JinaDocumentEmbedder

A component for computing Document embeddings using Jina AI models.

Basic Information

• Type: haystack_integrations.components.embedders.jina.document_embedder.JinaDocumentEmbedder

Inputs

Parameter	Type	Default	Description
documents	List[Document]		A list of Documents to embed.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A dictionary with following keys: - documents: List of Documents, each with an embedding field containing the computed embedding. - meta: A dictionary with metadata including the model name and usage statistics.
meta	Dict[str, Any]		A dictionary with following keys: - documents: List of Documents, each with an embedding field containing the computed embedding. - meta: A dictionary with metadata including the model name and usage statistics.

Overview

Work in Progress

Bear with us while we're working on adding pipeline examples and most common components connections.

A component for computing Document embeddings using Jina AI models. The embedding of each Document is stored in the embedding field of the Document.

Usage example:

from haystack import Document
from haystack_integrations.components.embedders.jina import JinaDocumentEmbedder

# Make sure that the environment variable JINA_API_KEY is set

document_embedder = JinaDocumentEmbedder(task="retrieval.query")

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

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

# [0.017020374536514282, -0.023255806416273117, ...]

Usage Example

components:
  JinaDocumentEmbedder:
    type: jina.src.haystack_integrations.components.embedders.jina.document_embedder.JinaDocumentEmbedder
    init_parameters:

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('JINA_API_KEY')	The Jina API key.
model	str	jina-embeddings-v3	The name of the Jina model to use. Check the list of available models on Jina documentation.
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	32	Number of Documents to encode at once.
progress_bar	bool	True	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	Optional[List[str]]	None	List of meta fields that should be embedded along with the Document text.
embedding_separator	str	\n	Separator used to concatenate the meta fields to the Document text.
task	Optional[str]	None	The downstream task for which the embeddings will be used. The model will return the optimized embeddings for that task. Check the list of available tasks on Jina documentation.
dimensions	Optional[int]	None	Number of desired dimension. Smaller dimensions are easier to store and retrieve, with minimal performance impact thanks to MRL.
late_chunking	Optional[bool]	None	A boolean to enable or disable late chunking. Apply the late chunking technique to leverage the model's long-context capabilities for generating contextual chunk embeddings. The support of task and late_chunking parameters is only available for jina-embeddings-v3.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
documents	List[Document]		A list of Documents to embed.