What the data for this category looks like
Marketing content data for e-commerce services targeting the financial industry primarily comes from marketing material libraries of financial institution e-commerce sections, product detail page copy, promotional activity configuration data, standardized customer service response templates from customer service interactions, and high-frequency question banks from user inquiries. Update rhythm follows marketing campaign cycles: daily new materials are added during major promotions, and existing materials are updated weekly during normal periods. The data includes structured fields such as activity_id, product_sku, and publish_time, as well as unstructured text such as long copy and live script transcripts. The length of individual entries varies widely, with no fixed character limit, and only requires linking to the unique identifier of the corresponding product or activity.
What constraints these characteristics impose on deployment and upgrade workflows
The multi-source mixed update feature of financial industry e-commerce service marketing content requires configuring multi-data source synchronization adapters during deployment, supporting two import paths: merchant backend API and local material library, to avoid excessive time spent on full indexing. The high-frequency update feature during campaign periods requires retaining incremental indexing capabilities during upgrades, to prevent service interruptions caused by full knowledge base reconstruction. The mixed structured and unstructured document structure requires presetting field mapping rules during deployment, separating structured metadata and text content for processing, to adapt to subsequent retrieval and ranking logic. The strong association between marketing content and product SKUs requires binding association indexes between SKUs and marketing content during deployment, and compatibility with old association rules during upgrades, without affecting retrieval of historical campaign data.
How to set configurations
| Config Item | Recommended Value | Basis for this Value |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | E-commerce marketing content includes long live script transcripts. Single file parsing takes significant time, and 600 seconds covers most long text parsing scenarios |
maxChunkSize | 800–1200 characters | E-commerce marketing copy varies widely in length. This range balances recall accuracy and context completeness, adapting to material content of different lengths |
RECALL_TOP_N | Top 8–12 entries | E-commerce user inquiries typically relate to single-scenario marketing content. A small number of recalls covers core needs, avoiding interference from redundant results |
SIMILARITY_THRESHOLD | 0.75–0.85 | Low-relevance generic marketing copy must be filtered out, retaining precise content with high matching degree to user questions, adapting to precise retrieval needs of e-commerce scenarios |
SYNC_INTERVAL_MINUTES | 15–30 minutes | Marketing content is updated frequently during major promotions. This interval balances synchronization delay and server load, adapting to high-frequency update demands during campaign periods |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports importing batches of live scripts and packaged product detail page files, avoiding impact on bulk material imports from single-file upload limits |
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: The WEB interface displays a
502 Bad Gatewayerror, and large model interface calls fail. Cause: Dynamic container address resolution is not configured. When the associated interface container restarts and updates its IP address, the previously configured fixed address becomes invalid. - Phenomenon: After full indexing of the knowledge base, retrieval returns empty results or far fewer entries than expected. Cause: A reasonable threshold for
SIMILARITY_THRESHOLDis not set, or chunking parameters do not match document length, leading to vector embedding deviations that cannot match user queries. - Phenomenon: The deployment process gets stuck at the vector database indexing step, with a
Connection timed outerror shown in logs. Cause: No timeout retry mechanism is configured for the vector database, and insufficient memory resources are reserved for processing indexing tasks for hundreds of thousands of e-commerce marketing content entries.
How to confirm correct configuration
- Run a parsing test for a single marketing copy, verify that the generated vector embedding fields and structured metadata are complete after parsing.
- Trigger an incremental synchronization task, verify that the number of new materials in the synchronization log matches the actual new volume.
- Simulate a user inquiry scenario, retrieve associated marketing content, verify that the number of recall results matches the preset
RECALL_TOP_Nparameter. - View connection status logs for the vector database and interface services, confirm there are no
Connection refusedor 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.