EdenAITextEmbedder

This component transforms a string into a vector using Eden AI's OpenAI-compatible API. Use it for embedding retrieval to transform your query into an embedding.

Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory init variables	api_key: The Eden AI API key. Can be set with EDENAI_API_KEY env var.
Mandatory run variables	text: A string
Output variables	embedding: A list of float numbers (vectors) 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

Use EdenAITextEmbedder to embed a simple string (such as a query) into a vector. For embedding lists of documents, use the EdenAIDocumentEmbedder, which enriches the document with the computed embedding, also known as vector.

Overview

EdenAITextEmbedder transforms a string into a vector that captures its semantics using an Eden AI embedding model. 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

EdenAITextEmbedder 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 EdenAITextEmbedder

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

Usage

On its own

Remember to set the EDENAI_API_KEY as an environment variable first or pass it in directly.

python

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

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

result = embedder.run(text="How can I use the Eden AI embedding models with Haystack?")

print(result["embedding"])
# [-0.0015687942504882812, 0.052154541015625, 0.037109375...]

In a pipeline

python

from haystack import Pipeline
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.dataclasses import Document
from haystack_integrations.components.embedders.edenai import (
    EdenAIDocumentEmbedder,
    EdenAITextEmbedder,
)

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

document_embedder = EdenAIDocumentEmbedder(model="openai/text-embedding-3-small")
documents_with_embeddings = document_embedder.run(documents)["documents"]
document_store.write_documents(documents_with_embeddings)

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", EdenAITextEmbedder(model="openai/text-embedding-3-small"))
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

result = query_pipeline.run({"text_embedder": {"text": "Who lives in Berlin?"}})

print(result["retriever"]["documents"][0])