AiHubMix Documentation Hub

Description

Nous avons intégré les trois interfaces principales de Jina AI, vous permettant de construire facilement de puissants agents intelligents. Ces interfaces sont principalement adaptées aux scénarios suivants :

Embeddings vectoriels (Embeddings) : applicables aux scénarios de questions-réponses RAG multimodaux, tels que le service client intelligent, le recrutement intelligent et les questions-réponses sur base de connaissances.
Reranking (Rerank) : en optimisant les résultats candidats des Embeddings et en les triant selon la pertinence thématique, on améliore significativement la qualité des réponses des grands modèles de langage.
Deep Search (DeepSearch) : effectue une recherche et un raisonnement approfondis jusqu’à trouver la meilleure réponse, particulièrement adapté aux tâches complexes telles que les projets de recherche et le développement de solutions produit.

Nous avons enrichi l’API Jina AI pour prendre en charge de futures extensions, l’utilisation peut donc différer légèrement de l’implémentation native officielle.

Démarrage rapide

Remplacez la API_KEY par AIHUBMIX_API_KEY et le lien d’endpoint du modèle ; les autres paramètres et l’utilisation sont entièrement cohérents avec ceux de Jina AI officiel. Remplacement de l’endpoint :

Embeddings vectoriels (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

Embeddings

L’Embedding de Jina AI prend en charge à la fois le texte brut et les images multimodales, et excelle dans la gestion des tâches multilingues.

Request Parameters

model

string

requis

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

input

array

requis

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.

embedding_format

string

défaut:"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

dimensions

integer

défaut:"1024"

The number of dimensions used in computation. Supported values:

1024
768

1. Multimodal Usage

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

2. Pure Text Usage

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

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

Rerank

Le Reranker vise à améliorer la pertinence des recherches et la précision du RAG. Il analyse en profondeur les résultats initiaux de la recherche, prend en compte les interactions subtiles entre la requête et le contenu des documents, et réordonne les résultats afin de placer les plus pertinents en tête.

Request Parameters

model

string

requis

Model name, available model list:

jina-reranker-m0：Multimodal multilingual document reranker, 10K context, 2.4B parameters, for visual document sorting

query

string

requis

Search query text, used to compare with candidate documents

top_n

integer

The number of most relevant documents to return. Default returns all documents

documents

array

requis

Array of candidate documents, will be reordered based on relevance to the query

max_chunk_per_doc

integer

défaut:"4096"

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.

1. Multimodal Usage

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": "数据提取么？为什么不用正则啊，你用正则不就全解决了么？"
        },
        {
            "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

Response Description

{
  "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.

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

DeepSearch

DeepSearch combine des capacités de recherche, de lecture et de raisonnement pour obtenir la meilleure réponse possible. Il est entièrement compatible avec le format Chat API d’OpenAI — il suffit de remplacer api.openai.com par aihubmix.com pour démarrer. Le flux renvoie également le processus de réflexion.

Request Parameters

model

string

requis

Model name, available models:

jina-deepsearch-v1：Default model, search, read and reason until the best answer is found

stream

boolean

défaut:"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

messages

array

requis

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

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

{
  "role": "user",
  "content": "hi"
}

2. Message with File Attachment

{
  "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

{
  "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.

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

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.

{
  "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:

Python

<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. 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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.

Dernière mise à jour : 2026-06-01

Documentation Index

​Description

​Démarrage rapide

​Embeddings

​Request Parameters

​1. Multimodal Usage

​2. Pure Text Usage

​Rerank

​Request Parameters

​1. Multimodal Usage

​Response Description

​2. Text Usage

​DeepSearch

​Request Parameters

​Multimodal Message Format

​1. Pure Text Message

​2. Message with File Attachment

​3. Message with Image

​Example of Calling

​Response Description

Description

Démarrage rapide

Embeddings

Request Parameters

1. Multimodal Usage

2. Pure Text Usage

Rerank

Request Parameters

1. Multimodal Usage

Response Description

2. Text Usage

DeepSearch

Request Parameters

Multimodal Message Format

1. Pure Text Message

2. Message with File Attachment

3. Message with Image

Example of Calling

Response Description