---
title: "JinaReaderConnector"
id: jinareaderconnector
slug: "/jinareaderconnector"
description: "Use Jina AI’s Reader API with Haystack."
---

# JinaReaderConnector

Use Jina AI’s Reader API with Haystack.

<div className="key-value-table">

|  |  |
| --- | --- |
| **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`)  <br /> <br />`api_key`: The Jina API key. Can be set with `JINA_API_KEY` env var. |
| **Mandatory run variables** | `query`: A query string |
| **Output variables** | `documents`: A list of documents |
| **API reference** | [Jina](/reference/integrations-jina) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/jina |

</div>

## 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](https://jina.ai/reader/).

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:

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

To get your API key, head to Jina AI’s [website](https://jina.ai/reranker/).

### Installation

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

```shell
pip install jina-haystack
```

## Usage

### On its own

Read mode:

```python
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:

```python
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:

```python
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.

```python
from haystack import Pipeline
from haystack.utils import Secret
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack_integrations.components.connectors.jina import JinaReaderConnector
from haystack.dataclasses import ChatMessage

reader_connector = JinaReaderConnector(mode="search")

prompt_template = [
    ChatMessage.from_system("You are a helpful assistant."),
    ChatMessage.from_user(
        "Given the information below:\n"
        "{% for document in documents %}{{ document.content }}{% endfor %}\n"
        "Answer question: {{ query }}.\nAnswer:"
    )
]

prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables={"query", "documents"})
llm = OpenAIChatGenerator(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.messages", "llm.messages")

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.
