What this category’s data looks like
Intelligent due diligence data for professional chains is sourced from internal POS systems, ERP supply chain ledgers, offline inspection reports, and local regulatory public information. Data update frequencies vary:
- Store revenue and in-store traffic data updates daily
- Inventory turnover data updates every three days
- Inspection compliance records update weekly
- Industrial and commercial compliance information synchronizes quarterly
Document structures include single-store basic information pages, operation details pages, supply chain ledger pages, and compliance check pages. Common formats are CSV, XLSX, PDF, and inspection photos. Fields include store unique identifier, business district attribute, store type, monthly total revenue, average daily in-store traffic, revenue per unit area, inventory turnover days, and supply chain response time.
What constraints these characteristics impose on model integration and configuration
Multi-source heterogeneous data formats require support for parsing multiple file types. Parsing parameters must be adjusted for different formats such as CSV and PDF. Data with different update frequencies needs flexible synchronization trigger rules. Fixed intervals cannot be used universally. Fields use multiple physical units. Explicit field-to-unit mapping must be configured to avoid model confusion of data meanings. Single-store due diligence documents usually contain thousands of detailed entries. Appropriate context length and segmentation rules must be configured to prevent key information from being truncated. Unstructured data such as offline inspection photos requires additional image parsing parameters to ensure correct content recognition.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_ALLOW_TYPES | ["csv", "xlsx", "pdf", "jpg", "png"] | Matches common formats of professional chain due diligence data, covers POS export files, purchase ledgers, inspection reports, and store photos |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Single-store due diligence documents usually contain multiple pages of details, requiring sufficient parsing time |
maxContext | 8000–16000 characters | Adapts to the total length of single-store due diligence reports, avoiding truncation of key operational data |
SYNC_DATA_INTERVAL | 00:00 daily | Store revenue data updates daily, daily synchronization ensures data timeliness |
RECALL_CHUNK_SIZE | 1000 characters | Single block length of single-store operation details is appropriate, facilitating accurate matching of due diligence dimensions by the model |
MODEL_TEMPERATURE | 0.1–0.3 | Due diligence reports require rigor and accuracy, reducing the probability of random generation |
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 specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Configuration Mistakes
- Phenomenon: Garbled characters appear in output results, and backend logs show "encoding mismatch". Cause: Default encoding parameter for file parsing is not specified, and the encoding of uploaded CSV or Excel files does not match the system default encoding.
- Phenomenon: No parsing result is returned after uploading DOC or image files, and the interface displays "file too large" prompt. Cause: The
UPLOAD_FILE_MAX_SIZEparameter is not adjusted, and the file size exceeds the default threshold. - Phenomenon: Model output contains redundant intermediate thinking processes, and compliant due diligence reports cannot be generated directly. Cause: The prompt template is not configured to block the model's intermediate reasoning steps, or the "show thinking process" switch is not turned off.
How to Confirm Successful Configuration
- Upload a single-store POS exported CSV file, check that the parsed text contains all preset fields without garbled characters, and verify that the parsing time meets expectations.
- Configure a scheduled synchronization task, manually trigger a synchronization, and check that files from the corresponding data source are correctly read and imported into the knowledge base.
- Initiate a due diligence report generation request, check that the output contains all required operational data dimensions and has no redundant intermediate steps.
- View model integration logs, confirm that API request status codes are normal response codes, with no authentication failures or timeout errors.
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.