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add completion costs notebook

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ishaan-jaff 2023-09-06 18:43:09 -07:00
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cookbook/LiteLLM_Completion_Cost.ipynb vendored Normal file
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Use LiteLLM to calculate costs for all your completion calls\n",
"In this notebook we'll use `litellm.completion_cost` to get completion costs"
],
"metadata": {
"id": "BgWr0PsUR3vV"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ViczFTjsDzSI"
},
"outputs": [],
"source": [
"!pip install litellm==0.1.549 # use 0.1.549 or later"
]
},
{
"cell_type": "markdown",
"source": [
"## Calculating costs for gpt-3.5 turbo completion()"
],
"metadata": {
"id": "k_1CWUwmSNtj"
}
},
{
"cell_type": "code",
"source": [
"from litellm import completion, completion_cost\n",
"import os\n",
"os.environ['OPENAI_API_KEY'] = \"\"\n",
"\n",
"messages = [{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
"response = completion(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages=messages,\n",
")\n",
"\n",
"print(response)\n",
"\n",
"cost = completion_cost(completion_response=response)\n",
"formatted_string = f\"Cost for completion call: ${float(cost):.10f}\"\n",
"print(formatted_string)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Tp0fyk-jD0pP",
"outputId": "ce885fb3-3237-41b2-9d8b-3fb30bba498b"
},
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"got response\n",
"{\n",
" \"id\": \"chatcmpl-7vyCApIZaCxP36kb9meUMN2DFSJPh\",\n",
" \"object\": \"chat.completion\",\n",
" \"created\": 1694050442,\n",
" \"model\": \"gpt-3.5-turbo-0613\",\n",
" \"choices\": [\n",
" {\n",
" \"index\": 0,\n",
" \"message\": {\n",
" \"role\": \"assistant\",\n",
" \"content\": \"Hello! I'm an AI and I don't have feelings, but I'm here to help you. How can I assist you today?\"\n",
" },\n",
" \"finish_reason\": \"stop\"\n",
" }\n",
" ],\n",
" \"usage\": {\n",
" \"prompt_tokens\": 13,\n",
" \"completion_tokens\": 28,\n",
" \"total_tokens\": 41\n",
" }\n",
"}\n",
"Cost for completion call: $0.0000755000\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Calculating costs for Together Computer completion()"
],
"metadata": {
"id": "AjDs4G-uS6PS"
}
},
{
"cell_type": "code",
"source": [
"from litellm import completion, completion_cost\n",
"import os\n",
"os.environ['TOGETHERAI_API_KEY'] = \"\"\n",
"\n",
"messages = [{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
"response = completion(\n",
" model=\"togethercomputer/llama-2-70b-chat\",\n",
" messages=messages,\n",
")\n",
"\n",
"print(response)\n",
"\n",
"cost = completion_cost(completion_response=response)\n",
"formatted_string = f\"Cost for completion call: ${float(cost):.10f}\"\n",
"print(formatted_string)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jMPsUV-KEa6a",
"outputId": "7a69b291-f149-4b9c-8a78-9c8142bac759"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"choices\": [\n",
" {\n",
" \"finish_reason\": \"stop\",\n",
" \"index\": 0,\n",
" \"message\": {\n",
" \"content\": \"Hello! I'm doing well, thanks for asking. I hope you're having a great\",\n",
" \"role\": \"assistant\",\n",
" \"logprobs\": null\n",
" }\n",
" }\n",
" ],\n",
" \"created\": 1694050771.2821715,\n",
" \"model\": \"togethercomputer/llama-2-70b-chat\",\n",
" \"usage\": {\n",
" \"prompt_tokens\": 12,\n",
" \"completion_tokens\": 18,\n",
" \"total_tokens\": 30\n",
" }\n",
"}\n",
"Cost for completion call: $0.0000900000\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Calculating costs for Replicate Llama2 completion()"
],
"metadata": {
"id": "vEa4s6-7TANS"
}
},
{
"cell_type": "code",
"source": [
"from litellm import completion, completion_cost\n",
"import os\n",
"os.environ['REPLICATE_API_KEY'] = \"\"\n",
"\n",
"messages = [{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
"response = completion(\n",
" model=\"replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf\",\n",
" messages=messages,\n",
")\n",
"\n",
"print(response)\n",
"\n",
"cost = completion_cost(completion_response=response)\n",
"formatted_string = f\"Cost for completion call: ${float(cost):.10f}\"\n",
"print(formatted_string)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Xf1TKRDuS1bR",
"outputId": "cfb2b484-a6e5-41ad-86c5-7e66aba27648"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{\n",
" \"choices\": [\n",
" {\n",
" \"finish_reason\": \"stop\",\n",
" \"index\": 0,\n",
" \"message\": {\n",
" \"content\": \" Hello! I'm doing well, thanks for asking. How about you? Is there anything you need help with today?\",\n",
" \"role\": \"assistant\",\n",
" \"logprobs\": null\n",
" }\n",
" }\n",
" ],\n",
" \"created\": 1694050893.4534576,\n",
" \"model\": \"replicate/llama-2-70b-chat:2796ee9483c3fd7aa2e171d38f4ca12251a30609463dcfd4cd76703f22e96cdf\",\n",
" \"usage\": {\n",
" \"prompt_tokens\": 6,\n",
" \"completion_tokens\": 24,\n",
" \"total_tokens\": 30\n",
" },\n",
" \"ended\": 1694050896.6689413\n",
"}\n",
"total_replicate_run_time 3.2154836654663086\n",
"Cost for completion call: $0.0045016771\n"
]
}
]
}
]
}