What the data for this category looks like
Data sources for professional chain investment research include four categories: publicly disclosed quarterly store operation data from brands, third-party monitored weekly offline foot traffic data, monthly SKU supply price lists from the supply chain, and unstructured industry research reports. Update rhythms vary significantly: public operation data is updated quarterly, foot traffic monitoring data is updated weekly, and supply chain price lists are adjusted alongside supply cycles.
Document structures fall into two categories: structured tables and unstructured text. Structured fields include store ID, sales per square meter per month (unit: yuan/square meter/month), customer unit price, daily foot traffic (unit: visits/day). SKU fields include unified product code and supply price.
Constraints on model access and configuration
Multi-tempo update data requires flexible trigger rules, distinguishing between scheduled synchronization and manual upload logic. Specific units and semantic associations of structured fields require models to support fine-grained field embedding, to avoid loss of business meaning during parsing. Knowledge bases with mixed document types need parsing parameters adapted to different formats, to ensure long tables and text fragments are fully processed. Investment research requirements for the same region and same business format require metadata filtering rules to filter irrelevant cross-region, non-same-format data, reducing invalid recall.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Chain supply chain documents often contain long tables with multiple SKUs, which take longer to parse. 300 seconds covers most scenarios |
maxContext | 1500–2000 characters | Single store operation data has many fields. A complete context must retain sufficient field information to ensure semantic association |
RECALL_TOP_N | Top 8 entries | Investment research requires comparing data across multiple stores and dimensions. 8 recalled entries cover valid comparison samples for the same region and business format |
VECTOR_EMBEDDING_MODEL | text-embedding-v3 (or same-level multi-field adaptive model) | Datasets with many structured fields require models to support fine-grained semantic embedding, improving retrieval accuracy |
METADATA_FILTER_RULES | Filter by store region and business format type | Investment research focuses on chain store data from the same region and category. Filtering reduces invalid recalls |
UPLOAD_FILE_MAX_SIZE | 500 MB | Supply chain price lists often contain large numbers of SKU details. Large files can be fully uploaded without truncation |
The parameter values provided on this page are common recommended 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 Errors
- Phenomenon: After calling the API to upload an image document containing store foot traffic reports, the model cannot parse the image content. Cause: The relevant configuration for image parsing is not enabled, or the upload format does not meet the official requirements of FastGPT.
- Phenomenon: The similarity scores of semantic retrieval return results remain consistently high, and there is no significant improvement after replacing the vector model. Cause: Metadata filtering rules are not configured, and a large number of irrelevant cross-region, non-same-format store data are recalled, leading to artificially inflated scores.
- Phenomenon: A user selection or form input component is configured in the workflow, but there is no interaction prompt when calling via API. Cause: The interaction mode parameter is not specified in the API call, only the background execution logic is triggered, and the front-end interaction component is not invoked.
How to Confirm Configuration is Complete
- Upload a structured document containing store ID, sales per square meter per month, and foot traffic, and check if the parsed fields fully match the preset business template.
- Initiate a semantic retrieval request, check if the returned results include store data that complies with metadata filtering rules, and exclude irrelevant content from non-same-format or cross-region sources.
- Call the API to trigger the configured workflow, check if the user selection or form input component can be correctly invoked, and obtain expected interactive feedback.
- Configure a scheduled sync task, wait for the preset trigger cycle, and check if the knowledge base has automatically updated the latest business data.
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.