mirror of https://github.com/hwchase17/langchain
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>pull/9208/head
Krish Dholakia
1 year ago
committed by
GitHub
parent
f11e5442d6
commit
49f1d8477c
@ -0,0 +1,185 @@
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "bf733a38-db84-4363-89e2-de6735c37230",
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"metadata": {},
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"source": [
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"# 🚅 LiteLLM\n",
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"\n",
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"[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
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"\n",
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"This notebook covers how to get started with using Langchain + the LiteLLM I/O library. "
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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": 1,
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"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.chat_models import ChatLiteLLM\n",
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"from langchain.prompts.chat import (\n",
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" ChatPromptTemplate,\n",
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" SystemMessagePromptTemplate,\n",
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" AIMessagePromptTemplate,\n",
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" HumanMessagePromptTemplate,\n",
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")\n",
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"from langchain.schema import AIMessage, HumanMessage, SystemMessage"
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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": 2,
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"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"chat = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
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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": 3,
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"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"messages = [\n",
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" HumanMessage(\n",
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" content=\"Translate this sentence from English to French. I love programming.\"\n",
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" )\n",
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"]\n",
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"chat(messages)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
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"metadata": {},
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"source": [
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"## `ChatLiteLLM` also supports async and streaming functionality:"
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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": 4,
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"id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.callbacks.manager import CallbackManager\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
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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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"id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"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'))])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"await chat.agenerate([messages])"
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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": 6,
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"id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" J'aime la programmation."
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]
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},
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{
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"data": {
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"text/plain": [
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"AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat = ChatLiteLLM(\n",
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" streaming=True,\n",
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" verbose=True,\n",
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" callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
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")\n",
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"chat(messages)"
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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": null,
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"id": "c253883f",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,449 @@
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"""Wrapper around LiteLLM's model I/O library."""
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from __future__ import annotations
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import logging
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from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Tuple,
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Union,
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)
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from pydantic import BaseModel, Field, root_validator
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.chat_models.base import BaseChatModel
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from langchain.llms.base import create_base_retry_decorator
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from langchain.schema import (
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ChatGeneration,
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ChatResult,
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)
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from langchain.schema.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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)
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from langchain.schema.output import ChatGenerationChunk
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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class ChatLiteLLMException(Exception):
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"""Error raised when there is an issue with the LiteLLM I/O Library"""
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def _truncate_at_stop_tokens(
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text: str,
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stop: Optional[List[str]],
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) -> str:
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"""Truncates text at the earliest stop token found."""
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if stop is None:
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return text
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for stop_token in stop:
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stop_token_idx = text.find(stop_token)
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if stop_token_idx != -1:
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text = text[:stop_token_idx]
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return text
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class FunctionMessage(BaseMessage):
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"""A Message for passing the result of executing a function back to a model."""
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name: str
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"""The name of the function that was executed."""
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@property
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def type(self) -> str:
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"""Type of the message, used for serialization."""
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return "function"
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class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
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pass
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def _create_retry_decorator(
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llm: ChatLiteLLM,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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"""Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
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import openai
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errors = [
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openai.error.Timeout,
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openai.error.APIError,
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openai.error.APIConnectionError,
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openai.error.RateLimitError,
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openai.error.ServiceUnavailableError,
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]
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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# Fix for azure
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# Also OpenAI returns None for tool invocations
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content = _dict.get("content", "") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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elif role == "function":
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return FunctionMessage(content=_dict["content"], name=_dict["name"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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async def acompletion_with_retry(
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llm: ChatLiteLLM,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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# Use OpenAI's async api https://github.com/openai/openai-python#async-api
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return await llm.client.acreate(**kwargs)
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return await _completion_with_retry(**kwargs)
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = _dict.get("role")
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content = _dict.get("content") or ""
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if _dict.get("function_call"):
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additional_kwargs = {"function_call": dict(_dict["function_call"])}
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else:
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additional_kwargs = {}
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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else:
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raise ValueError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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class ChatLiteLLM(BaseChatModel, BaseModel):
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"""Wrapper around the LiteLLM Model I/O library.
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To use you must have the google.generativeai Python package installed and
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either:
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1. The ``GOOGLE_API_KEY``` environment variable set with your API key, or
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2. Pass your API key using the google_api_key kwarg to the ChatGoogle
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constructor.
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Example:
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.. code-block:: python
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from langchain.chat_models import ChatGooglePalm
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chat = ChatGooglePalm()
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"""
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client: Any #: :meta private:
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model_name: str = "gpt-3.5-turbo"
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"""Model name to use."""
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openai_api_key: Optional[str] = None
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azure_api_key: Optional[str] = None
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anthropic_api_key: Optional[str] = None
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replicate_api_key: Optional[str] = None
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cohere_api_key: Optional[str] = None
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openrouter_api_key: Optional[str] = None
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streaming: bool = False
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api_base: Optional[str] = None
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organization: Optional[str] = None
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request_timeout: Optional[Union[float, Tuple[float, float]]] = None
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temperature: Optional[float] = None
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Run inference with this temperature. Must by in the closed
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interval [0.0, 1.0]."""
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top_p: Optional[float] = None
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"""Decode using nucleus sampling: consider the smallest set of tokens whose
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probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
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top_k: Optional[int] = None
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"""Decode using top-k sampling: consider the set of top_k most probable tokens.
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Must be positive."""
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n: int = 1
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"""Number of chat completions to generate for each prompt. Note that the API may
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not return the full n completions if duplicates are generated."""
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max_tokens: int = 256
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max_retries: int = 6
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling OpenAI API."""
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return {
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"model": self.model_name,
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"force_timeout": self.request_timeout,
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"max_tokens": self.max_tokens,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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**self.model_kwargs,
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}
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@property
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def _client_params(self) -> Dict[str, Any]:
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"""Get the parameters used for the openai client."""
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self.client.api_base = self.api_base
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self.client.organization = self.organization
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creds: Dict[str, Any] = {
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"model": self.model_name,
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"force_timeout": self.request_timeout,
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}
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return {**self._default_params, **creds}
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def completion_with_retry(
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self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
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) -> Any:
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"""Use tenacity to retry the completion call."""
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retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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@retry_decorator
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def _completion_with_retry(**kwargs: Any) -> Any:
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return self.client.completion(**kwargs)
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return _completion_with_retry(**kwargs)
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate api key, python package exists, temperature, top_p, and top_k."""
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try:
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import litellm
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except ImportError:
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raise ChatLiteLLMException(
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"Could not import google.generativeai python package. "
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"Please install it with `pip install google-generativeai`"
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)
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values["openai_api_key"] = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY", default=""
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)
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values["azure_api_key"] = get_from_dict_or_env(
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values, "azure_api_key", "AZURE_API_KEY", default=""
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)
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values["anthropic_api_key"] = get_from_dict_or_env(
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values, "anthropic_api_key", "ANTHROPIC_API_KEY", default=""
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)
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values["replicate_api_key"] = get_from_dict_or_env(
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values, "replicate_api_key", "REPLICATE_API_KEY", default=""
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)
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values["openrouter_api_key"] = get_from_dict_or_env(
|
||||
values, "openrouter_api_key", "OPENROUTER_API_KEY", default=""
|
||||
)
|
||||
values["client"] = litellm
|
||||
|
||||
if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
|
||||
raise ValueError("temperature must be in the range [0.0, 1.0]")
|
||||
|
||||
if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
|
||||
raise ValueError("top_p must be in the range [0.0, 1.0]")
|
||||
|
||||
if values["top_k"] is not None and values["top_k"] <= 0:
|
||||
raise ValueError("top_k must be positive")
|
||||
|
||||
return values
|
||||
|
||||
def _generate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if stream if stream is not None else self.streaming:
|
||||
generation: Optional[ChatGenerationChunk] = None
|
||||
for chunk in self._stream(
|
||||
messages=messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
return ChatResult(generations=[generation])
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs}
|
||||
response = self.completion_with_retry(
|
||||
messages=message_dicts, run_manager=run_manager, **params
|
||||
)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
|
||||
generations = []
|
||||
for res in response["choices"]:
|
||||
message = _convert_dict_to_message(res["message"])
|
||||
gen = ChatGeneration(
|
||||
message=message,
|
||||
generation_info=dict(finish_reason=res.get("finish_reason")),
|
||||
)
|
||||
generations.append(gen)
|
||||
token_usage = response.get("usage", {})
|
||||
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
|
||||
return ChatResult(generations=generations, llm_output=llm_output)
|
||||
|
||||
def _create_message_dicts(
|
||||
self, messages: List[BaseMessage], stop: Optional[List[str]]
|
||||
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
|
||||
params = self._client_params
|
||||
if stop is not None:
|
||||
if "stop" in params:
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
params["stop"] = stop
|
||||
message_dicts = [_convert_message_to_dict(m) for m in messages]
|
||||
return message_dicts, params
|
||||
|
||||
def _stream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> Iterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
for chunk in self.completion_with_retry(
|
||||
messages=message_dicts, run_manager=run_manager, **params
|
||||
):
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
delta = chunk["choices"][0]["delta"]
|
||||
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
|
||||
default_chunk_class = chunk.__class__
|
||||
yield ChatGenerationChunk(message=chunk)
|
||||
if run_manager:
|
||||
run_manager.on_llm_new_token(chunk.content)
|
||||
|
||||
async def _astream(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator[ChatGenerationChunk]:
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs, "stream": True}
|
||||
|
||||
default_chunk_class = AIMessageChunk
|
||||
async for chunk in await acompletion_with_retry(
|
||||
self, messages=message_dicts, run_manager=run_manager, **params
|
||||
):
|
||||
if len(chunk["choices"]) == 0:
|
||||
continue
|
||||
delta = chunk["choices"][0]["delta"]
|
||||
chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
|
||||
default_chunk_class = chunk.__class__
|
||||
yield ChatGenerationChunk(message=chunk)
|
||||
if run_manager:
|
||||
await run_manager.on_llm_new_token(chunk.content)
|
||||
|
||||
async def _agenerate(
|
||||
self,
|
||||
messages: List[BaseMessage],
|
||||
stop: Optional[List[str]] = None,
|
||||
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
||||
stream: Optional[bool] = None,
|
||||
**kwargs: Any,
|
||||
) -> ChatResult:
|
||||
if stream if stream is not None else self.streaming:
|
||||
generation: Optional[ChatGenerationChunk] = None
|
||||
async for chunk in self._astream(
|
||||
messages=messages, stop=stop, run_manager=run_manager, **kwargs
|
||||
):
|
||||
if generation is None:
|
||||
generation = chunk
|
||||
else:
|
||||
generation += chunk
|
||||
assert generation is not None
|
||||
return ChatResult(generations=[generation])
|
||||
|
||||
message_dicts, params = self._create_message_dicts(messages, stop)
|
||||
params = {**params, **kwargs}
|
||||
response = await acompletion_with_retry(
|
||||
self, messages=message_dicts, run_manager=run_manager, **params
|
||||
)
|
||||
return self._create_chat_result(response)
|
||||
|
||||
@property
|
||||
def _identifying_params(self) -> Dict[str, Any]:
|
||||
"""Get the identifying parameters."""
|
||||
return {
|
||||
"model_name": self.model_name,
|
||||
"temperature": self.temperature,
|
||||
"top_p": self.top_p,
|
||||
"top_k": self.top_k,
|
||||
"n": self.n,
|
||||
}
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "litellm-chat"
|
||||
@ -0,0 +1,64 @@
|
||||
"""Test Anthropic API wrapper."""
|
||||
from typing import List
|
||||
|
||||
from langchain.callbacks.manager import (
|
||||
CallbackManager,
|
||||
)
|
||||
from langchain.chat_models.litellm import ChatLiteLLM
|
||||
from langchain.schema import (
|
||||
ChatGeneration,
|
||||
LLMResult,
|
||||
)
|
||||
from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
|
||||
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
|
||||
|
||||
|
||||
def test_litellm_call() -> None:
|
||||
"""Test valid call to litellm."""
|
||||
chat = ChatLiteLLM(
|
||||
model="test",
|
||||
)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_litellm_generate() -> None:
|
||||
"""Test generate method of anthropic."""
|
||||
chat = ChatLiteLLM(model="test")
|
||||
chat_messages: List[List[BaseMessage]] = [
|
||||
[HumanMessage(content="How many toes do dogs have?")]
|
||||
]
|
||||
messages_copy = [messages.copy() for messages in chat_messages]
|
||||
result: LLMResult = chat.generate(chat_messages)
|
||||
assert isinstance(result, LLMResult)
|
||||
for response in result.generations[0]:
|
||||
assert isinstance(response, ChatGeneration)
|
||||
assert isinstance(response.text, str)
|
||||
assert response.text == response.message.content
|
||||
assert chat_messages == messages_copy
|
||||
|
||||
|
||||
def test_litellm_streaming() -> None:
|
||||
"""Test streaming tokens from anthropic."""
|
||||
chat = ChatLiteLLM(model="test", streaming=True)
|
||||
message = HumanMessage(content="Hello")
|
||||
response = chat([message])
|
||||
assert isinstance(response, AIMessage)
|
||||
assert isinstance(response.content, str)
|
||||
|
||||
|
||||
def test_litellm_streaming_callback() -> None:
|
||||
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
||||
callback_handler = FakeCallbackHandler()
|
||||
callback_manager = CallbackManager([callback_handler])
|
||||
chat = ChatLiteLLM(
|
||||
model="test",
|
||||
streaming=True,
|
||||
callback_manager=callback_manager,
|
||||
verbose=True,
|
||||
)
|
||||
message = HumanMessage(content="Write me a sentence with 10 words.")
|
||||
chat([message])
|
||||
assert callback_handler.llm_streams > 1
|
||||
Loading…
Reference in New Issue