What this category’s data looks like
Automated equipment research report data comes from securities research institute machinery industry teams, public industry association reports, earnings reports and technical whitepapers from leading automated equipment enterprises. Regular deep research reports are updated quarterly. Temporary industry dynamic reports are released in real time alongside technological breakthroughs or policy adjustments.
Document structures include core technical parameters, broken-down application scenarios, supply chain landscape, production capacity and revenue data modules. Fields include rated load (unit: kg), operating speed (unit: m/min), repeated positioning accuracy (unit: μm), annual production capacity (unit: units/year), and more. The word count of a single full research report varies widely. Brief reports range from 5000 to 10000 words. Deep reports can reach 30000 to 80000 words.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multiple professional parameters, varied document lengths, and inconsistent update rhythms of automated equipment research reports create multiple constraints for multi-turn dialogue and prompt configuration.
First, multiple technical parameters with dedicated units must be retained for precise matching in conversation context, to avoid unit confusion or parameter misalignment across turns. Second, differences in research report update rhythms require the dialogue system to distinguish between historical quarterly reports and real-time dynamic data, to ensure recalled content matches the timeliness of user questions. Third, the wide range of single-document lengths requires adaptation to different document splitting and context window management, to avoid information loss from long text overflow. Finally, standardized recognition of professional fields must be clearly defined in prompt rules, to ensure model output matches the field units used in research reports.
Configuration Setup
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Matches multi-turn dialogue context requirements for automated equipment research reports, avoids information loss from long text overflow, and retains cross-turn parameter association information |
segment length | 1500–2000 characters | Aligns with the paragraph structure of professional parameter groups and scenario descriptions in research reports, retains complete information units after splitting, and reduces the risk of information fragmentation |
recall count | top 6–8 results | Balances coverage of multi-dimensional research report parameters and context redundancy, avoids excessive irrelevant content interfering with multi-turn dialogue logic |
similarity threshold | 0.72–0.80 | Adapts to the precise matching requirements of automated equipment professional parameters, filters general industry discussion content, and ensures recalled content is highly relevant to user queries |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Adapts to the parsing time required for a single deep research report, avoids document parsing failure due to timeout, and ensures stable processing of long documents |
reranked return count | top 3–4 results | Focuses on recalled content related to core parameters, reduces unnecessary information interference, and adapts to the gradually refined question logic of users in multi-turn dialogue |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Frequent
Request Timeouterrors appear during dialogue, with a 504 status code. Cause: ThePARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted for the long document parsing requirements of automated equipment research reports, leading to timeout during document parsing or vector recall. - Symptom: Professional parameter units are mixed up in multi-turn dialogue responses. For example, the unit μm for repeated positioning accuracy is mistakenly replaced with kg. Cause: Prompt rules do not clearly define unit matching rules for professional fields, leading to parameter misalignment during cross-turn context association.
- Symptom: After uploading a research report file via API, retrieval results are empty, with no matching content returned in the response fields. Cause: The
similarity thresholdparameter was not set correctly. An overly high threshold prevents qualifying research report content from being recalled.
How to Verify Correct Configuration
- Initiate a parsing task for a single automated equipment research report, check the parsing completion status and time consumption, and confirm that the timeout parameter setting matches the parsing duration.
- Initiate a multi-turn query containing professional parameters, check the consistency of parameter units and fields across cross-turn dialogues, and confirm that the context window and prompt rules are active.
- Call the API to upload research report files of different lengths, check upload success rate and parsing results, and confirm that file size limit parameter settings are reasonable.
- Initiate a multi-dimensional parameter retrieval request, check the recall count and reranked results, and confirm that recall and rerank parameters meet 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.