Skip to main content
Version: 3.0

Eden AI

haystack_integrations.components.embedders.edenai.document_embedder​

EdenAIDocumentEmbedder​

Bases: OpenAIDocumentEmbedder

A component for computing Document embeddings using Eden AI's OpenAI-compatible API.

The embedding of each Document is stored in the embedding field of the Document.

Eden AI routes embedding requests to many providers (OpenAI, Mistral, Cohere, Google, Jina, and more) through a single API key, with EU data residency. Models use Eden AI's provider/model naming convention, for example "openai/text-embedding-3-small" or "mistral/mistral-embed".

Usage example:

python
from haystack import Document
from haystack_integrations.components.embedders.edenai import EdenAIDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = EdenAIDocumentEmbedder(model="mistral/mistral-embed")

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

SUPPORTED_MODELS​

python
SUPPORTED_MODELS: list[str] = [
    "openai/text-embedding-3-small",
    "openai/text-embedding-3-large",
    "mistral/mistral-embed",
    "cohere/embed-english-v3.0",
    "google/text-embedding-004",
]

A non-exhaustive list of embedding models supported by this component. See the Eden AI models catalog for the full list.

init​

python
__init__(
    *,
    model: str = "openai/text-embedding-3-small",
    api_key: Secret = Secret.from_env_var("EDENAI_API_KEY"),
    api_base_url: str | None = "https://api.edenai.run/v3",
    prefix: str = "",
    suffix: str = "",
    batch_size: int = 32,
    progress_bar: bool = True,
    meta_fields_to_embed: list[str] | None = None,
    embedding_separator: str = "\n",
    timeout: float | None = None,
    max_retries: int | None = None,
    http_client_kwargs: dict[str, Any] | None = None
) -> None

Creates an EdenAIDocumentEmbedder component.

Parameters:

  • model (str) – The name of the Eden AI embedding model to use, in provider/model format.
  • api_key (Secret) – The Eden AI API key. Defaults to the EDENAI_API_KEY environment variable.
  • api_base_url (str | None) – The Eden AI API base URL.
  • prefix (str) – A string to add to the beginning of each text.
  • suffix (str) – A string to add to the end of each text.
  • batch_size (int) – Number of Documents to encode at once.
  • progress_bar (bool) – Whether to show a progress bar or not. Can be helpful to disable in production deployments to keep the logs clean.
  • meta_fields_to_embed (list[str] | None) – List of meta fields that should be embedded along with the Document text.
  • embedding_separator (str) – Separator used to concatenate the meta fields to the Document text.
  • timeout (float | None) – Timeout for the API call. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
  • max_retries (int | None) – Maximum number of retries to contact Eden AI after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or set to 5.
  • http_client_kwargs (dict[str, Any] | None) – A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

haystack_integrations.components.embedders.edenai.text_embedder​

EdenAITextEmbedder​

Bases: OpenAITextEmbedder

A component for embedding strings using Eden AI's OpenAI-compatible API.

Eden AI routes embedding requests to many providers (OpenAI, Mistral, Cohere, Google, Jina, and more) through a single API key, with EU data residency. Models use Eden AI's provider/model naming convention, for example "openai/text-embedding-3-small" or "mistral/mistral-embed".

Usage example:

python
from haystack_integrations.components.embedders.edenai import EdenAITextEmbedder

text_embedder = EdenAITextEmbedder(model="mistral/mistral-embed")
print(text_embedder.run("I love pizza!"))

SUPPORTED_MODELS​

python
SUPPORTED_MODELS: list[str] = [
    "openai/text-embedding-3-small",
    "openai/text-embedding-3-large",
    "mistral/mistral-embed",
    "cohere/embed-english-v3.0",
    "google/text-embedding-004",
]

A non-exhaustive list of embedding models supported by this component. See the Eden AI models catalog for the full list.

init​

python
__init__(
    *,
    model: str = "openai/text-embedding-3-small",
    api_key: Secret = Secret.from_env_var("EDENAI_API_KEY"),
    api_base_url: str | None = "https://api.edenai.run/v3",
    prefix: str = "",
    suffix: str = "",
    timeout: float | None = None,
    max_retries: int | None = None,
    http_client_kwargs: dict[str, Any] | None = None
) -> None

Creates an EdenAITextEmbedder component.

Parameters:

  • model (str) – The name of the Eden AI embedding model to use, in provider/model format.
  • api_key (Secret) – The Eden AI API key. Defaults to the EDENAI_API_KEY environment variable.
  • api_base_url (str | None) – The Eden AI API base URL.
  • prefix (str) – A string to add to the beginning of each text.
  • suffix (str) – A string to add to the end of each text.
  • timeout (float | None) – Timeout for the API call. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
  • max_retries (int | None) – Maximum number of retries to contact Eden AI after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or set to 5.
  • http_client_kwargs (dict[str, Any] | None) – A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

haystack_integrations.components.generators.edenai.chat.chat_generator​

EdenAIChatGenerator​

Bases: OpenAIChatGenerator

A chat generator that uses Eden AI's OpenAI-compatible API to generate chat responses.

Eden AI is a unified 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.

This class extends Haystack's OpenAIChatGenerator to talk to Eden AI. It sets the api_base_url to Eden AI's OpenAI-compatible endpoint and keeps all the standard configurations available in the OpenAIChatGenerator.

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.

Usage example:

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

messages = [ChatMessage.from_user("What's Natural Language Processing?")]

client = EdenAIChatGenerator(model="mistral/mistral-large-latest")
response = client.run(messages)
print(response["replies"][0].text)

init​

python
__init__(
    *,
    api_key: Secret = Secret.from_env_var("EDENAI_API_KEY"),
    model: str = "openai/gpt-4o-mini",
    streaming_callback: StreamingCallbackT | None = None,
    generation_kwargs: dict[str, Any] | None = None,
    timeout: int | None = None,
    max_retries: int | None = None,
    tools: ToolsType | None = None,
    tools_strict: bool = False,
    http_client_kwargs: dict[str, Any] | None = None
) -> None

Creates an EdenAIChatGenerator instance.

Parameters:

  • api_key (Secret) – The Eden AI API key. Defaults to the EDENAI_API_KEY environment variable.
  • model (str) – The model to use, in Eden AI's provider/model format (e.g. "openai/gpt-4o-mini", "anthropic/claude-sonnet-4-5", "mistral/mistral-large-latest").
  • streaming_callback (StreamingCallbackT | None) – An optional callable invoked with each chunk of a streaming response.
  • generation_kwargs (dict[str, Any] | None) – Optional keyword arguments passed to the underlying generation API call, such as max_tokens, temperature, or top_p. Eden AI-specific parameters (for example a fallback model) are forwarded as-is to the Eden AI endpoint.
  • timeout (int | None) – The maximum time in seconds to wait for a response from the API.
  • max_retries (int | None) – The maximum number of times to retry a failed API request.
  • tools (ToolsType | None) – An optional list of tools or a Toolset the model can use for function calling.
  • tools_strict (bool) – If True, enable strict schema adherence for tool calls.
  • http_client_kwargs (dict[str, Any] | None) – Optional keyword arguments passed to the underlying HTTP client.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.