What data for this category looks like
Baijiu research report data primarily comes from public brokerage firm research reports, public reports from industry associations, and annual and semi-annual disclosure documents from leading baijiu enterprises. Updates align with key baijiu industry milestones, including quarterly earnings release cycles, periods before and after industry peak seasons, and new product launch dates. Documents typically include modules such as overall industry analysis, breakdown of operating data for key liquor enterprises, channel sales activation status, price trend analysis, and risk warnings. Core fields involved include per-ton liquor revenue, per-case selling price, channel inventory turnover days, and sales activation scale. Units are mostly yuan, ten thousand yuan, days, and kiloliters. Single document length varies widely. Some in-depth reports include multiple pages of charts and data tables.
What constraints do these characteristics impose on knowledge base retrieval and recall?
Dispersed research report sources lead to inconsistent field naming across different documents. For example, some documents use "per-ton liquor selling price" while others use "per-ton revenue". This increases the difficulty of text matching and standardization during retrieval. Update cycles are inconsistent, requiring regular synchronization of the latest earnings reports and research reports. Otherwise, recalled content may lag behind the latest industry developments. Single documents are lengthy and contain large amounts of structured data. Conventional chunking can easily lose business logic context, requiring more precise chunking rules. Core information is concentrated in operating data of key liquor enterprises. Retrieval should prioritize matching high-value fields to avoid broadly recalling irrelevant industry overview content.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Baijiu research reports contain lengthy descriptions of operating data with complex sentences. This range preserves the integrity of individual business logic units and avoids losing contextual connections after chunking. |
similarityThreshold | 0.72–0.85 | Baijiu research reports use a large number of specialized terms. A threshold that is too low will recall irrelevant industry documents, while a threshold that is too high may miss accurately matched in-depth research report content. |
rerankTopN | Top 8–12 results | Core information from baijiu research reports is scattered across different paragraphs. Reranked top results can cover multi-dimensional data of key liquor enterprises to meet retrieval requirements. |
UPLOAD_FILE_MAX_SIZE | 500 MB | Single in-depth baijiu research report PDF files often include multiple pages of charts and data tables. This size covers most bulk upload scenarios. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Text parsing and field extraction for large PDF files require longer processing time, preventing timeout interruptions. |
knowledgeBaseId | Bind by knowledge base ID | The baijiu research report knowledge base must be bound to a specific research report data source. Using ID binding simplifies invocation logic for multiple workflows and avoids duplicate configuration. |
The parameter values provided on this page are general recommendations to serve as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Issue: An error is prompted when passing knowledge base parameters during API workflow invocation, or the workflow cannot reference the configured global knowledge base variable. Cause: The knowledge base ID was not correctly passed as a parameter to the workflow node, or the binding scope of the global variable does not cover the current workflow.
- Issue: When uploading large PDF research reports, the upload progress reaches 90% before an "offset out of range" error occurs, and this issue appears across different deployment environments. Cause: Images or embedded objects within the PDF file cause abnormal byte offset calculation during parsing, and segmented upload verification logic for large files was not configured.
- Issue: Retrieval results recall food and beverage industry research reports unrelated to baijiu, or non-core data paragraphs from the same liquor enterprise. Cause: The similarity threshold was set too low, or a dedicated recall filtering rule was not configured for the specialized fields of baijiu research reports.
How to confirm configurations are correct
- Upload a test baijiu research report PDF, and check that the parsed text includes core operating data fields with no obvious chunking breaks.
- Invoke the retrieval API, pass precise queries such as "key baijiu enterprise per-ton liquor revenue", and verify that the knowledge base source of the returned results is the preset baijiu research report library.
- Adjust the similarity threshold and rerank count, test the number and relevance of recalled results under different configurations, and confirm the value range that meets business requirements.
- Trigger a bulk upload of research report files, and check that the upload progress completes fully without interruptions or errors.
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.