What the data for this category looks like
Glass industry financial report data comes primarily from public regular reports of listed glass manufacturing enterprises and operational data released by industry associations. Data update cycles fall into two categories: regular reports and industry monitoring data. Regular reports are released quarterly, semi-annually, and annually. Industry data is updated monthly or quarterly. Document structures include main financial statement tables, notes, and operational status discussion and analysis sections. Glass-related data concentrates in chapters such as main business composition, production capacity and output, and raw material costs. Unique fields include production and sales data broken down by product category, with units including weight box, ten thousand yuan, and others. It also covers common financial fields such as revenue, inventory, and cash flow.
What constraints these characteristics impose on multi-turn dialogue and prompting
Glass financial reports include a large number of segmented product categories. Multi-turn dialogue must guide users to explicitly specify specific categories such as float glass and special glass. The model cannot accurately match corresponding data without this clarification. Data updates follow cycles such as quarterly and monthly. Prompts must clearly require the model to label the reporting period of the data. This avoids confusion between information from different cycles. Glass financial reports have unique units such as weight box. Prompts must mandate that the model uses this unit consistently. This prevents unit discrepancies. Financial report documents contain large amounts of non-core financial content. Multi-turn dialogue must use prompts to limit the model to only extracting relevant data from the operational status discussion and analysis and main business composition chapters. This prevents the introduction of irrelevant information.
How to set the configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 16000–32000 token | A single parsed glass financial report document has multiple segments. A longer context retains key information such as product categories and time ranges from multi-turn dialogue, preventing key parameters from being lost due to context overflow |
PARSE_FILE_CHUNK_SIZE | 800–1200 characters | Glass financial reports include detailed data for segmented product categories. A moderate chunk length retains the complete context of production and sales data for a single product category, avoiding splitting that breaks field associations |
RECALL_TOP_K | Top 6–8 results | Glass financial reports have many unique fields. Recalling an appropriate number of segments covers relevant data for different product categories, preventing key information from being missed due to too few recalled segments |
SIMILARITY_THRESHOLD | 0.75–0.85 | Filters low-relevance document segments, preventing financial data from non-glass categories from being included in responses, while retaining matching accuracy for segmented product categories |
CHAT_FILE_EXPIRE_TIME | 7–30 days | Glass financial report analysis typically requires multi-round confirmation of data dimensions. Extending the expiration time retains dialogue context, meeting user needs for gradually refining queries |
PARSE_TIMEOUT_SECONDS | 300–600 seconds | Glass financial report documents are typically lengthy. A longer timeout ensures all segments are fully parsed, preventing data loss from interrupted parsing |
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 settings.
Three Common Mistakes
- Symptom: Subsequent dialogue cannot access previously uploaded financial report files when no interaction occurs for more than 7 days after the dialogue starts. Cause: The
CHAT_FILE_EXPIRE_TIMEparameter is not adjusted, and the default 7-day expiration setting remains in use. This triggers automatic cleanup of dialogue context and associated files. - Symptom: The model confuses production and sales data for different glass product categories during multi-turn dialogue. For example, it assigns float glass output to tempered glass. Cause: Prompts do not explicitly require the model to confirm the user-specified glass segmented product category before each response. This leads to matching against irrelevant product category data.
- Symptom: Execution order of multiple AI dialogue nodes is chaotic during batch workflow execution. Nodes cannot complete data extraction according to their assigned roles. Cause: No dependency relationships are configured between nodes. Parallel-executed dialogue nodes fail to pass parameters in the preset division-of-labor order.
How to Verify Correct Configuration
- Upload a single glass manufacturing enterprise financial report document, initiate a dialogue that includes a query for a segmented product category, and verify that the model’s returned content matches the specified glass product category and data dimensions.
- Modify the
CHAT_FILE_EXPIRE_TIMEparameter, initiate a dialogue after exceeding the default expiration duration, and verify that historical dialogue context and uploaded files remain accessible. - Configure user identifier association items, initiate multi-round queries, and verify that the dialogue log contains unique identifier information corresponding to the queries.
- Build a workflow with multi-node division of labor, run the workflow, and verify that each node’s output is passed according to the preset logic, with no parameter confusion issues.
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.