Integration: Eden AI

Use Eden AI to reach 500+ models from many providers through one OpenAI-compatible, EU-hosted API key.

Authors

Eden AI

GitHub Repo PyPI Package

Overview
Installation
Usage
License

Overview

Eden AI is a unified, OpenAI-compatible API that gives access to 500+ AI models from many providers (OpenAI, Anthropic, Mistral, Google, Cohere, and more) through a single API key, with built-in provider fallback and EU data residency. This makes it a convenient, sovereignty-friendly gateway for building LLM and RAG applications with Haystack.

Models are selected using Eden AI’s provider/model naming convention, for example openai/gpt-4o-mini, anthropic/claude-sonnet-4-5, or mistral/mistral-large-latest. See the Eden AI models catalog for the full list.

To follow along with this guide, create an API key in your Eden AI account and expose it as an environment variable, EDENAI_API_KEY.

Installation

pip install edenai-haystack

Usage

Components

This integration introduces 3 components:

The EdenAIChatGenerator: Generates chat responses using any Eden AI chat model through its OpenAI-compatible endpoint.
The EdenAITextEmbedder: Creates embeddings for texts (such as queries) using Eden AI embedding models.
The EdenAIDocumentEmbedder: Creates embeddings for Haystack Documents using Eden AI embedding models.

Use Eden AI Chat Models

import os
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.edenai import EdenAIChatGenerator

os.environ["EDENAI_API_KEY"] = "YOUR_EDENAI_API_KEY"

client = EdenAIChatGenerator(model="mistral/mistral-large-latest")

response = client.run(
    messages=[ChatMessage.from_user("What is the best French cheese?")]
)
print(response)

Eden AI models also support streaming responses if you pass a callback into the EdenAIChatGenerator:

import os
from haystack.components.generators.utils import print_streaming_chunk
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.edenai import EdenAIChatGenerator

os.environ["EDENAI_API_KEY"] = "YOUR_EDENAI_API_KEY"

client = EdenAIChatGenerator(
    model="mistral/mistral-large-latest",
    streaming_callback=print_streaming_chunk,
)

response = client.run(
    messages=[ChatMessage.from_user("What is the best French cheese?")]
)
print(response)

Embed Documents and Queries

import os
from haystack import Document
from haystack_integrations.components.embedders.edenai import (
    EdenAIDocumentEmbedder,
    EdenAITextEmbedder,
)

os.environ["EDENAI_API_KEY"] = "YOUR_EDENAI_API_KEY"

# Embed documents before writing them to a document store
document_embedder = EdenAIDocumentEmbedder(model="openai/text-embedding-3-small")
documents_with_embeddings = document_embedder.run([Document(content="I love pizza!")])["documents"]

# Embed a query at search time
text_embedder = EdenAITextEmbedder(model="openai/text-embedding-3-small")
query_embedding = text_embedder.run("What food do I love?")["embedding"]

This lets you build a fully sovereign RAG stack, retrieval and generation, on EU-hosted models through a single Eden AI key.

License

edenai-haystack is distributed under the terms of the Apache-2.0 license.