What data for this category looks like
Baijiu research reports primarily originate from domestic securities firm research institutes, liquor industry associations, and publicly disclosed research documents from listed liquor enterprises. Update frequency aligns with industry milestones, with concentrated releases before peak seasons and during earnings reporting periods. Most documents combine structured and semi-structured formats, containing fields such as title, publishing institution, release date, core operating data, channel analysis, and valuation models. Common industry-standard data units include ten thousand kiloliters (for production/sales volume), hundred million yuan (for revenue/profit), and yuan per bottle (for terminal wholesale price).
Constraints for Citation Source and Traceability
The multi-source nature of baijiu research reports requires precise binding of publishing institution and release date during traceability, to avoid mixing similar analyses from different organizations. Semi-structured document structures mean core data is often embedded in paragraphs, so contextual association must be retained to locate specific content blocks accurately during traceability. Industry-specific units and fields require automatic identification and dimension labeling during parsing, to prevent unit confusion during traceability. The concentrated release pattern requires traceability logic to support quick filtering of valid reports by release time range, to exclude expired content.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
recall_count | Top 8-12 entries | Baijiu research reports have high content density. Too many recall results will exceed the model context window, while too few will fail to cover core analysis dimensions |
chunk_size | 800-1200 characters | Baijiu research reports often include coherent channel analysis and operating data paragraphs. This segment length preserves complete context for single analysis content, enabling accurate traceability |
expire_days | 180 days | Baijiu industry research reports have strong timeliness. Industry trend analysis older than six months has reduced reference value. Automatically filtering expired documents improves traceability accuracy |
extract_metadata | Enable publishing institution and release date fields | Publishing institution and release date are core identifiers for baijiu research report traceability. These fields must be accurately extracted from document headers and bound to their corresponding content blocks |
rerank_count | Top 3-5 entries | Retain the most relevant research report fragments after reranking, preventing traceability results from being overly scattered and ensuring consistency between answers and citations |
max_context | 12000-16000 characters | Valuation logic and industry analysis in baijiu research reports have coherent connections. A sufficient context window ensures associated content is fully displayed during traceability |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: After importing baijiu research reports into the knowledge base, some documents are stored as raw text blocks without generating question-answer pairs. Cause: The
auto_generate_qaconfiguration is not enabled, or the proportion of structured data in documents is too high, causing the automatic question-answer generation logic to fail to match the semi-structured characteristics of baijiu research reports. - Phenomenon: When running a workspace knowledge base application, only one cited document is returned, and multiple research reports cannot be recalled. Cause: The
recall_countconfiguration value is too low, or the multi-document recall switch is not enabled, failing to meet the multi-source analysis requirements of baijiu research reports. - Phenomenon: After configuring an external model, an error triggers during testing, but the citation logic runs normally after forced ignoring. Cause: Temporary timeout of the model interface or failed permission verification. The citation traceability logic of FastGPT 4.8.20 and later versions does not include pre-checks for model testing, allowing core retrieval to execute even after an error occurs.
How to Verify Correct Configuration
- Upload a baijiu research report labeled with a release date, and check if the parsed metadata correctly extracts the publishing institution and release date fields.
- Initiate a retrieval request, and confirm that the returned citation list includes content from multiple research reports with different sources, matching the preset number of recalled entries.
- Import baijiu research reports with an expiration time exceeding the preset
expire_daysvalue, and verify that the system automatically filters these documents and excludes them from the retrieval scope. - Trigger a model response, then click the citation traceability entry, confirming that you can jump to the specific paragraph position of the corresponding research report.
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.