What Data for This Category Looks Like
Automated equipment investment research data comes from several sources. These include manufacturer public product manuals, industry association standard documents, patent databases, third-party supply chain quotation data, and monthly industry research reports for financial investment research. Update cycles vary widely. Manufacturer product manuals are updated quarterly with model iterations. Patent literature is released irregularly. Industry research reports are published monthly or quarterly. Document formats include structured technical parameter tables. These tables have unit-bearing fields such as rated power and rotational speed. There are also dozens of pages of installation and commissioning manuals, troubleshooting guides, and long-form annual industry analysis white papers.
Constraints Imposed on Context and Token Handling
Automated equipment investment research data has many structured parameters. It also has a high proportion of long documents. This directly raises token consumption caps. Recalling multiple documents in a single session can easily trigger context overflow. Document field units vary across sources. For example, power parameters may appear in both kW and horsepower. Failing to retain unit associations during chunking will invalidate context information. Continuous text from long-form industry white papers requires precise segmentation. This avoids splitting complete technical logic into disjoint fragments. Disjoint fragments increase context matching difficulty. Frequently updated supply chain data requires dynamic adjustment of the context window. This prevents old data from occupying excessive token resources.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContextTokens | 8000–12000 | Matches the parameter association needs of automated equipment long documents, and avoids single-round conversation token overflow |
chunkSize | 800–1200 characters | Adapts to the paragraph structure of technical manuals, retains complete technical logic units, and avoids splitting parameter associations |
topN | Top 6–8 results | Balances the recall needs of multi-document parameter comparison and token consumption, covers parameter information for core competing products and in-house equipment |
similarityThreshold | 0.75–0.85 | Filters low-relevance non-technical documents, reduces invalid token usage, and accurately matches automated equipment parameters and scenarios |
rerankTopN | Top 3–5 results | Focuses on the most relevant technical parameter fragments, reduces total context length, and avoids redundant information occupying tokens |
UPLOAD_FILE_MAX_SIZE | 500 MB | Adapts to the upload needs of large automated equipment manuals and white papers, allows full import of long documents |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Symptom: Uploading a document that exceeds the model's supported context length causes a parsing failure and returns a
413 Request Entity Too Largeerror. Cause: TheUPLOAD_FILE_MAX_SIZEconfiguration is not adjusted, and long documents are not pre-chunked. This causes the single-upload document token count to exceed the limit. - Symptom: During consecutive follow-up questions, subsequent responses fail to address the question and cannot associate previously discussed equipment parameters. Cause: Conversation context association configuration is not enabled, or the
maxContextTokenssetting is too small to retain prior conversation equipment parameter information. - Symptom: When calling a workflow API, the returned result does not associate with knowledge base context, and only generates a response based on the current prompt. Cause: No context recall step is configured in the workflow node, or no conversation history context parameters are passed in the API request.
How to Confirm Proper Configuration
- Upload a typical automated equipment product manual. Check that parsed segments retain complete technical parameter paragraphs with no field splitting errors.
- Initiate two consecutive conversations. Verify that the second question can associate with equipment parameters such as rated power and rotational speed mentioned in the first round, with coherent response logic.
- Call a workflow API. Check that request parameters include context recall configuration items, and that the returned result contains equipment technical information from the knowledge base.
- Review backend logs. Confirm that the single-recall context token count does not exceed the
maxContextTokenssetting, with no token overflow 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.