langchain/docs/versioned_docs/version-0.2.x/integrations/chat/litellm.ipynb

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  {
   "cell_type": "raw",
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   "metadata": {},
   "source": [
    "---\n",
    "sidebar_label: LiteLLM\n",
    "---"
   ]
  },
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   "cell_type": "markdown",
   "id": "bf733a38-db84-4363-89e2-de6735c37230",
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   "source": [
    "# ChatLiteLLM\n",
    "\n",
    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
    "\n",
    "This notebook covers how to get started with using Langchain + the LiteLLM I/O library. "
   ]
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   "metadata": {
    "tags": []
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   "source": [
    "from langchain_community.chat_models import ChatLiteLLM\n",
    "from langchain_core.messages import HumanMessage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "chat = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = [\n",
    "    HumanMessage(\n",
    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
    "    )\n",
    "]\n",
    "chat(messages)"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "## `ChatLiteLLM` also supports async and streaming functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.callbacks.manager import CallbackManager\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "await chat.agenerate([messages])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " J'aime la programmation."
     ]
    },
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat = ChatLiteLLM(\n",
    "    streaming=True,\n",
    "    verbose=True,\n",
    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
    ")\n",
    "chat(messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c253883f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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