Docqa Document QA

Document QA

2025-04-05
2025-12-01
2 min read

Let Documents Speak for Themselves: Document QA (DocQA)

DMflow.chat’s Document QA function is dedicated to transforming your massive enterprise documents into an interactive smart knowledge base. The system supports uploading various file formats and can automatically analyze and save content, allowing the bot to provide consistent, rapid, and multi-language precise answers based on this data. This not only significantly reduces the repetitive workload of customer service personnel but also significantly improves service quality and customer satisfaction.

Extensive Format Support

You can upload various types of knowledge documents to the platform, and the system will automatically parse and index them:

  • File Formats: CSV, JSON, HTML, DOCX, PDF
  • Voice Formats: Supports voice file upload; the system will automatically transcribe and analyze the content.

Exclusive Platform Advantages

Compared to other platforms, DMflow.chat offers the following powerful features:

1. Smart Sitemap Crawling

We provide sitemap.xml upload functionality. The platform will automatically crawl and analyze the web content of your site. When customers ask relevant information, the system can not only answer questions but also directly provide relevant web links, guiding customers to view the original data. Note: Currently Sitemap/RSS/Atom mainly supports static web page crawling; if you need to crawl dynamic web pages, please select “Single Page” mode.

2. Content Lifecycle Management

Information has timeliness. You can set an “Expiration Time” for documents. Once a document expires, the platform will no longer retrieve its content, ensuring the bot does not provide outdated information to customers.

3. User Feedback Loop

You can view real user feedback on Q&A via log tags (such as clicking “Helpful” or “Not Helpful”). These data allow you to optimize knowledge base content specifically and continuously improve answer quality.

4. Professional RAG Data Analysis

To help you build a more precise Retrieval-Augmented Generation (RAG) model, we provide professional analysis metrics:

  • Retrieval Precision
  • Retrieval Recall
  • Answer Relevancy
  • Answer Faithfulness
  • Historical Data Backtracking

Data Privacy and Interface Optimization

Whether it is AI Agent, Chat Role, or Document QA, as long as the knowledge base function is used, the system will record relevant Q&A data for analysis.

If you wish to make the user interface cleaner, you can choose to hide certain sensitive or unnecessary questions from the UI (via “Complete” operation).

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