What the data for this category looks like
Data sources include cooperative bank corporate financing systems, local supply chain financial service platforms, and financing filing databases of textile industry associations. Updates follow a daily T+1 schedule, covering new financing, approved financing, and changes to outstanding financing from the previous day. Document structure is split into a structured summary table and individual enterprise financing detail pages. The summary table is sorted by date and enterprise registration location. Fields include: unified social credit code of the financing subject, financing amount (unit: ten thousand yuan), financing term (unit: calendar days), guarantee method, corresponding production order number. Some detail pages include links to scanned copies of pledged warehouse receipts.
Constraints imposed by these characteristics on multi-turn dialogue and prompt engineering
Daily updated structured data requires that conversations retain critical context such as dates and enterprise names across multiple turns, to avoid users needing to repeat queries. Fields include unique identifiers such as unified social credit codes and production order numbers; prompts must explicitly limit responses to only financing data from the textile manufacturing industry, and must not confuse fields from other categories. Data from multiple sources must support multi-turn follow-up questions to confirm the user’s required data source type. The presence of warehouse receipt image attachments requires the dialogue module to support both text and image parsing, and adapt to unstructured voucher-style data.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–16000 characters | Adapts to context information such as multi-day financing data and enterprise names that need to be retained during multi-turn conversations, preventing key content from being truncated |
UPLOAD_FILE_MAX_SIZE | 500 MB | Textile manufacturing financing ledgers may contain batch enterprise production order attachments, requiring support for large file uploads |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Structured financing tables need to identify multi-field relationships, resulting in longer parsing times than general documents |
relevance_threshold | 0.75–0.85 | Filters out interference from financing data outside the textile manufacturing industry, ensuring retrieved content accurately matches the target category |
ENABLE_IMAGE_PARSE | Enabled | Some financing daily reports include pledged warehouse receipt images, requiring support for parsing and associating queries with image content |
PROMPT_TEMPLATE | Fixed prefix: "Only answer based on the provided textile manufacturing financing daily report data. If the corresponding information is not present in the data, state this directly and do not fabricate content. Output numerical values strictly in accordance with the specified field units." | Prevents the model from confusing data from other industries or fabricating non-existent financing information |
The parameter values provided on this page are general recommendations used as a starting point for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: When referencing yesterday’s financing data again during a multi-turn conversation, the model cannot associate the previously queried enterprise name. Cause: The
maxContextparameter is not configured, or its value is set too small, causing the context window to be truncated and historical content beyond the threshold to be automatically cleared. - Phenomenon: After adding image upload support in the configuration file, uploaded textile manufacturing warehouse receipt images still cannot be parsed. Cause: The
ENABLE_IMAGE_PARSEconfiguration item is not enabled, and the prompt does not specify that financing-related fields in the image need to be parsed. - Phenomenon: After integrating a third-party GLM-series model, the model selection list of the text extraction module only displays GPT-series models. Cause: The text extraction module and the dialogue module have independent model configurations, and the integrated third-party model was not added to the configuration page of the text extraction module.
How to Verify Successful Configuration
- Initiate two consecutive conversations to query textile manufacturing financing data for different dates, confirming that the model can correctly associate the mentioned enterprise name and date range with the context.
- Upload a textile manufacturing financing ledger file and corresponding warehouse receipt images, confirming that the correct financing amount and order number fields can be extracted after file parsing, and that images can be properly parsed and associated with corresponding financing records.
- After integrating a third-party model, test the model selection lists of the dialogue module and text extraction module separately, confirming that all integrated models are displayed in their respective lists.
- Orchestrate a multi-step workflow, confirming that only the output of the final dialogue node is displayed, and that intermediate step processing results are not shown on the front-end interface.
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.