Replicate

Replicate 在云端运行机器学习模型。我们有一个开源模型库，您可以通过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 可以轻松地将其大规模部署。

本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 Notebook，您需要创建一个 replicate 账户并安装 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

API 参考：LLMChain | Replicate | PromptTemplate

调用模型

在 replicate 探索页面上找到一个模型，然后以这种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."

再举一个例子，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

只有 model 参数是必需的，但我们可以在初始化时添加其他模型参数。

例如，如果我们运行稳定扩散并想更改图像尺寸

Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})

请注意，将只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 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

您可以选择在响应生成时进行流式传输，这有助于为耗时生成的用户提供交互性。有关更多信息，请参阅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)

API 参考：StreamingStdOutCallbackHandler

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。如果您对要解析的生成有一个明确的停止序列，那么在达到一个或多个停止序列时取消生成会更好（更便宜、更快！），而不是让模型喋喋不休直到达到指定的 max_length。无论是否处于流式模式，停止序列都有效，并且 Replicate 只会针对直到停止序列的生成向您收费。

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

调用链

LangChain 的全部意义在于...链式操作！这里有一个如何做到这一点的例子。

from langchain.chains import SimpleSequentialChain

API 参考：SimpleSequentialChain

首先，我们将此模型的 LLM 定义为 flan-5，将文本到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，用于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，我们根据提示 2 的描述输出来创建图像

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

LLM 概念指南
LLM 操作指南

设置​

调用模型​

流式响应​

停止序列

调用链​

相关​

设置

调用模型

流式响应

调用链

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