What the data for this category looks like
Research report data for professional services scenarios primarily comes from licensed financial research institutions and compliant financial information service platforms. Regular research reports are released in batches each trading day. Special research reports triggered by sudden industry events are added as needed.
Each document includes fields such as title, issuing institution, report number, release date, rating label, target price, core logic sections, and risk warnings. Document lengths range from thousands to tens of thousands of characters. Rating labels use standardized text expressions. Target prices are denominated in Renminbi.
What constraints do these data characteristics impose on vector models and indexing?
The data characteristics of these research reports impose multiple constraints on the vector model and indexing workflow. First, research reports are updated frequently and support incremental additions. This requires indexes to support incremental updates, avoiding resource consumption from full reindexing.
Second, single document lengths vary widely and include structured fields. Indexes must support metadata filtering, to narrow recall ranges using fields such as issuing institution and rating label. At the same time, semantic splitting of long documents must preserve logical connections between sections, preventing core viewpoints from being split apart. Additionally, semantic capture of professional financial terminology requires models adapted to industry-specific expression habits.
How to set configuration values
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Research reports contain long logical discussion sections. Excessively long segments cause semantic fragmentation, while excessively short segments damage the integrity of core viewpoints per segment. This range adapts to semantic coherence for professional documents. |
chunk_overlap | 100–150 characters | Preserves contextual connections between adjacent segments, avoids critical logic breaks after long document splitting. Adapts to the coherent discussion structure of research reports. |
embedding_model | text-embedding-3 or professional vector models of equivalent dimension | This model has stronger semantic adaptation to financial professional terminology, covers retrieval needs for professional scenarios, and matches common model iteration scenarios in the industry. |
retrieve_top_k | 10–15 results | Research report content is professional and has high information density. Too many recalled results introduce irrelevant content, while too few fail to cover core viewpoints. This range adapts to precise retrieval needs for professional service scenarios. |
metadata_filter_enabled | Enabled | Research reports include structured fields such as issuing institution and rating. Enabling this feature narrows recall ranges, improves retrieval precision, and meets targeted retrieval needs of professional users. |
index_refresh_interval | Every hour or triggered on demand | Adapts to the high-frequency update rhythm of research reports. Triggering on demand handles emergency special research reports, while regular refresh ensures timeliness of retrieved data. |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, 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 mistakes
- Symptom: An incompatible model error is returned when calling the knowledge base interface to generate an index, or old data fails to match vector results from the new model. Cause: Only the embedding model was replaced, and full or incremental reindexing was not triggered. Vector encodings for already imported documents were not updated synchronously.
- Symptom: Index generation takes too long after batch import of research reports, or retrieval responses time out. Cause: Reasonable segment length and overlap parameters were not configured, or metadata filtering was not enabled. This causes the total text volume processed by the index and recall range to exceed expected limits.
- Symptom: Irrelevant institution research reports appear in retrieval results, or core rating labels are not prioritized in recalled results. Cause: Metadata filtering configuration was not enabled, or index metadata was not built using the structured fields of research reports. This causes vector recall to not incorporate targeted screening needs for professional scenarios.
How to verify proper configuration
- Run a single research report upload test, review the segment parameters and actual split results in the upload log. Confirm that segment length and overlap parameters match the configured values.
- Call the retrieval interface, pass filter conditions including issuing institution and rating label. Verify that returned results only include qualifying research report fragments. Confirm that metadata filtering configuration is active.
- After replacing the embedding model, run a full reindexing task. Check that the index status updates to completed. Verify that vector encodings have been updated synchronously.
- Run a batch import test, calculate total index generation time. Compare time consumption changes before and after adjusting configuration parameters. Confirm that parameter configurations adapt to the current data scale.
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.