Heroku LLM Managed Inference Embedding

The llama-index-embeddings-heroku package contains LlamaIndex integrations for building applications with embeddings 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

python

%pip install llama-index-embeddings-heroku

Setup

1. Create a Heroku App

First, create an app in Heroku:

bash

heroku create $APP_NAME

2. Create and Attach AI Models

Create and attach a chat model to your app:

bash

heroku ai:models:create -a $APP_NAME cohere-embed-multilingual --as EMBEDDING

3. Export Configuration Variables

Export the required configuration variables:

bash

export EMBEDDING_KEY=$(heroku config:get EMBEDDING_KEY -a $APP_NAME)
export EMBEDDING_MODEL_ID=$(heroku config:get EMBEDDING_MODEL_ID -a $APP_NAME)
export EMBEDDING_URL=$(heroku config:get EMBEDDING_URL -a $APP_NAME)

Usage

Basic Usage

python

# Initialize the Heroku LLM
from llama_index.embeddings.heroku import HerokuEmbedding

# Initialize the Heroku Embedding
embedding_model = HerokuEmbedding()

# Get a single embedding
embedding = embedding_model.get_text_embedding("Hello, world!")
print(f"Embedding dimension: {len(embedding)}")

# Get embeddings for multiple texts
texts = ["Hello", "world", "from", "Heroku"]
embeddings = embedding_model.get_text_embedding_batch(texts)
print(f"Number of embeddings: {len(embeddings)}")

Using Environment Variables

The integration automatically reads from environment variables:

python

import os

# Set environment variables
os.environ["EMBEDDING_KEY"] = "your-embedding-key"
os.environ["EMBEDDING_URL"] = "https://us.inference.heroku.com"
os.environ["EMBEDDING_MODEL_ID"] = "claude-3-5-haiku"

# Initialize without parameters
llm = HerokuEmbedding()

Using Parameters

You can also pass parameters directly:

python

import os
from llama_index.embeddings.heroku import HerokuEmbedding

embedding_model = HerokuEmbedding(
    model=os.getenv("EMBEDDING_MODEL_ID", "cohere-embed-multilingual"),
    api_key=os.getenv("EMBEDDING_KEY", "your-embedding-key"),
    base_url=os.getenv("EMBEDDING_URL", "https://us.inference.heroku.com"),
    timeout=60.0,
)

print(embedding_model.get_text_embedding("Hello Heroku!"))

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.