langchain/docs/extras/integrations/chat/litellm.ipynb
Krish Dholakia 49f1d8477c
Adding ChatLiteLLM model (#9020)
Description: Adding a langchain integration for the LiteLLM library 
Tag maintainer: @hwchase17, @baskaryan
Twitter handle: @krrish_dh / @Berri_AI

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-14 07:43:40 -07:00

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4.3 KiB
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "bf733a38-db84-4363-89e2-de6735c37230",
"metadata": {},
"source": [
"# 🚅 LiteLLM\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. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.chat_models import ChatLiteLLM\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
" AIMessagePromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"from langchain.schema import AIMessage, HumanMessage, SystemMessage"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
"metadata": {
"tags": []
},
"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": {},
"cell_type": "markdown",
"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
"metadata": {},
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}