What Data for This Category Looks Like
Data sources for glass marketing include factory outgoing quality inspection reports from manufacturers, curtain wall parameter documents for financial institution business halls and insurance outlets, supply chain inventory ledgers, and marketing product brochures. Standard float glass updates parameters and pricing monthly. Specialty glass such as fire-resistant and bulletproof glass updates based on production schedules and customer custom requirements. Most documents use structured tables, with fields including thickness, light transmittance, compressive strength, dimensional specifications, pricing unit (yuan per square meter), and others. Some high-end products include PDF attachments of test reports, used for technical Q&A and solution creation for financial outlet renovations.
What Constraints These Characteristics Impose on Multiturn Conversations and Prompting
Glass product categories have numerous structured parameter fields with clear units. Multiturn conversations must retain contextual parameter associations to avoid repeating the same specification questions, improving marketing Q&A efficiency for financial clients. Documents include PDF test reports, so nodes that support multimodal parsing must be configured to process attachments and extract technical parameters for marketing materials. Parameter updates do not follow a fixed schedule, so multiturn conversations must support dynamic calls to the latest supply chain and pricing data. Relying solely on static knowledge bases cannot ensure the accuracy of marketing content. Physical parameter unit conversions are needed frequently. Prompts must explicitly specify unified unit output rules to avoid confusion between millimeters and inches, or between yuan per square meter and yuan per piece. This ensures the professionalism of marketing content for financial scenarios.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Glass products have many closely related parameters. Retaining specification and test report details across multiturn conversations prevents context overflow from disrupting marketing content generation |
multimodalParseEnabled | Enabled | Required to parse PDF attachments of glass test reports and extract structured parameters such as light transmittance and compressive strength for marketing materials |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Glass test report PDFs often contain multiple pages of charts and technical parameters. Extending the timeout ensures complete extraction |
textExtractChunkSize | 500–800 characters | Glass parameter documents are mostly structured tables. Chunk length adapts to the information density of table rows and columns, avoiding splitting that breaks parameter associations |
pluginNodeTimeout | 60 seconds | When calling supply chain APIs to pull the latest glass inventory and pricing data, sufficient network request time must be reserved to ensure smooth marketing quote generation |
Similarity Threshold | 0.75–0.85 | Glass parameters have high semantic similarity. Raising the threshold prevents irrelevant category parameters from being recalled, improving the accuracy of marketing Q&A |
The parameter values provided on this page are general recommendations for starting point configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: After connecting a text content extraction node to the workflow and configuring
Chat History: 6 entries, multiturn conversations fail to retain glass parameter context. Cause: The output of the text extraction node was not connected to the context passing chain of FastGPT v4.8.10, causing parameter context to reset with each conversation and preventing coherent marketing content generation. - Symptom: When calling a multimodal node to parse a glass test report PDF, a format error prompt appears. Cause: The
multimodalParseEnabledconfiguration was not enabled, or the uploaded PDF contains encrypted content that cannot be parsed normally, preventing extraction of technical parameters required for marketing. - Symptom: No response occurs after a knowledge base search is triggered in an online conversation, and the plugin node reports a service crash error. Cause: The
pluginNodeTimeouttimeout parameter was not set, or concurrency was too high when calling the supply chain API, causing service overload and affecting marketing content generation efficiency.
How to Verify Proper Configuration
- Enter basic glass specifications in the conversation interface, such as "10mm thick float glass", then follow up with "What is its light transmittance?" Check if the conversation associates the previously mentioned thickness parameter without repeating the question.
- Upload a glass test report PDF, trigger parsing, and verify that the extracted parameters include fields such as thickness, light transmittance, and compressive strength, and that units conform to preset rules.
- Test the plugin node calling the supply chain API in the workflow, verify that the returned glass quote matches current inventory and pricing rules, ensuring the timeliness of marketing content.
- Adjust the
maxContextparameter, then test asking five consecutive questions about different glass parameters in a multiturn conversation, verify that context is not lost or confused.
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.