What the Data for This Category Looks Like
Glass industry investment research data mainly comes from weekly production capacity reports released by industry associations, daily ex-factory quotes from third-party institutions, real-time monitoring data from furnace operations, and demand analysis documents from downstream application fields. Data update cycles cover daily, weekly, and monthly frequencies. Documents include structured tables (such as regional quotes for float glass of different thicknesses, daily melting capacity of furnaces) and industry research reports in PDF format. Core fields include glass thickness (unit: mm), ex-factory unit price (unit: yuan/weight box), inventory turnover days (unit: days). Some documents include cross-category comparison parameters.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
The multi-source nature of glass investment research data requires multi-turn dialogue to handle both structured quote data and unstructured research report content simultaneously, and clear calling rules for different data types must be defined in the prompt. The high data update frequency requires configuring real-time synchronized knowledge base indexes to prevent the model from using outdated data. The feature that fields come with clear units requires retaining the dimension parameters from the previous round in multi-turn dialogue, to avoid the model confusing glass data of different thicknesses or regions. Long document splitting must adapt to the contextual integrity of structured tables, to avoid breaking data logic during splitting.
Configuration Settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Glass investment research data includes multiple research reports and multiple sets of structured quotes. A single dialogue round needs to carry multi-source recalled content. This range can cover the core fragments of 3 to 5 research reports and more than 10 sets of structured data |
recallTopK | Top 8–12 entries | Glass investment research data has multiple dimensions (thickness, region, category). A sufficient number of candidate fragments must be recalled to cover cross-dimensional comparison needs and avoid missing key parameters |
similarityThreshold | 0.72–0.78 | Structured quote data has high matching accuracy requirements, while unstructured research reports need a certain degree of recall flexibility. This range balances precision and recall coverage |
chunkSize | 800–1200 characters | Glass research reports mostly include paragraph-based analysis and table fragments. This segment length can retain the contextual integrity of tables and avoid breaking structured data logic during splitting |
promptTemplate | Match data according to the user-specified glass category, region, and unit. Retain the parameter context from the previous round in multi-turn dialogue | Glass investment research conversations often involve cross-parameter comparisons. The model must be forced to bind the dimension parameters of the current session to avoid confusing quotes of different thicknesses or regions |
apiRequestTimeout | 30–45 seconds | Some real-time quote interfaces have high return delays. This timeout setting can prevent dialogue interruptions caused by data pulling timeouts |
The parameter values provided on this page are common recommended starting points for configuration. The actual values are affected by material forms, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing the configuration.
Three Common Misconfigurations
- Symptom: The debug preview dialogue works normally, but after publishing, the prompt indicates that no knowledge base is selected. Cause: In FastGPT V4.9.1, the associated glass investment research knowledge base permission was not checked when publishing the application, or the deployment environment did not load the index file of the corresponding knowledge base.
- Symptom: The model confuses glass quote data of different thicknesses or regions during multi-turn dialogue. Cause: The prompt did not force binding of the current session's dimension parameters, leading to the loss of key filtering conditions in the context.
- Symptom: When calling the OpenAI API interface to query dialogue data, the returned results include all dialogue records from both the web interface and the API side. Cause: The
chatSourcefield was not specified asapiin the API request, or the exclusive interface path for the corresponding session query was not used.
How to Verify Proper Configuration
- Enter the application configuration page, check whether the associated knowledge base list includes the glass investment research structured quotes and research report files, and confirm that the synchronization status shows completed.
- Initiate a multi-turn dialogue, ask for glass quotes of different thicknesses and regions in sequence, and verify whether the model's returned results retain the previous round's filtering parameters and do not show cross-dimensional parameter confusion.
- Call the FastGPT V4.9.1 dialogue query API, pass in the specified user's
chatIdandchatSource=apiparameters, and confirm that the returned results only include the dialogue records created by this user through the API. - Adjust the
similarityThresholdparameter to the range of 0.7 and 0.8, compare the number of recalled results and precision between the two times, and confirm that the current configuration value meets business requirements.
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.