What the data for this category looks like
Financial scenario research reports for brand agency operations (beauty and personal care category) draw data from three sources: brand agency operation ledgers from financial institutions, public beauty and personal care industry monitoring datasets, and e-commerce backend export data from partnered financial brands. The primary update cycle is biweekly. Each document includes modules such as brand account operation data, social media content interaction performance, competitor operation action breakdown, channel campaign effect analysis, and user feedback tag summary. Fields include account follower count, per-post interaction count, channel type, user comment keywords, and more. There is no unified fixed format: some content is structured tables, while other content is unstructured analytical text.
What constraints these characteristics impose on model integration and configuration
Multiple heterogeneous data sources require configuring multi-source data access verification rules to prevent format conflicts between differently formatted ledgers, monitoring data, and exported content. The mixed structured and unstructured document structure requires configuring adaptive parameters for long text segmentation and field extraction to avoid context overflow or field recognition errors. Exclusive beauty and personal care agency operation terminology requires configuring domain-adapted prompt templates to improve the large model's understanding accuracy of professional content. The fixed biweekly update cycle requires configuring timed synchronization trigger parameters to ensure the freshness of retrieved data.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Brand agency operation research reports are mostly long texts spliced from multiple modules. This range covers the core retrieval context of a single research report |
reranker_top_k | Top 10 results | Research report retrieval needs to balance coverage and accuracy. This value retains enough candidate results for reranking and filtering |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | A single research report includes multi-dimensional data parsing. Sufficient parsing time must be reserved to avoid mid-run interruptions |
DATA_SOURCE_SYNC_INTERVAL | 14 days | Matches the biweekly update cycle of brand agency operation research reports to ensure data freshness |
FIELD_MAPPING | Map by category: account data, competitor data, campaign data, user tags | Research report fields are numerous and clearly categorized. Categorized mapping improves the accuracy of subsequent retrieval and extraction |
RETRIEVAL_SIMILARITY_THRESHOLD | 0.72–0.78 | Calibrated through real-world testing, this range filters low-relevance research report content and retains results core to operation-related tasks |
The parameter values provided on this page are common starting points for configuration setup. Actual values will vary based on material format, data volume, and business rules. Evaluate each case individually, and test against your own samples before finalizing settings.
Three common configuration errors
- Symptom: After deploying the bge-reranker reranking model based on TEI, testing returns normal results, but all reranking results are false during formal retrieval. Cause: The correct compute node IP was not bound in the FastGPT reranking model configuration, or the reranking model call timeout parameter was not set.
- Symptom: Some large models fail to correctly extract agency operation-specific fields from research reports, such as influencer collaboration quotes and social media campaign effect data. Cause: No field extraction prompt template tailored to the beauty and personal care agency operation domain was configured, causing the generic model to fail to recognize exclusive terminology.
- Symptom: When calling large models such as DeepSeek Chat, no visual charts are generated, only text descriptions are returned. Cause: The model's visual output permission was not enabled, or the selected model does not support chart generation capabilities.
How to confirm successful configuration
- Upload a single brand agency operation research report, check that the parsed fields match the preset
FIELD_MAPPINGto confirm normal field extraction. - Submit a retrieval request targeting social media operation data, check that the number of returned results matches the
reranker_top_kconfiguration to confirm normal reranking model calls. - View the data synchronization task logs to confirm that the timed synchronization task triggers according to the
DATA_SOURCE_SYNC_INTERVALconfiguration cycle to confirm normal data updates. - Initiate a test conversation using the configured large model, check that it can correctly recognize and return professional content related to research reports to confirm normal model adaptation.
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.