What the data for this category looks like
Seasoning industry investment research data mainly comes from monthly/quarterly reports released by industry associations, periodic financial reports of listed companies, retail monitoring data from offline supermarkets and online channels, and price trend data for upstream raw materials such as soybeans and packaging materials. The data includes structured tables (such as sales volume, gross margin, and raw material cost proportion of each brand's SKUs), unstructured research report text, and channel interview minutes. Update rhythms vary significantly: raw material prices are updated daily, retail monitoring data is updated weekly, and industry financial reports and in-depth research reports are updated quarterly. Fields include brand name, SKU specification, unit price, year-over-year growth rate, and other items, with clear measurement units such as yuan/kilogram, ten thousand tons, and percentage.
What constraints do these characteristics impose on deployment and upgrade?
The multi-update cycle and multi-format features of seasoning industry investment research data impose clear constraints on the deployment and upgrade process. Differences in update rhythms across data sources require configuring multi-cycle incremental synchronization tasks during deployment. This avoids wasting computing resources on full repeated synchronization. The diversity of structured data fields and units requires presetting field verification rules during deployment. This ensures consistent format of imported data. Differences in length between long-text research reports and short-cycle retail data require configuring differentiated document segmentation and vectorization parameters during deployment. During upgrades, compatibility must be maintained for newly added supply chain data formats and field types.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Seasoning industry investment research documents include long-text research reports and batch structured tables, requiring sufficient time to complete parsing |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Single industry research report collections and batch quarterly financial report files have large sizes, adapting to large-file batch imports |
Segment Length | 800–1200 characters | Balances vectorization effects for short-cycle retail monitoring data and long-text research reports, avoiding context breaks |
Number of Retrieved Results | Top 8–10 results | Investment research requires multi-dimensional association of brand, channel, and raw material data, balancing retrieval accuracy and response speed |
Similarity Threshold | 0.72–0.8 | Distinguishes investment research data for seasoning sub-categories (soy sauce, seasoning sauce, hot pot base, etc.), reducing cross-category matching interference |
INCREMENTAL_SYNC_INTERVAL | Configured per data source: daily updated raw material data every 12 hours, weekly updated retail data every 7 days | Matches update rhythms of different data sources, avoiding invalid full synchronization |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, 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
- Symptom: Long conversation lag after Docker deployment, with
504 Gateway Timeouterror returned. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. Parsing of batch imported seasoning structured tables timed out, causing conversation requests to be blocked by the gateway. - Symptom: After deploying Ollama linked with FastGPT on Ubuntu, single-round conversation response takes over 30 seconds. Cause: The segment length configuration for seasoning data was not adapted. Too much redundant data was loaded during vector retrieval, leading to excessive model inference delay.
- Symptom: Local debugging works normally, but packaging as a Docker image prompts
database connection refusederror. Cause: Database connection environment variables were not configured in the Docker startup command. The image cannot read the database address configuration used during local debugging.
How to confirm proper configuration
- Upload a batch of seasoning industry research reports and structured tables, check that the parsed document segments cover the full content without obvious truncation.
- Initiate a search for revenue data of a specific seasoning brand, verify that the fields and units of the retrieved results match the imported data.
- After configuring the incremental synchronization task, manually trigger a synchronization for the daily updated data source, check that only new data is updated without full overwriting.
- After starting the Docker container, check database connection status via logs, with no
database connection refusederrors.
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.