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Eden AI

Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/)

This example goes over how to use LangChain to interact with Eden AI models

Accessing the EDENAI's API requires an API key,

which you can get by creating an account https://app.edenai.run/user/register and heading here https://app.edenai.run/admin/account/settings

Once we have a key we'll want to set it as an environment variable by running:

export EDENAI_API_KEY="..."

If you'd prefer not to set an environment variable you can pass the key in directly via the edenai_api_key named parameter

when initiating the EdenAI LLM class:

from langchain_community.llms import EdenAI
API Reference:EdenAI
llm = EdenAI(edenai_api_key="...", provider="openai", temperature=0.2, max_tokens=250)

Calling a model​

The EdenAI API brings together various providers, each offering multiple models.

To access a specific model, you can simply add 'model' during instantiation.

For instance, let's explore the models provided by OpenAI, such as GPT3.5

text generation​

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)
API Reference:LLMChain | PromptTemplate

image generation​

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)

text generation with callback​

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))

Chaining Calls​

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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