What the data for this category looks like
Paper manufacturing industry research report data comes primarily from brokerage institute reports, professional light manufacturing databases, and publicly available industry association materials. Updates occur in real time as new research reports are released. Database content is synced on a weekly or monthly basis. Document structures typically include four core modules: industry supply and demand analysis, raw material price trends, summary of financial reports for leading enterprises, and investment ratings. Fields include publishing institution, release date, core indicators (such as wood pulp price in yuan/ton, production capacity in ten thousand tons), rating type, and others. Each individual document contains a large number of specialized terms exclusive to specific sub-categories.
What constraints these characteristics impose on knowledge base retrieval and recall
Multi-source data sources require configuring multiple knowledge base aggregation rules. Differentiate update frequencies for brokerage research reports and database content, and create separate incremental sync tasks. Individual documents have large word counts. When splitting recall fragments, avoid breaking specialized terms and complete data logic. Specialized terms for specific sub-categories have high distinctiveness. Adapt dedicated semantic recall rules to avoid confusion with research reports from other light manufacturing categories. Core indicators have fixed units. Match unit dimensions during retrieval to prevent incorrect association between price and production capacity data.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Paper manufacturing research reports include long sections of supply and demand analysis and professional discourse. This range preserves complete logical units and avoids splitting specialized terms incorrectly |
recall_top_k | Top 10–15 results | Individual research reports have high information density. Too many recall results will exceed the context window. Too few will fail to cover complete industry-related information |
similarity_threshold | 0.75–0.85 | Paper industry terms have high distinctiveness. A threshold that is too low will introduce irrelevant light manufacturing category research reports. A threshold that is too high will miss weakly matched but relevant sub-category data |
rerank_top_k | Top 3–5 results | Retain the most relevant core research report fragments to avoid redundant information interfering with model generation |
UPLOAD_FILE_MAX_SIZE | 20 MB | Individual research report PDFs are typically 1–5 MB each. This capacity supports batch uploads of 10–20 research reports, balancing upload efficiency and storage requirements |
PARSE_FILE_TIMEOUT_SECONDS | 60 seconds | Paper manufacturing research report PDFs often include tables and long text. 60 seconds completes parsing for documents under 50 pages, avoiding timeout failures |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: QA search prompts "No available channel for model gpt-4o-mini under group default". Cause: No model call channel for the paper manufacturing research report knowledge base is configured in the corresponding group, or channel quota has been exhausted.
- Symptom: Image links in the knowledge base display as "input an image" in responses. Cause: Public access permissions for external image links are not enabled, or the parsing process failed to extract accessible image URLs.
- Symptom: Selecting a knowledge base via variable references fails to accurately recall paper manufacturing research reports. Cause: No label filtering parameters corresponding to the variable are configured, causing the recall range to cover all non-target documents.
How to Verify Successful Configuration
- Upload a single paper manufacturing research report PDF, view the parsed text fragments, and confirm that specialized terms such as "coated white board paper" and "corrugated base paper" are not split incorrectly.
- Enter a query including "2024 containerboard production capacity", check whether the recall results include research report data of the corresponding dimension, and adjust the similarity threshold to meet business requirements.
- Test batch upload of up to 10 research report files, confirm no timeout errors occur during upload progress, and check that file sizes fall within the configured limit.
- View the knowledge base tag management page, confirm that exclusive tags such as "paper manufacturing" and "light manufacturing" have been added for subsequent precise recall filtering.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.