What this category of data looks like
Computer equipment research report data for the financial industry primarily comes from official disclosure documents of hardware manufacturers, industry reports published by industry associations, and evaluation data from third-party consulting institutions. Update cycles fluctuate with new product launches, quarterly financial report disclosures, and industry events, with no fixed schedule. Core product categories see relatively high update frequency. Document structures include core parameter modules, performance test comparisons, supply chain cost breakdowns, compliance certification lists, and other sections. Fields often include model, clock speed, storage capacity, resolution, unit price, and more. Units include GHz, TB, dpi, CNY, and others.
What constraints these characteristics impose on the knowledge base retrieval and recall workflow
Computer equipment research reports for the financial industry contain a large number of structured parameters and long-form performance analysis. Retrieval and recall must accurately match field-level keywords to avoid generalized recall of irrelevant content, which could reduce the accuracy of decision-making for industry practitioners. If embedded parameter comparison tables in documents are split during parsing, retrieval will fail to associate complete parameter groups, damaging result integrity. Non-fixed update cycles require the knowledge base to support incremental synchronization, avoiding resource consumption from full re-scans. Long documents must be split while preserving parameter context, preventing different performance metrics for the same device model from being split into unrelated paragraphs.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 2000 MB | Computer equipment research reports often include high-definition test charts and long-form analysis. This setting covers the size limit of most single research reports |
PARSE_FILE_TIMEOUT_SECONDS | 900 seconds | Large performance test breakdown reports take longer to parse. This setting prevents premature timeout truncation of core parameter content |
Chunk Length | 800–1200 characters | Preserves complete parameter groups and associated analysis context for single device models, avoiding damage to parameter relevance through incorrect splitting |
Recall Count | Top 8 results | Covers multi-dimensional parameters and analysis content for the same device model, while controlling filtering costs for search results |
Similarity Threshold | 0.72–0.85 | Distinguishes parameter differences between different models in the same category, preventing irrelevant models with low matching scores from being recalled |
Reranked Return Count | Top 3 results | Prioritizes displaying the most relevant core parameters and authoritative analysis content, simplifying result filtering workflows |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific scenarios require individual analysis, and it is recommended to test against your own samples before finalizing settings.
Three common configuration mistakes
- Issue: Embedded tables in uploaded research reports fail to display correctly in search results, only showing blank spaces or unordered text. Cause: Parsing configuration does not enable structured table retention options, leading to incorrect conversion of table elements to plain text format.
- Issue: Large performance test research reports throw timeout errors during upload, or have truncated content after parsing. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter value is too low, and chunk length settings are unreasonable, leading to premature interruption of the parsing process. - Issue: Parameters for the same device model are split across multiple independent entries in search results, making complete parameter group information unavailable. Cause: Parameter context was not preserved during chunking, with different performance metrics for the same device model split into unrelated chunks, damaging information relevance.
How to confirm correct configuration
- Upload a test research report that includes embedded tables, verify the parsed document display to confirm table elements show normally.
- Upload a long test research report, verify parsing progress and result completeness to confirm no premature truncation or timeout errors occur.
- Submit a search request for a specific computer equipment model, verify the relevance of recall results to confirm complete parameter group information is available.
- Configure multiple knowledge bases divided by business module, submit search requests for corresponding modules to confirm results only come from the specified knowledge base set.
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.