc30efb54b693"},
 ]
 
 [package.dependencies]
+orjson = ">=3.9.14,<4.0.0"
 pydantic = ">=1,<3"
 requests = ">=2,<3"
 
@@ -3851,30 +4453,6 @@ importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
 docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
 testing = ["coverage", "pyyaml"]
 
-[[package]]
-name = "markdown-it-py"
-version = "3.0.0"
-description = "Python port of markdown-it. Markdown parsing, done right!"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"},
-    {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"},
-]
-
-[package.dependencies]
-mdurl = ">=0.1,<1.0"
-
-[package.extras]
-benchmarking = ["psutil", "pytest", "pytest-benchmark"]
-code-style = ["pre-commit (>=3.0,<4.0)"]
-compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"]
-linkify = ["linkify-it-py (>=1,<3)"]
-plugins = ["mdit-py-plugins"]
-profiling = ["gprof2dot"]
-rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
-testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
-
 [[package]]
 name = "markdownify"
 version = "0.11.6"
@@ -3961,78 +4539,77 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.20.2"
+version = "3.21.1"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "marshmallow-3.20.2-py3-none-any.whl", hash = "sha256:c21d4b98fee747c130e6bc8f45c4b3199ea66bc00c12ee1f639f0aeca034d5e9"},
-    {file = "marshmallow-3.20.2.tar.gz", hash = "sha256:4c1daff273513dc5eb24b219a8035559dc573c8f322558ef85f5438ddd1236dd"},
+    {file = "marshmallow-3.21.1-py3-none-any.whl", hash = "sha256:f085493f79efb0644f270a9bf2892843142d80d7174bbbd2f3713f2a589dc633"},
+    {file = "marshmallow-3.21.1.tar.gz", hash = "sha256:4e65e9e0d80fc9e609574b9983cf32579f305c718afb30d7233ab818571768c3"},
 ]
 
 [package.dependencies]
 packaging = ">=17.0"
 
 [package.extras]
-dev = ["pre-commit (>=2.4,<4.0)", "pytest", "pytz", "simplejson", "tox"]
-docs = ["alabaster (==0.7.15)", "autodocsumm (==0.2.12)", "sphinx (==7.2.6)", "sphinx-issues (==3.0.1)", "sphinx-version-warning (==1.1.2)"]
-lint = ["pre-commit (>=2.4,<4.0)"]
+dev = ["marshmallow[tests]", "pre-commit (>=3.5,<4.0)", "tox"]
+docs = ["alabaster (==0.7.16)", "autodocsumm (==0.2.12)", "sphinx (==7.2.6)", "sphinx-issues (==4.0.0)", "sphinx-version-warning (==1.1.2)"]
 tests = ["pytest", "pytz", "simplejson"]
 
 [[package]]
 name = "matplotlib"
-version = "3.7.4"
+version = "3.7.5"
 description = "Python plotting package"
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "matplotlib-3.7.4-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:b71079239bd866bf56df023e5146de159cb0c7294e508830901f4d79e2d89385"},
-    {file = "matplotlib-3.7.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:bf91a42f6274a64cb41189120b620c02e574535ff6671fa836cade7701b06fbd"},
-    {file = "matplotlib-3.7.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f757e8b42841d6add0cb69b42497667f0d25a404dcd50bd923ec9904e38414c4"},
-    {file = "matplotlib-3.7.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e4dfee00aa4bd291e08bb9461831c26ce0da85ca9781bb8794f2025c6e925281"},
-    {file = "matplotlib-3.7.4-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3640f33632beb3993b698b1be9d1c262b742761d6101f3c27b87b2185d25c875"},
-    {file = "matplotlib-3.7.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff539c4a17ecdf076ed808ee271ffae4a30dcb7e157b99ccae2c837262c07db6"},
-    {file = "matplotlib-3.7.4-cp310-cp310-win32.whl", hash = "sha256:24b8f28af3e766195c09b780b15aa9f6710192b415ae7866b9c03dee7ec86370"},
-    {file = "matplotlib-3.7.4-cp310-cp310-win_amd64.whl", hash = "sha256:3fa193286712c3b6c3cfa5fe8a6bb563f8c52cc750006c782296e0807ce5e799"},
-    {file = "matplotlib-3.7.4-cp311-cp311-macosx_10_12_universal2.whl", hash = "sha256:b167f54cb4654b210c9624ec7b54e2b3b8de68c93a14668937e7e53df60770ec"},
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 [package.dependencies]
@@ -4061,17 +4638,6 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
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-
 [[package]]
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 version = "3.0.2"
@@ -4085,35 +4651,34 @@ files = [
 
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+version = "2.11.1"
 description = "MLflow: A Platform for ML Development and Productionization"
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 python-versions = ">=3.8"
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 cloudpickle = "<4"
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 entrypoints = "<1"
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 packaging = "<24"
 protobuf = ">=3.12.0,<5"
-pytz = "<2024"
+pytz = "<2025"
 pyyaml = ">=5.1,<7"
 requests = ">=2.17.3,<3"
 sqlparse = ">=0.4.0,<1"
 
 [package.extras]
 aliyun-oss = ["aliyunstoreplugin"]
-databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "botocore (>1.34)", "google-cloud-storage (>=1.30.0)"]
-extras = ["azureml-core (>=1.2.0)", "boto3", "botocore", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1)", "mlserver-mlflow (>=1.2.0,!=1.3.1)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
-gateway = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "pydantic (>=1.0,<3)", "slowapi (<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
-genai = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "pydantic (>=1.0,<3)", "slowapi (<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
+databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "botocore", "google-cloud-storage (>=1.30.0)"]
+extras = ["azureml-core (>=1.2.0)", "boto3", "botocore", "google-cloud-storage (>=1.30.0)", "kubernetes", "mlserver (>=1.2.0,!=1.3.1,<1.4.0)", "mlserver-mlflow (>=1.2.0,!=1.3.1,<1.4.0)", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
+gateway = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "pydantic (>=1.0,<3)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
+genai = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "pydantic (>=1.0,<3)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<1)"]
 sqlserver = ["mlflow-dbstore"]
 xethub = ["mlflow-xethub"]
 
@@ -4160,22 +4725,43 @@ tests = ["pytest (>=4.6)"]
 
 [[package]]
 name = "msal"
-version = "1.26.0"
+version = "1.27.0"
 description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect."
 optional = true
 python-versions = ">=2.7"
 files = [
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-    {file = "msal-1.26.0.tar.gz", hash = "sha256:224756079fe338be838737682b49f8ebc20a87c1c5eeaf590daae4532b83de15"},
+    {file = "msal-1.27.0-py2.py3-none-any.whl", hash = "sha256:572d07149b83e7343a85a3bcef8e581167b4ac76befcbbb6eef0c0e19643cdc0"},
+    {file = "msal-1.27.0.tar.gz", hash = "sha256:3109503c038ba6b307152b0e8d34f98113f2e7a78986e28d0baf5b5303afda52"},
 ]
 
 [package.dependencies]
-cryptography = ">=0.6,<44"
+cryptography = ">=0.6,<45"
 PyJWT = {version = ">=1.0.0,<3", extras = ["crypto"]}
 requests = ">=2.0.0,<3"
 
 [package.extras]
-broker = ["pymsalruntime (>=0.13.2,<0.14)"]
+broker = ["pymsalruntime (>=0.13.2,<0.15)"]
+
+[[package]]
+name = "msrest"
+version = "0.7.1"
+description = "AutoRest swagger generator Python client runtime."
+optional = true
+python-versions = ">=3.6"
+files = [
+    {file = "msrest-0.7.1-py3-none-any.whl", hash = "sha256:21120a810e1233e5e6cc7fe40b474eeb4ec6f757a15d7cf86702c369f9567c32"},
+    {file = "msrest-0.7.1.zip", hash = "sha256:6e7661f46f3afd88b75667b7187a92829924446c7ea1d169be8c4bb7eeb788b9"},
+]
+
+[package.dependencies]
+azure-core = ">=1.24.0"
+certifi = ">=2017.4.17"
+isodate = ">=0.6.0"
+requests = ">=2.16,<3.0"
+requests-oauthlib = ">=0.5.0"
+
+[package.extras]
+async = ["aiodns", "aiohttp (>=3.0)"]
 
 [[package]]
 name = "multidict"
@@ -4278,31 +4864,27 @@ files = [
 
 [[package]]
 name = "multiprocess"
-version = "0.70.15"
+version = "0.70.16"
 description = "better multiprocessing and multithreading in Python"
 optional = true
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "multiprocess-0.70.16-py312-none-any.whl", hash = "sha256:fc0544c531920dde3b00c29863377f87e1632601092ea2daca74e4beb40faa2e"},
+    {file = "multiprocess-0.70.16-py38-none-any.whl", hash = "sha256:a71d82033454891091a226dfc319d0cfa8019a4e888ef9ca910372a446de4435"},
+    {file = "multiprocess-0.70.16-py39-none-any.whl", hash = "sha256:a0bafd3ae1b732eac64be2e72038231c1ba97724b60b09400d68f229fcc2fbf3"},
+    {file = "multiprocess-0.70.16.tar.gz", hash = "sha256:161af703d4652a0e1410be6abccecde4a7ddffd19341be0a7011b94aeb171ac1"},
 ]
 
 [package.dependencies]
-dill = ">=0.3.7"
+dill = ">=0.3.8"
 
 [[package]]
 name = "mwcli"
@@ -4436,6 +5018,20 @@ install-types = ["pip"]
 python2 = ["typed-ast (>=1.4.0,<2)"]
 reports = ["lxml"]
 
+[[package]]
+name = "mypy-boto3-s3"
+version = "1.34.14"
+description = "Type annotations for boto3.S3 1.34.14 service generated with mypy-boto3-builder 7.21.0"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "mypy-boto3-s3-1.34.14.tar.gz", hash = "sha256:71c39ab0623cdb442d225b71c1783f6a513cff4c4a13505a2efbb2e3aff2e965"},
+    {file = "mypy_boto3_s3-1.34.14-py3-none-any.whl", hash = "sha256:f9669ecd182d5bf3532f5f2dcc5e5237776afe157ad5a0b37b26d6bec5fcc432"},
+]
+
+[package.dependencies]
+typing-extensions = {version = ">=4.1.0", markers = "python_version < \"3.12\""}
+
 [[package]]
 name = "mypy-extensions"
 version = "1.0.0"
@@ -4486,13 +5082,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.16.0"
-description = "Converting Jupyter Notebooks"
+version = "7.16.2"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "nbconvert-7.16.0.tar.gz", hash = "sha256:813e6553796362489ae572e39ba1bff978536192fb518e10826b0e8cadf03ec8"},
+    {file = "nbconvert-7.16.2-py3-none-any.whl", hash = "sha256:0c01c23981a8de0220255706822c40b751438e32467d6a686e26be08ba784382"},
+    {file = "nbconvert-7.16.2.tar.gz", hash = "sha256:8310edd41e1c43947e4ecf16614c61469ebc024898eb808cce0999860fc9fb16"},
 ]
 
 [package.dependencies]
@@ -4607,18 +5203,18 @@ twitter = ["twython"]
 
 [[package]]
 name = "notebook"
-version = "7.0.7"
+version = "7.0.8"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "notebook-7.0.8-py3-none-any.whl", hash = "sha256:7f421b3fd46a17d91830e724b94e8e9ae922af152ebfd48b1e13ae4a07d8193c"},
+    {file = "notebook-7.0.8.tar.gz", hash = "sha256:3957ecd956056b0014677afc76d3bb44c2d2f29649f87b24d13606ff1d18938f"},
 ]
 
 [package.dependencies]
 jupyter-server = ">=2.4.0,<3"
-jupyterlab = ">=4.0.2,<5"
+jupyterlab = ">=4.0.2,<4.1"
 jupyterlab-server = ">=2.22.1,<3"
 notebook-shim = ">=0.2,<0.3"
 tornado = ">=6.2.0"
@@ -4630,13 +5226,13 @@ test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4
 
 [[package]]
 name = "notebook-shim"
-version = "0.2.3"
+version = "0.2.4"
 description = "A shim layer for notebook traits and config"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "notebook_shim-0.2.3-py3-none-any.whl", hash = "sha256:a83496a43341c1674b093bfcebf0fe8e74cbe7eda5fd2bbc56f8e39e1486c0c7"},
-    {file = "notebook_shim-0.2.3.tar.gz", hash = "sha256:f69388ac283ae008cd506dda10d0288b09a017d822d5e8c7129a152cbd3ce7e9"},
+    {file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"},
+    {file = "notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb"},
 ]
 
 [package.dependencies]
@@ -4756,20 +5352,20 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
 [[package]]
 name = "oci"
-version = "2.121.0"
+version = "2.124.0"
 description = "Oracle Cloud Infrastructure Python SDK"
 optional = true
 python-versions = "*"
 files = [
-    {file = "oci-2.121.0-py3-none-any.whl", hash = "sha256:6bfde96aaeaed08a0a0d21c81a79036d448face68f40414864231d62b93783f1"},
-    {file = "oci-2.121.0.tar.gz", hash = "sha256:d040678906867babf16976fa2db5b761dee62a297ddef87ed1101b51488b82ea"},
+    {file = "oci-2.124.0-py3-none-any.whl", hash = "sha256:7b757c825081dd582b359a1e23001b9e46810e592ec2e0135fdd9572e9e0d477"},
+    {file = "oci-2.124.0.tar.gz", hash = "sha256:4af009929976235003b86fa2ec8f66e9d3aabc2084722278bd22c9900a79ec29"},
 ]
 
 [package.dependencies]
 certifi = "*"
 circuitbreaker = ">=1.3.1,<2.0.0"
-cryptography = ">=3.2.1,<42.0.0"
-pyOpenSSL = ">=17.5.0,<24.0.0"
+cryptography = ">=3.2.1,<43.0.0"
+pyOpenSSL = ">=17.5.0,<25.0.0"
 python-dateutil = ">=2.5.3,<3.0.0"
 pytz = ">=2016.10"
 
@@ -4791,36 +5387,36 @@ requests = "*"
 
 [[package]]
 name = "onnxruntime"
-version = "1.17.0"
+version = "1.17.1"
 description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
 optional = true
 python-versions = "*"
 files = [
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 [package.dependencies]
@@ -4833,13 +5429,13 @@ sympy = "*"
 
 [[package]]
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-version = "1.11.1"
+version = "1.13.3"
 description = "The official Python library for the openai API"
 optional = false
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 [package.dependencies]
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     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
 ]
 
 [[package]]
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 python-versions = ">=3.8"
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 pytz = ">=2020.1"
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 testing = ["docopt", "pytest (<6.0.0)"]
 
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 version = "20221105"
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 [package.dependencies]
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 typing-extensions = ">=4.2.0"
 
 [package.extras]
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+version = "1.23.22"
 description = "MuPDF shared libraries for PyMuPDF."
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 python-versions = ">=3.8"
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 [[package]]
 name = "pyopenssl"
-version = "23.3.0"
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 description = "Python wrapper module around the OpenSSL library"
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
-cryptography = ">=41.0.5,<42"
+cryptography = ">=41.0.5,<43"
 
 [package.extras]
 docs = ["sphinx (!=5.2.0,!=5.2.0.post0,!=7.2.5)", "sphinx-rtd-theme"]
@@ -6131,13 +6739,13 @@ test = ["flaky", "pretend", "pytest (>=3.0.1)"]
 
 [[package]]
 name = "pyparsing"
-version = "3.1.1"
+version = "3.1.2"
 description = "pyparsing module - Classes and methods to define and execute parsing grammars"
 optional = true
 python-versions = ">=3.6.8"
 files = [
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 ]
 
 [package.extras]
@@ -6166,24 +6774,24 @@ image = ["Pillow (>=8.0.0)"]
 
 [[package]]
 name = "pypdfium2"
-version = "4.26.0"
+version = "4.27.0"
 description = "Python bindings to PDFium"
 optional = true
 python-versions = ">= 3.6"
 files = [
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 ]
 
 [[package]]
@@ -6246,12 +6854,12 @@ files = [
 
 [[package]]
 name = "pyspark"
-version = "3.5.0"
+version = "3.5.1"
 description = "Apache Spark Python API"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -6399,13 +7007,13 @@ watchdog = ">=2.0.0"
 
 [[package]]
 name = "python-dateutil"
-version = "2.8.2"
+version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -6438,13 +7046,13 @@ files = [
 
 [[package]]
 name = "python-jsonschema-objects"
-version = "0.5.2"
+version = "0.5.4"
 description = "An object wrapper for JSON Schema definitions"
 optional = true
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -6455,13 +7063,13 @@ six = ">=1.5.2"
 
 [[package]]
 name = "pytz"
-version = "2023.4"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -6489,17 +7097,17 @@ files = [
 
 [[package]]
 name = "pywinpty"
-version = "2.0.12"
+version = "2.0.13"
 description = "Pseudo terminal support for Windows from Python."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
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 [package.dependencies]
@@ -7663,15 +8289,33 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
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+optional = true
+python-versions = ">=3.7"
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+
+[package.extras]
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+
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 [[package]]
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+    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:64c86e5e068ac8b19204419ed8ca90f9d25db20578f5881e337d203b314f4104"},
+    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:de19c4dc503c612847edf833c82e9f73cd79926a384af9d801dcf93f110cea4e"},
+    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ea09acd2fe3324174063d61ad620dec3bcf042b495515f27f638270a7d466e8b"},
+    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:cf27fd43472e07b57cf420eee1e814549203d56de00b5af8659cb99885472f1f"},
+    {file = "tokenizers-0.15.2-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:7ca22bd897537a0080521445d91a58886c8c04084a6a19e6c78c586e0cfa92a5"},
+    {file = "tokenizers-0.15.2.tar.gz", hash = "sha256:e6e9c6e019dd5484be5beafc775ae6c925f4c69a3487040ed09b45e13df2cb91"},
 ]
 
 [package.dependencies]
@@ -8123,13 +8763,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.1"
+version = "4.66.2"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
-    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
+    {file = "tqdm-4.66.2-py3-none-any.whl", hash = "sha256:1ee4f8a893eb9bef51c6e35730cebf234d5d0b6bd112b0271e10ed7c24a02bd9"},
+    {file = "tqdm-4.66.2.tar.gz", hash = "sha256:6cd52cdf0fef0e0f543299cfc96fec90d7b8a7e88745f411ec33eb44d5ed3531"},
 ]
 
 [package.dependencies]
@@ -8336,6 +8976,17 @@ dev = ["autoflake (>=1.3.1,<2.0.0)", "flake8 (>=3.8.3,<4.0.0)", "pre-commit (>=2
 doc = ["cairosvg (>=2.5.2,<3.0.0)", "mdx-include (>=1.4.1,<2.0.0)", "mkdocs (>=1.1.2,<2.0.0)", "mkdocs-material (>=8.1.4,<9.0.0)", "pillow (>=9.3.0,<10.0.0)"]
 test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6.0.0)", "mypy (==0.910)", "pytest (>=4.4.0,<8.0.0)", "pytest-cov (>=2.10.0,<5.0.0)", "pytest-sugar (>=0.9.4,<0.10.0)", "pytest-xdist (>=1.32.0,<4.0.0)", "rich (>=10.11.0,<14.0.0)", "shellingham (>=1.3.0,<2.0.0)"]
 
+[[package]]
+name = "types-awscrt"
+version = "0.20.5"
+description = "Type annotations and code completion for awscrt"
+optional = true
+python-versions = ">=3.7,<4.0"
+files = [
+    {file = "types_awscrt-0.20.5-py3-none-any.whl", hash = "sha256:79d5bfb01f64701b6cf442e89a37d9c4dc6dbb79a46f2f611739b2418d30ecfd"},
+    {file = "types_awscrt-0.20.5.tar.gz", hash = "sha256:61811bbf4de95248939f9276a434be93d2b95f6ccfe8aa94e56999e9778cfcc2"},
+]
+
 [[package]]
 name = "types-chardet"
 version = "5.0.4.6"
@@ -8349,24 +9000,24 @@ files = [
 
 [[package]]
 name = "types-protobuf"
-version = "4.24.0.20240129"
+version = "4.24.0.20240302"
 description = "Typing stubs for protobuf"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-protobuf-4.24.0.20240129.tar.gz", hash = "sha256:8a83dd3b9b76a33e08d8636c5daa212ace1396418ed91837635fcd564a624891"},
-    {file = "types_protobuf-4.24.0.20240129-py3-none-any.whl", hash = "sha256:23be68cc29f3f5213b5c5878ac0151706182874040e220cfb11336f9ee642ead"},
+    {file = "types-protobuf-4.24.0.20240302.tar.gz", hash = "sha256:f22c00cc0cea9722e71e14d389bba429af9e35a74a949719c167203a5abbe2e4"},
+    {file = "types_protobuf-4.24.0.20240302-py3-none-any.whl", hash = "sha256:5c607990f50f14606c2edaf379f8acc7418fef1451b227aa3c6a8a2cbc6ff14a"},
 ]
 
 [[package]]
 name = "types-pyopenssl"
-version = "24.0.0.20240130"
+version = "24.0.0.20240228"
 description = "Typing stubs for pyOpenSSL"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-pyOpenSSL-24.0.0.20240130.tar.gz", hash = "sha256:c812e5c1c35249f75ef5935708b2a997d62abf9745be222e5f94b9595472ab25"},
-    {file = "types_pyOpenSSL-24.0.0.20240130-py3-none-any.whl", hash = "sha256:24a255458b5b8a7fca8139cf56f2a8ad5a4f1a5f711b73a5bb9cb50dc688fab5"},
+    {file = "types-pyOpenSSL-24.0.0.20240228.tar.gz", hash = "sha256:cd990717d8aa3743ef0e73e0f462e64b54d90c304249232d48fece4f0f7c3c6a"},
+    {file = "types_pyOpenSSL-24.0.0.20240228-py3-none-any.whl", hash = "sha256:a472cf877a873549175e81972f153f44e975302a3cf17381eb5f3d41ccfb75a4"},
 ]
 
 [package.dependencies]
@@ -8407,13 +9058,13 @@ files = [
 
 [[package]]
 name = "types-redis"
-version = "4.6.0.20240106"
+version = "4.6.0.20240218"
 description = "Typing stubs for redis"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-redis-4.6.0.20240106.tar.gz", hash = "sha256:2b2fa3a78f84559616242d23f86de5f4130dfd6c3b83fb2d8ce3329e503f756e"},
-    {file = "types_redis-4.6.0.20240106-py3-none-any.whl", hash = "sha256:912de6507b631934bd225cdac310b04a58def94391003ba83939e5a10e99568d"},
+    {file = "types-redis-4.6.0.20240218.tar.gz", hash = "sha256:5103d7e690e5c74c974a161317b2d59ac2303cf8bef24175b04c2a4c3486cb39"},
+    {file = "types_redis-4.6.0.20240218-py3-none-any.whl", hash = "sha256:dc9c45a068240e33a04302aec5655cf41e80f91eecffccbb2df215b2f6fc375d"},
 ]
 
 [package.dependencies]
@@ -8422,18 +9073,43 @@ types-pyOpenSSL = "*"
 
 [[package]]
 name = "types-requests"
-version = "2.31.0.20240125"
+version = "2.31.0.6"
+description = "Typing stubs for requests"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "types-requests-2.31.0.6.tar.gz", hash = "sha256:cd74ce3b53c461f1228a9b783929ac73a666658f223e28ed29753771477b3bd0"},
+    {file = "types_requests-2.31.0.6-py3-none-any.whl", hash = "sha256:a2db9cb228a81da8348b49ad6db3f5519452dd20a9c1e1a868c83c5fe88fd1a9"},
+]
+
+[package.dependencies]
+types-urllib3 = "*"
+
+[[package]]
+name = "types-requests"
+version = "2.31.0.20240218"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "types-requests-2.31.0.20240125.tar.gz", hash = "sha256:03a28ce1d7cd54199148e043b2079cdded22d6795d19a2c2a6791a4b2b5e2eb5"},
-    {file = "types_requests-2.31.0.20240125-py3-none-any.whl", hash = "sha256:9592a9a4cb92d6d75d9b491a41477272b710e021011a2a3061157e2fb1f1a5d1"},
+    {file = "types-requests-2.31.0.20240218.tar.gz", hash = "sha256:f1721dba8385958f504a5386240b92de4734e047a08a40751c1654d1ac3349c5"},
+    {file = "types_requests-2.31.0.20240218-py3-none-any.whl", hash = "sha256:a82807ec6ddce8f00fe0e949da6d6bc1fbf1715420218a9640d695f70a9e5a9b"},
 ]
 
 [package.dependencies]
 urllib3 = ">=2"
 
+[[package]]
+name = "types-s3transfer"
+version = "0.10.0"
+description = "Type annotations and code completion for s3transfer"
+optional = true
+python-versions = ">=3.7,<4.0"
+files = [
+    {file = "types_s3transfer-0.10.0-py3-none-any.whl", hash = "sha256:44fcdf0097b924a9aab1ee4baa1179081a9559ca62a88c807e2b256893ce688f"},
+    {file = "types_s3transfer-0.10.0.tar.gz", hash = "sha256:35e4998c25df7f8985ad69dedc8e4860e8af3b43b7615e940d53c00d413bdc69"},
+]
+
 [[package]]
 name = "types-toml"
 version = "0.10.8.7"
@@ -8445,6 +9121,17 @@ files = [
     {file = "types_toml-0.10.8.7-py3-none-any.whl", hash = "sha256:61951da6ad410794c97bec035d59376ce1cbf4453dc9b6f90477e81e4442d631"},
 ]
 
+[[package]]
+name = "types-urllib3"
+version = "1.26.25.14"
+description = "Typing stubs for urllib3"
+optional = false
+python-versions = "*"
+files = [
+    {file = "types-urllib3-1.26.25.14.tar.gz", hash = "sha256:229b7f577c951b8c1b92c1bc2b2fdb0b49847bd2af6d1cc2a2e3dd340f3bda8f"},
+    {file = "types_urllib3-1.26.25.14-py3-none-any.whl", hash = "sha256:9683bbb7fb72e32bfe9d2be6e04875fbe1b3eeec3cbb4ea231435aa7fd6b4f0e"},
+]
+
 [[package]]
 name = "typing"
 version = "3.7.4.3"
@@ -8458,13 +9145,13 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.9.0"
+version = "4.10.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
-    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
 
 [[package]]
@@ -8484,33 +9171,15 @@ typing-extensions = ">=3.7.4"
 
 [[package]]
 name = "tzdata"
-version = "2023.4"
+version = "2024.1"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
 files = [
-    {file = "tzdata-2023.4-py2.py3-none-any.whl", hash = "sha256:aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3"},
-    {file = "tzdata-2023.4.tar.gz", hash = "sha256:dd54c94f294765522c77399649b4fefd95522479a664a0cec87f41bebc6148c9"},
-]
-
-[[package]]
-name = "tzlocal"
-version = "5.2"
-description = "tzinfo object for the local timezone"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "tzlocal-5.2-py3-none-any.whl", hash = "sha256:49816ef2fe65ea8ac19d19aa7a1ae0551c834303d5014c6d5a62e4cbda8047b8"},
-    {file = "tzlocal-5.2.tar.gz", hash = "sha256:8d399205578f1a9342816409cc1e46a93ebd5755e39ea2d85334bea911bf0e6e"},
+    {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
+    {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
 ]
 
-[package.dependencies]
-"backports.zoneinfo" = {version = "*", markers = "python_version < \"3.9\""}
-tzdata = {version = "*", markers = "platform_system == \"Windows\""}
-
-[package.extras]
-devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"]
-
 [[package]]
 name = "update-checker"
 version = "0.18.0"
@@ -8559,6 +9228,22 @@ files = [
 [package.extras]
 dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
 
+[[package]]
+name = "urllib3"
+version = "1.26.18"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+files = [
+    {file = "urllib3-1.26.18-py2.py3-none-any.whl", hash = "sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07"},
+    {file = "urllib3-1.26.18.tar.gz", hash = "sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0"},
+]
+
+[package.extras]
+brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
+secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
+socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+
 [[package]]
 name = "urllib3"
 version = "2.0.7"
@@ -8587,43 +9272,23 @@ files = [
 ]
 
 [[package]]
-name = "validators"
-version = "0.22.0"
-description = "Python Data Validation for Humans™"
+name = "uvicorn"
+version = "0.23.2"
+description = "The lightning-fast ASGI server."
 optional = true
 python-versions = ">=3.8"
 files = [
-    {file = "validators-0.22.0-py3-none-any.whl", hash = "sha256:61cf7d4a62bbae559f2e54aed3b000cea9ff3e2fdbe463f51179b92c58c9585a"},
-    {file = "validators-0.22.0.tar.gz", hash = "sha256:77b2689b172eeeb600d9605ab86194641670cdb73b60afd577142a9397873370"},
-]
-
-[package.extras]
-docs-offline = ["myst-parser (>=2.0.0)", "pypandoc-binary (>=1.11)", "sphinx (>=7.1.1)"]
-docs-online = ["mkdocs (>=1.5.2)", "mkdocs-git-revision-date-localized-plugin (>=1.2.0)", "mkdocs-material (>=9.2.6)", "mkdocstrings[python] (>=0.22.0)", "pyaml (>=23.7.0)"]
-hooks = ["pre-commit (>=3.3.3)"]
-package = ["build (>=1.0.0)", "twine (>=4.0.2)"]
-runner = ["tox (>=4.11.1)"]
-sast = ["bandit[toml] (>=1.7.5)"]
-testing = ["pytest (>=7.4.0)"]
-tooling = ["black (>=23.7.0)", "pyright (>=1.1.325)", "ruff (>=0.0.287)"]
-tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"]
-
-[[package]]
-name = "vcrpy"
-version = "4.3.0"
-description = "Automatically mock your HTTP interactions to simplify and speed up testing"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "vcrpy-4.3.0-py2.py3-none-any.whl", hash = "sha256:8fbd4be412e8a7f35f623dd61034e6380a1c8dbd0edf6e87277a3289f6e98093"},
-    {file = "vcrpy-4.3.0.tar.gz", hash = "sha256:49c270ce67e826dba027d83e20d25b67a5885487697e97bca6dbdf53d750a0ac"},
+    {file = "uvicorn-0.23.2-py3-none-any.whl", hash = "sha256:1f9be6558f01239d4fdf22ef8126c39cb1ad0addf76c40e760549d2c2f43ab53"},
+    {file = "uvicorn-0.23.2.tar.gz", hash = "sha256:4d3cc12d7727ba72b64d12d3cc7743124074c0a69f7b201512fc50c3e3f1569a"},
 ]
 
 [package.dependencies]
-PyYAML = "*"
-six = ">=1.5"
-wrapt = "*"
-yarl = "*"
+click = ">=7.0"
+h11 = ">=0.8"
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
 
 [[package]]
 name = "vcrpy"
@@ -8637,6 +9302,7 @@ files = [
 
 [package.dependencies]
 PyYAML = "*"
+urllib3 = {version = "<2", markers = "platform_python_implementation == \"PyPy\" or python_version < \"3.10\""}
 wrapt = "*"
 yarl = "*"
 
@@ -8820,13 +9486,13 @@ files = [
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.9"
+version = "4.0.10"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.9-py3-none-any.whl", hash = "sha256:91452ca8445beb805792f206e560c1769284267a30ceb1cec9f5bcc887d15175"},
-    {file = "widgetsnbextension-4.0.9.tar.gz", hash = "sha256:3c1f5e46dc1166dfd40a42d685e6a51396fd34ff878742a3e47c6f0cc4a2a385"},
+    {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
+    {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
 ]
 
 [[package]]
@@ -8910,13 +9576,13 @@ files = [
 
 [[package]]
 name = "xata"
-version = "1.3.0"
+version = "1.3.1"
 description = "Python SDK for Xata.io"
 optional = true
 python-versions = ">=3.8,<4.0"
 files = [
-    {file = "xata-1.3.0-py3-none-any.whl", hash = "sha256:80467b15e5e32bb6277133e34b4d6e729347cdf43e3477061f1ef07dfea73f80"},
-    {file = "xata-1.3.0.tar.gz", hash = "sha256:e99d7dc3500d456526b785b69c95217a3f447657649972e3711872f92d99e360"},
+    {file = "xata-1.3.1-py3-none-any.whl", hash = "sha256:a52877fb2e9704113bc0a6b2107496d9724dd349f6ec64963a0bdde24bca0fd5"},
+    {file = "xata-1.3.1.tar.gz", hash = "sha256:72b9fdb7ac4f8ca4df0688d5e4a598d32d421d3e7a030ce44421d7248328d8b5"},
 ]
 
 [package.dependencies]
@@ -9190,9 +9856,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 
 [extras]
 cli = ["typer"]
-extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "xata", "xmltodict", "zhipuai"]
+extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cloudpickle", "cloudpickle", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "friendli-client", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", "oci", "openai", "openapi-pydantic", "oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "tidb-vector", "timescale-vector", "tqdm", "tree-sitter", "tree-sitter-languages", "upstash-redis", "xata", "xmltodict", "zhipuai"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "c8a7a435aabbfafc6c4210c2ceca2030b4a3724bd1d5804526498d2b52aa9db1"
+content-hash = "5d51faa8c1baf8deeed9ee3a2487285793c9bb4d25961bc47bc04818a29b2aa6"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index c63b434cad..16c0ac3c68 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -96,6 +96,7 @@ oci = {version = "^2.119.1", optional = true}
 rdflib = {version = "7.0.0", optional = true}
 nvidia-riva-client = {version = "^2.14.0", optional = true}
 tidb-vector = {version = ">=0.0.3,<1.0.0", optional = true}
+friendli-client = {version = "^1.2.4", optional = true}
 
 [tool.poetry.group.test]
 optional = true
@@ -266,6 +267,7 @@ extended_testing = [
  "rdflib",
  "tidb-vector",
  "cloudpickle",
+ "friendli-client"
 ]
 
 [tool.ruff]
diff --git a/libs/community/tests/integration_tests/chat_models/test_friendli.py b/libs/community/tests/integration_tests/chat_models/test_friendli.py
new file mode 100644
index 0000000000..ab9646c1eb
--- /dev/null
+++ b/libs/community/tests/integration_tests/chat_models/test_friendli.py
@@ -0,0 +1,105 @@
+"""Test Friendli chat API."""
+
+import pytest
+from langchain_core.messages.ai import AIMessage
+from langchain_core.messages.human import HumanMessage
+from langchain_core.outputs.generation import Generation
+from langchain_core.outputs.llm_result import LLMResult
+
+from langchain_community.chat_models.friendli import ChatFriendli
+
+
+@pytest.fixture
+def friendli_chat() -> ChatFriendli:
+    """Friendli LLM for chat."""
+    return ChatFriendli(temperature=0, max_tokens=10)
+
+
+def test_friendli_call(friendli_chat: ChatFriendli) -> None:
+    """Test call."""
+    message = HumanMessage(content="What is generative AI?")
+    output = friendli_chat([message])
+    assert isinstance(output, AIMessage)
+    assert isinstance(output.content, str)
+
+
+def test_friendli_invoke(friendli_chat: ChatFriendli) -> None:
+    """Test invoke."""
+    output = friendli_chat.invoke("What is generative AI?")
+    assert isinstance(output, AIMessage)
+    assert isinstance(output.content, str)
+
+
+async def test_friendli_ainvoke(friendli_chat: ChatFriendli) -> None:
+    """Test async invoke."""
+    output = await friendli_chat.ainvoke("What is generative AI?")
+    assert isinstance(output, AIMessage)
+    assert isinstance(output.content, str)
+
+
+def test_friendli_batch(friendli_chat: ChatFriendli) -> None:
+    """Test batch."""
+    outputs = friendli_chat.batch(["What is generative AI?", "What is generative AI?"])
+    for output in outputs:
+        assert isinstance(output, AIMessage)
+        assert isinstance(output.content, str)
+
+
+async def test_friendli_abatch(friendli_chat: ChatFriendli) -> None:
+    """Test async batch."""
+    outputs = await friendli_chat.abatch(
+        ["What is generative AI?", "What is generative AI?"]
+    )
+    for output in outputs:
+        assert isinstance(output, AIMessage)
+        assert isinstance(output.content, str)
+
+
+def test_friendli_generate(friendli_chat: ChatFriendli) -> None:
+    """Test generate."""
+    message = HumanMessage(content="What is generative AI?")
+    result = friendli_chat.generate([[message], [message]])
+    assert isinstance(result, LLMResult)
+    generations = result.generations
+    assert len(generations) == 2
+    for generation in generations:
+        gen_ = generation[0]
+        assert isinstance(gen_, Generation)
+        text = gen_.text
+        assert isinstance(text, str)
+        generation_info = gen_.generation_info
+        if generation_info is not None:
+            assert "token" in generation_info
+
+
+async def test_friendli_agenerate(friendli_chat: ChatFriendli) -> None:
+    """Test async generate."""
+    message = HumanMessage(content="What is generative AI?")
+    result = await friendli_chat.agenerate([[message], [message]])
+    assert isinstance(result, LLMResult)
+    generations = result.generations
+    assert len(generations) == 2
+    for generation in generations:
+        gen_ = generation[0]
+        assert isinstance(gen_, Generation)
+        text = gen_.text
+        assert isinstance(text, str)
+        generation_info = gen_.generation_info
+        if generation_info is not None:
+            assert "token" in generation_info
+
+
+def test_friendli_stream(friendli_chat: ChatFriendli) -> None:
+    """Test stream."""
+    stream = friendli_chat.stream("Say hello world.")
+    for chunk in stream:
+        assert isinstance(chunk, AIMessage)
+        assert isinstance(chunk.content, str)
+
+
+async def test_friendli_astream(friendli_chat: ChatFriendli) -> None:
+    """Test async stream."""
+    stream = friendli_chat.astream("Say hello world.")
+    async for chunk in stream:
+        assert isinstance(chunk, AIMessage)
+        assert isinstance(chunk.content, str)
diff --git a/libs/community/tests/integration_tests/llms/test_friendli.py b/libs/community/tests/integration_tests/llms/test_friendli.py
new file mode 100644
index 0000000000..38c6fd0646
--- /dev/null
+++ b/libs/community/tests/integration_tests/llms/test_friendli.py
@@ -0,0 +1,91 @@
+"""Test Friendli API."""
+
+import pytest
+from langchain_core.outputs.generation import Generation
+from langchain_core.outputs.llm_result import LLMResult
+
+from langchain_community.llms.friendli import Friendli
+
+
+@pytest.fixture
+def friendli_llm() -> Friendli:
+    """Friendli LLM."""
+    return Friendli(temperature=0, max_tokens=10)
+
+
+def test_friendli_call(friendli_llm: Friendli) -> None:
+    """Test call."""
+    output = friendli_llm("Say hello world.")
+    assert isinstance(output, str)
+
+
+def test_friendli_invoke(friendli_llm: Friendli) -> None:
+    """Test invoke."""
+    output = friendli_llm.invoke("Say hello world.")
+    assert isinstance(output, str)
+
+
+async def test_friendli_ainvoke(friendli_llm: Friendli) -> None:
+    """Test async invoke."""
+    output = await friendli_llm.ainvoke("Say hello world.")
+    assert isinstance(output, str)
+
+
+def test_friendli_batch(friendli_llm: Friendli) -> None:
+    """Test batch."""
+    outputs = friendli_llm.batch(["Say hello world.", "Say bye world."])
+    for output in outputs:
+        assert isinstance(output, str)
+
+
+async def test_friendli_abatch(friendli_llm: Friendli) -> None:
+    """Test async batch."""
+    outputs = await friendli_llm.abatch(["Say hello world.", "Say bye world."])
+    for output in outputs:
+        assert isinstance(output, str)
+
+
+def test_friendli_generate(friendli_llm: Friendli) -> None:
+    """Test generate."""
+    result = friendli_llm.generate(["Say hello world.", "Say bye world."])
+    assert isinstance(result, LLMResult)
+    generations = result.generations
+    assert len(generations) == 2
+    for generation in generations:
+        gen_ = generation[0]
+        assert isinstance(gen_, Generation)
+        text = gen_.text
+        assert isinstance(text, str)
+        generation_info = gen_.generation_info
+        if generation_info is not None:
+            assert "token" in generation_info
+
+
+async def test_friendli_agenerate(friendli_llm: Friendli) -> None:
+    """Test async generate."""
+    result = await friendli_llm.agenerate(["Say hello world.", "Say bye world."])
+    assert isinstance(result, LLMResult)
+    generations = result.generations
+    assert len(generations) == 2
+    for generation in generations:
+        gen_ = generation[0]
+        assert isinstance(gen_, Generation)
+        text = gen_.text
+        assert isinstance(text, str)
+        generation_info = gen_.generation_info
+        if generation_info is not None:
+            assert "token" in generation_info
+
+
+def test_friendli_stream(friendli_llm: Friendli) -> None:
+    """Test stream."""
+    stream = friendli_llm.stream("Say hello world.")
+    for chunk in stream:
+        assert isinstance(chunk, str)
+
+
+async def test_friendli_astream(friendli_llm: Friendli) -> None:
+    """Test async stream."""
+    stream = friendli_llm.astream("Say hello world.")
+    async for chunk in stream:
+        assert isinstance(chunk, str)
diff --git a/libs/community/tests/unit_tests/chat_models/test_friendli.py b/libs/community/tests/unit_tests/chat_models/test_friendli.py
new file mode 100644
index 0000000000..b337e185d3
--- /dev/null
+++ b/libs/community/tests/unit_tests/chat_models/test_friendli.py
@@ -0,0 +1,197 @@
+"""Test Friendli LLM for chat."""
+
+from unittest.mock import AsyncMock, MagicMock, Mock
+
+import pytest
+from langchain_core.pydantic_v1 import SecretStr
+from pytest import CaptureFixture, MonkeyPatch
+
+from langchain_community.adapters.openai import aenumerate
+from langchain_community.chat_models import ChatFriendli
+
+
+@pytest.fixture
+def mock_friendli_client() -> Mock:
+    """Mock instance of Friendli client."""
+    return Mock()
+
+
+@pytest.fixture
+def mock_friendli_async_client() -> AsyncMock:
+    """Mock instance of Friendli async client."""
+    return AsyncMock()
+
+
+@pytest.fixture
+def chat_friendli(
+    mock_friendli_client: Mock, mock_friendli_async_client: AsyncMock
+) -> ChatFriendli:
+    """Friendli LLM for chat with mock clients."""
+    return ChatFriendli(
+        friendli_token=SecretStr("personal-access-token"),
+        client=mock_friendli_client,
+        async_client=mock_friendli_async_client,
+    )
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_token_is_secret_string(capsys: CaptureFixture) -> None:
+    """Test if friendli token is stored as a SecretStr."""
+    fake_token_value = "personal-access-token"
+    chat = ChatFriendli(friendli_token=fake_token_value)
+    assert isinstance(chat.friendli_token, SecretStr)
+    assert chat.friendli_token.get_secret_value() == fake_token_value
+    print(chat.friendli_token, end="")  # noqa: T201
+    captured = capsys.readouterr()
+    assert captured.out == "**********"
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_token_read_from_env(
+    monkeypatch: MonkeyPatch, capsys: CaptureFixture
+) -> None:
+    """Test if friendli token can be parsed from environment."""
+    fake_token_value = "personal-access-token"
+    monkeypatch.setenv("FRIENDLI_TOKEN", fake_token_value)
+    chat = ChatFriendli()
+    assert isinstance(chat.friendli_token, SecretStr)
+    assert chat.friendli_token.get_secret_value() == fake_token_value
+    print(chat.friendli_token, end="")  # noqa: T201
+    captured = capsys.readouterr()
+    assert captured.out == "**********"
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_invoke(
+    mock_friendli_client: Mock, chat_friendli: ChatFriendli
+) -> None:
+    """Test invocation with friendli."""
+    mock_message = Mock()
+    mock_message.content = "Hello Friendli"
+    mock_message.role = "assistant"
+    mock_choice = Mock()
+    mock_choice.message = mock_message
+    mock_response = Mock()
+    mock_response.choices = [mock_choice]
+    mock_friendli_client.chat.completions.create.return_value = mock_response
+
+    result = chat_friendli.invoke("Hello langchain")
+    assert result.content == "Hello Friendli"
+    mock_friendli_client.chat.completions.create.assert_called_once_with(
+        messages=[{"role": "user", "content": "Hello langchain"}],
+        stream=False,
+        model=chat_friendli.model,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
+
+
+@pytest.mark.requires("friendli")
+async def test_friendli_ainvoke(
+    mock_friendli_async_client: AsyncMock, chat_friendli: ChatFriendli
+) -> None:
+    """Test async invocation with friendli."""
+    mock_message = Mock()
+    mock_message.content = "Hello Friendli"
+    mock_message.role = "assistant"
+    mock_choice = Mock()
+    mock_choice.message = mock_message
+    mock_response = Mock()
+    mock_response.choices = [mock_choice]
+    mock_friendli_async_client.chat.completions.create.return_value = mock_response
+
+    result = await chat_friendli.ainvoke("Hello langchain")
+    assert result.content == "Hello Friendli"
+    mock_friendli_async_client.chat.completions.create.assert_awaited_once_with(
+        messages=[{"role": "user", "content": "Hello langchain"}],
+        stream=False,
+        model=chat_friendli.model,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_stream(
+    mock_friendli_client: Mock, chat_friendli: ChatFriendli
+) -> None:
+    """Test stream with friendli."""
+    mock_delta_0 = Mock()
+    mock_delta_0.content = "Hello "
+    mock_delta_1 = Mock()
+    mock_delta_1.content = "Friendli"
+    mock_choice_0 = Mock()
+    mock_choice_0.delta = mock_delta_0
+    mock_choice_1 = Mock()
+    mock_choice_1.delta = mock_delta_1
+    mock_chunk_0 = Mock()
+    mock_chunk_0.choices = [mock_choice_0]
+    mock_chunk_1 = Mock()
+    mock_chunk_1.choices = [mock_choice_1]
+    mock_stream = MagicMock()
+    mock_chunks = [mock_chunk_0, mock_chunk_1]
+    mock_stream.__iter__.return_value = mock_chunks
+
+    mock_friendli_client.chat.completions.create.return_value = mock_stream
+    stream = chat_friendli.stream("Hello langchain")
+    for i, chunk in enumerate(stream):
+        assert chunk.content == mock_chunks[i].choices[0].delta.content
+
+    mock_friendli_client.chat.completions.create.assert_called_once_with(
+        messages=[{"role": "user", "content": "Hello langchain"}],
+        stream=True,
+        model=chat_friendli.model,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
+
+
+@pytest.mark.requires("friendli")
+async def test_friendli_astream(
+    mock_friendli_async_client: AsyncMock, chat_friendli: ChatFriendli
+) -> None:
+    """Test async stream with friendli."""
+    mock_delta_0 = Mock()
+    mock_delta_0.content = "Hello "
+    mock_delta_1 = Mock()
+    mock_delta_1.content = "Friendli"
+    mock_choice_0 = Mock()
+    mock_choice_0.delta = mock_delta_0
+    mock_choice_1 = Mock()
+    mock_choice_1.delta = mock_delta_1
+    mock_chunk_0 = Mock()
+    mock_chunk_0.choices = [mock_choice_0]
+    mock_chunk_1 = Mock()
+    mock_chunk_1.choices = [mock_choice_1]
+    mock_stream = AsyncMock()
+    mock_chunks = [mock_chunk_0, mock_chunk_1]
+    mock_stream.__aiter__.return_value = mock_chunks
+
+    mock_friendli_async_client.chat.completions.create.return_value = mock_stream
+    stream = chat_friendli.astream("Hello langchain")
+    async for i, chunk in aenumerate(stream):
+        assert chunk.content == mock_chunks[i].choices[0].delta.content
+
+    mock_friendli_async_client.chat.completions.create.assert_awaited_once_with(
+        messages=[{"role": "user", "content": "Hello langchain"}],
+        stream=True,
+        model=chat_friendli.model,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
diff --git a/libs/community/tests/unit_tests/chat_models/test_imports.py b/libs/community/tests/unit_tests/chat_models/test_imports.py
index 645a29f550..07f64c7ad6 100644
--- a/libs/community/tests/unit_tests/chat_models/test_imports.py
+++ b/libs/community/tests/unit_tests/chat_models/test_imports.py
@@ -43,6 +43,7 @@ EXPECTED_ALL = [
     "ChatZhipuAI",
     "ChatPerplexity",
     "ChatKinetica",
+    "ChatFriendli",
 ]
 
 
diff --git a/libs/community/tests/unit_tests/llms/test_friendli.py b/libs/community/tests/unit_tests/llms/test_friendli.py
new file mode 100644
index 0000000000..dae9eadf89
--- /dev/null
+++ b/libs/community/tests/unit_tests/llms/test_friendli.py
@@ -0,0 +1,179 @@
+"""Test Friendli LLM."""
+
+from unittest.mock import AsyncMock, MagicMock, Mock
+
+import pytest
+from langchain_core.pydantic_v1 import SecretStr
+from pytest import CaptureFixture, MonkeyPatch
+
+from langchain_community.adapters.openai import aenumerate
+from langchain_community.llms.friendli import Friendli
+
+
+@pytest.fixture
+def mock_friendli_client() -> Mock:
+    """Mock instance of Friendli client."""
+    return Mock()
+
+
+@pytest.fixture
+def mock_friendli_async_client() -> AsyncMock:
+    """Mock instance of Friendli async client."""
+    return AsyncMock()
+
+
+@pytest.fixture
+def friendli_llm(
+    mock_friendli_client: Mock, mock_friendli_async_client: AsyncMock
+) -> Friendli:
+    """Friendli LLM with mock clients."""
+    return Friendli(
+        friendli_token=SecretStr("personal-access-token"),
+        client=mock_friendli_client,
+        async_client=mock_friendli_async_client,
+    )
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_token_is_secret_string(capsys: CaptureFixture) -> None:
+    """Test if friendli token is stored as a SecretStr."""
+    fake_token_value = "personal-access-token"
+    chat = Friendli(friendli_token=fake_token_value)
+    assert isinstance(chat.friendli_token, SecretStr)
+    assert chat.friendli_token.get_secret_value() == fake_token_value
+    print(chat.friendli_token, end="")  # noqa: T201
+    captured = capsys.readouterr()
+    assert captured.out == "**********"
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_token_read_from_env(
+    monkeypatch: MonkeyPatch, capsys: CaptureFixture
+) -> None:
+    """Test if friendli token can be parsed from environment."""
+    fake_token_value = "personal-access-token"
+    monkeypatch.setenv("FRIENDLI_TOKEN", fake_token_value)
+    chat = Friendli()
+    assert isinstance(chat.friendli_token, SecretStr)
+    assert chat.friendli_token.get_secret_value() == fake_token_value
+    print(chat.friendli_token, end="")  # noqa: T201
+    captured = capsys.readouterr()
+    assert captured.out == "**********"
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_invoke(mock_friendli_client: Mock, friendli_llm: Friendli) -> None:
+    """Test invocation with friendli."""
+    mock_choice = Mock()
+    mock_choice.text = "Hello Friendli"
+    mock_response = Mock()
+    mock_response.choices = [mock_choice]
+    mock_friendli_client.completions.create.return_value = mock_response
+
+    result = friendli_llm.invoke("Hello langchain")
+    assert result == "Hello Friendli"
+    mock_friendli_client.completions.create.assert_called_once_with(
+        model=friendli_llm.model,
+        prompt="Hello langchain",
+        stream=False,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
+
+
+@pytest.mark.requires("friendli")
+async def test_friendli_ainvoke(
+    mock_friendli_async_client: AsyncMock, friendli_llm: Friendli
+) -> None:
+    """Test async invocation with friendli."""
+    mock_choice = Mock()
+    mock_choice.text = "Hello Friendli"
+    mock_response = Mock()
+    mock_response.choices = [mock_choice]
+    mock_friendli_async_client.completions.create.return_value = mock_response
+
+    result = await friendli_llm.ainvoke("Hello langchain")
+    assert result == "Hello Friendli"
+    mock_friendli_async_client.completions.create.assert_awaited_once_with(
+        model=friendli_llm.model,
+        prompt="Hello langchain",
+        stream=False,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
+
+
+@pytest.mark.requires("friendli")
+def test_friendli_stream(mock_friendli_client: Mock, friendli_llm: Friendli) -> None:
+    """Test stream with friendli."""
+    mock_chunk_0 = Mock()
+    mock_chunk_0.event = "token_sampled"
+    mock_chunk_0.text = "Hello "
+    mock_chunk_0.token = 0
+    mock_chunk_1 = Mock()
+    mock_chunk_1.event = "token_sampled"
+    mock_chunk_1.text = "Friendli"
+    mock_chunk_1.token = 1
+    mock_stream = MagicMock()
+    mock_chunks = [mock_chunk_0, mock_chunk_1]
+    mock_stream.__iter__.return_value = mock_chunks
+
+    mock_friendli_client.completions.create.return_value = mock_stream
+    stream = friendli_llm.stream("Hello langchain")
+    for i, chunk in enumerate(stream):
+        assert chunk == mock_chunks[i].text
+
+    mock_friendli_client.completions.create.assert_called_once_with(
+        model=friendli_llm.model,
+        prompt="Hello langchain",
+        stream=True,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
+
+
+@pytest.mark.requires("friendli")
+async def test_friendli_astream(
+    mock_friendli_async_client: AsyncMock, friendli_llm: Friendli
+) -> None:
+    """Test async stream with friendli."""
+    mock_chunk_0 = Mock()
+    mock_chunk_0.event = "token_sampled"
+    mock_chunk_0.text = "Hello "
+    mock_chunk_0.token = 0
+    mock_chunk_1 = Mock()
+    mock_chunk_1.event = "token_sampled"
+    mock_chunk_1.text = "Friendli"
+    mock_chunk_1.token = 1
+    mock_stream = AsyncMock()
+    mock_chunks = [mock_chunk_0, mock_chunk_1]
+    mock_stream.__aiter__.return_value = mock_chunks
+
+    mock_friendli_async_client.completions.create.return_value = mock_stream
+    stream = friendli_llm.astream("Hello langchain")
+    async for i, chunk in aenumerate(stream):
+        assert chunk == mock_chunks[i].text
+
+    mock_friendli_async_client.completions.create.assert_awaited_once_with(
+        model=friendli_llm.model,
+        prompt="Hello langchain",
+        stream=True,
+        frequency_penalty=None,
+        presence_penalty=None,
+        max_tokens=None,
+        stop=None,
+        temperature=None,
+        top_p=None,
+    )
diff --git a/libs/community/tests/unit_tests/llms/test_imports.py b/libs/community/tests/unit_tests/llms/test_imports.py
index 1f5489b2b3..3abee81450 100644
--- a/libs/community/tests/unit_tests/llms/test_imports.py
+++ b/libs/community/tests/unit_tests/llms/test_imports.py
@@ -30,6 +30,7 @@ EXPECT_ALL = [
     "FakeListLLM",
     "Fireworks",
     "ForefrontAI",
+    "Friendli",
     "GigaChat",
     "GPT4All",
     "GooglePalm",