
Name	JinaTextEmbedder
Source	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/jina
Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory input variables	“text”: A string
Output variables	“embedding”: A list of float numbers ”meta”: A dictionary of metadata

Overview

JinaTextEmbedder embeds a simple string (such as a query) into a vector. For embedding lists of documents, use the use the JinaDocumentEmbedder, which enriches the document with the computed embedding, also known as vector. To see the list of compatible Jina Embeddings models, head to Jina AI’s website. The default model for JinaTextEmbedder 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 = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"))

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

Usage

On its own

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

from haystack_integrations.components.embedders.jina import JinaTextEmbedder

text_to_embed = "I love pizza!"

text_embedder = JinaTextEmbedder(api_key=Secret.from_token("<your-api-key>"))

print(text_embedder.run(text_to_embed))

# {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
# 'meta': {'model': 'text-embedding-ada-002-v2',
#              'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}

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

In a pipeline

from haystack import Document
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.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")]

document_embedder = JinaDocumentEmbedder(api_key=Secret.from_token("<your-api-key>"))
documents_with_embeddings = document_embedder.run(documents)['documents']
document_store.write_documents(documents_with_embeddings)

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')