Replicate

Replicate (opens in a new tab)在云端运行机器学习模型。我们拥有一系列开源模型，只需几行代码即可运行。如果您正在构建自己的机器学习模型，Replicate可以轻松实现大规模部署。

这个例子介绍了如何使用LangChain与Replicate 模型 (opens in a new tab)进行交互。

设置#

要运行此笔记本电脑，您需要创建一个Replicate (opens in a new tab)账户并安装replicate python客户端 (opens in a new tab)。

!pip install replicate

# 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.llms import Replicate
from langchain import PromptTemplate, LLMChain

调用模型#

Find a model on the replicate explore page (opens in a new tab), and then paste in the model name and version in this format: model_name/version

For example, for this dolly model (opens in a new tab), click on the API tab. The model name/version would be: replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5

Only the model param is required, but we can add other model params when initializing.

For example, if we were running stable diffusion and wanted to change the image dimensions:

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

Note that only the first output of a model will be returned.

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)

'The legal driving age of dogs is 2. Cars are designed for humans to drive. Therefore, the final answer is yes.'

We can call any replicate model using this syntax. For example, we can call stable diffusion.

text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", 
                       input={'image_dimensions': '512x512'})

image_output = text2image("A cat riding a motorcycle by Picasso")
image_output

'https://replicate.delivery/pbxt/Cf07B1zqzFQLOSBQcKG7m9beE74wf7kuip5W9VxHJFembefKE/out-0.png'

The model spits out a URL. Let’s render it.

from PIL import Image
import requests
from io import BytesIO

response = requests.get(image_output)
img = Image.open(BytesIO(response.content))

img

Chaining Calls#

The whole point of langchain is to… chain! Here’s an example of how do that.

from langchain.chains import SimpleSequentialChain

First, let’s define the LLM for this model as a flan-5, and text2image as a stable diffusion model.

dolly_llm = Replicate(model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5")
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf")

First prompt in the chain

prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)

chain = LLMChain(llm=dolly_llm, prompt=prompt)

Second prompt to get the logo for company description

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，根据第二个提示输出的描述来创建图片

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)

> Entering new SimpleSequentialChain chain...
novelty socks
todd & co.
https://replicate.delivery/pbxt/BedAP1PPBwXFfkmeD7xDygXO4BcvApp1uvWOwUdHM4tcQfvCB/out-0.png

> Finished chain.
https://replicate.delivery/pbxt/BedAP1PPBwXFfkmeD7xDygXO4BcvApp1uvWOwUdHM4tcQfvCB/out-0.png

response = requests.get("https://replicate.delivery/pbxt/eq6foRJngThCAEBqse3nL3Km2MBfLnWQNd0Hy2SQRo2LuprCB/out-0.png")
img = Image.open(BytesIO(response.content))
img

Promptlayer Openai Runhouse