What the data for this category looks like
Military electronics investment research data primarily comes from public annual reports of military industrial groups, industry updates released by the National Defense Science, Technology and Industry Administration, public specification documents of military electronics component manufacturers, and supply chain white papers published by industry associations.
Update frequencies vary across sources. Corporate annual reports are updated quarterly. Component specification documents are updated irregularly alongside product iterations. Industry updates are released alongside policy changes or trade shows.
Public documents often include model parameter tables, production capacity data, and revenue breakdown fields. Field units include professional measurement standards such as GHz, mW, and ten thousand units per month. Some documents include supply chain association graph structures.
What constraints these characteristics impose on multi-turn dialogue and prompting
The multi-source nature of military electronics data requires multi-turn dialogue to gradually filter corresponding data sources, to avoid parameter confusion across research reports and financial reports.
Content with different update rhythms requires clear timeliness requirements in prompts, to ensure references to recent public data.
The diversity of professional fields and units requires prompts to enforce unified output formats, to avoid unit conversion errors.
The existence of long documents and graph structures requires multi-turn dialogue to retain context-specific model and node information, to ensure accurate matching for follow-up questions.
The question logic for investment research scenarios must also support switching dialogue branches across different segmented directions, to support gradual exploration requirements.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Military electronics documents are often long texts. Retaining model and parameter context across multi-turn dialogue prevents information loss between turns |
similarityTopK | Top 8–12 entries | Must cover multi-source research reports and financial data, to avoid missing professional parameters for segmented models |
rerankTopN | Top 3–5 entries | Screen highly matched professional content, filter irrelevant industry data, and improve the accuracy of investment research responses |
customPrompt | Fixed requirement to return units consistent with public document annotations, prioritize referencing public data from the past 6 months | Adapt to the multi-unit expression of military electronics parameters, meet timeliness requirements for investment research scenarios |
workflowInputParams | Includes queryType, targetModel | Support triggering different prompt logic based on investment research scenarios, adapt to branch requirements for multi-turn dialogue |
streamResponse | Enabled | Adapt to the reading experience of long content responses, align with the habit of gradually obtaining information in investment research scenarios |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing.
Three Common Misconfigurations
- Phenomenon: After upgrading to version v4.8.10, only for simple questions of a dozen characters, responses are output as a single result, without using streaming format. Cause: The
streamResponseconfiguration is not enabled on the dialogue node, or the streaming output link is not correctly connected in the workflow. - Phenomenon: The AI dialogue node in the workflow cannot obtain the output content of the code run, and the corresponding field is empty. Cause: The output of the code node is not bound to the input variable of the dialogue node, and the scope of the global variable is not set to workflow-level.
- Phenomenon: Repeatedly mentioned military electronics model parameters are confused in multi-turn dialogue, and unit errors appear in the output results. Cause: The prompt does not enforce binding the target model of the current turn, and the
maxContextconfiguration for retaining context information is not enabled.
How to Verify Proper Configuration
- Initiate a multi-turn dialogue containing military electronics model parameters, check that each round of replies retains the model information mentioned in the previous round, with no parameter confusion.
- Trigger the workflow to run the code node, check that the input variables of the dialogue node correctly load the code output content, with no empty fields.
- Test the streaming output switch for version v4.8.10, confirm that the dialogue returns results incrementally in streaming format after enabling.
- Initiate a query containing unit requirements, check that the units in the reply results are consistent with the annotations in the public documents, with no unit conversion errors.
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.