## 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/](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](https://app.edenai.run/user/register) and heading here [https://app.edenai.run/admin/account/settings](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
```

```
llm = EdenAI(edenai_api_key="...", provider="openai", temperature=0.2, max_tokens=250)
```

## Calling a model[​](#calling-a-model "Direct link to 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[​](#text-generation "Direct link to text generation")

```
from langchain.chains import LLMChainfrom langchain_core.prompts import PromptTemplatellm = 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)
```

### image generation[​](#image-generation "Direct link to image generation")

```
import base64from io import BytesIOfrom PIL import Imagedef 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[​](#text-generation-with-callback "Direct link to text generation with callback")

```
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerfrom langchain_community.llms import EdenAIllm = 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(prompt))
```

## Chaining Calls[​](#chaining-calls "Direct link to Chaining Calls")

```
from langchain.chains import LLMChain, SimpleSequentialChainfrom 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 imageprint_base64_image(output)
```