Heroku LLM Managed Inference¶

The llama-index-llms-heroku package contains LlamaIndex integrations for building applications with models on Heroku's Managed Inference platform. This integration allows you to easily connect to and use AI models deployed on Heroku's infrastructure.

Installation¶

In [ ]:

%pip install llama-index-llms-heroku

%pip install llama-index-llms-heroku

Setup¶

1. Create a Heroku App¶

First, create an app in Heroku:

heroku create $APP_NAME

2. Create and Attach AI Models¶

Create and attach a chat model to your app:

heroku ai:models:create -a $APP_NAME claude-3-5-haiku

3. Export Configuration Variables¶

Export the required configuration variables:

export INFERENCE_KEY=$(heroku config:get INFERENCE_KEY -a $APP_NAME)
export INFERENCE_MODEL_ID=$(heroku config:get INFERENCE_MODEL_ID -a $APP_NAME)
export INFERENCE_URL=$(heroku config:get INFERENCE_URL -a $APP_NAME)

Usage¶

Basic Usage¶

In [ ]:

from llama_index.llms.heroku import Heroku
from llama_index.core.llms import ChatMessage, MessageRole

# Initialize the Heroku LLM
llm = Heroku()

# Create chat messages
messages = [
    ChatMessage(
        role=MessageRole.SYSTEM, content="You are a helpful assistant."
    ),
    ChatMessage(
        role=MessageRole.USER,
        content="What are the most popular house pets in North America?",
    ),
]

# Get response
response = llm.chat(messages)
print(response)

from llama_index.llms.heroku import Heroku from llama_index.core.llms import ChatMessage, MessageRole # Initialize the Heroku LLM llm = Heroku() # Create chat messages messages = [ ChatMessage( role=MessageRole.SYSTEM, content="You are a helpful assistant." ), ChatMessage( role=MessageRole.USER, content="What are the most popular house pets in North America?", ), ] # Get response response = llm.chat(messages) print(response)

Using Environment Variables¶

The integration automatically reads from environment variables:

In [ ]:

import os

# Set environment variables
os.environ["INFERENCE_KEY"] = "your-inference-key"
os.environ["INFERENCE_URL"] = "https://us.inference.heroku.com"
os.environ["INFERENCE_MODEL_ID"] = "claude-3-5-haiku"

# Initialize without parameters
llm = Heroku()

import os # Set environment variables os.environ["INFERENCE_KEY"] = "your-inference-key" os.environ["INFERENCE_URL"] = "https://us.inference.heroku.com" os.environ["INFERENCE_MODEL_ID"] = "claude-3-5-haiku" # Initialize without parameters llm = Heroku()

Using Parameters¶

You can also pass parameters directly:

In [ ]:

import os

llm = Heroku(
    model=os.getenv("INFERENCE_MODEL_ID", "claude-3-5-haiku"),
    api_key=os.getenv("INFERENCE_KEY", "your-inference-key"),
    inference_url=os.getenv(
        "INFERENCE_URL", "https://us.inference.heroku.com"
    ),
    max_tokens=1024,
)

import os llm = Heroku( model=os.getenv("INFERENCE_MODEL_ID", "claude-3-5-haiku"), api_key=os.getenv("INFERENCE_KEY", "your-inference-key"), inference_url=os.getenv( "INFERENCE_URL", "https://us.inference.heroku.com" ), max_tokens=1024, )

Available Models¶

For a complete list of available models, see the Heroku Managed Inference documentation.

Error Handling¶

The integration includes proper error handling for common issues:

• Missing API key
• Invalid inference URL
• Missing model configuration

Additional Information¶

For more information about Heroku Managed Inference, visit the official documentation.