What the data for this category looks like
Professional chain research report data is sourced from internal brand operation systems, industry research databases, and public financial report excerpts. Data is updated on a monthly, quarterly, or annual cycle. Monthly updates cover real-time operation metrics such as in-store foot traffic and per-square-meter sales. Quarterly updates include industry trend data such as regional store distribution and supply chain turnover. Annual updates release deeply integrated chain brand analysis reports. Each individual research report document includes a core metrics table, regional store details, and a supply chain analysis module. Fields include average daily in-store foot traffic (person-times), average monthly per-square-meter sales (yuan/square meter), regional store coverage (number of stores), supply chain turnover days (days), and more. The order and details of fields vary across reports from different brands.
What constraints these characteristics impose on tool calling and plugins
The multi-source update cycles of professional chain research reports require tool calls to adapt to caching strategies for different data cycles. This prevents calling expired monthly operation data. The non-standardized field structure requires tool calls to configure flexible field mapping rules. This ensures accurate identification of core business metrics across different research reports. Long individual documents with multiple store detail entries require tool calls to reserve sufficient context window space. This prevents critical information from being truncated. Multi-source data origins require plugins to support synchronized configuration of multiple data sources. This ensures retrieval results cover internal operation data and industry research data.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Professional chain research reports include multiple store detail entries, and individual documents have long lengths. Extended parsing timeout is required |
UPLOAD_FILE_MAX_SIZE | 2000 MB | Individual in-depth research reports may integrate data from multiple regional stores. Large file upload support is required |
maxContext | 8000–12000 characters | Must accommodate multiple segments of content such as multi-store metrics and regional analysis, to avoid context truncation |
| recall count | Top 8–12 entries | Core metrics of professional chain research reports are concentrated. Excessive recall leads to redundant tool calling context |
| similarity threshold | 0.75–0.85 | Precise matching of core business fields such as store regions and per-square-meter sales is required. Irrelevant retrieval results must be filtered |
TOOL_CALL_MAX_RETRIES | 2 times | Tool calling involves pulling multi-source operation data. Retries reduce the impact of temporary data source fluctuations |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: Tool calling returns empty results, or fails to associate research report data in the knowledge base. Cause: No dedicated field mapping rules for professional chain research reports are configured. This prevents the tool from identifying core business fields such as store regions and per-square-meter sales.
- Symptom: After deploying version 4.8.21 via Docker, slow operation timeout errors appear in logs during research report parsing and upload. Cause: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration value is lower than the time required for actual parsing. Professional chain research report files have large sizes, exceeding default timeout limits. - Symptom: Global numeric counters fail to auto-increment. Variables are not updated after tool calling. Cause: The variable update trigger timing is not configured to trigger after answer completion, or counter variables are not bound to the tool calling workflow.
How to confirm proper configuration
- Upload a professional chain store research report document, run knowledge base parsing, and check if core fields such as in-store foot traffic and per-square-meter sales are successfully extracted in parsing logs.
- Configure a tool calling workflow, input store region keywords, and verify if the tool returns corresponding regional research report data.
- Trigger a single tool calling workflow, and check if the global counter variable auto-increments after answer completion.
- Adjust the similarity threshold, retrieve core business keywords, and check if the relevance of returned results meets business 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.