Tongyi Qianwen (also known as Qwen), developed by Alibaba Cloud, provides native long-document analysis, multimodal file parsing, and structured summarization capabilities designed to help users digest dense reports, academic papers, contracts, and spreadsheets. Powered by extensive context windows and integrated optical character recognition (OCR), the platform enables analysts, researchers, and enterprise professionals to extract critical metrics, generate visual outlines, and verify facts without manually combing through hundreds of pages. This guide outlines how to prepare and upload complex files, generate structured summaries and mind maps, query specific data points with source citations, and deploy reusable prompt workflows across single and multi-document tasks.
Supported File Formats and Upload Workflows
Tongyi Qianwen processes a broad range of document types through both its standard web interface and its dedicated document reading assistants. Understanding supported formats and ingestion limits ensures that files are parsed accurately without data loss.
Supported File Formats
The platform accepts text-heavy, tabular, and visual document formats:
- Text and Word Processing: PDF (
.pdf), Microsoft Word (.docx,.doc), Plain Text (.txt), and Markdown (.md). - Spreadsheets and Data Tables: Microsoft Excel (
.xlsx,.xls) and Comma-Separated Values (.csv). - Presentations and eBooks: Microsoft PowerPoint (
.pptx,.ppt) and EPUB (.epub). - Images and Scanned Documents: JPEG, PNG, BMP, and image-based PDFs, which Tongyi Qianwen processes using integrated OCR to recognize text, layout headers, and embedded tables.
Uploading Documents via the Web Interface
Users can upload documents directly into the conversation stream on Tongyi or the international Qwen Chat platform:
- Locate the Attachment Button: In the chat input box at the bottom of the interface, click the attachment or paperclip icon labeled Document or Upload.
- Select Files: Choose one or more files from your local drive. The platform supports batch uploading, allowing users to upload multiple related files into a single session for cross-referencing.
- Wait for Ingestion: A file badge appears above the input box showing parsing progress. Wait until the status indicates that indexing is complete before sending your query.
Generating Structured Summaries, Outlines, and Mind Maps
Once a document is indexed, Tongyi Qianwen can condense its contents into various formats depending on whether you need a high-level executive briefing or a detailed structural breakdown.
Executive Summaries and Core Findings
For immediate situational awareness, request an executive summary that highlights the problem statement, primary methodology, key results, and recommendations:
- Prompt Example: "Provide an executive summary of this document in three sections: Background, Key Findings, and Actionable Recommendations. Keep each section under 150 words."
- Focusing on Specific Audiences: You can instruct the model to tailor the summary for different stakeholders, such as non-technical executives, legal counsel, or engineering teams.
Section-by-Section Outlines
When dealing with lengthy technical specifications or multi-chapter research papers, an outline helps identify which sections require closer inspection:
- Prompt Example: "Generate a hierarchical chapter outline of this document. For each major heading, list two to three bullet points summarizing the core arguments and data presented."
- This allows readers to navigate 50-page reports rapidly by identifying relevant sections before diving into detailed queries.
Visual Logic and Mind Map Generation
Tongyi Qianwen can convert unstructured text into structured hierarchies suitable for mind mapping tools:
- Markdown Tree Format: Request the model to output a hierarchical Markdown list (using indentation and bullet points) representing concepts, sub-concepts, and supporting details.
- OPML or Text-Based Trees: Users can request OPML or structured plain-text outlines that can be imported directly into mind-mapping software such as XMind or MindNode.
Extracting Key Information and Tabular Data
Complex documents frequently contain quantitative metrics, financial disclosures, contractual clauses, or technical parameters buried inside narrative text. Tongyi Qianwen can isolate these specific elements and present them in structured formats.
Extracting Financial and Numerical Metrics
When analyzing earnings releases, annual reports, or audit findings, precision is essential:
- Instruct the model to extract specific financial indicators—such as revenue, year-over-year growth rates, gross margins, EBITDA, and forward-looking guidance.
- Prompt Example: "Scan the attached quarterly earnings report. Extract all revenue figures, net profit margins, and guidance figures into a Markdown table with columns for Metric, Q1 Value, Q2 Value, and YoY Change."
Reconstructing Tables and Complex Layouts
Scanned PDFs and multi-column reports often lose table structure when copy-pasted manually. Tongyi Qianwen reconstructs table relationships:
- The model interprets table headers, row alignments, and footnote references, rendering them into clean GitHub Flavored Markdown (GFM) tables.
- If a table spans multiple pages, specify that the model should merge the continuous rows under the same header structure.
Isolating Contractual Obligations and Deadlines
Legal and procurement teams can use document analysis to flag high-risk terms and critical milestones:
- Ask the model to extract all clauses concerning indemnity, termination notice periods, governing law, and payment terms.
- Prompt Example: "Identify all compliance obligations and contractual deadlines in this service agreement. List each obligation along with the responsible party, deadline date, and associated penalty."
Deep Q&A and Source Attribution
To prevent hallucinations and verify interpretations, Tongyi Qianwen provides interactive Q&A grounded directly in the uploaded file, accompanied by source attribution features.
Interactive Follow-Up Questions
Rather than relying solely on a single summary, engage in multi-turn dialogues to probe specific areas:
- Ask clarification questions: "On what empirical data does the author base the claim that efficiency improves by 25%?"
- Test edge cases: "Does this policy document specify exceptions for remote employees traveling internationally?"
Verifying Claims with Source Attribution
In Tongyi Qianwen's document reading mode, the interface includes interactive citation tags:
- When the model answers a question or presents a factual assertion, it attaches a reference marker (such as a superscript number or clickable tag) pointing to the original document.
- Clicking the reference tag opens the integrated document previewer and jumps directly to the exact page, paragraph, or table from which the information was derived.
- This verification step allows users to validate high-stakes claims before incorporating them into professional reports or client deliverables.
Multi-Document Comparative Analysis
One of the most effective applications of Tongyi Qianwen is synthesizing information across multiple related documents.
Cross-Referencing Multiple Files
Users can upload several files simultaneously—such as proposals from different vendors, research papers evaluating competing methodologies, or quarterly reports from peer companies:
- Upload all relevant files into the same conversation thread.
- Direct the model to compare them explicitly using standardized criteria.
Constructing Comparison Matrices
To evaluate alternatives objectively, instruct Tongyi Qianwen to generate a comparative matrix:
- Prompt Example: "Compare the three attached vendor proposals. Create a Markdown table comparing them across: Pricing Model, Implementation Timeline, SLA Guarantees, Security Certifications, and Identified Risks."
- Ask the model to highlight contradictions or discrepancies between documents, such as conflicting market projections or divergent baseline metrics.
Practical Prompt Templates for Complex Documents
Using standardized prompt templates ensures consistent, high-yield results across everyday analytical tasks.
Template 1: Comprehensive Executive Briefing
Act as a senior business analyst. Analyze the attached document and provide:
1. One-sentence thesis: The primary purpose or argument of the document.
2. Executive Summary: A concise 3-paragraph briefing covering context, methodology, and primary conclusions.
3. Top 5 Key Takeaways: High-impact findings with specific metrics where available.
4. Strategic Implications: What decisions or actions this document suggests for management.
Template 2: Structured Data Extraction to Table
Scan the attached file for all data related to [Target Topic / Financial Performance / Technical Specs].
Output the results as a Markdown table with the following columns:
- Parameter / Metric Name
- Reported Value / Finding
- Baseline / Comparison Value (if mentioned)
- Document Section / Page Reference
Do not summarize or omit items; list every instance found in the text.
Template 3: Risk and Compliance Audit
Review the attached agreement/policy from a compliance and risk perspective.
Identify and list:
1. Operational Risks: Bottlenecks, unrealistic timelines, or resource constraints.
2. Legal & Financial Liabilities: Indemnification clauses, penalty triggers, or unbounded commitments.
3. Ambiguities: Vague phrasing or unassigned responsibilities requiring clarification.
For each point, quote the relevant sentence or clause directly.
Template 4: Multi-Document Gap Analysis
Compare Document A and Document B.
1. Identify shared conclusions and consensus findings.
2. Highlight direct contradictions or discrepancies in data and recommendations.
3. Detail gaps where Document A provides critical information omitted in Document B, and vice versa.
Summarize the comparison in a structured matrix.
Best Practices and Operational Considerations
To maximize extraction accuracy and streamline document workflows, observe several practical guidelines:
- Verify Scanned Document Quality: While OCR is robust, low-resolution scans, heavy watermarks, or skewed pages can introduce transcription errors. Where possible, use clean digital exports rather than photographed paper documents.
- Scope Queries Narrowly: If a document exceeds several hundred pages, ask focused questions targeting specific chapters or topics rather than asking broad, open-ended questions in a single prompt.
- Double-Check Critical Figures: For high-stakes decisions—such as financial audits, legal filings, or medical research—always use the source attribution markers to verify critical numbers against the original text.
- Maintain Data Confidentiality: Check your organizational data governance policies before uploading proprietary code, sensitive personal identifiable information (PII), or confidential corporate contracts to cloud-based AI services.