What the data for this category looks like
Baijiu industry investment research data originates from several sources: broker consumer sector reports, listed company periodic reports, public statistical data from industry associations, official announcements from baijiu production enterprises, and offline channel and tasting survey minutes. Data updates include annual periodic disclosures, quarterly operating data releases, sudden industry policy and new product announcements, and irregular channel change information. Document types cover long-form in-depth research reports, structured financial and capacity tables, and scattered survey snippets. Relevant fields include annual production capacity, annual sales volume, ex-factory guide prices, terminal selling prices, and dealer network counts. Common units are kiloliters, tons, and ten thousand yuan.
Constraints for Document Parsing and Chunking
The diverse characteristics of baijiu investment research data require the document parsing step to support both structured tables and unstructured text. It must avoid splitting cross-page financial or capacity tables, which breaks data associations. Long-form in-depth research reports contain specialized terminology and coherent analysis of production processes and market trends. Chunking must preserve contextual semantic connections, and avoid breaking logical links between specialized content. Scattered survey snippets and short announcements require controlled chunk granularity, to prevent excessive splitting that causes information loss. Documents that include capacity and price fields have dedicated units. Parsing must retain original unit identifiers, to avoid unit confusion. Frequently updated channel and announcement documents require fast parsing response speeds, to prevent delays that reduce knowledge base update efficiency.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
parse_table_enable | true | Supports the large number of structured financial and capacity tables in baijiu investment research documents, ensuring complete extraction of table content |
chunk_max_size | 800–1200 characters | Balances contextual completeness for long-form research reports and chunk retrieval accuracy, avoids excessive context redundancy from overly large chunks, or semantic breaks from overly small chunks |
parse_image_ocr | true | Captures image-based data such as embedded capacity charts and price trend graphs in research reports, extracts text within charts |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches parsing durations for long-form in-depth research reports, prevents parsing timeouts caused by overly large documents |
chunk_overlap | 150–200 characters | Retains contextual overlap between chunks, ensures specialized terminology and associated data are not split during retrieval |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploads of large single research report collections, prevents blocking due to overly large file sizes |
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 targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Scenario: After uploading a baijiu research report, reference field information is missing from the parsing result. Cause: Relevant reference extraction configuration is not enabled, or parsing rules do not cover reference paragraphs at the end of research reports.
- Scenario: After passing an image to the parsing script via HTTP request, the returned result is empty or contains no valid text. Cause:
parse_image_ocrconfiguration is not enabled, or OCR recognition parameters do not match the font style of handwritten survey minutes in baijiu research reports. - Scenario: The pdf-marker function works normally locally, but no parsing entry appears when uploading a baijiu enterprise annual report PDF to the FastGPT knowledge base. Cause: PDF format support is not added to FastGPT's file parsing configuration, or the parsing plugin configuration mounted to the container is not updated synchronously.
How to Verify Proper Configuration
- Uploading a baijiu research report PDF that contains structured tables allows verification of whether table content is fully retained in the parsing result.
- Uploading a research report image with embedded charts allows verification of whether text within the charts is extracted in the parsing result.
- Reviewing FastGPT backend parsing logs allows confirmation that parsing duration for a single document does not exceed the preset timeout threshold.
- Uploading a long-form in-depth research report allows checking whether contextual connections in the chunk results match the configured overlap and length requirements.
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.