What the Data for This Category Looks Like
Data sources for glass industry research reports include operational data released by industry associations, public reports from securities firm building materials research teams, and public disclosures from glass manufacturers. Update frequency falls into three categories: spot price data is updated daily, monthly industry operation reports are released monthly, and in-depth research reports are released quarterly or at major policy milestones.
Document structures typically include sections on industry supply and demand, capacity distribution, cost composition, downstream application scenarios, price trends, and policy impacts. Fields include production capacity, output, inventory, ex-factory price, and raw material cost, with corresponding units: ten thousand heavy containers, tons, ten thousand heavy containers, yuan per square meter, and yuan per ton.
Constraints for Multi-turn Conversation and Prompt Configuration
Glass research report data sources are scattered, update cycles vary widely, and document structures are highly specialized. These factors create multiple constraints for multi-turn conversation and prompt configuration.
Data coverage differs across sources, so multi-turn interactions must first clarify the specific data type the user is asking for, to avoid mixed recalled content. Differences in update frequency require prompts to guide users to specify a time range for data, preventing the use of outdated information.
Downstream glass application scenarios cover real estate, automotive, photovoltaic and other fields, with many professional terms. Prompts must explicitly require users to specify the glass product type, and multi-turn conversations must follow up on scenario details to ensure accurate recall of content.
Unit expressions vary across research reports, so prompts must standardize unit rules to prevent the model from confusing data calibers.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Glass research report single documents are lengthy, and multi-turn conversations need to retain interaction context to avoid losing critical professional information |
Recall Count | Top 8–12 entries | Relevant content for glass research reports may be spread across multiple documents. Too many entries will overload the context, too few will miss critical data |
Similarity Threshold | 0.72–0.78 | Glass research reports contain many professional terms. This range balances recall relevance and coverage, avoiding irrelevant building materials research reports |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing a single in-depth glass research report takes significant time, so sufficient timeout is needed to avoid parsing failures |
Chunk Length | 1000–1500 characters | Professional paragraphs in glass research reports are lengthy. Too short chunks will break professional logic, too long will reduce recall accuracy |
Reranked Return Count | Top 5–7 entries | Further filter the most relevant content based on initial recall, adapting to the context length limits of multi-turn conversations |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Each scenario requires specific analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Symptom: After calling the API to start a conversation, the generated reply does not include content from the knowledge base. Cause: The target knowledge base is not correctly bound in the request parameters, or knowledge base recall configuration is not enabled.
- Symptom: An uncaught exception is thrown when the AI conversation component runs, and logs show configuration file loading failed. Cause: In a private deployment scenario, database connection or storage path parameters in the configuration file are configured incorrectly.
- Symptom: When using version 4.8.20, the conversation page or knowledge base page crashes. Cause: A known front-end rendering compatibility issue exists in this version, which does not adapt to some browser kernels or system environments.
How to Verify Proper Configuration
- Upload a single glass research report to the knowledge base, ask a targeted professional question, and confirm whether the reply includes specific data content from the document.
- Initiate consecutive multi-turn interactions, sequentially ask questions about glass industry issues in different detailed scenarios, and confirm whether the system retains context information from the previous round.
- View FastGPT parsing logs to confirm that the uploaded glass research report has been correctly split and indexed.
- Call the official API to send a conversation request, and confirm whether the returned results include associated identifiers for knowledge base documents.
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.