What the data for this category looks like
Data sources include daily spot price ledgers from building materials industry associations and credit financing interfaces from partner banks. The update cadence is daily T+1, with full datasets for the previous trading day released each day. Each daily report document uses structured entries with fixed fields:
- Report date (format: YYYY-MM-DD)
- Glass product name (e.g. float flat glass, tempered glass)
- Production location
- Single financing amount (unit: ten thousand yuan)
- Annualized financing interest rate (unit: %)
- Credit period (unit: days)
- Financing subject type
- Fund provider type
Data is stored as structured JSON or CSV. Individual data entries are small, but there are many segmented product categories.
What constraints these characteristics impose on deployment and upgrade
The daily T+1 update cadence requires configuring fixed-interval scheduled sync tasks during deployment. This avoids overusing server resources with full sync operations. The multiple segmented product categories require the knowledge base to support multi-dimensional tag classification. During upgrades, the system must maintain compatibility with old version tag mapping rules. This prevents historical data from losing its classification. Structured fields with clear units require configuring strict field extraction rules in the knowledge base parser. This prevents unit information from being lost. When deploying a knowledge base with more than 30 segmented product categories, the pgvector storage engine’s limits will restrict deployment scale. An upgrade path for the vector storage engine must be planned in advance.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SYNC_INTERVAL_MINUTES | 1440 | Matches the daily T+1 update cadence, ensures only one full sync of latest data runs per day |
VECTOR_STORE_TYPE | milvus | Breaks the 30-knowledge-base limit of pgvector, supports deployment of financing daily report knowledge bases for multiple segmented glass product categories |
FIELD_MAPPING_RULES | Map according to "report date, glass product category, financing amount, annualized interest rate" | Matches the fixed field structure of glass financing daily reports, ensures core information is extracted correctly |
PARSE_STRICT_MODE | Enabled | Preserves unit information in structured data, prevents automatic filtering of financing amount and interest rate units |
MAX_BATCH_INSERT_SIZE | 500 entries | Adapts to the batch write scale of daily incremental data, reduces the probability of vector database write timeouts |
RECALL_TOP_K | Top 10 entries | Matches the single-data relevance recall requirements of financing daily reports, returns precise retrieval results |
The parameter values provided on this page are common starting points 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
- Vector database connection error, status code 500, with prompt "knowledge base count exceeds limit". Cause: Did not switch from pgvector to milvus for storage. The 30-knowledge-base limit of pgvector prevents deployment of financing daily report knowledge bases for more than 30 glass segmented product categories.
- Unable to modify login password after Docker deployment, with authentication failure prompt when accessing the interface. Cause: Did not mount a custom configuration file to modify password parameters in the Docker startup command, or did not restart the service to apply new configurations.
- Unable to start service after Docker deployment, with prompts "port mapping error" or "configuration file missing". Cause: FastGPT 4.9 version Docker deployment requires correct port 8080 mapping and persistent data volume mounting. Failure to follow official configuration procedures leads to startup failure.
How to Confirm Successful Configuration
- Check the scheduled sync task run logs. Confirm there are successful incremental sync records at the daily T+1 time point, with no failed errors.
- Retrieve financing daily reports for a specified glass product category. Confirm returned results include complete fields and corresponding unit information, with no missing or incorrect data.
- Check the vector database storage dashboard. Confirm the current deployed knowledge base count does not exceed the limit of the selected storage engine.
- Log in to the FastGPT backend using the modified password, with no permission authentication 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.