forked from phoenix/litellm-mirror
update cookbook
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ishaan-jaff
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2 changed files with 198 additions and 58 deletions
198
cookbook/LiteLLM_AB_TestLLMs.ipynb
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198
cookbook/LiteLLM_AB_TestLLMs.ipynb
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# LiteLLM A/B Testing LLMs in production\n",
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"\n",
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"* LiteLLM allows you to use 100+ LLMs as a drop in replacement for `gpt-3.5-turbo`\n",
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"\n",
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"This tutorial walks through how to use LiteLLM to easily A/B Test LLMs in production"
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],
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"metadata": {
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"id": "ODpmJQ5u4rXI"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Example 1: A/B Test GPT-4 & GPT-3.5\n",
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"\n",
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"# Step 1\n",
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"👉 Get your `id` from here: https://admin.litellm.ai/"
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],
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"metadata": {
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"id": "YamUetcC5Ke7"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from litellm import completion_with_split_tests\n",
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"import os\n",
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"\n",
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"## set ENV variables\n",
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"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
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"\n",
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"\n",
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"# define a dict of model id and % of requests for model\n",
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"# see models here: https://docs.litellm.ai/docs/completion/supported\n",
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"split_per_model = {\n",
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"\t\"gpt-4\": 0.3,\n",
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"\t\"gpt-3.5-turbo\": 0.7\n",
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"}\n",
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"\n",
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"messages = [{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
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"\n",
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"completion_with_split_tests(messages=messages, use_client=True,\n",
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" id=\"91fad14a-8c0f-4e99-8eaa-68245435aa80\") # enter your id"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "7XGfv0958k70",
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"outputId": "91a069a5-c7d4-4fb0-e345-5ebf383edbbc"
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},
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"last_fetched_at: 1693624804.2941535\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<OpenAIObject chat.completion id=chatcmpl-7uBT4QHc8BAoZKkU7JoH4ahmXvu0M at 0x7c2895c9e890> JSON: {\n",
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" \"id\": \"chatcmpl-7uBT4QHc8BAoZKkU7JoH4ahmXvu0M\",\n",
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" \"object\": \"chat.completion\",\n",
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" \"created\": 1693624806,\n",
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" \"model\": \"gpt-3.5-turbo-0613\",\n",
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" \"choices\": [\n",
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" {\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"role\": \"assistant\",\n",
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" \"content\": \"Hello! I'm an AI, so I don't have emotions, but I'm here to assist you. How can I help you today?\"\n",
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" },\n",
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" \"finish_reason\": \"stop\"\n",
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" }\n",
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" ],\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 13,\n",
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" \"completion_tokens\": 29,\n",
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" \"total_tokens\": 42\n",
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" }\n",
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"}"
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]
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},
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"metadata": {},
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"execution_count": 4
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## A/B Test GPT-4 and Claude-2"
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],
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"metadata": {
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"id": "Y12cxhZt58v8"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "0k6rshtR8i9q",
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"outputId": "31ac9d73-9e35-4697-d1ff-5d51048566f8"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"last_fetched_at: 1693624809.3467667\n"
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]
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},
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<OpenAIObject chat.completion id=chatcmpl-7uBTA6gotsTksvCU7GffJ64ybfHUw at 0x7c28aa288630> JSON: {\n",
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" \"id\": \"chatcmpl-7uBTA6gotsTksvCU7GffJ64ybfHUw\",\n",
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" \"object\": \"chat.completion\",\n",
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" \"created\": 1693624812,\n",
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" \"model\": \"gpt-4-0613\",\n",
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" \"choices\": [\n",
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" {\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"role\": \"assistant\",\n",
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" \"content\": \"As an AI, I don't have feelings, but I'm here and ready to assist you. How can I help you today?\"\n",
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" },\n",
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" \"finish_reason\": \"stop\"\n",
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" }\n",
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" ],\n",
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" \"usage\": {\n",
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" \"prompt_tokens\": 13,\n",
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" \"completion_tokens\": 27,\n",
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" \"total_tokens\": 40\n",
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" }\n",
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"}"
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]
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},
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"metadata": {},
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"execution_count": 5
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}
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],
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"source": [
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"from litellm import completion_with_split_tests\n",
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"import os\n",
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"\n",
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"## set ENV variables\n",
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"os.environ[\"ANTHROPIC_API_KEY\"] = \"\"\n",
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"\n",
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"# define a dict of model id and % of requests for model\n",
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"split_per_model = {\n",
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"\t\"gpt-4\": 0.3,\n",
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"\t\"claude-2\": 0.7\n",
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"}\n",
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"\n",
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"messages = [{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
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"\n",
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"\n",
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"completion_with_split_tests(messages=messages, use_client=True,\n",
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" id=\"91fad14a-8c0f-4e99-8eaa-68245435aa80\") # enter your id"
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]
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},
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{
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"cell_type": "markdown",
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"source": [],
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"metadata": {
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"id": "hzzbsAIp4pnr"
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}
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}
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]
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}
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@ -1,58 +0,0 @@
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# -*- coding: utf-8 -*-
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"""LiteLLM_AB_TestLLMs.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/1Y7zcpuJT5rqJYEELeg9UfP1Zf8gFVR_o
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# LiteLLM A/B Testing LLMs in production
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* LiteLLM allows you to use 100+ LLMs as a drop in replacement for `gpt-3.5-turbo`
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This tutorial walks through how to use LiteLLM to easily A/B Test LLMs in production
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# Example 1: A/B Test GPT-4 & GPT-3.5
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# Step 1
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👉 Get your `id` from here: https://admin.litellm.ai/
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"""
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from litellm import completion_with_split_tests
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import os
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## set ENV variables
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os.environ["OPENAI_API_KEY"] = "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"
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# define a dict of model id and % of requests for model
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# see models here: https://docs.litellm.ai/docs/completion/supported
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split_per_model = {
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"gpt-4": 0.3,
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"gpt-3.5-turbo": 0.7
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}
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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completion_with_split_tests(messages=messages, use_client=True,
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id="91fad14a-8c0f-4e99-8eaa-68245435aa80") # enter your id
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"""## A/B Test GPT-4 and Claude-2"""
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from litellm import completion_with_split_tests
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import os
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## set ENV variables
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os.environ["ANTHROPIC_API_KEY"] = ""
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# define a dict of model id and % of requests for model
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split_per_model = {
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"gpt-4": 0.3,
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"claude-2": 0.7
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}
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messages = [{ "content": "Hello, how are you?","role": "user"}]
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completion_with_split_tests(messages=messages, use_client=True,
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id="91fad14a-8c0f-4e99-8eaa-68245435aa80") # enter your id
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