What Data for This Category Looks Like
Data sources for condiment investment research include channel monitoring data released by industry associations, regular financial reports of listed condiment companies, dealer terminal sales ledgers, quotation information from upstream raw material suppliers, and in-depth analysis documents from professional food and beverage research institutions.
The update rhythm of data varies. Raw material quotations are updated daily or weekly. Terminal sales data is aggregated monthly. Corporate financial reports are released quarterly or annually.
Document formats include structured Excel tables with fields such as SKU code, product specification, ex-factory price, and procurement cost, multi-chapter PDF/Word research reports, and semi-structured dealer interview minutes. Fields and units use concrete numerical values. For example, ex-factory price is measured in yuan per kilogram, channel sales volume is measured in cases or units, and there are no vague statistical expressions.
Constraints on Multi-turn Dialogue and Prompt Engineering
The varying update rhythms of data sources require explicit specification of data time ranges in multi-turn dialogue. This avoids confusion between statistical results from different cycles.
The diversity of document formats requires multi-turn dialogue to support mixed-format context retrieval. It also requires distinguishing processing logic between structured table data and unstructured text.
The concrete nature of fields and units requires prompts to mandate that output results carry the units of original fields. This prevents unit mismatches.
The existence of large-volume documents requires adjusting multi-turn dialogue context windows and segmentation parameters for long-text retrieval and splicing. This avoids context overflow or reduced retrieval accuracy.
Configuration Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Condiment research reports often contain long paragraphs of industry analysis and detailed single-SKU information. This segmentation length balances context completeness and retrieval accuracy |
RECALL_TOP_K | Top 6–8 results | Condiment investment research needs to cover macro trends, segmented brand data, and raw material prices. Additional retrievals can cover multi-dimensional information |
SIMILARITY_THRESHOLD | 0.72–0.80 | There is a large semantic gap between structured data such as Excel sales tables and unstructured text such as research reports. This range filters irrelevant retrieval results |
maxContext | 12000–15000 characters | Multi-turn dialogue needs to retain 3–5 valid investment research questions and corresponding retrieved data. This window covers a complete context |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Supports uploading research report documents with 100,000 Chinese characters and Excel channel data with over 15,000 rows |
API_REQUEST_TIMEOUT | 600 seconds | Sufficient timeout time is required to avoid interruptions when processing large file parsing and multi-turn retrieval splicing |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Phenomenon: When calling the dialogue interface to upload a 100,000-Chinese-character document or Excel file with 15,000 rows, a
413 Request Entity Too Largeerror is returned. Cause: TheUPLOAD_FILE_MAX_SIZEparameter was not adjusted, and the upload failed because the default file size limit was exceeded. - Phenomenon: Unit confusion appears in multi-turn dialogue output results, such as incorrectly labeling raw material procurement cost as "yuan/case" instead of "yuan/kilogram". Cause: The prompt did not mandate carrying the units of fields, and the retrieved context did not retain the unit information of original data.
- Phenomenon: After private deployment, the token consumption statistics in the backend do not match the actual call volume, making cost accounting inaccurate. Cause: The
TOKEN_CALCULATE_MODEconfiguration was not set to count Chinese characters and punctuation. The default English token calculation rules cannot adapt to the length calculation of Chinese investment research text.
How to Verify Correct Configuration
- Upload a single research report document with 100,000 Chinese characters and Excel channel data with 15,000 rows, and check whether the upload progress completes normally without error prompts.
- Initiate a multi-turn dialogue covering raw material prices, brand revenue, and terminal sales, and verify whether the output results carry the unit information of all involved fields.
- Check the token statistics panel in the private deployment backend, and verify whether the token consumption of each dialogue matches the length of the input text.
- Simulate initiating 5 consecutive investment research dialogues, and check whether the interface returns normal status without timeout 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.