What This Category's Data Looks Like
Snack food investment research data is sourced from dealer inventory and sales ledgers, offline terminal POS sales logs, third-party e-commerce platform sales rankings, industry association monthly supply and demand reports, and upstream raw material purchase quotes. Data updates follow distinct cycles: terminal sales data is synced daily, inventory ledgers are updated weekly, and industry reports are released monthly. Single data entries include fields such as SKU code, product name, net content, sales region, purchase price, retail price, and inventory turnover days. Net content is typically measured in grams or kilograms, prices are in yuan, and turnover days are measured in calendar days.
What Constraints Do These Characteristics Impose on Database and Operations
Multi-source heterogeneous data sources require the database to support batch import of multiple formats and incremental data pulling via API. Daily terminal sales data has a large volume, so deploy a sharded cluster to distribute write pressure. Configure a composite index on SKU code and time range to speed up queries. Weekly updated inventory ledgers require sync triggered by scheduled tasks. Set up task scheduling and failure retry operation interfaces. Fields include multiple unit types and unique SKU identifiers. Perform unit validation and primary key deduplication before data ingestion to prevent dirty data from entering the knowledge base. Some regional terminal data is missing. Configure null value filling rules to ensure data integrity in the knowledge base.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
mongodb_shard_count | 3–5 shards | Daily terminal sales data has a large volume. 3–5 shards balance write performance and operational complexity |
mysql_batch_insert_size | 1000–2000 records per batch | Snack food has a large number of SKUs. Batch insertion reduces database connection overhead and write latency |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Some industry supply and demand report documents are lengthy, so sufficient time must be reserved for file parsing |
recall_top_k | Top 8–12 records | Investment research scenarios require a balance between information comprehensiveness and context capacity. Too many recalled results will exceed the large model context limit |
db_connection_timeout | 15 seconds | Multi-source database connections require a controlled timeout threshold to prevent overall task blocking from single node exceptions |
field_validation_enabled | Enabled | Field format and unit compliance must be validated to prevent dirty data from entering the investment research knowledge base |
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 specific analysis, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- The interface displays the
1045 Access deniederror, indicating a database connection failure. The cause is incorrect configuration of MongoDB or MySQL account credentials for Docker deployment, or the account credentials have not been granted read/write permissions for the corresponding database. - Investment research results returned by the large model do not include original snippets from the MySQL database. The cause is that the
Function CALLresult snippet extraction configuration is not enabled, or the original content field name of the returned field is not specified. - Scheduled data synchronization tasks return the
504 Gateway Timeoutstatus code and fail to execute. The cause is that themysql_batch_insert_sizeparameter is not adjusted based on single batch data volume, and the single insertion data volume exceeds the database load limit.
How to Confirm Configuration Is Complete
- Run a single batch SKU data import test, check that the database write log has no errors, and verify that the field mapping and unit validation rules take effect.
- Initiate a simulated investment research conversation, trigger a
Function CALLto retrieve MySQL sales data, and check that the returned results include original content snippets of the specified fields. - View the database monitoring dashboard, confirm that the write load of the sharded cluster is in a stable range, and no node overload occurs.
- Manually trigger the weekly inventory ledger synchronization task, check that the task execution log has no timeout or failure records, and the data synchronization process completes normally.
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.