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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": [
"# Migrating to LiteLLM Proxy from OpenAI/Azure OpenAI\n",
"\n",
"Covers:\n",
"\n",
"* /chat/completion\n",
"* /embedding\n",
"\n",
"\n",
"These are **selected examples**. LiteLLM Proxy is **OpenAI-Compatible**, it works with any project that calls OpenAI. Just change the `base_url`, `api_key` and `model`.\n",
"\n",
"For more examples, [go here](https://docs.litellm.ai/docs/proxy/user_keys)\n",
"\n",
"To pass provider-specific args, [go here](https://docs.litellm.ai/docs/completion/provider_specific_params#proxy-usage)\n",
"\n",
"To drop unsupported params (E.g. frequency_penalty for bedrock with librechat), [go here](https://docs.litellm.ai/docs/completion/drop_params#openai-proxy-usage)\n"
],
"metadata": {
"id": "kccfk0mHZ4Ad"
}
},
{
"cell_type": "markdown",
"source": [
"## /chat/completion\n",
"\n"
],
"metadata": {
"id": "nmSClzCPaGH6"
}
},
{
"cell_type": "markdown",
"source": [
"### OpenAI Python SDK"
],
"metadata": {
"id": "_vqcjwOVaKpO"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "x1e_Ok3KZzeP"
},
"outputs": [],
"source": [
"import openai\n",
"client = openai.OpenAI(\n",
" api_key=\"anything\",\n",
" base_url=\"http://0.0.0.0:4000\"\n",
")\n",
"\n",
"# request sent to model set on litellm proxy, `litellm --model`\n",
"response = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"this is a test request, write a short poem\"\n",
" }\n",
" ],\n",
" extra_body={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params\n",
" \"metadata\": { # 👈 use for logging additional params (e.g. to langfuse)\n",
" \"generation_name\": \"ishaan-generation-openai-client\",\n",
" \"generation_id\": \"openai-client-gen-id22\",\n",
" \"trace_id\": \"openai-client-trace-id22\",\n",
" \"trace_user_id\": \"openai-client-user-id2\"\n",
" }\n",
" }\n",
")\n",
"\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"source": [
"## Function Calling"
],
"metadata": {
"id": "AqkyKk9Scxgj"
}
},
{
"cell_type": "code",
"source": [
"from openai import OpenAI\n",
"client = OpenAI(\n",
" api_key=\"sk-1234\", # [OPTIONAL] set if you set one on proxy, else set \"\"\n",
" base_url=\"http://0.0.0.0:4000\",\n",
")\n",
"\n",
"tools = [\n",
" {\n",
" \"type\": \"function\",\n",
" \"function\": {\n",
" \"name\": \"get_current_weather\",\n",
" \"description\": \"Get the current weather in a given location\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"location\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
" },\n",
" \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
" },\n",
" \"required\": [\"location\"],\n",
" },\n",
" }\n",
" }\n",
"]\n",
"messages = [{\"role\": \"user\", \"content\": \"What's the weather like in Boston today?\"}]\n",
"completion = client.chat.completions.create(\n",
" model=\"gpt-4o\", # use 'model_name' from config.yaml\n",
" messages=messages,\n",
" tools=tools,\n",
" tool_choice=\"auto\"\n",
")\n",
"\n",
"print(completion)\n"
],
"metadata": {
"id": "wDg10VqLczE1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Azure OpenAI Python SDK"
],
"metadata": {
"id": "YYoxLloSaNWW"
}
},
{
"cell_type": "code",
"source": [
"import openai\n",
"client = openai.AzureOpenAI(\n",
" api_key=\"anything\",\n",
" base_url=\"http://0.0.0.0:4000\"\n",
")\n",
"\n",
"# request sent to model set on litellm proxy, `litellm --model`\n",
"response = client.chat.completions.create(\n",
" model=\"gpt-3.5-turbo\",\n",
" messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"this is a test request, write a short poem\"\n",
" }\n",
" ],\n",
" extra_body={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params\n",
" \"metadata\": { # 👈 use for logging additional params (e.g. to langfuse)\n",
" \"generation_name\": \"ishaan-generation-openai-client\",\n",
" \"generation_id\": \"openai-client-gen-id22\",\n",
" \"trace_id\": \"openai-client-trace-id22\",\n",
" \"trace_user_id\": \"openai-client-user-id2\"\n",
" }\n",
" }\n",
")\n",
"\n",
"print(response)"
],
"metadata": {
"id": "yA1XcgowaSRy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Langchain Python"
],
"metadata": {
"id": "yl9qhDvnaTpL"
}
},
{
"cell_type": "code",
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
" SystemMessagePromptTemplate,\n",
")\n",
"from langchain.schema import HumanMessage, SystemMessage\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"anything\"\n",
"\n",
"chat = ChatOpenAI(\n",
" openai_api_base=\"http://0.0.0.0:4000\",\n",
" model = \"gpt-3.5-turbo\",\n",
" temperature=0.1,\n",
" extra_body={\n",
" \"metadata\": {\n",
" \"generation_name\": \"ishaan-generation-langchain-client\",\n",
" \"generation_id\": \"langchain-client-gen-id22\",\n",
" \"trace_id\": \"langchain-client-trace-id22\",\n",
" \"trace_user_id\": \"langchain-client-user-id2\"\n",
" }\n",
" }\n",
")\n",
"\n",
"messages = [\n",
" SystemMessage(\n",
" content=\"You are a helpful assistant that im using to make a test request to.\"\n",
" ),\n",
" HumanMessage(\n",
" content=\"test from litellm. tell me why it's amazing in 1 sentence\"\n",
" ),\n",
"]\n",
"response = chat(messages)\n",
"\n",
"print(response)"
],
"metadata": {
"id": "5MUZgSquaW5t"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Curl"
],
"metadata": {
"id": "B9eMgnULbRaz"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"```\n",
"curl -X POST 'http://0.0.0.0:4000/chat/completions' \\\n",
" -H 'Content-Type: application/json' \\\n",
" -d '{\n",
" \"model\": \"gpt-3.5-turbo\",\n",
" \"messages\": [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"what llm are you\"\n",
" }\n",
" ],\n",
" \"metadata\": {\n",
" \"generation_name\": \"ishaan-test-generation\",\n",
" \"generation_id\": \"gen-id22\",\n",
" \"trace_id\": \"trace-id22\",\n",
" \"trace_user_id\": \"user-id2\"\n",
" }\n",
"}'\n",
"```\n",
"\n"
],
"metadata": {
"id": "VWCCk5PFcmhS"
}
},
{
"cell_type": "markdown",
"source": [
"### LlamaIndex"
],
"metadata": {
"id": "drBAm2e1b6xe"
}
},
{
"cell_type": "code",
"source": [
"import os, dotenv\n",
"\n",
"from llama_index.llms import AzureOpenAI\n",
"from llama_index.embeddings import AzureOpenAIEmbedding\n",
"from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext\n",
"\n",
"llm = AzureOpenAI(\n",
" engine=\"azure-gpt-3.5\", # model_name on litellm proxy\n",
" temperature=0.0,\n",
" azure_endpoint=\"http://0.0.0.0:4000\", # litellm proxy endpoint\n",
" api_key=\"sk-1234\", # litellm proxy API Key\n",
" api_version=\"2023-07-01-preview\",\n",
")\n",
"\n",
"embed_model = AzureOpenAIEmbedding(\n",
" deployment_name=\"azure-embedding-model\",\n",
" azure_endpoint=\"http://0.0.0.0:4000\",\n",
" api_key=\"sk-1234\",\n",
" api_version=\"2023-07-01-preview\",\n",
")\n",
"\n",
"\n",
"documents = SimpleDirectoryReader(\"llama_index_data\").load_data()\n",
"service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)\n",
"index = VectorStoreIndex.from_documents(documents, service_context=service_context)\n",
"\n",
"query_engine = index.as_query_engine()\n",
"response = query_engine.query(\"What did the author do growing up?\")\n",
"print(response)\n"
],
"metadata": {
"id": "d0bZcv8fb9mL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Langchain JS"
],
"metadata": {
"id": "xypvNdHnb-Yy"
}
},
{
"cell_type": "code",
"source": [
"import { ChatOpenAI } from \"@langchain/openai\";\n",
"\n",
"\n",
"const model = new ChatOpenAI({\n",
" modelName: \"gpt-4\",\n",
" openAIApiKey: \"sk-1234\",\n",
" modelKwargs: {\"metadata\": \"hello world\"} // 👈 PASS Additional params here\n",
"}, {\n",
" basePath: \"http://0.0.0.0:4000\",\n",
"});\n",
"\n",
"const message = await model.invoke(\"Hi there!\");\n",
"\n",
"console.log(message);\n"
],
"metadata": {
"id": "R55mK2vCcBN2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### OpenAI JS"
],
"metadata": {
"id": "nC4bLifCcCiW"
}
},
{
"cell_type": "code",
"source": [
"const { OpenAI } = require('openai');\n",
"\n",
"const openai = new OpenAI({\n",
" apiKey: \"sk-1234\", // This is the default and can be omitted\n",
" baseURL: \"http://0.0.0.0:4000\"\n",
"});\n",
"\n",
"async function main() {\n",
" const chatCompletion = await openai.chat.completions.create({\n",
" messages: [{ role: 'user', content: 'Say this is a test' }],\n",
" model: 'gpt-3.5-turbo',\n",
" }, {\"metadata\": {\n",
" \"generation_name\": \"ishaan-generation-openaijs-client\",\n",
" \"generation_id\": \"openaijs-client-gen-id22\",\n",
" \"trace_id\": \"openaijs-client-trace-id22\",\n",
" \"trace_user_id\": \"openaijs-client-user-id2\"\n",
" }});\n",
"}\n",
"\n",
"main();\n"
],
"metadata": {
"id": "MICH8kIMcFpg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Anthropic SDK"
],
"metadata": {
"id": "D1Q07pEAcGTb"
}
},
{
"cell_type": "code",
"source": [
"import os\n",
"\n",
"from anthropic import Anthropic\n",
"\n",
"client = Anthropic(\n",
" base_url=\"http://localhost:4000\", # proxy endpoint\n",
" api_key=\"sk-s4xN1IiLTCytwtZFJaYQrA\", # litellm proxy virtual key\n",
")\n",
"\n",
"message = client.messages.create(\n",
" max_tokens=1024,\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Hello, Claude\",\n",
" }\n",
" ],\n",
" model=\"claude-3-opus-20240229\",\n",
")\n",
"print(message.content)"
],
"metadata": {
"id": "qBjFcAvgcI3t"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## /embeddings"
],
"metadata": {
"id": "dFAR4AJGcONI"
}
},
{
"cell_type": "markdown",
"source": [
"### OpenAI Python SDK"
],
"metadata": {
"id": "lgNoM281cRzR"
}
},
{
"cell_type": "code",
"source": [
"import openai\n",
"from openai import OpenAI\n",
"\n",
"# set base_url to your proxy server\n",
"# set api_key to send to proxy server\n",
"client = OpenAI(api_key=\"<proxy-api-key>\", base_url=\"http://0.0.0.0:4000\")\n",
"\n",
"response = client.embeddings.create(\n",
" input=[\"hello from litellm\"],\n",
" model=\"text-embedding-ada-002\"\n",
")\n",
"\n",
"print(response)\n"
],
"metadata": {
"id": "NY3DJhPfcQhA"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Langchain Embeddings"
],
"metadata": {
"id": "hmbg-DW6cUZs"
}
},
{
"cell_type": "code",
"source": [
"from langchain.embeddings import OpenAIEmbeddings\n",
"\n",
"embeddings = OpenAIEmbeddings(model=\"sagemaker-embeddings\", openai_api_base=\"http://0.0.0.0:4000\", openai_api_key=\"temp-key\")\n",
"\n",
"\n",
"text = \"This is a test document.\"\n",
"\n",
"query_result = embeddings.embed_query(text)\n",
"\n",
"print(f\"SAGEMAKER EMBEDDINGS\")\n",
"print(query_result[:5])\n",
"\n",
"embeddings = OpenAIEmbeddings(model=\"bedrock-embeddings\", openai_api_base=\"http://0.0.0.0:4000\", openai_api_key=\"temp-key\")\n",
"\n",
"text = \"This is a test document.\"\n",
"\n",
"query_result = embeddings.embed_query(text)\n",
"\n",
"print(f\"BEDROCK EMBEDDINGS\")\n",
"print(query_result[:5])\n",
"\n",
"embeddings = OpenAIEmbeddings(model=\"bedrock-titan-embeddings\", openai_api_base=\"http://0.0.0.0:4000\", openai_api_key=\"temp-key\")\n",
"\n",
"text = \"This is a test document.\"\n",
"\n",
"query_result = embeddings.embed_query(text)\n",
"\n",
"print(f\"TITAN EMBEDDINGS\")\n",
"print(query_result[:5])"
],
"metadata": {
"id": "lX2S8Nl1cWVP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Curl Request"
],
"metadata": {
"id": "oqGbWBCQcYfd"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"\n",
"```curl\n",
"curl -X POST 'http://0.0.0.0:4000/embeddings' \\\n",
" -H 'Content-Type: application/json' \\\n",
" -d ' {\n",
" \"model\": \"text-embedding-ada-002\",\n",
" \"input\": [\"write a litellm poem\"]\n",
" }'\n",
"```\n",
"\n"
],
"metadata": {
"id": "7rkIMV9LcdwQ"
}
}
]
} |