DeepSearch API
与 OpenAI 的 Chat API schema 完全兼容，只需把
api.openai.com 换成 deepsearch.jina.ai 即可开始使用。与 DeepSearch 聊天
用简洁的聊天界面上手体验。DeepSearch 最适合那些需要反复推理、依赖世界知识或最新信息的复杂问题。
open_in_new
消息
用户与助手之间构成当前对话的消息列表。您可以在消息中附加图片（webp、png、jpeg）或文件（txt、pdf）。
附加图片/文档
支持多种消息类型（模态），如文本（.txt、.pdf）、图片（.png、.webp、.jpeg）。文件最大 10MB，且必须预先编码为 data URI。
{
"role": "user",
"content": "hi"
}
upload
请求
curl https://deepsearch.jina.ai/v1/chat/completions \
-H "Content-Type: application/json"\
-H "Authorization: Bearer " \
-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,
"reasoning_effort": "medium"
}
EOFEOF
download
响应
fiber_manual_record 200 OK
timer
0.0 s
straighten
196,526 词元
{
"id": "1742181758589",
"object": "chat.completion.chunk",
"created": 1742181758,
"model": "jina-deepsearch-v1",
"system_fingerprint": "fp_1742181758589",
"choices": [
{
"index": 0,
"delta": {
"content": "The latest blog post from Jina AI is titled \"Snippet Selection and URL Ranking in DeepSearch/DeepResearch,\" published on March 12, 2025 [^1]. This post discusses how to improve the quality of DeepSearch by using late-chunking embeddings for snippet selection and rerankers to prioritize URLs before crawling. You can read the full post here: https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch\n\n[^1]: Since our DeepSearch release on February 2nd 2025 we ve discovered two implementation details that greatly improved quality In both cases multilingual embeddings and rerankers are used in an in context manner operating at a much smaller scale than the traditional pre computed indices these models typically require [jina.ai](https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch)",
"type": "text",
"annotations": [
{
"type": "url_citation",
"url_citation": {
"title": "Snippet Selection and URL Ranking in DeepSearch/DeepResearch",
"exactQuote": "Since our DeepSearch release on February 2nd 2025, we've discovered two implementation details that greatly improved quality. In both cases, multilingual embeddings and rerankers are used in an _\"in-context\"_ manner - operating at a much smaller scale than the traditional pre-computed indices these models typically require.",
"url": "https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch",
"dateTime": "2025-03-13 06:48:01"
}
}
]
},
"logprobs": null,
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 169670,
"completion_tokens": 27285,
"total_tokens": 196526
},
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"https://github.com/jina-ai/node-DeepResearch/blob/main/src/utils/url-tools.ts",
"https://huggingface.co/jinaai/jina-embeddings-v3",
"https://github.com/jina-ai/reader",
"https://zilliz.com/blog/training-text-embeddings-with-jina-ai",
"https://threads.net/@unwind_ai/post/DGmhWCVswbe/media",
"https://twitter.com/JinaAI_/status/1899840196507820173",
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"https://milvus.io/docs/embed-with-jina.md",
"https://seedtable.com/best-startups-in-china",
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"https://youtube.com/watch?v=QxHE4af5BQE",
"https://sdxcentral.com/articles/news/cisco-engages-businesses-on-ai-strategies-at-greater-bay-area-2025/2025/02",
"https://aws.amazon.com/blogs/machine-learning/build-rag-applications-using-jina-embeddings-v2-on-amazon-sagemaker-jumpstart",
"https://reddit.com/r/perplexity_ai/comments/1ejbdqa/fastest_open_source_ai_search_engine",
"https://search.jina.ai/",
"https://sebastian-petrus.medium.com/build-openais-deep-research-open-source-alternative-4f21aed6d9f0",
"https://medium.com/@elmo92/jina-reader-transforming-web-content-to-feed-llms-d238e827cc27",
"https://openai.com/index/introducing-deep-research",
"https://python.langchain.com/docs/integrations/tools/jina_search",
"https://varindia.com/news/meta-is-in-talks-for-usd200-billion-ai-data-center-project",
"https://varindia.com/news/Mira-Murati%E2%80%99s-new-AI-venture-eyes-$9-billion-valuation",
"https://53ai.com/news/RAG/2025031401342.html",
"https://arxiv.org/abs/2409.04701",
"https://bigdatawire.com/this-just-in/together-ai-raises-305m-series-b-to-power-ai-model-training-and-inference",
"https://github.blog/",
"https://cdn-uploads.huggingface.co/production/uploads/660c3c5c8eec126bfc7aa326/MvwT9enRT7gOESHA_tpRj.jpeg",
"https://cdn-uploads.huggingface.co/production/uploads/660c3c5c8eec126bfc7aa326/JNs_DrpFbr6ok_pSRUK4j.jpeg",
"https://app.dealroom.co/lists/33530",
"https://api-docs.deepseek.com/news/news250120",
"https://sdxcentral.com/articles/news/ninjaone-raises-500-million-valued-at-5-billion/2025/02",
"https://linkedin.com/sharing/share-offsite?url=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
"https://twitter.com/intent/tweet?url=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
"https://platform.openai.com/docs/api-reference/chat/create",
"https://mp.weixin.qq.com/s/-pPhHDi2nz8hp5R3Lm_mww",
"https://huggingface.us17.list-manage.com/subscribe?id=9ed45a3ef6&u=7f57e683fa28b51bfc493d048",
"https://automatio.ai/",
"https://sdk.vercel.ai/docs/introduction",
"https://app.eu.vanta.com/jinaai/trust/vz7f4mohp0847aho84lmva",
"https://apply.workable.com/huggingface/j/AF1D4E3FEB",
"https://facebook.com/sharer/sharer.php?u=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
"https://facebook.com/sharer/sharer.php?u=http%3A%2F%2F127.0.0.1%3A3000%2Fen-US%2Fnews%2Fsnippet-selection-and-url-ranking-in-deepsearch-deepresearch%2F",
"https://reddit.com/submit?url=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
"https://apply.workable.com/huggingface",
"https://news.ycombinator.com/submitlink?u=https%3A%2F%2Fjina.ai%2Fnews%2Fa-practical-guide-to-implementing-deepsearch-deepresearch%2F",
"https://news.ycombinator.com/submitlink?u=http%3A%2F%2F127.0.0.1%3A3000%2Fen-US%2Fnews%2Fsnippet-selection-and-url-ranking-in-deepsearch-deepresearch%2F",
"https://docs.github.com/site-policy/privacy-policies/github-privacy-statement",
"https://discord.jina.ai/",
"https://docs.github.com/site-policy/github-terms/github-terms-of-service",
"https://bigdatawire.com/this-just-in/qumulo-announces-30-million-funding",
"https://x.ai/blog/grok-3",
"https://m-ric-open-deep-research.hf.space/",
"https://youtu.be/sal78ACtGTc?feature=shared&t=52",
"https://mp.weixin.qq.com/s/apnorBj4TZs3-Mo23xUReQ",
"https://perplexity.ai/hub/blog/introducing-perplexity-deep-research",
"https://githubstatus.com/",
"https://github.blog/changelog/2021-09-30-footnotes-now-supported-in-markdown-fields",
"https://openai.com/index/introducing-operator",
"mailto:[email protected]",
"https://resources.github.com/learn/pathways",
"https://status.jina.ai/",
"https://reuters.com/technology/artificial-intelligence/tencents-messaging-app-weixin-launches-beta-testing-with-deepseek-2025-02-16",
"https://scmp.com/tech/big-tech/article/3298981/baidu-adopts-deepseek-ai-models-chasing-tencent-race-embrace-hot-start",
"https://microsoft.com/en-us/research/articles/magentic-one-a-generalist-multi-agent-system-for-solving-complex-tasks",
"javascript:UC_UI.showSecondLayer();",
"https://resources.github.com/",
"https://storm-project.stanford.edu/research/storm",
"https://blog.google/products/gemini/google-gemini-deep-research",
"https://youtu.be/vrpraFiPUyA",
"https://chat.baidu.com/search?extParamsJson=%7B%22enter_type%22%3A%22ai_explore_home%22%7D&isShowHello=1&pd=csaitab&setype=csaitab&usedModel=%7B%22modelName%22%3A%22DeepSeek-R1%22%7D",
"https://app.dover.com/jobs/jinaai",
"http://localhost:3000/",
"https://docs.cherry-ai.com/",
"https://en.wikipedia.org/wiki/Delayed_gratification",
"https://support.github.com/?tags=dotcom-footer",
"https://docs.jina.ai/",
"https://skills.github.com/",
"https://partner.github.com/",
"https://help.x.com/resources/accessibility",
"https://business.twitter.com/en/help/troubleshooting/how-twitter-ads-work.html",
"https://business.x.com/en/help/troubleshooting/how-twitter-ads-work.html",
"https://support.twitter.com/articles/20170514",
"https://support.x.com/articles/20170514",
"https://t.co/jnxcxPzndy",
"https://t.co/6EtEMa9P05",
"https://help.x.com/using-x/x-supported-browsers",
"https://legal.twitter.com/imprint.html"
],
"readURLs": [
"https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch",
"https://github.com/jina-ai/node-DeepResearch",
"https://huggingface.co/blog/open-deep-research",
"https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch",
"https://x.com/jinaai_?lang=en",
"https://jina.ai/news",
"https://x.com/joedevon/status/1896984525210837081",
"https://github.com/jina-ai/node-DeepResearch/blob/main/src/tools/jina-latechunk.ts"
],
"numURLs": 98
}
DeepSearch 参数指南
了解如何设置正确的参数并获得最佳结果。
质量控制
在 DeepSearch 中通常存在一个权衡：系统执行的步骤越多，结果质量越高，但消耗的词元也越多。质量的提升来自更广泛、更详尽的搜索和更深入的反思。控制 DeepSearch 质量的主要参数有四个：budget_tokens、max_attempts、team_size 和 reasoning_effort。其中 reasoning_effort 本质上是 budget_tokens 和 max_attempts 的预设组合，且经过了精心调优。对大多数用户而言，调整 reasoning_effort 是最简单的做法。
词元预算
budget_tokens 设定整个 DeepSearch 流程允许消耗的最大词元数，涵盖网页搜索、读取网页、反思、摘要和编码等全部操作。预算越大，回答质量自然越好。预算耗尽或找到满意答案时（以先发生者为准），DeepSearch 流程即告停止。如果预算先耗尽，您仍会拿到答案，但它可能不是最终打磨完成的回答，因为尚未通过 max_attempts 所定义的全部质量检查。
最大尝试次数
max_attempts 决定系统在 DeepSearch 流程中重试解决问题的次数。DeepSearch 每生成一个答案，都必须通过内部评估器设定的质量测试。若答案未通过，评估器会给出反馈，系统据此继续搜索并优化答案。max_attempts 设得过低，出结果虽快，但答案可能没通过全部质量检查，质量因此打折；设得过高，则流程容易陷入反复尝试、反复失败的死循环。
当 budget_tokens 或 max_attempts 被突破（以先发生者为准），或者答案在预算和尝试次数仍有剩余时通过了全部测试，系统就会返回最终答案。
团队规模
team_size 影响质量的方式与 max_attempts、budget_tokens 截然不同。当 team_size 大于 1 时，系统会把原始问题拆解成若干子问题，分别独立研究。这类似 Map-Reduce 模式：一个大任务被拆成多个小任务并行执行，最终答案则是各个执行单元结果的综合。之所以叫 team_size，是因为它模拟了一支研究团队——多个智能体分别调研同一问题的不同侧面，再协作产出最终报告。
请注意，所有智能体的词元消耗都计入您的 budget_tokens 总额，但每个智能体各自拥有独立的 max_attempts。这意味着在 budget_tokens 不变的情况下调大 team_size，智能体可能会因预算吃紧而比预期更早给出答案。建议同时调大 team_size 和 budget_tokens，让每个智能体都有充足资源把工作做透。
最后，可以把 team_size 理解为控制搜索的广度——决定研究多少个不同侧面；而 budget_tokens 和 max_attempts 控制搜索的深度——决定每个侧面挖得多深。
信息来源控制
DeepSearch 高度依赖事实依据，也就是它所采用的信息来源。质量不只取决于算法的深度和广度；DeepSearch 从哪里获取信息同样重要，往往还是决定性因素。下面来看控制这一点的关键参数。
不直接回答
no_direct_answer 是一个简单的开关，用于阻止系统在第 1 步就直接给出答案。启用后，系统无法调用内部知识，必须先搜索网页。开启此项会让系统对简单问题也“过度思考”，比如“今天星期几”“你好吗”，或者“美国第 40 任总统是谁”这类模型训练数据中必然包含的基本事实。
域名控制
boost_hostnames、bad_hostnames 和 only_hostnames 这三个参数告诉 DeepSearch 优先访问、避开还是只访问哪些网页。要理解它们的作用，可以回顾一下 DeepSearch 的搜索与读取流程：
- 搜索阶段：系统搜索网络，得到一批网站 URL 及其摘要
- 选择阶段：系统决定实际访问哪些 URL（受时间和成本限制，不会全部访问）
boost_hostnames：此处列出的域名优先级更高，更有可能被访问bad_hostnames：这些域名永远不会被访问only_hostnames：一旦设定，只访问匹配这些域名的 URL
关于域名参数，有几点需要说明。首先，系统始终以搜索引擎返回的摘要作为构建推理链的初始线索。这些域名参数只影响系统访问哪些网页，不影响它如何组织搜索查询。
其次，如果收集到的 URL 中不含 only_hostnames 指定的域名，系统可能会完全停止读取网页。建议仅在您对研究问题足够熟悉、清楚答案可能出现在哪里（或绝对不会出现在哪里）时才使用这些参数。
特殊情况：学术研究
做学术研究时，您可能希望把搜索和读取限定在 arxiv.org。此时只需设置 "search_provider": "arxiv"，所有内容都会以 arxiv 为唯一来源。不过在这一限制下，通用或简单的问题可能得不到高效的答案，因此请只在严肃的学术研究中使用 "search_provider": "arxiv"。
搜索语言代码
search_language_code 是另一个影响网络来源的参数，它强制系统用指定语言生成查询，与原始输入和中间推理步骤所用语言无关。一般情况下，系统会自动选择查询语言以获得最佳搜索覆盖，但有时手动控制会很有用。
语言控制的适用场景
国际市场调研：研究本土品牌或公司在国际市场的影响力时，可用 "search_language_code": "en" 强制始终使用英语查询以覆盖全球，也可以改用当地语言获取更贴合区域的信息。
用非英语提示做全球调研：如果您的输入始终是中文或日语（因为终端用户主要使用这些语言），但调研范围是全球性的，而不限于中文或日语网站，系统可能会自动偏向提示所用的语言。此时可用该参数强制使用英语查询，以获得更广的国际覆盖。
与 DeepSearch 聊天
用简洁的聊天界面上手体验。DeepSearch 最适合那些需要反复推理、依赖世界知识或最新信息的复杂问题。
我们刚刚推出了全新的 DeepSearch 界面，极速、简洁且免费。访问 https://search.jina.ai 了解，或点击下方按钮试用！open_in_new访问新 UI
聊天客户端
为获得最佳体验，建议使用专业的聊天客户端。DeepSearch 与 OpenAI 的 Chat API schema 完全兼容，可轻松搭配任何兼容 OpenAI 的客户端使用。
TypingMind
Chatwise
Cherry Studio
Chatbox
LobeChat
NextChat
什么是 DeepSearch？
DeepSearch 把网页搜索、读取和推理结合起来，做全面的调研。可以把它看作一个智能体：您交给它一项研究任务，它会广泛搜索、多轮迭代，然后给出答案。
标准大模型
attach_money
约 1000 个词元
access_time
约 1 秒
check
常识问题的快速答案
close
无法获取实时或训练后的信息
答案完全来自预训练知识，知识截止日期固定
RAG 与带检索的大模型
attach_money
约 10,000 个词元
access_time
约 3 秒
check
需要当前或特定领域信息的问题
close
难以应对需要多跳推理的复杂问题
汇总单轮搜索结果生成答案
可获取训练截止日期之后的最新信息
DeepSearch
attach_money
约 500,000 个词元
access_time
约 50 秒
check
需要深入研究和推理的复杂问题
info
比简单的大模型或 RAG 方法花费的时间更长
自主智能体，反复搜索、读取和推理
根据当前发现动态决定下一步行动
在返回结果之前自我评估答案质量
可通过多轮搜索与推理循环深挖主题
API 定价
API 按词元用量计费。一个 API 密钥即可访问所有搜索底座产品。
使用 Jina Search Foundation API
访问我们全部产品最简单的方式，按需充值词元。
为此 API 密钥充值更多词元
请输入正确的 API 密钥以充值
了解速率限制
速率限制指每个 IP 地址/API 密钥每分钟可发起的最大请求数（RPM）。各产品和各档位的速率限制详见下表。
keyboard_arrow_down
code
我可以在所有 Jina API 中使用同一个 API 密钥吗？
keyboard_arrow_down
code
我可以查看 API 密钥的词元用量吗？
keyboard_arrow_down
code
如果我忘记了 API 密钥，该怎么办？
keyboard_arrow_down
code
API 密钥会过期吗？
keyboard_arrow_down
code
可以在不同 API 密钥之间转移词元吗？
keyboard_arrow_down
code
我可以销毁我的 API 密钥吗？
keyboard_arrow_down
code
为什么有些模型的首次请求比较慢？
keyboard_arrow_down
code
我的 API 数据会被用来训练你们的模型吗？
keyboard_arrow_down
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Jina API 的速率限制是多少？
keyboard_arrow_down
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API 有批量大小限制吗？
keyboard_arrow_down
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Jina API 与 Elastic 中的 Jina 模型是同一回事吗？
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速率限制
速率限制按以下维度统计：RPM（每分钟请求数）和 TPM（每分钟词元数）。限制按 IP/API 密钥分别计算，RPM 或 TPM 任一先达到阈值即触发限制。若您在请求头中提供了 API 密钥，我们将按密钥而非 IP 地址统计速率限制。
| Product | API Endpoint | Descriptionarrow_upward | w/o API Keykey_off | w/ Free API Keykey | w/ Paid API Keykey | w/ Premium API Keykey | Average Latency | Token Usage Counting | Allowed Request | |
|---|---|---|---|---|---|---|---|---|---|---|
| Reader API | https://r.jina.ai | Converts a URL to LLM-friendly text | 20 RPM | 500 RPM | 500 RPM | trending_up5000 RPM | 7.9s | Count the number of tokens in the output response. | GET/POST | |
| Reader API | https://s.jina.ai | Search the web and convert results to LLM-friendly text | block | 100 RPM | 100 RPM | trending_up1000 RPM | 2.5s | Every request costs a fixed number of tokens, starting from 10000 tokens | GET/POST | |
| Embedding API | https://api.jina.ai/v1/embeddings | Convert text/images to fixed-length vectors | block | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | trending_up5,000 RPM & 50,000,000 TPM | ssid_chart depends on the input size help | Count the number of tokens in the input request. | POST | |
| Reranker API | https://api.jina.ai/v1/rerank | Rank documents by query | block | 100 RPM & 100,000 TPM | 500 RPM & 2,000,000 TPM | trending_up5,000 RPM & 50,000,000 TPM | ssid_chart depends on the input size help | Count the number of tokens in the input request. | POST |
与计费相关的常见问题
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API 是按句子数还是按请求数计费？
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新用户可以免费试用吗？
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失败的请求是否会扣除词元？
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接受哪些付款方式？
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充值后可以开具发票吗？
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如何购买商业许可证而不是 API 词元？
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我可以用发票或采购订单付款，而不是用信用卡吗？
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我已付款，但余额或速率限制没有变化，该检查什么？
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如何取消、关闭自动充值或删除已保存的付款方式？
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