Replicate
Replicate 在云中运行机器学习模型。我们有一个开源模型库，您可以用几行代码运行这些模型。如果您正在构建自己的机器学习模型，Replicate 可以轻松地将它们大规模部署。
本示例介绍如何使用 LangChain 与Replicate 模型 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 帐户并安装 replicate python 客户端。
!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，然后以以下格式粘贴模型名称和版本：model_name/version。
例如，这是 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."
另一个示例，对于此 dolly 模型，单击 API 选项卡。模型名称/版本将是：replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5
仅model 参数是必需的，但在初始化时可以添加其他模型参数。
例如，如果我们正在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'})
请注意，只会返回模型的第一个输出。
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'
我们可以使用此语法调用任何复制模型。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
您可以选择在生成时流式传输响应，这有助于为耗时的生成向用户显示交互性。有关更多信息，请参阅有关 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。如果您为要解析的生成设置了一个明确的停止序列，那么在生成完成时取消生成要好得多（更便宜、更快！），而不是让模型一直闲聊到指定的max_length。停止序列无论您是否处于流式模式都会起作用，Replicate 只会向您收取直到停止序列的生成费用。
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
链接调用
Langchain 的全部意义在于......链接！以下是如何执行此操作的示例。
from langchain.chains import SimpleSequentialChain
API 参考：SimpleSequentialChain
首先，让我们将此模型的 LLM 定义为 flan-5，将文本转图像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="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=dolly_llm, prompt=prompt)
第二个提示获取公司描述的徽标
second_prompt = PromptTemplate(
input_variables=["company_name"],
template="Write a description of a logo for this company: {company_name}",
)
chain_two = LLMChain(llm=dolly_llm, prompt=second_prompt)
第三个提示，让我们根据提示 2 的输出描述创建图像
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！
# Run the chain specifying only the input variable for the first chain.
overall_chain = SimpleSequentialChain(
chains=[chain, chain_two, chain_three], verbose=True
)
catchphrase = overall_chain.run("colorful socks")
print(catchphrase)
[1m> Entering new SimpleSequentialChain chain...[0m
[36;1m[1;3mColorful socks could be named after a song by The Beatles or a color (yellow, blue, pink). A good combination of letters and digits would be 6399. Apple also owns the domain 6399.com so this could be reserved for the Company.
[0m
[33;1m[1;3mA colorful sock with the numbers 3, 9, and 99 screen printed in yellow, blue, and pink, respectively.
[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