What the Data for This Category Looks Like
Ordnance equipment investment research data primarily comes from publicly available defense industry research reports, annual and quarterly official disclosures, standard documents released by industry associations, and public reports from equipment finalization tests. Data update rhythm falls into two categories: regular disclosure and temporary announcements. Regular disclosure follows quarterly and annual cycles. Temporary announcements are released alongside test progress and procurement updates. Document structure includes three types: structured parameter tables, long-text test analysis, and supply chain details. Fields include equipment model, finalization time, performance parameters, procurement unit price, and others. Parameter fields must match the units specified in the documents, such as kilometers, ten thousand yuan, days, and others.
What Constraints These Characteristics Impose on Multi-turn Dialogue and Prompt Engineering
Ordnance equipment investment research data contains a large volume of structured parameters and long-text analysis content, and updates follow two rhythms: regular and temporary. This creates three constraints for multi-turn dialogue and prompt engineering configurations. First, multi-turn dialogue must accurately track the equipment models and corresponding parameters mentioned in each turn, to avoid context loss that leads to incorrect parameter matching. Second, prompt engineering must explicitly require the model to only use publicly available data from the knowledge base, not generate undisclosed equipment information, and unify the expression of parameter units. Third, temporarily updated announcement data must be pulled in real time during dialogue, to ensure the model uses the latest investment research information when responding.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Adapts to the typical length of a single ordnance equipment research report, prevents context overflow that causes content truncation |
recallTopK | Top 6–8 entries | Balances the accuracy of structured parameter recall and context redundancy, avoids interference from excessive irrelevant data |
temperature | 0.1–0.3 | Investment research scenarios require accurate parameters; a lower temperature value reduces the randomness of model outputs |
globalVarSyncMode | Real-time synchronization within session | Adapts to temporarily modified equipment parameters during multi-turn dialogue, ensures global variables take effect in real time within the session |
promptTemplate | Fixed prefix: Only respond based on publicly available ordnance equipment investment research data in the knowledge base, clearly mark the chapter or page number of the data source, and uniformly use the units specified in the documents for responses | Prevents the model from generating undisclosed information, ensures parameter units are consistent with the documents |
dialogueHistoryFetchLimit | Top 3–5 turns | Retains key investment research follow-up history, avoids redundant historical dialogue occupying context space |
The parameter values provided on this page are common recommended 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
- Phenomenon: The dialogue data returned by the
getDialogueListAPI does not distinguish between user questions and AI replies by role, making matching impossible. Cause: ThedialogueRoleMarkparameter is not enabled, so the returned dialogue records are not tagged by role. - Phenomenon: After modifying a global variable during a multi-turn dialogue, subsequent dialogue cannot read the updated variable value. Cause:
globalVarSyncModeis not configured for real-time synchronization within the session; the variable is only loaded when the session initializes, and subsequent modifications are not synced. - Phenomenon: After three consecutive questions, only the last user question is retrieved, and the content of the first two questions cannot be obtained. Cause: The recall range for the
dialogueHistoryFetchLimitparameter is not configured; the default setting only retains dialogue data from the current turn.
How to Verify a Correct Configuration
- The dialogue record retrieval API is invoked. Returned data is checked for fields including
roleandcontent, and correct mapping of each record’s role to its content is confirmed. - A global variable is modified within a session. A new investment research-related question is submitted, and the model output is verified to reference the updated variable value.
- Three consecutive equipment parameter follow-up questions are submitted. The historical dialogue retrieval API is invoked, and returned data is confirmed to include all three rounds of user questions and corresponding replies.
- The advanced configuration interface of the AI dialogue component is accessed. The large model parameter settings entry is checked for existence, and adjustment of required model parameters is confirmed to be possible.
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.