给技术文档查时效性：用 ADE 和 Parallel Decisions 逐条核对 PDF 说法

给技术文档检查时效性：从 PDF 提取关键说法，再用实时网页逐条核对 来自 @Sumanth_077 的分享：先找出 PDF 里容易过期的接口、版本和模型信息，再联网核对，标记为「仍有效」「已过时」或「需复核」，并提供来源供人工判断。 github.com/Sumanth077/Han… 它解决什么问题 技术文档不一定整篇失效，往往只是某个 API 路径、模型名称、价格或依赖版本已经变了。作者没有让 AI 直接重写全文，而是先定位这些容易变化的说法，再逐项检查，缩小人工审查范围。 核心流程 上传 PDF 后，LandingAI 的文档提取服务 ADE 将其转为保留结构的 Markdown，并抽取版本、API、配置、弃用提示和依赖等信息。 程序去重后，把每条说法连同原句和上下文，分别交给 Parallel Decisions API。这个接口结合实时网页检索，在预设选项中作出判断，返回结论、概率和引用来源。证据不足或互相矛盾时，归入「需复核」，而不是强行判定。 按项目说明，Parallel 收到的是抽取后的说法及简短上下文，不是整份 PDF；但 PDF 本身仍需交给 ADE 解析。 演示结果 Sumanth 用一份旧 OpenAI API Beta 文档测试，抽出 12 条说法：7 条过时、1 条仍有效、4 条需复核。过时项包括旧的 Engines 路由、davinci 示例和历史价格。 Sumanth @Sumanth_077 I built a document agent with a live web-grounded decision engine! Decision models like Jev are powerful, but they don’t have live context by default. They can only reason over what they learned during training and the context you give them at inference time. That becomes a problem when you’re working with documents containing information that can change over time. An API referenced in a document could have been deprecated, a package or model recommendation could be outdated, or an instruction that was correct when the document was written may no longer work today. So I built an agent that checks these time-sensitive claims against the live web. I first use Agentic Document Extraction to parse the uploaded document while preserving its structure and content. From that extracted content, the agent identifies claims that are actually freshness-sensitive rather than sending every sentence for verification. For each claim, it keeps the original statement and the surrounding document context, then turns the verification into a decision task for Parallel’s Decisions API. Parallel searches the live web for evidence relevant to that specific claim and returns a grounded decision with supporting sources. The agent then maps the result into one of three states: - VALID: current evidence still supports the claim - OUTDATED: newer evidence contradicts or supersedes it - NEEDS REVIEW: the available evidence is not strong enough to make a reliable call For example, if an older document says: “Use API v1 for this integration.” the verification request includes that claim and its document context. Parallel can search the current API docs, check whether v1 is still supported, and ground the decision in the latest sources it finds. The final output keeps both sides together: the claim extracted from the original document and the current web evidence used to evaluate it. So instead of treating a document as static context, the agent can reason about which parts of it may have drifted since it was written. Github Repo: github.com/Sumanth077/Han… This can be useful across technical docs, research reports, internal knowledge bases, implementation guides, product documentation, policies, and other documents where some claims remain stable while others age quickly. 🔗 View Quoted Tweet 💬 4 🔄 0 ❤️ 4 👀 306 📊 5 ⚡