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Eden AI

Eden AI 正在通过联合最佳的 AI 提供商来革新 AI 领域，赋能用户解锁无限可能，挖掘人工智能的真正潜力。通过一个全面且无忧的平台，它允许用户快速将 AI 功能部署到生产环境，使用户能够通过单一 API 轻松访问全方位的 AI 能力。 (网站: https://edenai.co/)

本示例介绍了如何使用 LangChain 与 Eden AI 模型进行交互

访问 EDENAI 的 API 需要一个 API 密钥，

您可以通过创建一个账户 https://app.edenai.run/user/register 来获取，并前往这里 https://app.edenai.run/admin/account/settings

一旦我们有了密钥，我们将希望通过运行以下命令将其设置为环境变量：

export EDENAI_API_KEY="..."

如果您不想设置环境变量，可以通过名为 edenai_api_key 的参数直接传递密钥

在初始化 EdenAI LLM 类时：

<!--IMPORTS:[{"imported": "EdenAI", "source": "langchain_community.llms", "docs": "https://python.langchain.com/api_reference/community/llms/langchain_community.llms.edenai.EdenAI.html", "title": "Eden AI"}]-->
from langchain_community.llms import EdenAI
llm = EdenAI(edenai_api_key="...", provider="openai", temperature=0.2, max_tokens=250)

调用模型​

EdenAI API 汇集了各种提供商，每个提供商提供多个模型。

要访问特定模型，您只需在实例化时添加 'model'。

例如，让我们探索 OpenAI 提供的模型，如 GPT3.5

文本生成​

<!--IMPORTS:[{"imported": "LLMChain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html", "title": "Eden AI"}, {"imported": "PromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.prompt.PromptTemplate.html", "title": "Eden AI"}]-->
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate

llm = EdenAI(
    feature="text",
    provider="openai",
    model="gpt-3.5-turbo-instruct",
    temperature=0.2,
    max_tokens=250,
)

prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""

llm(prompt)

图像生成​

import base64
from io import BytesIO

from PIL import Image


def print_base64_image(base64_string):
    # Decode the base64 string into binary data
    decoded_data = base64.b64decode(base64_string)

    # Create an in-memory stream to read the binary data
    image_stream = BytesIO(decoded_data)

    # Open the image using PIL
    image = Image.open(image_stream)

    # Display the image
    image.show()
text2image = EdenAI(feature="image", provider="openai", resolution="512x512")
image_output = text2image("A cat riding a motorcycle by Picasso")
print_base64_image(image_output)

带回调的文本生成​

<!--IMPORTS:[{"imported": "EdenAI", "source": "langchain_community.llms", "docs": "https://python.langchain.com/api_reference/community/llms/langchain_community.llms.edenai.EdenAI.html", "title": "Eden AI"}, {"imported": "StreamingStdOutCallbackHandler", "source": "langchain_core.callbacks", "docs": "https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.streaming_stdout.StreamingStdOutCallbackHandler.html", "title": "Eden AI"}]-->
from langchain_community.llms import EdenAI
from langchain_core.callbacks import StreamingStdOutCallbackHandler

llm = EdenAI(
    callbacks=[StreamingStdOutCallbackHandler()],
    feature="text",
    provider="openai",
    temperature=0.2,
    max_tokens=250,
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
print(llm.invoke(prompt))

链式调用​

<!--IMPORTS:[{"imported": "LLMChain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.llm.LLMChain.html", "title": "Eden AI"}, {"imported": "SimpleSequentialChain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.sequential.SimpleSequentialChain.html", "title": "Eden AI"}, {"imported": "PromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.prompt.PromptTemplate.html", "title": "Eden AI"}]-->
from langchain.chains import LLMChain, SimpleSequentialChain
from langchain_core.prompts import PromptTemplate
llm = EdenAI(feature="text", provider="openai", temperature=0.2, max_tokens=250)
text2image = EdenAI(feature="image", provider="openai", resolution="512x512")
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)

chain = LLMChain(llm=llm, prompt=prompt)
second_prompt = PromptTemplate(
    input_variables=["company_name"],
    template="Write a description of a logo for this company: {company_name}, the logo should not contain text at all ",
)
chain_two = LLMChain(llm=llm, prompt=second_prompt)
third_prompt = PromptTemplate(
    input_variables=["company_logo_description"],
    template="{company_logo_description}",
)
chain_three = LLMChain(llm=text2image, prompt=third_prompt)
# Run the chain specifying only the input variable for the first chain.
overall_chain = SimpleSequentialChain(
    chains=[chain, chain_two, chain_three], verbose=True
)
output = overall_chain.run("hats")
# print the image
print_base64_image(output)

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