What the data for this category looks like
Condiment investment research knowledge bases draw data from industry association public weekly reports, listed company financial reports, in-store retail monitoring logs, and supply chain raw material quotation sheets. Update rhythms vary significantly: terminal retail data updates daily, financial reports update quarterly or annually, and industry research reports update biweekly.
Two types of document structures exist. The first is structured table documents, which include fields such as batch number, ex-factory price, terminal retail price, and channel share. Units are mostly yuan/500g, yuan/bottle, and yuan/case. The second is unstructured research report documents, which include market analysis, competitor updates, and consumer preferences. Some documents include detail fields such as packaging specifications and shelf life, with units including grams, milliliters, and days.
What constraints these characteristics impose on model integration and configuration
Structured documents contain fields with multiple specialized units. Configure field mapping rules to adapt to these specific units, otherwise the model will mix up the correspondence between numerical values and units.
There is a wide span in data update frequencies. Configure incremental sync trigger conditions to cover high-frequency retail data while accommodating low-frequency financial reports and research reports.
Document lengths vary significantly, from single-page retail logs to dozens of pages of industry research reports. Configure reasonable chunking and parsing timeout parameters to avoid redundant parsing for short documents or interrupted parsing for long documents.
There are many segmented product categories. Adjust similarity thresholds to distinguish related data of different products in the same category, preventing irrelevant information from being recalled.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1000 MB | Condiment investment research data includes multi-page logs and long research reports. Single file size usually does not exceed 800 MB, with a reasonable buffer reserved |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Structured log documents require field unit verification line by line. Long document parsing takes longer than general text, so sufficient timeout time is reserved |
Chunk Length | 800–1200 characters | Condiment data contains a large amount of numerical content with units. Overly long chunks will cause the model to confuse the association between units and numerical values |
Recall Count | Top 8–12 results | Investment research scenarios require multi-dimensional related data such as raw material costs, channels, and competitors. Too few results will miss critical information |
Similarity Threshold | 0.72–0.80 | There are many segmented product categories. A lower threshold is required to recall related data in the same category while avoiding irrelevant recalls |
Incremental Sync Trigger Frequency | Every 6 hours | Retail data is updated daily, industry research reports are updated weekly. Configured according to actual data source update rhythm, balancing real-time performance and resource usage |
The parameter values provided on this page are all common recommended starting points 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.
Three common configuration errors
- Symptom: A 3 MB condiment industry research report PDF is uploaded, and the interface displays a "File parsing failed" pop-up. Logs show the
PARSE_ERRORerror code. Cause: Structured table parsing rules are not configured, so the cost breakdown table in the research report is not correctly identified as structured data, causing the parsing process to interrupt. - Symptom: A VL model is connected, and the price data returned by the model does not match the input retail logs. Cause: The document parsing adaptation switch for the VL model is not enabled, and the dedicated prompt for
VL_MODEL_PROMPTis not configured, so the model cannot recognize the correspondence between packaging specifications and units. - Symptom: A Docker-deployed knowledge base remains in the indexing state. The interface prompts "Access point configuration error", and the call returns the
403 Forbiddenerror code. Cause: The correctAPI_ACCESS_ENDPOINTis not filled in. The domestic access point is mistakenly written as an overseas access point, causing network requests to fail to connect.
How to confirm the configuration is complete
- Upload a condiment industry research report PDF that contains structured tables, and verify that the parsed results include fields such as ex-factory price and terminal retail price, and that units are correctly identified in the corresponding format.
- Review vector database recall logs to confirm that the recall count falls within the configured
8–12 resultsrange, and that the similarity score falls within the0.72–0.80range. - Submit a model call with the input "October 2024 terminal retail price of soy sauce in East China", and verify that the returned results include the corresponding regional retail data, with no unit identification errors.
- Review scheduled sync task logs to confirm that the incremental sync trigger time matches the data source update rhythm, with no duplicate sync or missed sync records.
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.