What the Data for This Category Looks Like
Glass-related market and yield rate data comes primarily from domestic mainstream building materials spot trading platforms and glass futures exchanges. The data falls into two categories: spot quotes and futures market trends. Spot quotes update at fixed times each day, while futures market trends update in real time during trading hours. Each data document includes fields such as producing area name, glass type (for example, float glass, tempered glass), thickness specification, daily quote, previous trading day’s quote, and market inventory surplus. Most quotes use yuan per weight box or yuan per square meter as units, and most inventory measurements use weight boxes. Data documents are typically stored, categorized by producing area and type, with a single document covering historical data across multiple cycles.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompting
Because data sources are scattered and have different update rhythms, multi-turn dialogue must clarify the data type and update time in each interaction. Prompts must include mandatory requirements for data tracing. Because fields include detailed parameters for multiple specifications and producing areas, multi-turn dialogue must first guide users to specify the exact type and producing area to avoid returning generalized invalid results. Because field structures vary across different data sources, prompts must limit the range of returned fields to prevent irrelevant inventory or cycle data from being output. Additionally, data updates occur at fixed times, so time verification logic must be added to conversations to ensure that when a user asks for that day’s data, the update period has passed.
Configuration Setup
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
maxContext | 12000–15000 characters | Multi-turn dialogue for glass-related content must retain context such as producing area, specification, and data type to prevent key parameters from being lost due to context overflow |
Recall Count | Top 6–8 entries | Glass data has many field dimensions. Too many recalled entries will make the prompt overly long, while too few will fail to cover the multi-dimensional information users need |
Similarity Threshold | 0.72–0.78 | Specifications and producing areas of glass products vary widely, so a high matching degree is required to avoid recalling irrelevant data from other building material categories |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Glass historical quote documents usually contain data from multiple producing areas and cycles, so parsing takes longer and the timeout period must be extended |
HTTP_REQUEST_TIMEOUT | 600 seconds | Interfaces connected to third-party spot or futures platforms may have long response times, preventing requests from being interrupted mid-process |
system_prompt | Fixed template: Combine recalled glass spot or futures data to answer user questions about yield rates and daily market trends, clearly mark data sources and update times, and limit returned fields to producing area, specification, quote, and update time | Uniformly constrain the model’s output format, avoid irrelevant content, and ensure responses align with users’ specific scenario needs |
The parameter values provided on this page are all conventional recommendations used as 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 on your own samples before finalizing settings.
Three Common Misconfigurations
- Issue: After calling the HTTP request orchestration node in version 4.6.9, the node’s output content does not appear in the conversation result. Cause: The configuration item "Connect HTTP Node Output to Conversation Context" is not enabled, so the output is not included in the conversation flow.
- Issue: Multi-turn dialogue repeatedly asks the user for glass type and specification, and fails to retain key parameters from previous interactions. Cause: The
maxContextconfiguration value is too small, causing the context of multi-turn interactions to be truncated, so previous type and producing area information cannot be retained. - Issue: After configuring a full-modal model, only text conversations are supported, and image or file input cannot be processed. Cause: The full-modal support option is not enabled in the model configuration, and the prompt does not explicitly require compatibility with multimodal input.
How to Confirm Successful Configuration
- Initiate a multi-turn dialogue that includes glass type, producing area, and data type, and verify whether the conversation context retains key parameters from the previous round.
- Test calling the HTTP request node, and check whether the conversation output includes the node’s return content.
- After configuring a full-modal model, upload a glass quote sheet image, and verify whether the model can correctly identify the data fields in the image.
- Trigger the workflow process, and verify whether the AI conversation jumps to the corresponding node according to the rules after completion.
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.