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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.

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Beschreibung

Wir haben die drei Kern-Schnittstellen von Jina AI integriert, mit denen Sie einfach leistungsfähige intelligente Agenten aufbauen können. Diese Schnittstellen eignen sich vor allem für folgende Szenarien:
  • Vektor-Embeddings (Embeddings): Geeignet für multimodale RAG-Frage-Antwort-Szenarien wie intelligenten Kundenservice, intelligentes Recruiting und Wissensdatenbank-Q&A.
  • Reranking (Rerank): Optimiert die Embedding-Kandidaten und ordnet sie nach Themenrelevanz neu, was die Antwortqualität großer Sprachmodelle deutlich verbessert.
  • Tiefe Suche (DeepSearch): Führt tiefe Suche und Reasoning durch, bis die optimale Antwort gefunden wird – besonders geeignet für komplexe Aufgaben wie Forschungsprojekte und Produktlösungsentwicklung.
Wir haben die Jina-AI-API für zukünftige Erweiterungen ergänzt, daher kann die Verwendung leicht von der offiziellen nativen Implementierung abweichen.

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Schnellstart

Ersetzen Sie API_KEY durch AIHUBMIX_API_KEY und den Modell-Endpoint-Link – alle anderen Parameter und die Verwendung sind vollständig konsistent mit Jina AI offiziell. Endpoint-Ersetzung:
  • Vektor-Embeddings (Embeddings): https://jina.ai/embeddings → https://aihubmix.com/v1/embeddings
  • Reranking (Rerank): https://api.jina.ai/v1/rerank → https://aihubmix.com/v1/rerank
  • Tiefe Suche (DeepSearch): https://deepsearch.jina.ai/v1/chat/completions → https://aihubmix.com/v1/chat/completions

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Embeddings

Jina AIs Embedding unterstützt sowohl reinen Text als auch multimodale Bilder und überzeugt insbesondere bei mehrsprachigen Aufgaben.

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Anfrage-Parameter

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model
string
erforderlich
Modellname, verfügbare Modellliste:
  • jina-clip-v2: Multimodal, mehrsprachig, 1024 Dimensionen, 8K-Kontextfenster, 865M Parameter
  • jina-embeddings-v3: Textmodell, mehrsprachig, 1024 Dimensionen, 8K-Kontextfenster, 570M Parameter
  • jina-colbert-v2: Mehrsprachiges ColBERT-Modell, 8K-Token-Kontext, 560M Parameter, verwendet für Embedding und Reranking
  • jina-embeddings-v2-base-code: Für Code- und Dokumentensuche optimiertes Modell, 768 Dimensionen, 8K-Kontextfenster, 137M Parameter
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input
array
erforderlich
Eingabetext oder -bild; unterschiedliche Modelle unterstützen unterschiedliche Eingabeformate. Für Text: ein Array von Strings; für multimodale Modelle: ein Array von Objekten mit den Feldern text oder image.
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embedding_format
string
Standard:"float"
Rückgabedatentyp, mögliche Werte:
  • float: Standard, gibt ein Float-Array zurück. Häufigstes und am einfachsten zu nutzendes Format
  • binary_int8: Rückgabe als gepacktes int8-Binärformat. Effizienter für Speicherung, Suche und Übertragung
  • binary_uint8: Rückgabe als gepacktes uint8-Binärformat. Effizienter für Speicherung, Suche und Übertragung
  • base64: Rückgabe als base64-kodierter String. Effizienter für die Übertragung
​
dimensions
integer
Standard:"1024"
Anzahl der bei der Berechnung verwendeten Dimensionen. Unterstützte Werte:
  • 1024
  • 768

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1. Multimodale Nutzung

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. Nutzung mit reinem Text

Geben Sie nur ein Array von Text-Strings an; das Feld image wird nicht angegeben. 
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

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Rerank

Reranker zielt darauf ab, die Suchrelevanz und RAG-Genauigkeit zu verbessern. Er analysiert die ursprünglichen Suchergebnisse tiefgehend, berücksichtigt die subtilen Wechselwirkungen zwischen Anfrage und Dokumentinhalt und ordnet die Suchergebnisse neu, sodass die relevantesten Treffer ganz oben stehen.

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Anfrage-Parameter

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model
string
erforderlich
Modellname, verfügbare Modellliste:
  • jina-reranker-m0: Multimodaler mehrsprachiger Dokument-Reranker, 10K-Kontext, 2,4B Parameter, für die Sortierung visueller Dokumente
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query
string
erforderlich
Suchanfrage-Text, der mit den Kandidatendokumenten verglichen wird
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top_n
integer
Anzahl der zurückzugebenden relevantesten Dokumente. Standardmäßig werden alle Dokumente zurückgegeben.
​
documents
array
erforderlich
Array der Kandidatendokumente, die nach Relevanz zur Anfrage neu geordnet werden
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max_chunk_per_doc
integer
Standard:"4096"
Maximale Chunk-Länge pro Dokument, gilt nur für Cohere (nicht von Jina unterstützt). Standardwert: 4096. Lange Dokumente werden automatisch auf die angegebene Token-Anzahl gekürzt.

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1. Multimodale Nutzung

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

​
Antwortbeschreibung

{
  "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
  }
}
Erfolgreiche Antwort:
  • model: Name des verwendeten Modells
  • results: Array der Reranking-Ergebnisse, absteigend nach Relevanz-Score sortiert. Jedes Element enthält:
    • index: Indexposition im ursprünglichen Dokument-Array
    • relevance_score: Relevanzwert zwischen 0 und 1; höhere Werte bedeuten höhere Relevanz zur Anfrage
  • usage: Nutzungsstatistik
    • total_tokens: Gesamtanzahl der in dieser Anfrage verarbeiteten Tokens

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2. Text-Nutzung

Text-Reranking unterstützt sowohl mehrsprachige als auch reguläre Aufgaben, ähnlich der Embedding-Nutzung: Übergeben Sie ein 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 kombiniert Such-, Lese- und Reasoning-Fähigkeiten, um die bestmögliche Antwort zu liefern. Vollständig kompatibel mit dem OpenAI Chat-API-Format — ersetzen Sie einfach api.openai.com durch aihubmix.com, um loszulegen. Der Stream gibt den Denkprozess zurück.

​
Anfrage-Parameter

​
model
string
erforderlich
Modellname, verfügbare Modelle:
  • jina-deepsearch-v1: Standardmodell, sucht, liest und reasoniert, bis die beste Antwort gefunden ist
​
stream
boolean
Standard:"true"
Ob Streaming-Antworten aktiviert werden. Es wird dringend empfohlen, diese Option aktiviert zu lassen — DeepSearch-Anfragen können lange dauern; ohne Streaming kann es zu einem 524 Timeout-Fehler kommen.
​
messages
array
erforderlich
Liste der Konversationsnachrichten zwischen Nutzer und Assistent. Unterstützt mehrere Typen (modal) wie Text (.txt, .pdf), Bilder (.png, .webp, .jpeg) usw. Maximale Dateigröße: 10MB.

​
Multimodales Nachrichtenformat

DeepSearch unterstützt mehrere Nachrichtenformate, darunter reinen Text (message), Dateien (file) und Bilder (image). Beispiele für verschiedene Formate:

​
1. Reine Textnachricht

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

​
2. Nachricht mit Datei-Anhang

{
  "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. Nachricht mit Bild

{
  "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"
    }
  ]
}
Alle Dateien und Bilder müssen vorher im Data-URI-Format kodiert sein, mit einer maximalen Dateigröße von 10MB.

​
Aufruf-Beispiel

Bitte beachten Sie: Der Python-Streaming-Aufruf von Jina AI auf der offiziellen Website liefert keine Antwort — verwenden Sie stattdessen unser Beispiel. 
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

​
Antwortbeschreibung

Die Antwort von DeepSearch wird standardmäßig gestreamt — sowohl Zwischenschritte des Reasonings als auch die endgültige Antwort. Der letzte Stream-Block enthält die finale Antwort, eine Liste der besuchten URLs und Details zur Token-Nutzung. Ohne Streaming wird nur die endgültige Antwort zurückgegeben — Zwischenschritte des „Denkens” werden weggelassen. Hinweis: Dieses JSON-Objekt unterscheidet sich vom Format, das Jina AI verwendet. 
{
  "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",
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data: [DONE]
Python-Rückgabebeispiel:
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. 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)
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Zuletzt aktualisiert: 2026-06-01