What data for this category looks like
Professional chain investment research data comes primarily from inventory and sales reports in the headquarters ERP system, daily store inspection records, regional franchisee operation ledgers, and industry regional retail policy documents. Update cycles cover daily inventory and sales data, weekly member consumption data, monthly regional operation review documents, and irregular competitor store research notes. Documents include structured report files, semi-structured inspection ledgers, and unstructured operation analysis notes. Fields include store ID (string), sales per square meter per day (yuan/sqm/day), single-store revenue (yuan), SKU sales ratio (proportion), and member visit frequency (times/week).
What constraints these characteristics impose on deployment and upgrade
Deployments must adapt multi-format parsing plugins to handle heterogeneous data from multiple sources. Upgrades must maintain compatibility with new system export formats added by chain brands. Deployments must configure incremental sync triggers for high-frequency daily updated data, to avoid excessive server resource usage from full syncs. Weekly and monthly review documents require scheduled full update tasks. Deployments must enable field standardization mapping for operation fields with specific units, to prevent retrieval deviations caused by inconsistent units. Upgrades must synchronize updates to field mapping rules to adapt to new operation indicator fields added by chain brands. Store data volume expands as the number of stores increases. Deployments must adjust vector database sharding configurations. Upgrades must support expanded vector storage.
How to set configurations
| Configuration item | Recommended value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Chain store inspection reports and monthly review documents are typically lengthy, requiring sufficient parsing time |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Bulk inventory and sales reports and regional operation review documents have large file sizes, requiring support for large file uploads |
maxContext | 8000–12000 characters | Investment research documents include multiple cross-store operation data segments, requiring sufficient context for associated analysis |
| Retrieval count | Top 8–12 entries | Chain investment research data has multiple dimensions, requiring coverage of sufficient store and operation indicator information |
| Similarity threshold | 0.75–0.85 | Low-related competitor data must be filtered out, retaining precise content related to store operations |
PARSE_INCREMENTAL_SYNC_INTERVAL | Every 4 hours | Adapts to the sync frequency of daily updated inventory and sales data, balancing real-time performance and server load |
The parameter values provided on this page are common recommendations used as a starting point for configuration. Actual values are affected by material format, data volume and business rules. Each situation requires separate analysis. It is recommended to test on your own samples before finalizing.
Three common errors
- Phenomenon: After upgrading to version 4.9.13, model chat responses end with traceability symbols such as
[SOI]or[EOI]. Cause: The chat traceability display switch is not disabled in the system configuration, and rule traceability display is enabled by default. - Phenomenon: A
workflow error {"message":"Dangerous behavior"}error is triggered during workflow debugging. Cause: No security access whitelist is configured for the workflow, allowing unauthorized external interface calls. - Phenomenon: A locally deployed m3e-large model cannot be connected to the knowledge base as an embedding tool. Cause: The correct model interface address and request header are not configured via
onapi, or the locally deployed endpoint is not specified in FastGPT's embedding model settings.
How to confirm the configuration is correct
- Upload a store inventory and sales report, check if parsed fields match preset mapping rules, and confirm no field unit deviations occur.
- Trigger an incremental sync task, check if the system only syncs files with an update time later than the last sync, and confirm that the incremental sync configuration is effective.
- Initiate an investment research retrieval, check if the number of returned results falls within the configured retrieval count range, and confirm that the similarity threshold filtering is effective.
- Initiate a model chat, check that no traceability symbols appear at the end of the response, and confirm that the chat traceability switch is disabled.
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.