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人工智能培训和地面化

面向 AI、RAG 和代理的 Web 数据

构建 AI 训练和评估数据集，或更新 RAG 与智能体使用的网页内容。选择 API、数据集、定期数据流或托管采集。

扩展您的AI数据管道探索人工智能数据基础
采集带来源链接的内容，用于训练、检索或智能体的实时上下文。

开始从工作量开始

选择用于训练、评估、RAG 或智能体的数据。

选择使用场景，查看记录示例和可采集的数据。

批量数据操作

建立一个团队可以复制的体体.

在扩展量之前定义源列表,捕获窗口,包含规则,方案,正常化,重复政策和数据集版本.

  • 专业预训或精细调节输入
  • 版本的大量记录,含有源和捕获的元数据
  • 报告质量和差距与已达成的接受规则相反
Plan a corpus sample

智能人工智能数据基础

开始提供您工作量需求的数据.

选择一个准备好的数据集,历史网页快照,多模组收集,请求时间API或完全运行的程序.每个路径保持源,记录形状,更新模式和交付责任清晰.

多型式培训

视频数据

建立视频片段,音频,转录和视频语言模型,多模式系统和物理人工智能的视频片段,转录和元数据以场景为中心的集合.

Explore video data
物理人工智能视频数据

场景的视频片段,其中包括行动,环境,视角和时间界限的背景.

准备好和可配置人工智能数据包

根据样本记录,方案,历史记录,更新序列和交付来评估包组.

准备使用数据市场

浏览有清晰的源头,领域,新鲜性,格式和交付信息的结构化数据集.

复制的背景数据馈送

通过定义的更新节奏和交付路径保持支持的结构记录.

历史背景网页归档

保存的公页快照中,填写知识库和研究时间表.

运营的交付托管数据

WebScrapingAPI处理收集,提取,质量监测,源维护和交付.

记录设计图像,视频和音频

检查媒体对象,输出,转录,元数据,以及您的管道收到的来源.

请求时采集让公共网络的环境成为您自己的管道.

使用数据 API 对于支持的结构化来源,计划一个有限的多页工作爬取 API 任何一个代理面向的通道都能在一个地方进行比较.

Explore Agentic Web Access

两个数据视野

训练和地面化需要不同的数据规格.

它们可以使用相同的源基础,但收集时刻,记录单位,质量重点和变化政策不应相互交换.

01 · Controlled snapshot

Training & evaluation corpus

A defined set that can be reproduced, reviewed, versioned, and compared across model or pipeline iterations.

Collection
Fixed capture window or agreed batch.
Record unit
Document or structured observation with a stable key.
Quality focus
Coverage, schema, duplicate handling, parse states, and manifest integrity.
Change policy
A new dataset version is issued when the specification or source snapshot changes.
Downstream
Your team owns labeling, tokenization, training, evaluation, and model behavior unless separately scoped.

02 · Refreshable context

Grounding, RAG & agents

A defined source collection refreshed on an agreed schedule, or a supported page retrieved when the application requests it.

Collection
Scheduled refresh or request-time retrieval.
Record unit
Source-linked content or structured response with capture context.
Quality focus
Freshness, retrieval state, provenance, changed content, and observable gaps.
Change policy
New, changed, unchanged, and unavailable states can be represented in the delivery plan.
Downstream
Your team owns chunking, embeddings, indexing, retrieval logic, citations, and response behavior unless separately scoped.

Fields and sample records

See the fields in a sample record.

“AI-ready” is not a file extension. A useful record specification defines provenance, capture context, schema, versioning, quality states, and known gaps before the data enters a model workflow.

01
Observed content stays distinct

Keep collected fields separate from normalized, inferred, summarized, or generated values.

02
Version keys make change visible

Use a stable record key and content checksum to identify updates without hiding the prior state.

03
Gaps are part of the delivery

Report unavailable, failed, excluded, or needs-review states instead of silently dropping them.

illustrative_record.jsonlschema 1.4
// Synthetic example for specification review
{
  "record_id": "doc:7d3b:20260716",
  "source_url": "https://docs.example.com/deploy",
  "captured_at": "2026-07-16T08:30:00Z",
  "requested_context": {
    "locale": "en-US",
    "market": "global"
  },
  "content_type": "technical_article",
  "title": "Regional inference deployment",
  "content": "Observed page content…",
  "content_hash": "sha256:b81c…8a0e",
  "previous_hash": "sha256:2f10…c143",
  "record_state": "changed",
  "quality_state": "accepted",
  "quality_flags": [],
  "source_review_state": "scoped",
  "use_note_ref": "scope:ai-grounding-pilot",
  "schema_version": "1.4",
  "scope_id": "ai-grounding-pilot"
}

形状字段,转换,格式化,保留和交付工作负载.

Identity & provenance

Stable record key, source URL, source class, request context, and capture time.

Observed content

Selected page content or structured public fields, kept distinct from downstream transformation.

Version & state

Schema version, content checksum, prior checksum, and new, changed, unchanged, unavailable, or review state when scoped.

Quality & gaps

Extraction status, required-field checks, duplicate state, exclusions, and failed collection context.

Governance controls

Source scope, review state, retention, access, and intended-use references travel with the delivery so reviewers can trace each record through the workflow.

01Source eligibility

Requested source, access state, and project scope are reviewed before expansion.

Scoped
02Retrieval state

Successful, unavailable, excluded, or failed collection remains observable.

Reported
03Schema conformance

Required fields, types, and format checks follow the agreed record definition.

Checked
04Field fidelity

The acceptance plan defines how sampled records are reconciled with source observations and how discrepancies are classified.

Defined
05Duplicate handling

Stable keys and content hashes support the selected duplicate rules.

Tracked
06Change detection

New and changed content can be distinguished when history is in scope.

Versioned
07Delivery integrity

File, batch, or response delivery is checked against the selected output definition.

Verified

Measurable quality

Measure freshness and gaps—not “AI readiness.”

The quality scorecard changes with the workload. A batch corpus needs reproducibility and manifest integrity; a grounding collection needs observable freshness and change states.

For training & evaluation

Define capture window, source coverage, required-field completeness, source-to-record sample checks, discrepancy states, duplicate policy, parse failures, record counts, and dataset version.

For grounding & retrieval

Define last successful capture, refresh window, changed and unavailable states, provenance coverage, source-to-record sampling, failed retrievals, and late-delivery handling.

Use the scorecard to separate collection quality from model quality: inspect source coverage, freshness, versions, and delivery first, then measure retrieval, citation, and application results in your evaluation suite.

Refresh data without hiding what changed.

When change tracking is part of the specification, each collection can compare a stable record key and content checksum. The result is an explicit state—not an overwritten file with no history.

Snapshot 01First observed version

Source, capture time, content, schema, and checksum establish the baseline.

state: new
Refresh 02Content unchanged

The page remains available and the content checksum matches the prior capture.

state: unchanged
Refresh 03New version observed

The record retains its identity while the changed content receives a new checksum.

state: changed
Refresh 04Source not observed

A missing response is classified as unavailable, excluded, or failed according to the agreed policy.

state: unavailable | excluded | failed

交付的选择

选择数据获取方式。

在应用中使用 API，定期接收记录，或交由我们负责采集。

代理

保留现有采集程序，使用我们的代理网络。

将住宅、数据中心或 ISP 代理接入团队现有的采集程序。
功能详情
您获得的内容用于自有请求的代理访问
我们负责代理网络和访问
您负责的部分采集、提取、存储和分析

适合 · 适合已有采集程序的团队。

探索代理基础设施

Source governance

Scale AI data with the source context intact.

Bring source access, intended use, data categories, retention, and jurisdiction into the collection plan before volume grows.

01Source eligibility

Select publicly accessible sources, access conditions, geography, and explicit exclusions before scale.

02Data categories

Define the fields and content types needed; minimize personal or sensitive data that the workload does not require.

03Intended use

Keep each delivery aligned to its defined training, evaluation, retrieval, or agent workload.

04Retention & deletion

Set the storage period, access, update, removal, and deletion workflow for the project.

05Observed vs. generated

Keep collected content distinguishable from translation, summarization, inference, labeling, or other generated transformations.

Clear operating split: WebScrapingAPI runs the selected collection and delivery layer. Your team applies the data within its model and application workflow, with any added transformations defined in the project plan.

评估过程

在扩展之前,请证明数据的形状.

试点应该在生产量之前暴露领域,来源,缺口,更新行为和所有权.

01 · Define

Name the workload

Set the AI job, representative sources, markets, content types, capture window, and intended downstream use.

02 · Sample

Inspect real records

Review source context, observed fields, quality states, exclusions, and a representative output shape.

03 · Specify

Define the record

Set schema, versioning, cadence, checks, gap handling, retention, destination, and operating model.

04 · Operate

Launch and maintain

WSA operates the selected layer and reports against the collection and delivery rules in the project plan.

Review sample records and source coverage before agreeing the production schedule.

常见问题

智能化数据买家在发射之前问什么.

这些数据可以支持哪些类型的人工智能工作负载?

解决方案可以支持针对定义的来源和预期用途的培训和评估数据集,重复的知识库或RAG收集,以及代理工作流的请求时间访问.每个需要自己的源,方案,质量,更新和所有权规范.

哪些来源,市场和内容类型可以包含?

通过本页面来规划培训,评估,RAG和代理人的文本和结构化网络工作负载.对于视频片段,音频,转录和媒体元数据,继续访问视频数据为AI;多模数据指南解释了底层媒体记录.来源和市场覆盖范围围绕选择的产品或管理程序.

能否保存源属性和捕获文本?

是的. 添加源URL,捕获时间,本地或市场,内容类型,方案版本,记录键,收集状态和质量标志,以便随着每次发送而传输.

提供哪些输出格式和输送方法?

使用文件的API响应来访问请求时间和已达成的文件或批量交付计划.目前的API格式列出在产品文件. 在管理交付过程中,记录格式,包装,批量大小,压缩和目的地都得到确认;这里所示的JSONL记录是说明性的,而不是默认格式的承诺.

页面的修改,删除和未能更新的表征是什么?

当历史和变化跟踪是交付的一部分时,稳定键和检查数区分新的,改变的,未改变的,不可用,排除的和失败的状态.选择完整的快照,增量更新或仅变化交付,以匹配下游工作流.

没有WebScrapingAPI它们是如何分化,嵌入,索引,标签或训练数据?

标准交付包括收集和结构化输出.如果你还需要分块化,嵌入,索引,标签或其他转换,请将其添加到项目简报中,以便输出准备好到达你的管道.

谁会在一个来源发生变化时保持收藏?

WSA 维护记录的终端点和它运营的定制连接器,用于计划或管理的交付.代理客户维护自己的收藏器;网络访问 API 客户维护他们的定制提取,体积准备和下游管道.

我们如何评估人工智能数据程序的来源?

首先要考虑公众可访问性,然后再审视源头条款,数据类别,预期模型使用,保留和管辖权.

这项服务能否提高模型的准确性,或消除幻觉?

较好的输入使评估变得更容易.WSA提供源文本,新鲜性,版本化和可观察的质量状态,以便您的团队可以测量数据如何改变检索,引用和应用结果.

什么决定了范围和成本?

自动服务产品遵循发布的计划. 定制范围反映了源数和复杂性,记录量,市场,捕获窗口,序列,字段,转换,复制和版本政策,质量检查,历史,保留,格式和交付.

采购可以在哪里审查数据处理和服务条款?

检查我们的隐私政策, GDPR信息，以及 服务协议对于定制交付,项目计划记录了源域,数据处理,存储,访问,变更处理和交付角色.

验证数据简报

定义您的AI系统所需的来源,新鲜性和方案.

分享代表性来源,工作负载类型,市场,输出要求和更新窗口. 我们将评估可行性,并提出合适的收集界限.

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