What the Data for This Category Looks Like
Textile manufacturing research report data mainly comes from public industry association reports, research reports from consumer research teams of top securities firms, regular disclosure documents of listed textile enterprises, and import and export data of textile products from national customs authorities. Update cycles cover quarterly industry prosperity reports, monthly raw material price updates, and annual corporate annual reports. Document structures include core indicator tables, supply chain link analysis, and policy interpretation modules. Fields include yarn count, fabric gram weight, export FOB price, and capacity utilization rate. Most units use granular measurement standards such as meters, kilograms, yuan/ton, million USD, and similar units.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
The segmented fields and diverse units of textile manufacturing research reports require precise alignment of measurement logic in multi-turn dialogue, to avoid confusion between the unit associations of yarn count, gram weight, and export unit price. The layered update data source structure requires multi-turn dialogue to dynamically distinguish the timeliness of monthly raw material prices, quarterly prosperity reports, and annual corporate financial reports, to avoid recalling expired data. The document structure with a large number of embedded tables requires multi-turn dialogue to identify field associations within tables, and conduct linked analysis of capacity utilization rate and export FOB price. Differences in statistical caliber across data sources require prompts to clearly specify data source verification rules, and unify statistical standards.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Single textile manufacturing research report often contains multiple tables and long-form text analysis, requiring sufficient context to retain cross-turn indicator queries |
RECALL_TOP_N | Top 8–10 entries | Textile manufacturing research reports have many segmented indicators, requiring recall of sufficient relevant fragments to cover data across dimensions such as yarn, fabric, and exports |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Single research report often contains a large number of embedded tables and images, leading to longer parsing time |
SYSTEM_PROMPT | Answer solely based on uploaded textile manufacturing research report data, clearly label data sources and statistical units, and prioritize public statistical standards from national textile industry associations when caliber differences occur | Unify data verification rules in multi-turn dialogue, avoid unit confusion and caliber conflicts |
USER_HISTORY_IGNORE_KEYWORDS | Designated Reply, Plugin Call Log | Filter non-business related historical records to avoid interfering with context association in multi-turn dialogue |
MAX_HISTORY_LENGTH | Top 6–8 turns | Textile manufacturing research report queries often involve multi-dimensional indicator linkage, requiring sufficient context retention but avoiding interference from redundant information |
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 on-site analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: When calling a designated large model, the file parsing tool cannot be invoked, and an empty answer with no document association is returned. Cause: The tool calling permission of the large model is not enabled, or the
TOOL_INVOKE_WHITELISTparameter is not configured to allow file parsing tool calls. - Symptom: Historical dialogue contains plugin invocation logs or specified reply content, leading to contextual deviation in subsequent multi-turn queries. Cause: The
USER_HISTORY_IGNORE_KEYWORDSparameter is not configured, and non-business historical record fields are not filtered. - Symptom: Newly deployed system cannot communicate normally, returning an authentication failure prompt. Cause: The
API_KEYparameter is not correctly filled in the platform configuration page, or the key permission of the corresponding large model is not bound.
How to Verify Successful Configuration
- Upload a textile manufacturing research report containing multiple segmented indicator tables, initiate a cross-turn indicator association query, and check whether the system can accurately link data of different dimensions, confirming that the context and recall configuration meet business requirements.
- Initiate a test dialogue containing plugin calls, check whether the system automatically filters plugin logs and specified reply content in history, confirming that the history filtering configuration takes effect.
- Upload a single long research report, wait for the parsing to complete, then initiate a query, check whether the parsing process is completed within the preset duration, confirming that the timeout configuration is reasonable.
- Call the API interface to conduct multi-user testing, check whether the chat records of different users are isolated from each other, confirming that the user isolation configuration is correct.
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.