What this category's data looks like
Data for party building learning Q&A in the metal material processing sector comes primarily from party building policy documents released by party committees of the iron and steel and nonferrous metals industries, internal party committee learning archives of enterprises, and post-adapted learning courses customized for production scenarios. Update cadence follows a quarterly sync of latest industry party building policies, and monthly updates for learning content related to processes such as steel rolling and forging. Most documents are structured text, with a length of 1000 to 5000 characters per piece. Content includes original policy texts, learning key points, and compliance requirements for corresponding production posts. Fields include release date, affiliated party committee level, and associated production process number. Units of "copy", "class hour", and "item" are used to distinguish different document types.
Constraints Imposed by These Characteristics on Multi-turn Dialogue and Prompt Engineering
Documents include production post-related fields. Multi-turn dialogue must adjust the Q&A direction based on post information included in input. Therefore, prompts must preset logic for extracting post parameters. Update frequency is relatively high. Prompts must regularly adapt to the expression methods of latest policies to avoid retrieving outdated content. Individual documents have long lengths. The historical context of multi-turn dialogue must be controlled within a reasonable range to prevent exceeding the model's token limit. Fields include production process numbers. Prompts must add retrieval restrictions related to processes to improve the matching degree between party building learning Q&A and production scenarios. Multi-turn dialogue must distinguish context between different sessions to avoid cross-session information interference. Therefore, the usage logic of chatId must be standardized.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Maximum Context Length | 8000–12000 token | Adapts to the total length of a single party building document plus multi-turn dialogue history, and avoids exceeding the default model limit of FastGPT v4.17.0 |
| Question Optimization | Enabled | Optimizes professional expressions of party building policies to make retrieval more accurately match party building content in metal processing scenarios |
| Single Recall Upper Limit | Top 6 entries | Matches the monthly updated learning content batches for this category, and avoids returning redundant outdated documents |
| Custom Context Variables | Release Date, Associated Production Post | Extracted from the variables parameter, to align dialogue with the relevant metal processing post scenario |
| Response Token Upper Limit | Calibrated via actual testing | Meets the interpretation needs of long documents, and avoids truncating key policy texts or learning key points |
Three Common Misconfigurations
- Phenomenon: An error is triggered in the debug preview interface after input guidance is enabled. Cause: The party building post keywords related to metal processing such as steel rolling and forging are not bound in the custom thesaurus, resulting in missing fields during thesaurus loading.
- Phenomenon: The response token count for specific professional party building questions automatically drops to below 200. Cause: No global response token upper limit is set, or the historical dialogue carried in the
messagesparameter exceeds the preset length, triggering automatic model truncation. - Phenomenon: The question optimization process takes more than 5 seconds. Cause: The optimization scope is not limited to party building policy-related content, causing the model to process a large amount of irrelevant non-party building text and extending optimization duration.
How to Confirm Correct Configuration
- Launch a multi-turn dialogue that includes a specific production post such as steel rolling or forging, and check whether the reply content is associated with the party building learning requirements of the corresponding post.
- View the
chatIdfield in the dialogue log, and confirm that each dialogue session has a unique ID with no duplicates. - Test dialogue with more than 3 consecutive rounds, and confirm that information such as release date and associated post in the context parameter
variablesis correctly carried and applied. - Input unoptimized party building professional terminology, and check that after the question optimization switch is enabled, the retrieved documents fit the current dialogue scenario better.
The values provided here are common starting points, and should be measured against samples relevant to the specific deployment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.