What the Data for This Category Looks Like
Data for this category mainly comes from pledge financing registration platforms for the building materials industry, shipping financing ledgers of glass manufacturers, and futures delivery warehouse receipt management systems. The data update schedule is that full synchronization of the previous natural day is completed every early morning. Documents use structured CSV or JSON format, with each row corresponding to one glass pledge financing business. Core fields include the full name of the financing entity, glass specification (unit: millimeter), pledged glass weight (unit: ton), financing loan amount (unit: ten thousand yuan), loan date, origin of the pledged goods, daily market guidance price (unit: yuan/square meter), plus identification fields for segmented categories such as float glass and tempered glass.
Constraints Imposed on Multi-turn Dialogue and Prompt Engineering by These Data Characteristics
These data characteristics impose multiple constraints on the multi-turn dialogue and prompt engineering link. First, the data contains multi-dimensional segmented fields and differentiated units. Multi-turn dialogue must gradually clarify parameters such as the glass category and specification unit that users care about, to avoid confusion across categories. Second, the data is updated on a T+1 basis. Multi-turn dialogue must clearly inform users of the data time validity range at the start, to prevent misunderstanding of the data as real-time market quotes. In addition, a single business corresponds to multiple sets of associated fields. Multi-turn interaction must guide users to supplement information in sequence according to business logic, such as confirming the financing entity first, then matching the corresponding pledged glass weight and guidance price, to avoid field misalignment. At the same time, the field names of structured data contain industry-specific terms. Prompt engineering must pre-set common industry field explanations to reduce AI matching errors.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Glass financing daily reports have many fields per batch of data. A longer context can retain complete business association information and avoid truncating key pledge and financing correspondence |
recall count | Top 8–12 entries | Glass financing businesses have high correlation per single data entry. Too many recalled entries will introduce irrelevant businesses, while too few will fail to cover the same-category financing cases required by users |
similarity threshold | 0.75–0.85 | There are many segmented glass categories. Fields such as specification and origin must be strictly matched to avoid recalling low-relevance non-glass financing data |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Structured financing daily report documents may contain tens of thousands of business records. Parsing takes a long time, so sufficient time must be reserved for full data loading |
system_prompt_template | Exclusive preset template including glass category filtering and data time validity instructions | Must clearly inform the AI that the data is updated on a T+1 basis, and filter by glass category and unit to avoid confusion with data from other categories |
tool_call_max_steps | 3–5 | Multi-turn dialogue requires sequentially confirming information such as financing entity, glass specification, and pledged weight. Too many steps will increase interaction burden |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test with samples tailored to the actual deployment before finalizing.
Three Common Misconfiguration Issues
- The symptom is waiting more than 3 seconds for a response when initiating a conversation for the first time, with normal interaction speed in subsequent interactions. The cause is that the first request triggers full knowledge base data parsing. Glass financing daily reports have a large data volume, resulting in long parsing time.
- The symptom is empty charts generated after calling the chart tool. The cause is failing to explicitly specify the glass specification unit and category in the prompt. The AI cannot match the corresponding field data, resulting in missing chart data sources.
- The symptom is financing data returned in the dialogue confusing float glass and tempered glass businesses. The cause is not setting category filtering recall rules, resulting in recalled cross-category data.
How to Verify Proper Configuration
- Initiate a first conversation, observe the startup waiting time, and adjust
PARSE_FILE_TIMEOUT_SECONDSto a duration that meets business expectations. - Enter a query containing a specific glass specification, and check whether the category and unit of the returned data match the preset rules.
- Call the chart tool to verify whether the generated chart contains field data corresponding to glass financing.
- Test multi-turn interaction to confirm that the AI can sequentially guide the supplement of necessary information such as financing entity and glass specification.
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.