> ## Documentation Index
> Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Jina AI Integration

## Description

We have integrated the five core interfaces of Jina AI, helping you easily build powerful intelligent agents. These interfaces are primarily suitable for the following scenarios:

* **Vector Embeddings (Embeddings)**：Applicable to multi-modal RAG question-answering scenarios, such as smart customer service, smart recruitment, and knowledge base question-answering.
* **Reranking (Rerank)**：By optimizing the Embedding candidate results, sorting them based on topic relevance, significantly improving the quality of the answers from large language models.
* **Deep Search (DeepSearch)**：Perform deep search and reasoning until the optimal answer is found, particularly suitable for complex tasks such as research projects and product solution development.
* **Web Search (Search)**：Pass a query and get the clean body text of the search engine results page (SERP), ready to feed straight into an LLM for web-grounded Q\&A and RAG.
* **Web Reader (Reader)**：Pass any URL and get the clean markdown body of that page after conversion, ideal for scraping web content to feed to an LLM.

<Tip>
  We've enhanced the Jina AI API to support future extensions, so the usage may differ slightly from the official native implementation.
</Tip>

## Quick Start

Replace the `API_KEY` with [AIHUBMIX\_API\_KEY](https://aihubmix.com/token) and the model endpoint link, and the other parameters and usage are fully consistent with [Jina AI official](https://jina.ai/).

**Endpoint Replacement:**

* **Vector Embeddings (Embeddings)**：`https://jina.ai/embeddings` -> `https://aihubmix.com/v1/embeddings`
* **Reranking (Rerank)**：`https://api.jina.ai/v1/rerank` -> `https://aihubmix.com/v1/rerank`
* **Deep Search (DeepSearch)**：`https://deepsearch.jina.ai/v1/chat/completions` -> `https://aihubmix.com/v1/chat/completions`
* **Web Search (Search)**：`https://s.jina.ai/?q=` -> `https://aihubmix.com/v1/jina/search?q=`
* **Web Reader (Reader)**：`https://r.jina.ai/<url>` -> `https://aihubmix.com/v1/jina/reader/<url>`

  <Note>
    If the current primary API address is unavailable, replace the domain in this configuration with the backup address `https://api.inferera.com`; keep the path unchanged.
  </Note>

## Embeddings

Jina AI's Embedding supports both plain text and multi-modal images, and performs excellently in handling multi-language tasks.

### Request Parameters

<ParamField body="model" type="string" required>
  Model name, available model list:

  * `jina-clip-v2`：Multi-modal, multilingual, 1024-dimensional, 8K context window, 865M parameters
  * `jina-embeddings-v3`：Text model, multilingual, 1024-dimensional, 8K context window, 570M parameters
  * `jina-colbert-v2`：Multi-language ColBERT model, 8K token context, 560M parameters, used for embedding and reranking
  * `jina-embeddings-v2-base-code`：Model optimized for code and document search, 768-dimensional, 8K context window, 137M parameters
</ParamField>

<ParamField body="input" type="array" required>
  Input text or image, different models support different input formats. For text, provide an array of strings; for multi-modal models, provide an array of objects containing text or image fields.
</ParamField>

<ParamField body="embedding_format" type="string" default="float">
  Data type returned, optional values:

  * `float`：Default, return a float array. The most common and easy-to-use format, return a list of floats
  * `binary_int8`：Return as int8 packed binary format. More efficient storage, search, and transmission
  * `binary_uint8`：Return as uint8 packed binary format. More efficient storage, search, and transmission
  * `base64`：Return as base64 encoded string. More efficient transmission
</ParamField>

<ParamField body="dimensions" default="1024" type="integer">
  The number of dimensions used in computation. Supported values:

  * 1024
  * 768
</ParamField>

### 1. Multimodal Usage

<CodeGroup>
  ```shell Curl theme={null}
  curl https://aihubmix.com/v1/embeddings \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer sk-***" \
    -d @- <<EOFEOF
    {
      "model": "jina-clip-v2",
      "input": [
          {
              "text": "A beautiful sunset over the beach"
          },
          {
              "text": "Un beau coucher de soleil sur la plage"
          },
          {
              "text": "海滩上美丽的日落"
          },
          {
              "text": "浜辺に沈む美しい夕日"
          },
          {
              "image": "https://i.ibb.co/nQNGqL0/beach1.jpg"
          },
          {
              "image": "https://i.ibb.co/r5w8hG8/beach2.jpg"
          },
          {
              "image": "R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7"
          }
      ]
    }
  EOFEOF
  ```

  ```py Python theme={null}
  import requests
  import json

  url = 'https://aihubmix.com/v1/embeddings'
  headers = {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' # Replace it by your AiHubMix Key
  }

  data = {
      "model": "jina-clip-v2",
      "input": [
          {
              "text": "A beautiful sunset over the beach"
          },
          {
              "text": "Un beau coucher de soleil sur la plage"
          },
          {
              "text": "海滩上美丽的日落"
          },
          {
              "text": "浜辺に沈む美しい夕日"
          },
          {
              "image": "https://i.ibb.co/nQNGqL0/beach1.jpg"
          },
          {
              "image": "https://i.ibb.co/r5w8hG8/beach2.jpg"
          },
          {
              "image": "R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7"
          }
      ]
  }

  response = requests.post(url, headers=headers, data=json.dumps(data))

  print(response.json())
  ```

  ```ts TypeScript theme={null}
  import fetch from 'node-fetch';

  const url = 'https://aihubmix.com/v1/embeddings';
  const headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer sk-***' // Replace it by your AiHubMix Key
  };
  const data = {
    "model": "jina-clip-v2",
    "input": [
      {
        "text": "A beautiful sunset over the beach"
      },
      {
        "text": "Un beau coucher de soleil sur la plage"
      },
      {
        "text": "海滩上美丽的日落"
      },
      {
        "text": "浜辺に沈む美しい夕日"
      },
      {
        "image": "https://i.ibb.co/nQNGqL0/beach1.jpg"
      },
      {
        "image": "https://i.ibb.co/r5w8hG8/beach2.jpg"
      },
      {
        "image": "R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7"
      }
    ]
  };

  fetch(url, {
    method: 'POST',
    headers: headers,
    body: JSON.stringify(data)
  })
    .then(response => response.json())
    .then(data => console.log(data))
    .catch(err => console.error(err));

  ```
</CodeGroup>

### 2. Pure Text Usage

Only provide an array of text strings, do not provide the `image` field.

<CodeGroup>
  ```shell Curl theme={null}
  curl https://aihubmix.com/v1/rerank \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer sk-***" \
    -d @- <<EOFEOF
    {
      "model": "jina-embeddings-v3",
      "input": [
          "A beautiful sunset over the beach",
          "Un beau coucher de soleil sur la plage",
          "海滩上美丽的日落",
          "浜辺に沈む美しい夕日"
      ]
    }
  EOFEOF
  ```

  ```py Python theme={null}
  import requests

  url = 'https://aihubmix.com/v1/embeddings'
  headers = {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' # Replace it by your AiHubMix Key
  }
  data = {
      'model': 'jina-embeddings-v3',
      'input': [
          'A beautiful sunset over the beach',
          'Un beau coucher de soleil sur la plage',
          '海滩上美丽的日落',
          '浜辺に沈む美しい夕日'
      ]
  }

  response = requests.post(url, headers=headers, json=data)

  print(response.json())
  ```

  ```ts TypeScript theme={null}
  import * as https from 'https';

  const options = {
    hostname: 'aihubmix.com',
    path: '/v1/embeddings',   
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' // Replace it by your AiHubMix Key
    }
  };

  const data = JSON.stringify({
    "model": "jina-embeddings-v3",
    "input": [
      "A beautiful sunset over the beach",
      "Un beau coucher de soleil sur la plage",
      "海滩上美丽的日落",
      "浜辺に沈む美しい夕日"
    ]
  });

  const req = https.request(options, res => {
    let responseData = '';
    res.on('data', chunk => {
      responseData += chunk;
    });
    res.on('end', () => {
      console.log(responseData);
    });
  });

  req.write(data);
  req.end();

  ```
</CodeGroup>

## Rerank

Reranker aims to improve search relevance and RAG accuracy. It deep analyzes the initial search results, considers the subtle interactions between the query and document content, and reorders the search results to place the most relevant results at the top.

### Request Parameters

<ParamField body="model" type="string" required>
  Model name, available model list:

  * `jina-reranker-m0`：Multimodal multilingual document reranker, 10K context, 2.4B parameters, for visual document sorting
</ParamField>

<ParamField body="query" type="string" required>
  Search query text, used to compare with candidate documents
</ParamField>

<ParamField body="top_n" type="integer">
  The number of most relevant documents to return. Default returns all documents
</ParamField>

<ParamField body="documents" type="array" required>
  Array of candidate documents, will be reordered based on relevance to the query
</ParamField>

<ParamField body="max_chunk_per_doc" default="4096" type="integer">
  Maximum chunk length per document, applicable **only** to Cohere (not supported by Jina). Defaults to 4096.\
  Long documents will be automatically truncated to the specified number of tokens.
</ParamField>

### 1. Multimodal Usage

<CodeGroup>
  ```shell Curl theme={null}
  curl https://aihubmix.com/v1/rerank \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer sk-***" \
    -d @- <<EOFEOF
    {
      "model": "jina-reranker-m0",
      "query": "small language model data extraction",
      "documents": [
          {
              "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png"
          },
          {
              "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"
          },
          {
              "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/wired-preview.png"
          },
          {
              "text": "We present ReaderLM-v2, a compact 1.5 billion parameter language model designed for efficient web content extraction. Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models. The models effectiveness results from two key innovations: (1) a three-stage data synthesis pipeline that generates high quality, diverse training data by iteratively drafting, refining, and critiquing web content extraction; and (2) a unified training framework combining continuous pre-training with multi-objective optimization. Intensive evaluation demonstrates that ReaderLM-v2 outperforms GPT-4o-2024-08-06 and other larger models by 15-20% on carefully curated benchmarks, particularly excelling at documents exceeding 100K tokens, while maintaining significantly lower computational requirements."
          },
          {
              "image": "https://jina.ai/blog-banner/using-deepseek-r1-reasoning-model-in-deepsearch.webp"
          },
          {
              "text": "Data extraction? Why not just use regular expressions—wouldn't regex solve it all?"
          },
          {
              "text": "During the California Gold Rush, some merchants made more money selling supplies to miners than the miners made finding gold."
          },
          {
              "text": "Die wichtigsten Beiträge unserer Arbeit sind zweifach: Erstens führen wir eine neuartige dreistufige Datensynthese-Pipeline namens Draft-Refine-Critique ein, die durch iterative Verfeinerung hochwertige Trainingsdaten generiert; und zweitens schlagen wir eine umfassende Trainingsstrategie vor, die kontinuierliches Vortraining zur Längenerweiterung, überwachtes Feintuning mit spezialisierten Kontrollpunkten, direkte Präferenzoptimierung (DPO) und iteratives Self-Play-Tuning kombiniert. Um die weitere Forschung und Anwendung der strukturierten Inhaltsextraktion zu erleichtern, ist das Modell auf Hugging Face öffentlich verfügbar."
          },
          {
              "image": "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"
          }
      ]
    }
  EOFEOF
  ```

  ```py Python theme={null}
  import requests

  url = 'https://aihubmix.com/v1/rerank'
  headers = {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' # Replace it by your AiHubMix Key 
  }
  data = {
      "model": "jina-reranker-m0",
      "query": "small language model data extraction",
      "documents": [
          {
              "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png"
          },
          {
              "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png"
          },
          {
              "image": "https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/wired-preview.png"
          },
          {
              "text": "We present ReaderLM-v2, a compact 1.5 billion parameter language model designed for efficient web content extraction. Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models. The models effectiveness results from two key innovations: (1) a three-stage data synthesis pipeline that generates high quality, diverse training data by iteratively drafting, refining, and critiquing web content extraction; and (2) a unified training framework combining continuous pre-training with multi-objective optimization. Intensive evaluation demonstrates that ReaderLM-v2 outperforms GPT-4o-2024-08-06 and other larger models by 15-20% on carefully curated benchmarks, particularly excelling at documents exceeding 100K tokens, while maintaining significantly lower computational requirements."
          },
          {
              "image": "https://jina.ai/blog-banner/using-deepseek-r1-reasoning-model-in-deepsearch.webp"
          },
          {
              "text": "Data extraction? Why not just use regular expressions—wouldn't regex solve it all?"
          },
          {
              "text": "During the California Gold Rush, some merchants made more money selling supplies to miners than the miners made finding gold."
          },
          {
              "text": "Die wichtigsten Beiträge unserer Arbeit sind zweifach: Erstens führen wir eine neuartige dreistufige Datensynthese-Pipeline namens Draft-Refine-Critique ein, die durch iterative Verfeinerung hochwertige Trainingsdaten generiert; und zweitens schlagen wir eine umfassende Trainingsstrategie vor, die kontinuierliches Vortraining zur Längenerweiterung, überwachtes Feintuning mit spezialisierten Kontrollpunkten, direkte Präferenzoptimierung (DPO) und iteratives Self-Play-Tuning kombiniert. Um die weitere Forschung und Anwendung der strukturierten Inhaltsextraktion zu erleichtern, ist das Modell auf Hugging Face öffentlich verfügbar."
          },
          {
              "image": "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"
          }
      ]
  }

  response = requests.post(url, headers=headers, json=data)

  print(response.json())

  ```

  ```ts TypeScript theme={null}
  import fetch from 'node-fetch';

  const url = 'https://aihubmix.com/v1/rerank';

  const options = {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' // Replace it by your AiHubMix Key
    },
    body: JSON.stringify({
      model: 'jina-reranker-m0',
      query: 'small language model data extraction',
      documents: [
        {
          image: 'https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/handelsblatt-preview.png'
        },
        {
          image: 'https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/paper-11.png'
        },
        {
          image: 'https://raw.githubusercontent.com/jina-ai/multimodal-reranker-test/main/wired-preview.png'
        },
        {
          text: 'We present ReaderLM-v2, a compact 1.5 billion parameter language model designed for efficient web content extraction. Our model processes documents up to 512K tokens, transforming messy HTML into clean Markdown or JSON formats with high accuracy -- making it an ideal tool for grounding large language models. The models effectiveness results from two key innovations: (1) a three-stage data synthesis pipeline that generates high quality, diverse training data by iteratively drafting, refining, and critiquing web content extraction; and (2) a unified training framework combining continuous pre-training with multi-objective optimization. Intensive evaluation demonstrates that ReaderLM-v2 outperforms GPT-4o-2024-08-06 and other larger models by 15-20% on carefully curated benchmarks, particularly excelling at documents exceeding 100K tokens, while maintaining significantly lower computational requirements.'
        },
        {
          image: 'https://jina.ai/blog-banner/using-deepseek-r1-reasoning-model-in-deepsearch.webp'
        },
        {
          text: 'Data extraction? Why not just use regular expressions—wouldn\'t regex solve it all?'
        },
        {
          text: 'During the California Gold Rush, some merchants made more money selling supplies to miners than the miners made finding gold.'
        },
        {
          text: 'Die wichtigsten Beiträge unserer Arbeit sind zweifach: Erstens führen wir eine neuartige dreistufige Datensynthese-Pipeline namens Draft-Refine-Critique ein, die durch iterative Verfeinerung hochwertige Trainingsdaten generiert; und zweitens schlagen wir eine umfassende Trainingsstrategie vor, die kontinuierliches Vortraining zur Längenerweiterung, überwachtes Feintuning mit spezialisierten Kontrollpunkten, direkte Präferenzoptimierung (DPO) und iteratives Self-Play-Tuning kombiniert. Um die weitere Forschung und Anwendung der strukturierten Inhaltsextraktion zu erleichtern, ist das Modell auf Hugging Face öffentlich verfügbar.'
        },
        {
          image: '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'
        }
      ]
    })
  };

  fetch(url, options)
    .then(response => response.json())
    .then(data => console.log(data))
    .catch(error => console.error('Error:', error));
  ```
</CodeGroup>

### Response Description

```json theme={null}
{
  "model": "jina-reranker-m0",
  "results": [
    {
      "index": 1,
      "relevance_score": 0.8814517277012487
    },
    {
      "index": 3,
      "relevance_score": 0.7756727858283531
    },
    {
      "index": 7,
      "relevance_score": 0.6128658982982312
    }
  ],
  "usage": {
    "total_tokens": 2894
  }
}
```

**Response successful:**

* `model`: The name of the model used
* `results`: An array of reranking results sorted by relevance score in descending order, each element contains:
  * `index`: The index position in the original document array
  * `relevance_score`: A relevance score between 0-1, higher scores indicate greater relevance to the query
* `usage`: Usage statistics
  * `total_tokens`: Total number of tokens processed in this request

### 2. Text Usage

Text reranking supports both multilingual and regular tasks, similar to embedding usage, by passing in an array.

<CodeGroup>
  ```shell Curl theme={null}
  curl https://aihubmix.com/v1/rerank \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer sk-***" \
    -d @- <<EOFEOF
    {
      "model": "jina-reranker-v2-base-multilingual",
      "query": "Organic skincare products for sensitive skin",
      "top_n": 3,
      "documents": [
          "Organic skincare for sensitive skin with aloe vera and chamomile: Imagine the soothing embrace of nature with our organic skincare range, crafted specifically for sensitive skin. Infused with the calming properties of aloe vera and chamomile, each product provides gentle nourishment and protection. Say goodbye to irritation and hello to a glowing, healthy complexion.",
          "New makeup trends focus on bold colors and innovative techniques: Step into the world of cutting-edge beauty with this seasons makeup trends. Bold, vibrant colors and groundbreaking techniques are redefining the art of makeup. From neon eyeliners to holographic highlighters, unleash your creativity and make a statement with every look.",
          "Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille: Erleben Sie die wohltuende Wirkung unserer Bio-Hautpflege, speziell für empfindliche Haut entwickelt. Mit den beruhigenden Eigenschaften von Aloe Vera und Kamille pflegen und schützen unsere Produkte Ihre Haut auf natürliche Weise. Verabschieden Sie sich von Hautirritationen und genießen Sie einen strahlenden Teint.",
          "Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken: Tauchen Sie ein in die Welt der modernen Schönheit mit den neuesten Make-up-Trends. Kräftige, lebendige Farben und innovative Techniken setzen neue Maßstäbe. Von auffälligen Eyelinern bis hin zu holografischen Highlightern – lassen Sie Ihrer Kreativität freien Lauf und setzen Sie jedes Mal ein Statement.",
          "Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla: Descubre el poder de la naturaleza con nuestra línea de cuidado de la piel orgánico, diseñada especialmente para pieles sensibles. Enriquecidos con aloe vera y manzanilla, estos productos ofrecen una hidratación y protección suave. Despídete de las irritaciones y saluda a una piel radiante y saludable.",
          "Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras: Entra en el fascinante mundo del maquillaje con las tendencias más actuales. Colores vivos y técnicas innovadoras están revolucionando el arte del maquillaje. Desde delineadores neón hasta iluminadores holográficos, desata tu creatividad y destaca en cada look.",
          "针对敏感肌专门设计的天然有机护肤产品：体验由芦荟和洋甘菊提取物带来的自然呵护。我们的护肤产品特别为敏感肌设计，温和滋润，保护您的肌肤不受刺激。让您的肌肤告别不适，迎来健康光彩。",
          "新的化妆趋势注重鲜艳的颜色和创新的技巧：进入化妆艺术的新纪元，本季的化妆趋势以大胆的颜色和创新的技巧为主。无论是霓虹眼线还是全息高光，每一款妆容都能让您脱颖而出，展现独特魅力。",
          "敏感肌のために特別に設計された天然有機スキンケア製品: アロエベラとカモミールのやさしい力で、自然の抱擁を感じてください。敏感肌用に特別に設計された私たちのスキンケア製品は、肌に優しく栄養を与え、保護します。肌トラブルにさようなら、輝く健康な肌にこんにちは。",
          "新しいメイクのトレンドは鮮やかな色と革新的な技術に焦点を当てています: 今シーズンのメイクアップトレンドは、大胆な色彩と革新的な技術に注目しています。ネオンアイライナーからホログラフィックハイライターまで、クリエイティビティを解き放ち、毎回ユニークなルックを演出しましょう。"
      ]
    }
  EOFEOF
  ```

  ```py Python theme={null}
  import requests

  url = 'https://aihubmix.com/v1/rerank'
  headers = {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' # Replace it by your AiHubMix Key
  }
  data = {
      "model": "jina-reranker-v2-base-multilingual",
      "query": "Organic skincare products for sensitive skin",
      "top_n": 3,
      "documents": [
          "Organic skincare for sensitive skin with aloe vera and chamomile: Imagine the soothing embrace of nature with our organic skincare range, crafted specifically for sensitive skin. Infused with the calming properties of aloe vera and chamomile, each product provides gentle nourishment and protection. Say goodbye to irritation and hello to a glowing, healthy complexion.",
          "New makeup trends focus on bold colors and innovative techniques: Step into the world of cutting-edge beauty with this seasons makeup trends. Bold, vibrant colors and groundbreaking techniques are redefining the art of makeup. From neon eyeliners to holographic highlighters, unleash your creativity and make a statement with every look.",
          "Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille: Erleben Sie die wohltuende Wirkung unserer Bio-Hautpflege, speziell für empfindliche Haut entwickelt. Mit den beruhigenden Eigenschaften von Aloe Vera und Kamille pflegen und schützen unsere Produkte Ihre Haut auf natürliche Weise. Verabschieden Sie sich von Hautirritationen und genießen Sie einen strahlenden Teint.",
          "Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken: Tauchen Sie ein in die Welt der modernen Schönheit mit den neuesten Make-up-Trends. Kräftige, lebendige Farben und innovative Techniken setzen neue Maßstäbe. Von auffälligen Eyelinern bis hin zu holografischen Highlightern – lassen Sie Ihrer Kreativität freien Lauf und setzen Sie jedes Mal ein Statement.",
          "Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla: Descubre el poder de la naturaleza con nuestra línea de cuidado de la piel orgánico, diseñada especialmente para pieles sensibles. Enriquecidos con aloe vera y manzanilla, estos productos ofrecen una hidratación y protección suave. Despídete de las irritaciones y saluda a una piel radiante y saludable.",
          "Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras: Entra en el fascinante mundo del maquillaje con las tendencias más actuales. Colores vivos y técnicas innovadoras están revolucionando el arte del maquillaje. Desde delineadores neón hasta iluminadores holográficos, desata tu creatividad y destaca en cada look.",
          "针对敏感肌专门设计的天然有机护肤产品：体验由芦荟和洋甘菊提取物带来的自然呵护。我们的护肤产品特别为敏感肌设计，温和滋润，保护您的肌肤不受刺激。让您的肌肤告别不适，迎来健康光彩。",
          "新的化妆趋势注重鲜艳的颜色和创新的技巧：进入化妆艺术的新纪元，本季的化妆趋势以大胆的颜色和创新的技巧为主。无论是霓虹眼线还是全息高光，每一款妆容都能让您脱颖而出，展现独特魅力。",
          "敏感肌のために特別に設計された天然有機スキンケア製品: アロエベラとカモミールのやさしい力で、自然の抱擁を感じてください。敏感肌用に特別に設計された私たちのスキンケア製品は、肌に優しく栄養を与え、保護します。肌トラブルにさようなら、輝く健康な肌にこんにちは。",
          "新しいメイクのトレンドは鮮やかな色と革新的な技術に焦点を当てています: 今シーズンのメイクアップトレンドは、大胆な色彩と革新的な技術に注目しています。ネオンアイライナーからホログラフィックハイライターまで、クリエイティビティを解き放ち、毎回ユニークなルックを演出しましょう。"
      ]
  }

  response = requests.post(url, headers=headers, json=data)

  print(response.json())
  ```

  ```ts TypeScript theme={null}
  const https = require('https');

  const requestData = {
    model: "jina-reranker-v2-base-multilingual",
    query: "Organic skincare products for sensitive skin",
    top_n: 3,
    documents: [
      "Organic skincare for sensitive skin with aloe vera and chamomile: Imagine the soothing embrace of nature with our organic skincare range, crafted specifically for sensitive skin. Infused with the calming properties of aloe vera and chamomile, each product provides gentle nourishment and protection. Say goodbye to irritation and hello to a glowing, healthy complexion.",
      "New makeup trends focus on bold colors and innovative techniques: Step into the world of cutting-edge beauty with this seasons makeup trends. Bold, vibrant colors and groundbreaking techniques are redefining the art of makeup. From neon eyeliners to holographic highlighters, unleash your creativity and make a statement with every look.",
      "Bio-Hautpflege für empfindliche Haut mit Aloe Vera und Kamille: Erleben Sie die wohltuende Wirkung unserer Bio-Hautpflege, speziell für empfindliche Haut entwickelt. Mit den beruhigenden Eigenschaften von Aloe Vera und Kamille pflegen und schützen unsere Produkte Ihre Haut auf natürliche Weise. Verabschieden Sie sich von Hautirritationen und genießen Sie einen strahlenden Teint.",
      "Neue Make-up-Trends setzen auf kräftige Farben und innovative Techniken: Tauchen Sie ein in die Welt der modernen Schönheit mit den neuesten Make-up-Trends. Kräftige, lebendige Farben und innovative Techniken setzen neue Maßstäbe. Von auffälligen Eyelinern bis hin zu holografischen Highlightern – lassen Sie Ihrer Kreativität freien Lauf und setzen Sie jedes Mal ein Statement.",
      "Cuidado de la piel orgánico para piel sensible con aloe vera y manzanilla: Descubre el poder de la naturaleza con nuestra línea de cuidado de la piel orgánico, diseñada especialmente para pieles sensibles. Enriquecidos con aloe vera y manzanilla, estos productos ofrecen una hidratación y protección suave. Despídete de las irritaciones y saluda a una piel radiante y saludable.",
      "Las nuevas tendencias de maquillaje se centran en colores vivos y técnicas innovadoras: Entra en el fascinante mundo del maquillaje con las tendencias más actuales. Colores vivos y técnicas innovadoras están revolucionando el arte del maquillaje. Desde delineadores neón hasta iluminadores holográficos, desata tu creatividad y destaca en cada look.",
      "针对敏感肌专门设计的天然有机护肤产品：体验由芦荟和洋甘菊提取物带来的自然呵护。我们的护肤产品特别为敏感肌设计，温和滋润，保护您的肌肤不受刺激。让您的肌肤告别不适，迎来健康光彩。",
      "新的化妆趋势注重鲜艳的颜色和创新的技巧：进入化妆艺术的新纪元，本季的化妆趋势以大胆的颜色和创新的技巧为主。无论是霓虹眼线还是全息高光，每一款妆容都能让您脱颖而出，展现独特魅力。",
      "敏感肌のために特別に設計された天然有機スキンケア製品：アロエベラとカモミールのやさしい力で、自然の抱擁を感じてください。敏感肌用に特別に設計された私たちのスキンケア製品は、肌に優しく栄養を与え、保護します。肌トラブルにさようなら、輝く健康な肌にこんにちは。",
      "新しいメイクのトレンドは鮮やかな色と革新的な技術に焦点を当てています：今シーズンのメイクアップトレンドは、大胆な色彩と革新的な技術に注目しています。ネオンアイライナーからホログラフィックハイライターまで、クリエイティビティを解き放ち、毎回ユニークなルックを演出しましょう。"
    ]
  };

  const options = {
    hostname: 'aihubmix.com',
    port: 443,
    path: '/v1/rerank',
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' // Replace it by your AiHubMix Key
    }
  };

  const req = https.request(options, res => {
    let responseData = '';
    res.on('data', chunk => {
      responseData += chunk;
    });
    res.on('end', () => {
      console.log(responseData);
    });
  });

  req.write(JSON.stringify(requestData));
  req.end();
  ```
</CodeGroup>

## DeepSearch

DeepSearch combines search, reading, and reasoning capabilities to pursue the best possible answer. It's fully compatible with OpenAI's Chat API format—just replace api.openai.com with aihubmix.com to get started.\
The stream will return the thinking process.

### Request Parameters

<ParamField body="model" type="string" required>
  Model name, available models:

  * `jina-deepsearch-v1`：Default model, search, read and reason until the best answer is found
</ParamField>

<ParamField body="stream" type="boolean" default="true">
  Whether to enable streaming response. It is strongly recommended to keep this option enabled, DeepSearch requests may take a long time to complete, disabling streaming may result in a '524 Timeout' error
</ParamField>

<ParamField body="messages" type="array" required>
  The list of conversation messages between the user and the assistant. Supports multiple types (modal) messages, such as text (.txt, .pdf), images (.png, .webp, .jpeg), etc. The maximum file size is 10MB
</ParamField>

### Multimodal Message Format

DeepSearch supports multiple types of message formats, which can include pure text (message), files (file), and images (image). The following are examples of different formats:

#### 1. Pure Text Message

```json theme={null}
{
  "role": "user",
  "content": "hi"
}
```

#### 2. Message with File Attachment

```json theme={null}
{
  "role": "user",
  "content": [
    {
      "type": "text",
      "text": "what's in this file?"
    },
    {
      "type": "file",
      "data": "data:application/pdf;base64,JVBERi0xLjQKJfbk...", // PDF 文件的 base64 编码
      "mimeType": "application/pdf"
    }
  ]
}
```

#### 3. Message with Image

```json theme={null}
{
  "role": "user",
  "content": [
    {
      "type": "text",
      "text": "what's in the image?"
    },
    {
      "type": "image",
      "image": "data:image/webp;base64,UklGRoDOAAB...", // the base64 encoding of images
      "mimeType": "image/webp"
    }
  ]
}
```

All files and images must be encoded in data URI format (data URI) in advance, with a maximum file size of 10MB.

### Example of Calling

Please note that **Jina AI's Python streaming call on the official website will not have a response**; please refer to our example.

<CodeGroup>
  ```shell Curl theme={null}
  curl https://aihubmix.com/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer sk-***" \
    -d @- <<EOFEOF
    {
      "model": "jina-deepsearch-v1",
      "messages": [
          {
              "role": "user",
              "content": "Hi!"
          },
          {
              "role": "assistant",
              "content": "Hi, how can I help you?"
          },
          {
              "role": "user",
              "content": "what's the latest blog post from jina ai?"
          }
      ],
      "stream": true
    }
  EOFEOF
  ```

  ```py Python theme={null}
  import requests
  import json

  url = 'https://aihubmix.com/v1/chat/completions'
  headers = {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer sk-***' # Replace it by your AiHubMix Key
  }

  data = {
      "model": "jina-deepsearch-v1",
      "messages": [
          {
              "role": "user",
              "content": "Hi!"
          },
          {
              "role": "assistant",
              "content": "Hi, how can I help you?"
          },
          {
              "role": "user",
              "content": "what's the latest blog post from jina ai?"
          }
      ],
      "stream": True
  }

  # Use stream=True in the request to handle streaming responses
  response = requests.post(url, headers=headers, json=data, stream=True)

  # Check if the request was successful
  if response.status_code == 200:
      # Iterate over the response content line by line
      for line in response.iter_lines():
          if line:
              # Decode line and print (assuming SSE format: "data: {...}")
              decoded_line = line.decode('utf-8')
              if decoded_line.startswith('data: '):
                  # Handle potential end signal like "data: [DONE]"
                  if decoded_line[len('data: '):].strip() == '[DONE]':
                      print("\nStream finished.")
                      break
                  try:
                      # Extract the JSON part
                      json_data = json.loads(decoded_line[len('data: '):])
                      # Process the JSON data
                      if 'choices' in json_data and len(json_data['choices']) > 0:
                          delta = json_data['choices'][0].get('delta', {})
                          content_to_print = delta.get('content') or delta.get('reasoning_content') # Check both fields

                          if content_to_print:
                              print(content_to_print, end='', flush=True)
                  except json.JSONDecodeError:
                      # Ignore lines that are not valid JSON after "data: "
                      # print(f"\nCould not decode JSON from line: {decoded_line}")
                      pass # Optionally log or handle non-JSON data lines if needed
              # Handle lines that don't start with "data: " if necessary
              # else:
              #    print(f"Received non-data line: {decoded_line}")

      print() # Add a newline at the end
  elif response.status_code == 401:
       print(f"Error: {response.status_code} - Unauthorized. Please check your API key.")
       try:
           print(response.json())
       except json.JSONDecodeError:
           print(response.text)
  else:
      print(f"Error: {response.status_code}")
      try:
          print(response.json()) # Print error details if available
      except json.JSONDecodeError:
          print(response.text) # Print raw text if not JSON
  ```

  ```ts TypeScript theme={null}
  const https = require('https');

  const data = JSON.stringify({
    model: "jina-deepsearch-v1",
    messages: [
      {
        role: "user",
        content: "Hi!"
      },
      {
        role: "assistant",
        content: "Hi, how can I help you?"
      },
      {
        role: "user",
        content: "what's the latest blog post from jina ai?"
      }
    ],
    stream: true
  });

  const options = {
    hostname: 'aihubmix.com',
    path: '/v1/chat/completions',
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Content-Length': data.length,
      'Authorization': 'Bearer sk-***' // Replace it by your AiHubMix Key
    }
  };

  const req = https.request(options, res => {
    console.log(`statusCode: ${res.statusCode}`);

    res.on('data', d => {
      process.stdout.write(d);
    });
  });

  req.on('error', error => {
    console.error(error);
  });

  req.write(data);
  req.end();
  ```
</CodeGroup>

### Response Description

The response from DeepSearch is streamed by default, including both intermediate reasoning steps and the final answer.
The last block of the stream contains the final response, a list of visited URLs, and token usage details.
If streaming is disabled, only the final answer will be returned—intermediate "thinking" steps will be omitted.
**Note:** This JSON object differs from the format used by Jina AI.

```json theme={null}
{
  "id": "1745506101379",
  "object": "chat.completion.chunk",
  "created": 1745506101,
  "model": "jina-deepsearch-v1",
  "choices": [
    {
      "index": 0,
      "delta": {
        "role": "assistant",
        "reasoning_content": "<think>"
      }
    }
  ],
  "system_fingerprint": "fp_1745506101379"
}

// Streaming reason
{
  "id": "1745506101379",
  "object": "chat.completion.chunk",
  "created": 1745506101,
  "model": "jina-deepsearch-v1",
  "choices": [
    {
      "index": 0,
      "delta": {
        "reasoning_content": "thinking parts"
      }
    }
  ],
  "system_fingerprint": "fp_1745506101379"
}

// Reasoning finished
{
  "id": "1745506101379",
  "object": "chat.completion.chunk",
  "created": 1745506101,
  "model": "jina-deepsearch-v1",
  "choices": [
    {
      "index": 0,
      "delta": {
        "reasoning_content": "</think>\n\n"
      },
      "finish_reason": "thinking_end"
    }
  ],
  "system_fingerprint": "fp_1745506101379"
}

// The Final Response with URL
{
  "id": "1745506101379",
  "object": "chat.completion.chunk",
  "created": 1745506101,
  "model": "jina-deepsearch-v1",
  "choices": [
    {
      "index": 0,
      "delta": {
        "content": "Response content",
        "type": "text",
        "annotations": [
          {
            "type": "url_citation",
            "url_citation": {
              "url": "https://example.com",
              "title": "Page Title",
              "start_index": 0,
              "end_index": 0
            }
          }
        ]
      },
      "finish_reason": "stop"
    }
  ],
  "system_fingerprint": "fp_1745506101379",
  "usage": {
    "prompt_tokens": 673423,
    "completion_tokens": 109286,
    "total_tokens": 583555
  }
}

data: [DONE]
```

**Python Return Example:**

```py Python theme={null}
<think>I need to check the Jina AI blog for their most recent post, which requires up-to-date information. I need to find the latest blog post from Jina AI. I will use a search engine to find the Jina AI blog and then identify the most recent post. Let me search for latest blog post from Jina AI to gather more information. Okay, I've created some queries to find the latest Jina AI blog post. First, a general search for the Jina AI blog updated in the past week. Then, some focused queries on specific Jina AI products like DeepSearch and neural search, checking for updates in the last month. Also, I included queries about embedding models and API updates, again looking at the past month. And I added a query about Elasticsearch integration from the past year. Finally, I've added a query to find any criticisms or limitations of Jina AI, to get a balanced perspective. Let me search for Jina AI Elasticsearch integration, Jina AI criticism limitations, Jina AI deepsearch updates, Jina AI neural search, Jina AI embedding models to gather more information. To accurately answer the user's question about the latest blog post from Jina AI, I need to visit the provided URLs and extract the publication dates and titles of the blog posts. This will allow me to identify the most recent one. I'll start with the most relevant URLs based on the weights assigned during the search action. Let me read https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch, https://jina.ai/news/auto-gpt-unmasked-hype-hard-truths-production-pitfalls, https://jinaai.cn/news/a-practical-guide-to-implementing-deepsearch-deepresearch, https://businesswire.com/news/home/20250220781575/en/Elasticsearch-Open-Inference-API-now-Supports-Jina-AI-Embeddings-and-Rerank-Model, https://gurufocus.com/news/2709507/elastic-nv-estc-enhances-elasticsearch-with-jina-ai-integration to gather more information. Content of https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch is too long, let me cherry-pick the relevant parts. Content of https://jinaai.cn/news/a-practical-guide-to-implementing-deepsearch-deepresearch is too long, let me cherry-pick the relevant parts. Content of https://jina.ai/news/auto-gpt-unmasked-hype-hard-truths-production-pitfalls is too long, let me cherry-pick the relevant parts. I have found several blog posts and news articles related to Jina AI. I will summarize the most recent information available to answer the user's question. But wait, let me evaluate the answer first. The answer provides a summary of recent blog posts from Jina AI, covering different aspects of their activities. This constitutes a definitive response as it directly addresses the question with specific information. The answer discusses recent blog posts and news from Jina AI. Tech news has a max age of 7 days, and since the blog posts are only a few days old, the answer is still fresh. I am sorry, but the answer is too generic. While it mentions a few blog posts, it doesn't identify the absolute latest one. A perfect answer should pinpoint the most recent blog post with its exact title and, if possible, a direct link. The current answer provides a summary of several recent posts, which isn't precise enough for what I'm looking for. I messed up by summarizing multiple blog posts instead of pinpointing the single latest one. I needed to focus on finding the most recent date and title. I should prioritize identifying the latest date associated with a blog post and then provide its title and a direct link if available. Okay, I need to find the absolute latest blog post from Jina AI. The previous answer was too generic. I need to be laser-focused on identifying the most recent post. I'll revisit the Jina AI news page and look for specific dates and titles. I'll prioritize URLs that are likely to contain blog posts or news announcements directly from Jina AI. Let me read https://jina.ai/news, https://jina.ai/deepsearch, https://zilliz.com/blog/training-text-embeddings-with-jina-ai, https://github.com/jina-ai/node-DeepResearch, https://x.com/jinaai_?lang=en to gather more information. Content of https://zilliz.com/blog/training-text-embeddings-with-jina-ai is too long, let me cherry-pick the relevant parts. Content of https://jina.ai/deepsearch is too long, let me cherry-pick the relevant parts. I have reviewed my knowledge and can confidently answer the user's question about the latest blog post from Jina AI. I will provide the title and a direct link. But wait, let me evaluate the answer first. The answer provides a clear and direct response to the question, including the title, publication date, and a link to the latest blog post from Jina AI. There are no uncertainty markers or hedging. The blog post was published on April 16, 2025, which is 8 days ago from today (April 24, 2025). Since the blog post falls under 'Tech News,' the maximum age should be 7 days. Therefore, the answer is outdated. Okay, I jumped the gun and didn't double-check the date of the blog post against the current date. My bad for not ensuring it was within the acceptable timeframe! Next time, I'll make absolutely sure to verify the publication date and confirm it's fresh enough before confidently declaring it the latest blog post. Gotta keep those dates in check! I need to find the very latest blog post from Jina AI. The previous attempt failed because it wasn't recent enough. I need to revisit the Jina AI news page to check for any even more recent posts. I'll focus on extracting the title and URL of the most recent entry. Let me read https://jina.ai/news?tag=tech-blog, https://news.ycombinator.com/item?id=38020109, https://github.com/sthagen/jina-ai-jina, https://parsers.vc/news/250222-the-new-frontier-of-ai--elasticsearch-and, https://jina.ai/embeddings to gather more information. Content of https://jina.ai/embeddings is too long, let me cherry-pick the relevant parts. Content of https://news.ycombinator.com/item?id=38020109 is too long, let me cherry-pick the relevant parts. I have gathered information from various sources about Jina AI's blog posts. I will now synthesize this information to provide the most recent blog post title and a direct link, if available. But wait, let me evaluate the answer first. The answer makes a direct statement about the latest blog post from Jina AI, providing the title, publication date, and a link to the post. There are no hedges or qualifications, so it is definitive. Let me finalize the answer. Let Let me cross-reference the information from the web to verify the answer. </think>

The relentless pursuit of better search demands we confront a harsh reality: size matters, and not always in a good way. Jina AI's latest blog post, "On the Size Bias of Text Embeddings and Its Impact in Search," published on April 16, 2025, tackles this head-on. It's not just another piece of content; it's a critical examination of how the length of text inputs can skew similarity metrics, leading search systems to favor verbose, often irrelevant documents over concise, precise matches.

Why should you care? Because in the age of information overload, relevance is king. We're drowning in data, and search engines are our life rafts. But if those rafts are steered by algorithms that are easily fooled by sheer volume, we're all going down with the ship.

The post likely delves into the mechanics of text embeddings, those numerical representations of text that allow machines to understand and compare semantic meaning. The core issue, as Jina AI points out, is that these embeddings can be influenced by the length of the input text, a phenomenon they term "size bias." This means that a longer document, even if only marginally relevant, might appear more similar to a query than a shorter, more focused one.[^1]

To truly grasp the implications, consider the following:

*   **What is Size Bias?** Size bias refers to how the length of text inputs affects similarity, regardless of semantic relevance. It explains why search systems sometimes return long, barely-relevant documents instead of shorter, more precise matches to your query.[^2]
*   **Who is impacted?** Anyone relying on semantic search, from researchers sifting through academic papers to businesses trying to surface the most pertinent information for their customers, is vulnerable to the distortions caused by size bias.
*   **Where does this problem manifest?** This issue isn't confined to a specific search engine or platform. It's a systemic challenge inherent in the way many text embedding models are designed and implemented.
*   **When did this become a pressing concern?** As context windows grow, and models are ingesting larger and larger documents, the problem of size bias becomes amplified.
*   **Why does this happen?** The reasons are complex, but it boils down to the mathematical properties of high-dimensional spaces and the way similarity is calculated. Longer vectors simply have more "surface area" to overlap with a query vector, even if the semantic alignment is weak.
*   **How can we fix it?** Jina AI's blog post likely explores potential mitigation strategies. These might include normalization techniques, architectural modifications to embedding models, or novel similarity metrics that are less susceptible to length-related distortions.

Jina AI's work here isn't just academic; it's a practical intervention. By identifying and analyzing size bias, they're paving the way for more accurate and reliable search technologies. This has real-world implications, influencing everything from information retrieval to content recommendation and beyond.

The latest blog post can be found here: https://jina.ai/news

Ultimately, Jina AI's willingness to confront the inconvenient truths about text embeddings is a testament to their commitment to advancing the field. It's a reminder that progress isn't just about building bigger and more complex models; it's about understanding the nuances and limitations of those models and striving for solutions that prioritize accuracy and relevance above all else. And that's a size-independent truth worth embracing.



[^1]: Size bias refers to how the length of text inputs affects similarity regardless of semantic relevance It explains why search systems sometimes return long barely relevant documents instead of shorter more precise matches to your query [Newsroom - Jina AI](https://jina.ai/news)

[^2]: Size bias refers to how the length of text inputs affects similarity regardless of semantic relevance It explains why search systems sometimes return long barely relevant documents instead of shorter more precise matches to your query [Newsroom - Jina AI](https://jina.ai/news?tag=tech-blog)
Stream finished.
```

## Web Search (Search)

Powered by Jina AI's `s.jina.ai`, pass a query and get the clean body text of the search engine results page (SERP), ready to feed straight into an LLM for web-grounded Q\&A and RAG. The endpoint supports both `GET` and `POST`.

<Note>
  **Response format (markdown by default)**: by default returns a concatenated **markdown** list of results, ready to feed to an LLM; when you need structured data (each result's `title` / `url` / `content` and `usage`), add the request header `Accept: application/json` to get JSON instead.
</Note>

### Request Parameters

<ParamField query="q" type="string" required>
  Query text. Must be URL-encoded when called from code.
</ParamField>

<ParamField query="num" type="integer" default="5">
  Maximum number of results to return; the actual count depends on how many results are available.
</ParamField>

<ParamField query="gl" type="string">
  Country / region code, e.g. `US`.
</ParamField>

<ParamField query="hl" type="string">
  Interface language, e.g. `en`.
</ParamField>

<ParamField query="site" type="string">
  Restrict the search to specific sites; can be passed multiple times, e.g. `site=jina.ai&site=github.com`.
</ParamField>

<ParamField header="X-Respond-With" type="string" default="markdown">
  Body format of the results, one of `markdown` / `html` / `text`.
</ParamField>

<ParamField header="X-Retain-Images" type="string">
  Image retention policy; pass `none` to remove images and save tokens.
</ParamField>

<ParamField header="X-No-Cache" type="boolean">
  Skip the cache and fetch the latest results.
</ParamField>

In addition, search invokes Reader to extract the body of each hit, so all the `X-*` request headers used to control body formatting listed under "Web Reader (Reader)" also apply to search results.

### Example of Calling

The query and parameters can be passed as URL query parameters via `GET` (recommended, most concise), or in a JSON request body via `POST`; both hit the same endpoint and return the same result. The examples below add `Accept: application/json` to return JSON by default; **remove that header to get a clean markdown list of results** (see the first `Curl-markdown` example).

<CodeGroup>
  ```shell Curl-markdown theme={null}
  # Without the Accept header → returns a concatenated markdown list of results
  curl "https://aihubmix.com/v1/jina/search?q=AIHubMix&num=5&gl=US&hl=en" \
    -H "Authorization: Bearer sk-***"
  ```

  ```shell Curl-GET theme={null}
  curl "https://aihubmix.com/v1/jina/search?q=AIHubMix&num=5&gl=US&hl=en" \
    -H "Authorization: Bearer sk-***" \
    -H "Accept: application/json"
  ```

  ```shell Curl-POST theme={null}
  curl -X POST "https://aihubmix.com/v1/jina/search" \
    -H "Authorization: Bearer sk-***" \
    -H "Accept: application/json" \
    -H "Content-Type: application/json" \
    -d '{
      "q": "AIHubMix",
      "num": 5,
      "gl": "US",
      "hl": "en"
    }'
  ```

  ```py Python theme={null}
  import requests

  url = 'https://aihubmix.com/v1/jina/search'
  headers = {
      'Authorization': 'Bearer sk-***',  # Replace with your AiHubMix key
      'Accept': 'application/json'
  }
  params = {'q': 'AIHubMix', 'num': 5, 'gl': 'US', 'hl': 'en'}

  response = requests.get(url, headers=headers, params=params)
  print(response.json())
  ```

  ```ts TypeScript theme={null}
  const url = 'https://aihubmix.com/v1/jina/search?q=AIHubMix&num=5&gl=US&hl=en';

  fetch(url, {
    method: 'GET',
    headers: {
      'Authorization': 'Bearer sk-***', // Replace with your AiHubMix key
      'Accept': 'application/json'
    }
  })
    .then(res => res.json())
    .then(data => console.log(data))
    .catch(err => console.error(err));
  ```
</CodeGroup>

### Response Description

**By default (without `Accept`) it returns a concatenated markdown list**, each entry giving the title, source link, description (if any) and body in turn:

```text theme={null}
[1] Title: AIHubMix - One Interface, Router All LLMs
[1] URL Source: https://aihubmix.com/?lang=en
[1] Description: Access every major LLM through a single, unified interface. Connect to ChatGPT, Claude, Gemini, DeepSeek and more.
[1] Content:
If requests to http://aihubmix.com fail, you can try using a VPN, or switch to the alternative baseURL: https://api.inferera.com …

[2] Title: AI Models & Pricing - AIHubMix
[2] URL Source: https://aihubmix.com/models?lang=en
[2] Content:
…
```

**With `Accept: application/json` it returns structured JSON**:

```json theme={null}
{
  "code": 200,
  "status": 200,
  "data": [
    {
      "title": "AIHubMix - One Interface, Router All LLMs",
      "url": "https://aihubmix.com/?lang=en",
      "content": "If requests to http://aihubmix.com fail, you can try using a VPN, or switch to the alternative …",
      "usage": { "tokens": 4244 }
    },
    {
      "title": "AI Models & Pricing - AIHubMix",
      "url": "https://aihubmix.com/models?lang=en",
      "content": "…",
      "usage": { "tokens": 4303 }
    }
  ]
}
```

* `data`: array of search results (the count is controlled by `num`; the example above returns 5 but only the first 2 are shown here; `content` is the full body, truncated in the example), each containing `title`, `url`, `content`, `usage.tokens`.
* **Billing**: charged by the sum of each result's `usage.tokens`; Jina officially charges a minimum of **10000 tokens** per search, so the final charge is the larger of the two, i.e. `max(10000, sum of tokens)`.

## Web Reader (Reader)

Powered by Jina AI's `r.jina.ai`, pass any URL and get the clean markdown body of that page after conversion, convenient for scraping web content to feed to an LLM. Besides web pages, it also supports parsing **images** (described by a vision model) and **local files** (PDF, Word / Excel / PPT, HTML, images).

<Note>
  **Response format (markdown by default)**: by default returns the **clean markdown body** directly, ready to feed to an LLM; when you need structured **JSON** with `usage` and fields like `title` / `url` (the body sits in `data.content`), add the request header `Accept: application/json`.
</Note>

### Request Parameters

<ParamField path="target URL" type="string" required>
  The web address to read, appended directly to the end of the endpoint path, e.g. `/v1/jina/reader/https://jina.ai`.
</ParamField>

<ParamField body="file" type="file">
  The local file to upload; supports PDF, Word / Excel / PPT, HTML, images, passed in the `file` field via `POST` as `multipart/form-data`.
</ParamField>

<ParamField body="url" type="string">
  Required when uploading an HTML file, used as the reference address for resolving relative links in the page; not needed when uploading a PDF.
</ParamField>

<ParamField header="X-Respond-With" type="string" default="markdown">
  Return format, one of `markdown` / `html` / `text` / `screenshot` / `pageshot`.
</ParamField>

<ParamField header="X-Retain-Images" type="string" default="all">
  Image retention policy, one of `all` / `none` (remove images to save tokens) / `alt`.
</ParamField>

<ParamField header="X-Retain-Links" type="string" default="all">
  Link retention policy, one of `all` / `none` / `text`.
</ParamField>

<ParamField header="X-With-Generated-Alt" type="boolean">
  Automatically generate descriptive text for images without `alt`.
</ParamField>

<ParamField header="X-With-Links-Summary" type="boolean">
  Summarize all links at the end of the body.
</ParamField>

<ParamField header="X-With-Images-Summary" type="boolean">
  Summarize all images at the end of the body.
</ParamField>

<ParamField header="X-Engine" type="string">
  Fetching engine, one of `browser` / `direct` / `cf-browser-rendering`.
</ParamField>

<ParamField header="X-Target-Selector" type="string">
  CSS selector; extract only the matching page region.
</ParamField>

<ParamField header="X-Remove-Selector" type="string">
  CSS selector; remove matching elements (e.g. `header, footer, nav`).
</ParamField>

<ParamField header="X-Timeout" type="integer">
  Fetch timeout in seconds, max 180.
</ParamField>

<ParamField header="X-No-Cache" type="boolean">
  Skip the cache and fetch the latest.
</ParamField>

<ParamField header="X-Md-Heading-Style" type="string" default="atx">
  Markdown heading style, one of `atx` (`#`) / `setext` (underline).
</ParamField>

<ParamField header="X-Md-Bullet-List-Marker" type="string">
  Markdown bullet marker, one of `-` / `+` / `*`.
</ParamField>

<ParamField header="X-Md-Hr" type="string">
  Markdown horizontal rule style, e.g. `***`.
</ParamField>

<ParamField header="X-Md-Link-Style" type="string">
  Markdown link style, one of `inlined` / `referenced` / `discarded`.
</ParamField>

The above are only the common options. **All** `X-*` request headers supported by Jina (including the entire `X-Md-*` family) as well as `POST` body fields (such as the injected script `injectPageScript`) are **forwarded as-is** by the gateway; for the complete list and values, refer to the [official Jina documentation](https://r.jina.ai/docs).

### Multimodal Input Format

Reader supports three kinds of input. **Web pages and images** are appended directly to the end of the endpoint path (`GET`); **local files** are uploaded via `POST` as `multipart/form-data`.

#### 1. Web page URL

```text theme={null}
GET /v1/jina/reader/https://example.com
```

#### 2. Image URL (returns a visual description)

The image address is likewise appended to the end of the path. Reader uses a vision model to generate a **description** (a caption, not verbatim OCR) for the image and puts it in `content`.

```text theme={null}
GET /v1/jina/reader/https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png
```

#### 3. Upload a local file (PDF / Word·Excel·PPT / HTML / image)

```text theme={null}
POST /v1/jina/reader
Content-Type: multipart/form-data

file=@./doc.pdf              # put the file in the file field
url=https://example.com/...  # only needed when uploading HTML, as the reference address for resolving relative links
```

### Example of Calling

By default it returns the markdown body directly; add `Accept: application/json` to return structured JSON. Optional parameters are passed as `X-*` request headers and are **all forwarded as-is** by the gateway to Jina (for the full list see "Request Parameters" above).

#### 1. Read a web page

<CodeGroup>
  ```shell Basic (markdown) theme={null}
  # Without Accept → returns the clean markdown body directly
  curl "https://aihubmix.com/v1/jina/reader/https://example.com" \
    -H "Authorization: Bearer sk-***"
  ```

  ```shell Advanced (multiple parameters) theme={null}
  # Combine multiple X-* headers, all forwarded as-is by the gateway to Jina:
  #   remove images to save tokens · extract only the #bodyContent region · summarize links at the end
  curl "https://aihubmix.com/v1/jina/reader/https://en.wikipedia.org/wiki/Large_language_model" \
    -H "Authorization: Bearer sk-***" \
    -H "Accept: application/json" \
    -H "X-Retain-Images: none" \
    -H "X-Target-Selector: #bodyContent" \
    -H "X-With-Links-Summary: true"
  ```

  ```shell JSON theme={null}
  curl "https://aihubmix.com/v1/jina/reader/https://example.com" \
    -H "Authorization: Bearer sk-***" \
    -H "Accept: application/json"
  ```

  ```py Python theme={null}
  import requests

  url = 'https://aihubmix.com/v1/jina/reader/https://example.com'
  headers = {
      'Authorization': 'Bearer sk-***',  # Replace with your AiHubMix key
      'Accept': 'application/json'
  }

  response = requests.get(url, headers=headers)
  print(response.json())
  ```

  ```ts TypeScript theme={null}
  const url = 'https://aihubmix.com/v1/jina/reader/https://example.com';

  fetch(url, {
    method: 'GET',
    headers: {
      'Authorization': 'Bearer sk-***', // Replace with your AiHubMix key
      'Accept': 'application/json'
    }
  })
    .then(res => res.json())
    .then(data => console.log(data))
    .catch(err => console.error(err));
  ```
</CodeGroup>

#### 2. Read an image

```shell Curl theme={null}
curl "https://aihubmix.com/v1/jina/reader/https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png" \
  -H "Authorization: Bearer sk-***" \
  -H "Accept: application/json"
```

#### 3. Upload a local file

Upload via `POST` + `multipart/form-data`; when uploading **HTML** you must also include the `url` field as the reference address. Billing is the same as reading a URL.

<CodeGroup>
  ```shell PDF / Image / Office theme={null}
  curl -X POST "https://aihubmix.com/v1/jina/reader" \
    -H "Authorization: Bearer sk-***" \
    -H "Accept: application/json" \
    -F "file=@./doc.pdf"
  ```

  ```shell HTML (url required) theme={null}
  curl -X POST "https://aihubmix.com/v1/jina/reader" \
    -H "Authorization: Bearer sk-***" \
    -H "Accept: application/json" \
    -F "file=@./local.html" \
    -F "url=https://example.com/local.html"
  ```
</CodeGroup>

### Response Description

**By default (without `Accept`) it returns the markdown body directly** (i.e. the content of `data.content` in the JSON below). For example, reading `https://example.com`:

```text theme={null}
This domain is for use in documentation examples without needing permission. Avoid use in operations.

[Learn more](https://iana.org/domains/example)
```

**With `Accept: application/json`** it returns structured JSON. The JSON structure is the same for all three input types: `data` is a single object containing `title` / `url` / `content` / `usage.tokens`. Below are the **real responses** for the three input types (when `content` is too long, the beginning is kept and the rest is elided with `…`).

**① Reading a web page** (reading `https://example.com`):

```json theme={null}
{
  "code": 200,
  "status": 20000,
  "data": {
    "title": "Example Domain",
    "url": "https://example.com/",
    "content": "This domain is for use in documentation examples without needing permission. Avoid use in operations.\n\n[Learn more](https://iana.org/domains/example)",
    "usage": { "tokens": 29 }
  }
}
```

**② Reading an image** (`content` is the description generated by the vision model):

```json theme={null}
{
  "code": 200,
  "status": 20000,
  "data": {
    "title": "googlelogo_color_272x92dp.png",
    "url": "https://www.google.com/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png",
    "content": "The logo for Google, consisting of the word Google in lowercase letters, with its colors being blue, red, yellow, and green, representing the company's innovative approach to information and computing services",
    "usage": { "tokens": 38 }
  }
}
```

**③ Uploading a local file** (uploading a PDF paper; `content` is long, only the beginning is shown):

```json theme={null}
{
  "code": 200,
  "status": 20000,
  "data": {
    "title": "Unnoticeable Backdoor Attacks on Graph Neural Networks",
    "url": "blob:df586d587956e0ca72e50e9e12dc06fc44b7c4b480b8a640a6db7d6f488a7c91",
    "content": "# Unnoticeable Backdoor Attacks on Graph Neural Networks\n\n# Enyan Dai ∗\n\nemd5759@psu.edu\n\nThe Pennsylvania State University\n\nState College, USA\n\n## ABSTRACT\n\nGraph Neural Networks (GNNs) have achieved promising results in various tasks such as node classification and graph classification. …",
    "usage": { "tokens": 20535 }
  }
}
```

* `status`: the business status code returned by Jina upstream; on a successful reader call it is `20000` (consistent with the outer HTTP `200`).
* **Billing**: charged by `data.usage.tokens` (the actual number of output tokens), **with no minimum charge** (unlike search's "10000 tokens per call minimum"); for extremely short content a minimum billing unit is applied as a floor, so a zero charge never occurs.

***

Last updated: 2026-07-03