Replicate
Replicate 在云中运行机器学习模型。我们拥有一个开源模型库，您可以用几行代码运行。如果您正在构建自己的机器学习模型，Replicate 使其易于大规模部署。
此示例介绍了如何使用 LangChain 与 Replicate 模型 进行交互。
设置
# magics to auto-reload external modules in case you are making changes to langchain while working on this notebook
%load_ext autoreload
%autoreload 2
要运行此笔记本，您需要创建一个 replicate 账户并安装 replicate python client。
!poetry run pip install replicate
Collecting replicate
Using cached replicate-0.25.1-py3-none-any.whl.metadata (24 kB)
Requirement already satisfied: httpx<1,>=0.21.0 in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from replicate) (0.24.1)
Requirement already satisfied: packaging in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from replicate) (23.2)
Requirement already satisfied: pydantic>1.10.7 in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from replicate) (1.10.14)
Requirement already satisfied: typing-extensions>=4.5.0 in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from replicate) (4.10.0)
Requirement already satisfied: certifi in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from httpx<1,>=0.21.0->replicate) (2024.2.2)
Requirement already satisfied: httpcore<0.18.0,>=0.15.0 in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from httpx<1,>=0.21.0->replicate) (0.17.3)
Requirement already satisfied: idna in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from httpx<1,>=0.21.0->replicate) (3.6)
Requirement already satisfied: sniffio in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from httpx<1,>=0.21.0->replicate) (1.3.1)
Requirement already satisfied: h11<0.15,>=0.13 in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from httpcore<0.18.0,>=0.15.0->httpx<1,>=0.21.0->replicate) (0.14.0)
Requirement already satisfied: anyio<5.0,>=3.0 in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from httpcore<0.18.0,>=0.15.0->httpx<1,>=0.21.0->replicate) (3.7.1)
Requirement already satisfied: exceptiongroup in /Users/charlieholtz/miniconda3/envs/langchain/lib/python3.9/site-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx<1,>=0.21.0->replicate) (1.2.0)
Using cached replicate-0.25.1-py3-none-any.whl (39 kB)
Installing collected packages: replicate
Successfully installed replicate-0.25.1
# get a token: https://replicate.com/account
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.chains import LLMChain
from langchain_community.llms import Replicate
from langchain_core.prompts import PromptTemplate
调用模型
在replicate 探索页面上找到一个模型，然后以此格式粘贴模型名称和版本：model_name/version。
例如，这里是Meta Llama 3。
llm = Replicate(
model="meta/meta-llama-3-8b-instruct",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
llm(prompt)
"Let's break this down step by step:\n\n1. A dog is a living being, specifically a mammal.\n2. Dogs do not possess the cognitive abilities or physical characteristics necessary to operate a vehicle, such as a car.\n3. Operating a car requires complex mental and physical abilities, including:\n\t* Understanding of traffic laws and rules\n\t* Ability to read and comprehend road signs\n\t* Ability to make decisions quickly and accurately\n\t* Ability to physically manipulate the vehicle's controls (e.g., steering wheel, pedals)\n4. Dogs do not possess any of these abilities. They are unable to read or comprehend written language, let alone complex traffic laws.\n5. Dogs also lack the physical dexterity and coordination to operate a vehicle's controls. Their paws and claws are not adapted for grasping or manipulating small, precise objects like a steering wheel or pedals.\n6. Therefore, it is not possible for a dog to drive a car.\n\nAnswer: No."
作为另一个例子，对于这个dolly 模型，点击API选项卡。模型名称/版本将是：replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5
仅需要model参数，但我们可以在初始化时添加其他模型参数。
例如，如果我们要运行稳定扩散并想要更改图像尺寸：
Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})
注意，只有模型的第一个输出会被返回。
llm = Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
)
prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
llm(prompt)
'No, dogs lack some of the brain functions required to operate a motor vehicle. They cannot focus and react in time to accelerate or brake correctly. Additionally, they do not have enough muscle control to properly operate a steering wheel.\n\n'
我们可以使用这种语法调用任何replicate模型。例如，我们可以调用稳定扩散。
text2image = Replicate(
model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
model_kwargs={"image_dimensions": "512x512"},
)
image_output = text2image("A cat riding a motorcycle by Picasso")
image_output
'https://pbxt.replicate.delivery/bqQq4KtzwrrYL9Bub9e7NvMTDeEMm5E9VZueTXkLE7kWumIjA/out-0.png'
模型输出一个URL。让我们渲染它。
!poetry run pip install Pillow
Requirement already satisfied: Pillow in /Users/bagatur/langchain/.venv/lib/python3.9/site-packages (9.5.0)
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.2[0m[39;49m -> [0m[32;49m23.2.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m
from io import BytesIO
import requests
from PIL import Image
response = requests.get(image_output)
img = Image.open(BytesIO(response.content))
img
流式响应
您可以选择在生成过程中流式传输响应，这有助于向用户展示在耗时生成过程中的交互性。有关更多信息，请参见流式传输的详细文档。
from langchain_core.callbacks import StreamingStdOutCallbackHandler
llm = Replicate(
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()],
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
_ = llm.invoke(prompt)
1. Dogs do not have the physical ability to operate a vehicle.
停止序列
您还可以指定停止序列。如果您有明确的停止序列用于生成并且您将解析它，最好（更便宜且更快！）在达到一个或多个停止序列时取消生成，而不是让模型继续生成直到指定的 max_length。停止序列无论您是否处于流模式下都有效，并且 Replicate 只会对直到停止序列的生成收费。
import time
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},
)
prompt = """
User: What is the best way to learn python?
Assistant:
"""
start_time = time.perf_counter()
raw_output = llm.invoke(prompt) # raw output, no stop
end_time = time.perf_counter()
print(f"Raw output:\n {raw_output}")
print(f"Raw output runtime: {end_time - start_time} seconds")
start_time = time.perf_counter()
stopped_output = llm.invoke(prompt, stop=["\n\n"]) # stop on double newlines
end_time = time.perf_counter()
print(f"Stopped output:\n {stopped_output}")
print(f"Stopped output runtime: {end_time - start_time} seconds")
Raw output:
There are several ways to learn Python, and the best method for you will depend on your learning style and goals. Here are a few suggestions:
1. Online tutorials and courses: Websites such as Codecademy, Coursera, and edX offer interactive coding lessons and courses that can help you get started with Python. These courses are often designed for beginners and cover the basics of Python programming.
2. Books: There are many books available that can teach you Python, ranging from introductory texts to more advanced manuals. Some popular options include "Python Crash Course" by Eric Matthes, "Automate the Boring Stuff with Python" by Al Sweigart, and "Python for Data Analysis" by Wes McKinney.
3. Videos: YouTube and other video platforms have a wealth of tutorials and lectures on Python programming. Many of these videos are created by experienced programmers and can provide detailed explanations and examples of Python concepts.
4. Practice: One of the best ways to learn Python is to practice writing code. Start with simple programs and gradually work your way up to more complex projects. As you gain experience, you'll become more comfortable with the language and develop a better understanding of its capabilities.
5. Join a community: There are many online communities and forums dedicated to Python programming, such as Reddit's r/learnpython community. These communities can provide support, resources, and feedback as you learn.
6. Take online courses: Many universities and organizations offer online courses on Python programming. These courses can provide a structured learning experience and often include exercises and assignments to help you practice your skills.
7. Use a Python IDE: An Integrated Development Environment (IDE) is a software application that provides an interface for writing, debugging, and testing code. Popular Python IDEs include PyCharm, Visual Studio Code, and Spyder. These tools can help you write more efficient code and provide features such as code completion, debugging, and project management.
Which of the above options do you think is the best way to learn Python?
Raw output runtime: 25.27470933299992 seconds
Stopped output:
There are several ways to learn Python, and the best method for you will depend on your learning style and goals. Here are some suggestions:
Stopped output runtime: 25.77039254200008 seconds
链式调用
langchain 的整个重点在于... 链接！以下是如何做到这一点的示例。
from langchain.chains import SimpleSequentialChain
首先，让我们将此模型的 LLM 定义为 flan-5，将 text2image 定义为稳定扩散模型。
dolly_llm = Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
)
text2image = Replicate(
model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf"
)
链中的第一个提示
prompt = PromptTemplate(
input_variables=["product"],
template="为生产 {product} 的公司起个好名字？",
)
chain = LLMChain(llm=dolly_llm, prompt=prompt)
第二个提示以获取公司描述的标志
second_prompt = PromptTemplate(
input_variables=["company_name"],
template="为这个公司写一个标志的描述：{company_name}",
)
chain_two = LLMChain(llm=dolly_llm, prompt=second_prompt)
第三个提示，让我们根据提示 2 的描述输出创建图像
third_prompt = PromptTemplate(
input_variables=["company_logo_description"],
template="{company_logo_description}",
)
chain_three = LLMChain(llm=text2image, prompt=third_prompt)
现在让我们运行它！
# 运行链，仅指定第一个链的输入变量。
overall_chain = SimpleSequentialChain(
chains=[chain, chain_two, chain_three], verbose=True
)
catchphrase = overall_chain.run("彩色袜子")
print(catchphrase)
[1m> Entering new SimpleSequentialChain chain...[0m
[36;1m[1;3m彩色袜子可以以披头士乐队的一首歌或一种颜色（黄色、蓝色、粉色）命名。字母和数字的良好组合可以是 6399。苹果公司也拥有域名 6399.com，因此这可以为公司保留。
[0m
[33;1m[1;3m一双彩色袜子，上面印有数字 3、9 和 99，分别使用黄色、蓝色和粉色印刷。
[0m
[38;5;200m[1;3mhttps://pbxt.replicate.delivery/P8Oy3pZ7DyaAC1nbJTxNw95D1A3gCPfi2arqlPGlfG9WYTkRA/out-0.png[0m
[1m> Finished chain.[0m
https://pbxt.replicate.delivery/P8Oy3pZ7DyaAC1nbJTxNw95D1A3gCPfi2arqlPGlfG9WYTkRA/out-0.png
response = requests.get(
"https://replicate.delivery/pbxt/682XgeUlFela7kmZgPOf39dDdGDDkwjsCIJ0aQ0AO5bTbbkiA/out-0.png"
)
img = Image.open(BytesIO(response.content))
img