What Data Looks Like for This Category
Data for integrated services investment research knowledge bases primarily comes from public industry research reports, periodic and interim announcements of listed companies, public regulatory documents, and third-party industry databases. Update cycles align with source release timelines: research reports are updated by publication date, announcements are synced in real time, and regulatory documents are updated per their release cycles. Most documents combine structured and semi-structured formats, with fixed fields such as publishing entity, publication date, and document number, plus quantitative indicator fields with units including RMB yuan, percentage, multiples, and others.
Constraints on Vector Models and Indexing From These Characteristics
Wide structural variation across data sources such as public research reports and announcements requires vector models to support multi-format text encoding, while also adapting to feature extraction for semi-structured fields. High-frequency updates across multiple sources requires indexes to support incremental synchronization and incremental rebuilding, avoiding the resource consumption of full indexing. Differences in units for quantitative indicator fields require vector models to support normalization configuration, adapting to vector scales from different embedding outputs. A high proportion of long texts requires reasonable chunking rules to balance context completeness and vector recall accuracy.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
segment_length | 800–1200 characters | Adapts to long text chunking for investment research documents, balancing context completeness and vector granularity |
recall_top_k | Top 10–20 results | Matches recall needs for multi-source knowledge bases, avoiding insufficient recall or redundant results |
similarity_threshold | 0.75–0.85 | Filters low-relevance search results, adapting to the professional matching accuracy required for investment research content |
vector_normalization | Enabled | Adapts to unnormalized embedding model outputs, must be enabled in version 4.8.23 and above |
index_incremental_sync | Triggered by document update time | Adapts to multi-source high-frequency update knowledge bases, reducing resource usage from full indexing |
max_context_token | 10000–15000 characters | Matches the long context requirements of investment research documents, avoiding truncation of critical information |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- After switching vector models, the interface displays an "Index Rebuilding" status that does not change for a long time and cannot revert to the original model configuration. The cause is that the background task for forced index rebuilding is not triggered, and only the configuration is modified without synchronously updating the vector database metadata.
- Search requests return timeouts or have excessive resource usage, and background logs show that context tokens exceed the preset range. The cause is that the maximum context token value is not restricted, and tokens from fully recalled documents are directly concatenated.
- Results from multiple searches for the same topic have large fluctuations in similarity. The cause is that vector normalization configuration is not enabled, leading to deviations in adapting to vector output scales from non-normalized embedding models.
How to Confirm Correct Configuration
- Submit a single typical investment research document, view the generated vector chunks, and confirm that the chunking rules match the preset configuration.
- Send a professional search request, check the number of returned recall results and similarity matching accuracy, and confirm that the search parameters are effective.
- Upload updated source documents, view the index update status, and confirm that the incremental synchronization logic triggers normally.
- After switching vector models, run a batch search test, verify the consistency of vector outputs and search accuracy, and confirm that the model switching process works normally.
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.