
Most common position in a pipeline	As the first component in a pipeline that passes the resulting document downstream
Mandatory init variables	“mode”: The operation mode for the reader (`read`, `search`, or `ground`) ”api_key”: The Jina API key. Can be set with `JINA_API_KEY` env var.
Mandatory run variables	“query”: A query string
Output variables	“document”: A list of documents
API reference	Jina
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/jina

Overview

JinaReaderConnector interacts with Jina AI’s Reader API to process queries and output documents.

You need to select one of the following modes of operations when initializing the component:

read: Processes a URL and extracts the textual content.
search: Searches the web and returns textual content from the most relevant pages.
ground: Performs fact-checking using a grounding engine.

You can find more information on these modes in the Jina Reader documentation.

You can additionally control the response format from the Jina Reader API using the component’s json_response parameter:

True (default) requests a JSON response for documents enriched with structured metadata.
False requests a raw response, resulting in one document with minimal metadata.

Authorization

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

ranker = JinaRanker(api_key=Secret.from_token("<your-api-key>"))

To get your API key, head to Jina AI’s website.

Installation

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

pip install jina-haystack

Usage

On its own

Read mode:

from haystack_integrations.components.connectors.jina import JinaReaderConnector

reader = JinaReaderConnector(mode="read")
query = "https://example.com"
result = reader.run(query=query)

print(result)
# {'documents': [Document(id=fa3e51e4ca91828086dca4f359b6e1ea2881e358f83b41b53c84616cb0b2f7cf,
# content: 'This domain is for use in illustrative examples in documents. You may use this domain in literature ...',
# meta: {'title': 'Example Domain', 'description': '', 'url': 'https://example.com/', 'usage': {'tokens': 42}})]}

Search mode:

from haystack_integrations.components.connectors.jina import JinaReaderConnector

reader = JinaReaderConnector(mode="search")
query = "UEFA Champions League 2024"
result = reader.run(query=query)

print(result)
# {'documents': Document(id=6a71abf9955594232037321a476d39a835c0cb7bc575d886ee0087c973c95940,
# content: '2024/25 UEFA Champions League: Matches, draw, final, key dates | UEFA Champions League | UEFA.com...',
# meta: {'title': '2024/25 UEFA Champions League: Matches, draw, final, key dates',
# 'description': 'What are the match dates? Where is the 2025 final? How will the competition work?',
# 'url': 'https://www.uefa.com/uefachampionsleague/news/...',
# 'usage': {'tokens': 5581}}), ...]}

Ground mode:

from haystack_integrations.components.connectors.jina import JinaReaderConnector

reader = JinaReaderConnector(mode="ground")
query = "ChatGPT was launched in 2017"
result = reader.run(query=query)

print(result)
# {'documents': [Document(id=f0c964dbc1ebb2d6584c8032b657150b9aa6e421f714cc1b9f8093a159127f0c,
# content: 'The statement that ChatGPT was launched in 2017 is incorrect. Multiple references confirm that ChatG...',
# meta: {'factuality': 0, 'result': False, 'references': [
# {'url': 'https://en.wikipedia.org/wiki/ChatGPT',
# 'keyQuote': 'ChatGPT is a generative artificial intelligence (AI) chatbot developed by OpenAI and launched in 2022.',
# 'isSupportive': False}, ...],
# 'usage': {'tokens': 10188}})]}

In a pipeline

Query pipeline with search mode

The following pipeline example, the JinaReaderConnector first searches for relevant documents, then feeds them along with a user query into a prompt template, and finally generates a response based on the retrieved context.

from haystack import Pipeline
from haystack.utils import Secret
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack_integrations.components.connectors.jina import JinaReaderConnector

reader_connector = JinaReaderConnector(mode="search")

template = """Given the information below: \n
            {% for document in documents %}
                {{ document.content }}
            {% endfor %}
            Answer question: {{ query }}. \n Answer:"""

prompt_builder = PromptBuilder(template=template)
llm = OpenAIGenerator(model="gpt-4o-mini", api_key=Secret.from_token("<your-api-key>"))

pipe = Pipeline()
pipe.add_component("reader_connector", reader_connector)
pipe.add_component("prompt_builder", prompt_builder)
pipe.add_component("llm", llm)

pipe.connect("reader_connector.documents", "prompt_builder.documents")
pipe.connect("prompt_builder.prompt", "llm.prompt")

query = "What is the most famous landmark in Berlin?"

result = pipe.run(data={"reader_connector":{"query":query}, "prompt_builder":{"query": query}})
print(result)

# {'llm': {'replies': ['The most famous landmark in Berlin is the **Brandenburg Gate**. It is considered the symbol of the city and represents reunification.'], 'meta': [{'model': 'gpt-4o-mini-2024-07-18', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 27, 'prompt_tokens': 4479, 'total_tokens': 4506, 'completion_tokens_details': CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), 'prompt_tokens_details': PromptTokensDetails(audio_tokens=0, cached_tokens=0)}}]}}

The same component in search mode could also be used in an indexing pipeline.