JinaDocumentEmbedder

This component computes the embeddings of a list of documents and stores the obtained vectors in the embedding field of each document. It uses Jina AI Embeddings models. The vectors computed by this component are necessary to perform embedding retrieval on a collection of documents. At retrieval time, the vector representing the query is compared with those of the documents to find the most similar or relevant documents.


Most common position in a pipeline	Before a `DocumentWriter` in an indexing pipeline
Mandatory init variables	"api_key": The Jina API key. Can be set with `JINA_API_KEY` env var.
Mandatory run variables	“documents”: A list of documents
Output variables	“documents”: A list of documents (enriched with embeddings) ”meta”: A dictionary of metadata
API reference	Jina
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/jina

Overview

JinaDocumentEmbedder enriches the metadata of documents with an embedding of their content. To embed a string, you should use the JinaTextEmbedder. To see the list of compatible Jina Embeddings models, head to Jina AI’s website. The default model for JinaDocumentEmbedder is jina-embeddings-v2-base-en.

To start using this integration with Haystack, install the package with:

pip install jina-haystack

The component uses a JINA_API_KEY environment variable by default. Otherwise, you can pass an API key at initialization with api_key:

embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))

To get a Jina Embeddings API key, head to https://jina.ai/embeddings/.

Embedding Metadata

Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.

You can do this easily by using the Document Embedder:

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

doc = Document(content="some text",
	       meta={"title": "relevant title",
			"page number": 18})

embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"), meta_fields_to_embed=["title"])

docs_w_embeddings = embedder.run(documents=[doc])["documents"]

Usage

On its own

Here is how you can use the component on its own:

from haystack_integrations.components.embedders.jina import JinaDocumentEmbedder

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

document_embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))

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

# [0.017020374536514282, -0.023255806416273117, ...]

📘
We recommend setting JINA_API_KEY as an environment variable instead of setting it as a parameter.

In a pipeline

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.jina import JinaDocumentEmbedder
from haystack_integrations.components.embedders.jina import JinaTextEmbedder
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

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")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>")))
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>")))
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

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

# Document(id=..., mimetype: 'text/plain',
#  text: 'My name is Wolfgang and I live in Berlin')

Additional References

🧑‍🍳 Cookbook: Using the Jina-embeddings-v2-base-en model in a Haystack RAG pipeline for legal document analysis