What the data for this category looks like
Data sources include official financing press releases from cosmetics brands, disclosure documents from domestic and overseas stock exchanges, and third-party industry financing monitoring platforms. Updates are released daily for financing events from the previous calendar day. Each financing entry includes financing entity name, financing round, financing amount (unit: ten thousand or hundred million RMB), list of investors, announcement release date, and brand's main business track. Text length of individual entries mostly falls between 300 and 800 characters. Total length of daily summary documents fluctuates based on the number of financing events on the day.
What constraints these characteristics impose on deployment and upgrade
Multiple data sources create adaptation constraints. Custom parsing rules must be configured to adapt to unstructured text from brand press releases and standardized table formats from exchange disclosures. The daily update schedule requires configuring a 24-hour interval incremental pull task during deployment, to avoid resource consumption from full pull tasks. Differences in financing amount units require configuring unit normalization conversion parameters during deployment, to prevent numerical deviations in subsequent analysis. Fluctuations in daily summary document length require configuring a vector database segmentation threshold, to prevent ultra-long texts from failing to generate vectors. During upgrades, parsing rules must be updated synchronously to adapt to newly appearing financing disclosure formats or new expressions in brand press releases.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CRON_SCHEDULE | 0 2 * * * | Most financing events are released the previous evening; pulling data at 2 AM covers all same-day updates |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Daily summary documents contain multiple financing entries, leading to long parsing times; 600 seconds avoids timeout interruptions |
VECTOR_SEGMENT_LENGTH | 800–1200 characters | Individual financing entry text mostly ranges from 300–800 characters; this segmentation threshold covers single and combined entries, avoiding vector generation anomalies |
DATA_DUPLICATE_CHECK_FIELD | Announcement release date + Financing entity name | Accurately identifies duplicate financing events, preventing duplicate entries in storage |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Total file size of daily summary documents typically does not exceed this threshold, meeting daily operational needs |
UNIT_CONVERSION_RULE | Automatically convert ten thousand/hundred million RMB to a unified unit | Financing amounts use different unit expressions; unifying units ensures accuracy in subsequent analysis |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis, and testing on applicable samples is recommended before finalizing configuration settings.
Three Common Errors
- Phenomenon: A
token encoder not founderror occurs when calling a large language model after deployment, and the service restarts indefinitely. Cause: API keys and interface addresses for large language model access are not configured correctly, or Docker container network policies restrict external access. - Phenomenon: Insufficient number of knowledge base recall results, leading to low answer accuracy. Cause: Similarity threshold and recall count parameters are not set correctly; an overly high threshold or too few recall entries causes valid information to not be recalled.
- Phenomenon: The content extraction module fails to properly extract financing amount or main business track fields. Cause: Custom parsing rules are not configured to adapt to unstructured expressions in cosmetics brand financing press releases, leading to field recognition failure.
How to Confirm Configuration Is Complete
- Manually trigger a data pull task, check task logs to confirm there are no timeout or parsing failure errors, and that the pull range covers financing events from the previous day.
- Check vector database ingestion records to confirm each financing entry is correctly segmented and vectors are generated, with no cases of ultra-long texts failing to be stored.
- Test the large language model call link to confirm interface connection is normal, with no errors in key or address configuration.
- Randomly select 3 to 5 financing entries to check whether field extraction and unit conversion meet expectations.
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.