What this category of data looks like
Publishing industry investment research data primarily comes from publicly published industry research reports, professional journals, industry yearbooks, and internally compiled investment research monographs. Update cycles are primarily quarterly or annual. Some niche domain journals update monthly. Documents mostly use PDF or EPUB formats. Their structures include abstracts, chaptered main text, structured data tables, references, and publication identifiers. Fields include report number, issuing institution, publication date, industry classification, and core business indicators. Indicator units mostly use standardized measurement units such as ten thousand yuan, percentage, per mille, and similar units.
What constraints these characteristics impose on the "knowledge base retrieval and recall" link
The long text, structured features, and fixed update cycles of publishing industry investment research data create multiple constraints for the retrieval and recall workflow. Long documents and multi-table structures require retrieval to retain contextual associations between chapters and tables. This avoids breaking professional logic after splitting. The presence of structured tables requires retrieval to support field-level precise matching. Combine this with full-text search to cover different query needs. Fixed update cycles require incremental updates to only process newly added or updated documents. This avoids re-parsing existing content. Multi-dimensional metadata fields require retrieval to support filtering by conditions such as issuing institution and industry classification. This narrows the recall scope and improves efficiency.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Recall count | Top 8-12 entries | Single publishing industry investment research documents have relatively long content. Too many recalled entries will exceed the large model's context window. Too few will miss relevant viewpoints from niche domains |
Similarity threshold | 0.72-0.85 | Investment research documents contain many professional terms and niche viewpoints. A threshold that is too low introduces irrelevant content. A threshold that is too high misses relatively relevant niche materials |
Chunk size | 800-1200 characters | Publishing industry investment research documents mostly contain long paragraphs and cross-page tables. Segments that are too long lose contextual associations. Segments that are too short destroy the logical integrity of professional discussions |
PARSE_TABLE_ENABLE | Enabled | Publishing industry investment research documents contain many structured data tables. Enabling this setting extracts table fields and values, supporting field-level precise recall |
Incremental Update Trigger Rule | Triggered by publication date | Publishing industry investment research data updates quarterly or annually. Triggering incremental updates by publication date avoids re-parsing already processed existing documents |
maxContext | 8000-12000 characters | Single investment research documents have relatively long content. Splicing recalled document fragments requires retaining sufficient context for the large model to complete viewpoint integration and output |
The parameter values provided on this page are all common recommended 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 settings.
Three Common Mistakes
- Symptom: Knowledge base search for a single publishing industry investment research document takes over 10 seconds, with noticeable return delay. Cause: The
Chunk sizeparameter is not adjusted. Splitting long documents into too many fragments causes excessive fragment traversal during retrieval. - Symptom: The knowledge base backend can retrieve matching investment research content, but no results are returned after calling the large model interface associated with the knowledge base. Cause: The
maxContextparameter value is too small. Splicing recalled document fragments exceeds the large model's context window, so the large model cannot read complete content. - Symptom: After creating a subdirectory using the
parentIdfield, investment research documents are still mounted to the root directory. Cause: The parent ID does not point to an existing valid directory node, or the parameter format does not meet interface requirements.
How to Confirm Proper Configuration
- Upload a publishing industry investment research document that contains structured tables. Check if table fields and corresponding values are extracted in the parsing results.
- Enter professional term keywords to perform a retrieval test. Verify that the number and relevance of recalled results meet preset filtering requirements.
- Trigger an incremental update task. Check that only newly added investment research documents are parsed, and existing documents are not re-parsed.
- Call the interface associated with the knowledge base. Confirm that recalled fragments can be read completely. Confirm that the large model can output integrated results based on the recalled content.
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.