What the data for this category looks like
Research reports related to brand agency operations mainly come from third-party beauty and personal care industry monitoring institutions, operational data exported from e-commerce platform backends, and brand monthly sales review documents. Update frequency follows a monthly baseline, with weekly updates added before major promotion periods. Document structures include fields such as brand channel sales, customer unit price, user age range, regional distribution, and competitive benchmarking metrics. Most units are yuan and person-times. Common document formats are PDF with tables and structured Excel files.
What constraints do these characteristics impose on workflow orchestration?
Multi-source research report data includes both structured tables and unstructured analytical text. Configure a format validation node in the workflow first to filter imported data with invalid formats, and avoid interfering with subsequent retrieval. The monthly update rhythm with weekly updates before promotions requires the workflow to adapt to timed trigger logic for different cycles. This ensures retrieved data uses the latest published operational reports. Documents with numeric fields such as sales and customer unit price need a field extraction node in the workflow. Convert structured data into retrievable text blocks, and unify the expression of numeric units to avoid confusion. The correlation of competitive benchmarking data requires a cross-document association node in the workflow. Bind competitive research report content of the same category with current brand data for joint retrieval.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Knowledge base recall count | Top 8–12 entries | Brand agency operation research reports include three core data types: sales, user data, and competitive data. Sufficient recall is needed to cover core dimensions and avoid missing key information |
Similarity threshold | 0.72–0.78 | Research report content has high professionality. Balance recall accuracy and coverage. Avoid missing detailed operational data if the threshold is too high, and introducing irrelevant reports if the threshold is too low |
Workflow Scheduled trigger interval | Configure according to business cycle: 30 days for monthly tasks, 7 days for pre-promotion tasks | Match the research report update rhythm, and ensure retrieved data uses the latest published operational reports |
Document Parsing Chunk size | 1000–1200 characters | Research reports include long sections of industry analysis and text parsed from structured tables. Segments that are too long cause context fragmentation, while segments that are too short destroy indicator correlation |
Multimodal Data Processing Switch | Enable only for uploaded e-commerce backend screenshot data | Most brand agency operation research reports use text and structured tables. Only call multimodal models for screenshot-based multimodal data, and use Qwen2 tool calls for remaining plain text |
Workflow Knowledge Base Citation limit | 3000 characters | Match the default configuration of FastGPT workflow nodes, avoid exceeding the context bearing limit of a single node, and adapt to the average length of a single research report |
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, and it is recommended to test on your own samples before finalizing.
Three Common Mistakes
- Phenomenon: The workflow fails to distinguish between multimodal and plain text data. Plain text queries mistakenly trigger multimodal model calls, leading to abnormal return result formats. Cause: No data type judgment node is added to the workflow, and the corresponding processing model is not bound based on the input content type.
- Phenomenon: The number of results returned when the workflow calls knowledge base search is far lower than the configured value, or a context truncation error occurs. Cause: The
Workflow Knowledge Base Citation limitsetting is incorrect, or does not match the actual length of a single research report document, resulting in exceeding the node's bearing threshold. - Phenomenon: The timed-triggered workflow does not update research report data on time, and retrieval results use expired content. Cause: The correct trigger cycle is not configured according to the research report update rhythm, or the time zone setting of the timed node does not match the time zone of research report release.
How to Confirm Proper Configuration
- Manually trigger the workflow once, input different types of test data, and check whether the corresponding processing model is called correctly.
- View the workflow running logs to confirm that the number of results returned by the knowledge base search node matches the configured recall count.
- Verify that the cycle setting of the timed trigger node matches the current business's research report update rhythm.
- Upload a brand agency operation research report document, and check whether the parsed segments meet the configured requirements.
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.