EdenAIDocumentEmbedder

This component computes the embeddings of a list of documents using Eden AI's OpenAI-compatible API.

Most common position in a pipeline	Before a DocumentWriter in an indexing pipeline
Mandatory init variables	api_key: The Eden AI API key. Can be set with EDENAI_API_KEY env var.
Mandatory run variables	documents: A list of documents to be embedded
Output variables	documents: A list of documents (enriched with embeddings) meta: A dictionary of metadata strings
API reference	Eden AI
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/edenai
Package name	edenai-haystack

This component should be used to embed a list of Documents. To embed a string, use the EdenAITextEmbedder.

Overview

EdenAIDocumentEmbedder computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Eden AI's OpenAI-compatible API. Models are selected using Eden AI's provider/model naming convention, for example openai/text-embedding-3-small (default) or mistral/mistral-embed. For the full list of available models, see the Eden AI models catalog.

To start using this integration with Haystack, install it with:

shell

pip install edenai-haystack

EdenAIDocumentEmbedder needs an Eden AI API key to work. It uses an EDENAI_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with api_key:

python

from haystack.utils import Secret
from haystack_integrations.components.embedders.edenai import EdenAIDocumentEmbedder

embedder = EdenAIDocumentEmbedder(
    api_key=Secret.from_token("<your-api-key>"),
    model="openai/text-embedding-3-small",
)

Usage

On its own

python

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

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

document_embedder = EdenAIDocumentEmbedder(model="openai/text-embedding-3-small")

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

# [0.017020374536514282, -0.023255806416273117, ...]

In an indexing pipeline

python

from haystack import Pipeline
from haystack.components.converters import TextFileToDocument
from haystack.components.writers import DocumentWriter
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.edenai import EdenAIDocumentEmbedder

document_store = InMemoryDocumentStore()

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("converter", TextFileToDocument())
indexing_pipeline.add_component("embedder", EdenAIDocumentEmbedder(model="openai/text-embedding-3-small"))
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))

indexing_pipeline.connect("converter", "embedder")
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"converter": {"sources": ["./my_document.txt"]}})